From 2a5914fa66616c81b9ba7e6add4f4e11455db41e Mon Sep 17 00:00:00 2001 From: nursnaaz Date: Tue, 3 Sep 2019 01:40:36 +0530 Subject: [PATCH] LSTM --- .../4. YOLO-OpenCV/yolov3-coco/.DS_Store | Bin 0 -> 6148 bytes .../1. Basic/01. RNN Structure.ipynb | 550 +++++++ .../1. Basic/02. Simple RNN.ipynb | 594 ++++++++ .../2. Advanced/1. Bi-Directional LSTM.ipynb | 357 +++++ .../2. Advanced/2. CNN-LSTM.ipynb | 491 +++++++ .../2. Advanced/3. GRU - CuDNN LSTM.ipynb | 494 +++++++ ...Air Passenger forecasting using LSTM.ipynb | 661 +++++++++ .../AirPassengers.csv | 145 ++ .../Bitcoin Price Prediction.ipynb | 1276 +++++++++++++++++ ...ord [Sentence Completion] using LSTM.ipynb | 404 ++++++ .../3. Sample LSTM Application/output.txt | 0 .../shakespeare.txt | 0 03 - Day - 3 Reccurent NN/TensorFlow-RNNs.pdf | Bin 0 -> 2378680 bytes .../1. Sequential-API.ipynb | 506 +++++++ .../2. Functional API.ipynb | 726 ++++++++++ .../3. Importing Images from url.ipynb | 234 +++ ...s - Model Checkpoint, Early Stopping.ipynb | 712 +++++++++ ...er tuning in Keras with GridSearchcv.ipynb | 461 ++++++ ...te Class imbalanced problem in keras.ipynb | 755 ++++++++++ .../7. Model Weights save and load.ipynb | 358 +++++ .../8. GPU check.ipynb | 112 ++ .../best_model.hdf5 | Bin 0 -> 238808 bytes .../saved_model.hdf5 | Bin 0 -> 238776 bytes 04 - Day - 4 Appendix on Keras/weights | Bin 0 -> 15872 bytes 24 files changed, 8836 insertions(+) create mode 100644 02 - Day - 2 Convolution NN/4. YOLO-OpenCV/yolov3-coco/.DS_Store create mode 100644 03 - Day - 3 Reccurent NN/1. Basic/01. RNN Structure.ipynb create mode 100644 03 - Day - 3 Reccurent NN/1. Basic/02. Simple RNN.ipynb create mode 100644 03 - Day - 3 Reccurent NN/2. Advanced/1. Bi-Directional LSTM.ipynb create mode 100644 03 - Day - 3 Reccurent NN/2. Advanced/2. CNN-LSTM.ipynb create mode 100644 03 - Day - 3 Reccurent NN/2. Advanced/3. GRU - CuDNN LSTM.ipynb create mode 100644 03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Air Passenger forecasting using LSTM.ipynb create mode 100644 03 - Day - 3 Reccurent NN/3. Sample LSTM Application/AirPassengers.csv create mode 100644 03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Bitcoin Price Prediction.ipynb create mode 100644 03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Predict the next word [Sentence Completion] using LSTM.ipynb create mode 100644 03 - Day - 3 Reccurent NN/3. Sample LSTM Application/output.txt create mode 100644 03 - Day - 3 Reccurent NN/3. Sample LSTM Application/shakespeare.txt create mode 100644 03 - Day - 3 Reccurent NN/TensorFlow-RNNs.pdf create mode 100644 04 - Day - 4 Appendix on Keras/1. Sequential-API.ipynb create mode 100644 04 - Day - 4 Appendix on Keras/2. Functional API.ipynb create mode 100644 04 - Day - 4 Appendix on Keras/3. Importing Images from url.ipynb create mode 100644 04 - Day - 4 Appendix on Keras/4. Keras-callbacks - Model Checkpoint, Early Stopping.ipynb create mode 100644 04 - Day - 4 Appendix on Keras/5. Hyperparameter tuning in Keras with GridSearchcv.ipynb create mode 100644 04 - Day - 4 Appendix on Keras/6. Mitigate Class imbalanced problem in keras.ipynb create mode 100644 04 - Day - 4 Appendix on Keras/7. Model Weights save and load.ipynb create mode 100644 04 - Day - 4 Appendix on Keras/8. GPU check.ipynb create mode 100644 04 - Day - 4 Appendix on Keras/best_model.hdf5 create mode 100644 04 - Day - 4 Appendix on Keras/saved_model.hdf5 create mode 100644 04 - Day - 4 Appendix on Keras/weights diff --git a/02 - Day - 2 Convolution NN/4. YOLO-OpenCV/yolov3-coco/.DS_Store b/02 - Day - 2 Convolution NN/4. YOLO-OpenCV/yolov3-coco/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..32feaeac375cbeae552ae8067528dfb498278dcf GIT binary patch literal 6148 zcmeH~OHW%d5QUH1s6v1$c4g@$n-Ct|vAKd&Z6!n(Rgt=)z2P=Q3M9&-6}!9P$Mjd0 zZ)~d~7mCy_P!)Ni@$qBE<2jM-Yf0{Ke!eShNLoP+TdNcgh>e@xlV_>t1?a*YZ4LER zr7AE>W*{^0zcXNacTGKwg5KS~)Az$suN;m_EbMqVK(~Gcp(d@X&pw~&OqY0{hdsZo zsV*9RKO26ly2gHpe%HRXiTh3cMIZO7;q$WL^Mhyu;$INIu}*jMH@@Rpc|4!aCiqnP z#OfQ{DQGnDq)SjFUNY4MN-NgkMLmVaV&VKIj;2qvH_$NT-b6WwQPIU+j?0ql}UD zNy_?BJLbusN2Va`S^rI z!a5=&h>NaZbam;8;i5bJKG_us>xj`^E\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.models import Model, Sequential\n", + "from keras.layers import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.5'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import keras\n", + "keras.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. SimpleRNN " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Input shape of SimpleRNN should be 3D tensor => (batch_size, timesteps, input_dim)\n", + "- **batch_size**: ommitted when creating RNN instance (== None). Usually designated when fitting model.\n", + "- **timesteps**: number of input sequence per batch\n", + "- **input_dim**: dimensionality of input sequence" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# for instance, consider below array\n", + "x = np.array([[\n", + " [1, # => input_dim 1\n", + " 2, # => input_dim 2 \n", + " 3], # => input_dim 3 # => timestep 1 \n", + " [4, 5, 6] # => timestep 2\n", + " ], # => batch 1\n", + " [[7, 8, 9], [10, 11, 12]], # => batch 2\n", + " [[13, 14, 15], [16, 17, 18]] # => batch 3\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[[1, 2, 3], [4, 5, 6]],\n", + " [[7, 8, 9], [10, 11, 12]],\n", + " [[13, 14, 15], [16, 17, 18]]]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]],[[13,14,15],[16,17,18]]]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Batch size, timesteps, input_dim) = (3, 2, 3)\n" + ] + } + ], + "source": [ + "print('(Batch size, timesteps, input_dim) = ',x.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# rnn = SimpleRNN(50)(Input(shape = (10,))) => error\n", + "# rnn = SimpleRNN(50)(Input(shape = (10, 30, 40))) => error\n", + "rnn = SimpleRNN(50)(Input(shape = (10, 30)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**return_state** = **return_sequences** = **False** ====> output_shape = **(batch_size = None, num_units)**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, 50)\n" + ] + } + ], + "source": [ + "rnn = SimpleRNN(50)(Input(shape = (10, 30)))\n", + "print(rnn.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**return_sequences = True** ====> output_shape = **(batch_size, timesteps, num_units)**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, ?, 50)\n" + ] + } + ], + "source": [ + "rnn = SimpleRNN(50, return_sequences = True)(Input(shape = (10, 30)))\n", + "print(rnn.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "return_state = True ===> outputs list of tensor: **[output, state]**\n", + "- if return_sequences == False =>> output_shape = (batch_size, num_units)\n", + "- if return_sequences == True =>> output_shape = (batch_size, timesteps, num_units)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "rnn = SimpleRNN(50, return_sequences = False, return_state = True)(Input(shape = (10, 30)))\n", + "print(rnn[0].shape) # shape of output\n", + "print(rnn[1].shape) # shape of last state" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, ?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "rnn = SimpleRNN(50, return_sequences = True, return_state = True)(Input(shape = (10, 30)))\n", + "print(rnn[0].shape) # shape of output\n", + "print(rnn[1].shape) # shape of last state\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Current output and state can be unpacked as below" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "output, state = SimpleRNN(50, return_sequences = True, return_state = True)(Input(shape = (10, 30)))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, ?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "print(output.shape)\n", + "print(state.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. LSTM\n", + "- Outputs of LSTM are quite similar to those of RNNs, but there exist subtle differences\n", + "- If you compare two diagrams below, there is one more type of \"state\" that is preserved to next module\n", + "\n", + "
\n", + "\n", + "\n", + "
Standard RNN
\n", + "\n", + "
\n", + "\n", + "\n", + "
LSTM
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to \"hidden state (ht)\" in RNN, there exist \"cell state (Ct)\" in LSTM structure\n", + "\n", + "
\n", + "\n", + "\n", + "
Hidden State
\n", + "\n", + "
\n", + "\n", + "\n", + "
Cell State
" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "lstm = LSTM(50)(Input(shape = (10, 30)))" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, 50)\n" + ] + } + ], + "source": [ + "print(lstm.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, 50)\n", + "(?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "lstm = LSTM(50, return_sequences = False, return_state = True)(Input(shape = (10, 30)))\n", + "print(lstm[0].shape) # shape of output\n", + "print(lstm[1].shape) # shape of hidden state\n", + "print(lstm[2].shape) # shape of cell state" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, ?, 50)\n", + "(?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "lstm = LSTM(50, return_sequences = True, return_state = True)(Input(shape = (10, 30)))\n", + "print(lstm[0].shape) # shape of output\n", + "print(lstm[1].shape) # shape of hidden state\n", + "print(lstm[2].shape) # shape of cell state" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "output, hidden_state, cell_state = LSTM(50, return_sequences = True, return_state = True)(Input(shape = (10, 30)))" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, ?, 50)\n", + "(?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "print(output.shape)\n", + "print(hidden_state.shape)\n", + "print(cell_state.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. GRU\n", + "- GRU, Popular variant of LSTM, does not have cell state\n", + "- Hence, it has only hidden state, as simple RNN" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "gru = GRU(50)(Input(shape = (10, 30)))" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, 50)\n" + ] + } + ], + "source": [ + "print(gru.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "gru = GRU(50, return_sequences = False, return_state = True)(Input(shape = (10, 30)))\n", + "print(gru[0].shape) # shape of output\n", + "print(gru[1].shape) # shape of hidden state" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, ?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "gru = GRU(50, return_sequences = True, return_state = True)(Input(shape = (10, 30)))\n", + "print(gru[0].shape) # shape of output\n", + "print(gru[1].shape) # shape of hidden state" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "output, hidden_state = GRU(50, return_sequences = True, return_state = True)(Input(shape = (10, 30)))" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, ?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "print(output.shape)\n", + "print(hidden_state.shape)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03 - Day - 3 Reccurent NN/1. Basic/02. Simple RNN.ipynb b/03 - Day - 3 Reccurent NN/1. Basic/02. Simple RNN.ipynb new file mode 100644 index 0000000..cbd0ec3 --- /dev/null +++ b/03 - Day - 3 Reccurent NN/1. Basic/02. Simple RNN.ipynb @@ -0,0 +1,594 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic RNN\n", + "- Objective: to understand basics of RNN & LSTM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recurrent Neural Networks\n", + "- Feedforward neural networks (e.g. MLPs and CNNs) are powerful, but they are not optimized to handle \"sequential\" data\n", + "- In other words, they do not possess \"memory\" of previous inputs\n", + "- For instance, consider the case of translating a corpus. You need to consider the **\"context\"** to guess the next word to come forward\n", + "\n", + "\n", + "\n", + "
\n", + "- RNNs are suitable for dealing with sequential format data since they have **\"recurrent\"** structure\n", + "- To put it differently, they keep the **\"memory\"** of earlier inputs in the sequence\n", + "
\n", + "\n", + "\n", + "
\n", + "- However, in order to reduce the number of parameters, every layer of different time steps shares same parameters\n", + "
\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from sklearn.metrics import accuracy_score\n", + "from keras.datasets import reuters\n", + "from keras.preprocessing.sequence import pad_sequences\n", + "from keras.utils import to_categorical" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# parameters for data load\n", + "num_words = 30000\n", + "maxlen = 50\n", + "test_split = 0.3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://s3.amazonaws.com/text-datasets/reuters.npz\n", + "2113536/2110848 [==============================] - 7s 3us/step\n" + ] + } + ], + "source": [ + "(X_train, y_train), (X_test, y_test) = reuters.load_data(num_words = num_words, maxlen = maxlen, test_split = test_split)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19,\n", + " 20, 21, 23, 24, 25, 28, 30, 32, 34, 36, 37, 38, 39, 45])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# pad the sequences with zeros \n", + "# padding parameter is set to 'post' => 0's are appended to end of sequences\n", + "X_train = pad_sequences(X_train, padding = 'post')\n", + "X_test = pad_sequences(X_test, padding = 'post')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train = np.array(X_train).reshape((X_train.shape[0], X_train.shape[1], 1))\n", + "X_test = np.array(X_test).reshape((X_test.shape[0], X_test.shape[1], 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_data = np.concatenate((y_train, y_test))\n", + "y_data = to_categorical(y_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_train = y_data[:1395]\n", + "y_test = y_data[1395:]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1395, 49, 1)\n", + "(599, 49, 1)\n", + "(1395, 46)\n", + "(599, 46)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(y_train.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Vanilla RNN\n", + "- Vanilla RNNs have a simple structure\n", + "- However, they suffer from the problem of \"long-term dependencies\"\n", + "- Hence, they are not able to keep the **sequential memory\" for long\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense, SimpleRNN, Activation\n", + "from keras import optimizers\n", + "from keras.wrappers.scikit_learn import KerasClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def vanilla_rnn():\n", + " model = Sequential()\n", + " model.add(SimpleRNN(50, input_shape = (49,1), return_sequences = False))\n", + " model.add(Dense(46))\n", + " model.add(Activation('softmax'))\n", + " \n", + " adam = optimizers.Adam(lr = 0.001)\n", + " model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = vanilla_rnn, epochs = 200, batch_size = 50, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 50/599 [=>............................] - ETA: 0s" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_test_ = np.argmax(y_test, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.74958263773\n" + ] + } + ], + "source": [ + "print(accuracy_score(y_pred, y_test_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Stacked Vanilla RNN\n", + "- RNN layers can be stacked to form a deeper network\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def stacked_vanilla_rnn():\n", + " model = Sequential()\n", + " model.add(SimpleRNN(50, input_shape = (49,1), return_sequences = True)) # return_sequences parameter has to be set True to stack\n", + " model.add(SimpleRNN(50, return_sequences = False))\n", + " model.add(Dense(46))\n", + " model.add(Activation('softmax'))\n", + " \n", + " adam = optimizers.Adam(lr = 0.001)\n", + " model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = stacked_vanilla_rnn, epochs = 200, batch_size = 50, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500/599 [========================>.....] - ETA: 0s" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.746243739566\n" + ] + } + ], + "source": [ + "print(accuracy_score(y_pred, y_test_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. LSTM\n", + "- LSTM (long short-term memory) is an improved structure to solve the problem of long-term dependencies\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.layers import LSTM" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def lstm():\n", + " model = Sequential()\n", + " model.add(LSTM(50, input_shape = (49,1), return_sequences = False))\n", + " model.add(Dense(46))\n", + " model.add(Activation('softmax'))\n", + " \n", + " adam = optimizers.Adam(lr = 0.001)\n", + " model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = lstm, epochs = 200, batch_size = 50, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500/599 [========================>.....] - ETA: 0s" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.844741235392\n" + ] + } + ], + "source": [ + "# accuracy improves by adopting LSTM structure\n", + "print(accuracy_score(y_pred, y_test_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Stacked LSTM\n", + "- LSTM layers can be stacked as well" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def stacked_lstm():\n", + " model = Sequential()\n", + " model.add(LSTM(50, input_shape = (49,1), return_sequences = True))\n", + " model.add(LSTM(50, return_sequences = False))\n", + " model.add(Dense(46))\n", + " model.add(Activation('softmax'))\n", + " \n", + " adam = optimizers.Adam(lr = 0.001)\n", + " model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = stacked_lstm, epochs = 200, batch_size = 50, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500/599 [========================>.....] - ETA: 0s" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.858096828047\n" + ] + } + ], + "source": [ + "print(accuracy_score(y_pred, y_test_))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03 - Day - 3 Reccurent NN/2. Advanced/1. Bi-Directional LSTM.ipynb b/03 - Day - 3 Reccurent NN/2. Advanced/1. Bi-Directional LSTM.ipynb new file mode 100644 index 0000000..3871a5e --- /dev/null +++ b/03 - Day - 3 Reccurent NN/2. Advanced/1. Bi-Directional LSTM.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from sklearn.metrics import accuracy_score\n", + "from keras.datasets import reuters\n", + "from keras.preprocessing.sequence import pad_sequences\n", + "from keras.utils import to_categorical" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# parameters for data load\n", + "num_words = 30000\n", + "maxlen = 50\n", + "test_split = 0.3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "(X_train, y_train), (X_test, y_test) = reuters.load_data(num_words = num_words, maxlen = maxlen, test_split = test_split)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# pad the sequences with zeros \n", + "# padding parameter is set to 'post' => 0's are appended to end of sequences\n", + "X_train = pad_sequences(X_train, padding = 'post')\n", + "X_test = pad_sequences(X_test, padding = 'post')\n", + "\n", + "X_train = np.array(X_train).reshape((X_train.shape[0], X_train.shape[1], 1))\n", + "X_test = np.array(X_test).reshape((X_test.shape[0], X_test.shape[1], 1))\n", + "\n", + "y_data = np.concatenate((y_train, y_test))\n", + "y_data = to_categorical(y_data)\n", + "y_train = y_data[:1395]\n", + "y_test = y_data[1395:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Deep RNN\n", + "- RNNs can be made deep, with multiple layers, like CNNs or MLPs\n", + "- Beware that RNNs take long to train compared to CNNs" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Activation\n", + "from keras import optimizers\n", + "from keras.wrappers.scikit_learn import KerasClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def deep_lstm():\n", + " model = Sequential()\n", + " model.add(LSTM(20, input_shape = (49,1), return_sequences = True))\n", + " model.add(LSTM(20, return_sequences = True))\n", + " model.add(LSTM(20, return_sequences = True))\n", + " model.add(LSTM(20, return_sequences = False))\n", + " model.add(Dense(46))\n", + " model.add(Activation('softmax'))\n", + " \n", + " adam = optimizers.Adam(lr = 0.001)\n", + " model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = deep_lstm, epochs = 200, batch_size = 50, verbose = 1)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "599/599 [==============================] - 0s \n" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "y_test_ = np.argmax(y_test, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.816360601002\n" + ] + } + ], + "source": [ + "print(accuracy_score(y_pred, y_test_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Bidirectional RNN\n", + "- Bidirectional RNNs consider not only one-way influence of sequence, but also the other way\n", + "- Actually, they can be thought as building two separate RNNs, and merging them\\\n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.layers import Bidirectional" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def bidirectional_lstm():\n", + " model = Sequential()\n", + " model.add(Bidirectional(LSTM(20, return_sequences = False), input_shape = (49,1)))\n", + " model.add(Dense(46))\n", + " model.add(Activation('softmax'))\n", + " \n", + " adam = optimizers.Adam(lr = 0.001)\n", + " model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = bidirectional_lstm, epochs = 200, batch_size = 50, verbose = 1)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500/599 [========================>.....] - ETA: 0s" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "y_test_ = np.argmax(y_test, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.841402337229\n" + ] + } + ], + "source": [ + "print(accuracy_score(y_pred, y_test_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Deep Bidirectional RNN\n", + "- Bidirectional RNNs can be stacked\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def deep_bidirectional_lstm():\n", + " model = Sequential()\n", + " model.add(Bidirectional(LSTM(10, return_sequences = True), input_shape = (49,1)))\n", + " model.add(Bidirectional(LSTM(10, return_sequences = True)))\n", + " model.add(Bidirectional(LSTM(10, return_sequences = True)))\n", + " model.add(Bidirectional(LSTM(10, return_sequences = False)))\n", + " model.add(Dense(46))\n", + " model.add(Activation('softmax'))\n", + " \n", + " adam = optimizers.Adam(lr = 0.001)\n", + " model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = deep_bidirectional_lstm, epochs = 200, batch_size = 50, verbose = 1)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "599/599 [==============================] - 1s \n" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "y_test_ = np.argmax(y_test, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.824707846411\n" + ] + } + ], + "source": [ + "print(accuracy_score(y_pred, y_test_))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03 - Day - 3 Reccurent NN/2. Advanced/2. CNN-LSTM.ipynb b/03 - Day - 3 Reccurent NN/2. Advanced/2. CNN-LSTM.ipynb new file mode 100644 index 0000000..1942ee5 --- /dev/null +++ b/03 - Day - 3 Reccurent NN/2. Advanced/2. CNN-LSTM.ipynb @@ -0,0 +1,491 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced RNN - 2\n", + "- Objective: try various types of NN architectures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Dataset\n", + "- CIFAR-10 dataset\n", + "- source: https://www.cs.toronto.edu/~kriz/cifar.html\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from sklearn.metrics import accuracy_score\n", + "from keras.datasets import cifar10\n", + "from keras.utils import to_categorical" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "(X_train, y_train), (X_test, y_test) = cifar10.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_train = to_categorical(y_train)\n", + "y_test = to_categorical(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(50000, 32, 32, 3)\n", + "(10000, 32, 32, 3)\n", + "(50000, 10)\n", + "(10000, 10)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(y_train.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. CNN-RNN\n", + "- Perform convolution & pooling operation first, and then perform recurrent operation\n", + "- Similar to the structure used in image captioning\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.models import Sequential, Model\n", + "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, LSTM, Input, Activation, Reshape, concatenate\n", + "from keras import optimizers" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = Sequential()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.add(Conv2D(input_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3]), filters = 50, kernel_size = (3,3), strides = (1,1), padding = 'same'))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPooling2D(pool_size = (2,2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(None, 16, 16, 50)\n" + ] + } + ], + "source": [ + "print(model.output_shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.add(Reshape(target_shape = (16*16, 50)))" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.add(LSTM(50, return_sequences = False))" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.add(Dense(10))\n", + "model.add(Activation('softmax'))" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "adam = optimizers.Adam(lr = 0.001)\n", + "model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv2d_8 (Conv2D) (None, 32, 32, 50) 1400 \n", + "_________________________________________________________________\n", + "activation_18 (Activation) (None, 32, 32, 50) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d_7 (MaxPooling2 (None, 16, 16, 50) 0 \n", + "_________________________________________________________________\n", + "reshape_6 (Reshape) (None, 256, 50) 0 \n", + "_________________________________________________________________\n", + "lstm_13 (LSTM) (None, 50) 20200 \n", + "_________________________________________________________________\n", + "dense_18 (Dense) (None, 10) 510 \n", + "_________________________________________________________________\n", + "activation_19 (Activation) (None, 10) 0 \n", + "=================================================================\n", + "Total params: 22,110\n", + "Trainable params: 22,110\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "print(model.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 3h 54min 45s\n" + ] + } + ], + "source": [ + "%%time\n", + "history = model.fit(X_train, y_train, epochs = 100, batch_size = 100, verbose = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10000/10000 [==============================] - 20s \n" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.5927\n" + ] + } + ], + "source": [ + "print('Test Accuracy: ', results[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. CNN-RNN-2\n", + "- Perform convolution-pooling operations and recurrent operations independently, and sum their results up\n", + "- Similar to the structure used in visual question answering\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "input_layer = Input(shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3]))\n", + "conv_layer = Conv2D(filters = 50, kernel_size = (3,3), strides = (1,1), padding = 'same')(input_layer)\n", + "activation_layer = Activation('relu')(conv_layer)\n", + "pooling_layer = MaxPooling2D(pool_size = (2,2), padding = 'same')(activation_layer)\n", + "flatten = Flatten()(pooling_layer)\n", + "dense_layer_1 = Dense(100)(flatten)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "reshape = Reshape(target_shape = (X_train.shape[1]*X_train.shape[2], X_train.shape[3]))(input_layer)\n", + "lstm_layer = LSTM(50, return_sequences = False)(reshape)\n", + "dense_layer_2 = Dense(100)(lstm_layer)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "merged_layer = concatenate([dense_layer_1, dense_layer_2])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "output_layer = Dense(10, activation = 'softmax')(merged_layer)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = Model(inputs = input_layer, outputs = output_layer)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "adam = optimizers.Adam(lr = 0.001)\n", + "model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "____________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "====================================================================================================\n", + "input_4 (InputLayer) (None, 32, 32, 3) 0 \n", + "____________________________________________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, 32, 32, 50) 1400 input_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_8 (Activation) (None, 32, 32, 50) 0 conv2d_6[0][0] \n", + "____________________________________________________________________________________________________\n", + "max_pooling2d_5 (MaxPooling2D) (None, 16, 16, 50) 0 activation_8[0][0] \n", + "____________________________________________________________________________________________________\n", + "reshape_4 (Reshape) (None, 1024, 3) 0 input_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "flatten_2 (Flatten) (None, 12800) 0 max_pooling2d_5[0][0] \n", + "____________________________________________________________________________________________________\n", + "lstm_4 (LSTM) (None, 50) 10800 reshape_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "dense_6 (Dense) (None, 100) 1280100 flatten_2[0][0] \n", + "____________________________________________________________________________________________________\n", + "dense_7 (Dense) (None, 100) 5100 lstm_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "concatenate_2 (Concatenate) (None, 200) 0 dense_6[0][0] \n", + " dense_7[0][0] \n", + "____________________________________________________________________________________________________\n", + "dense_8 (Dense) (None, 10) 2010 concatenate_2[0][0] \n", + "====================================================================================================\n", + "Total params: 1,299,410\n", + "Trainable params: 1,299,410\n", + "Non-trainable params: 0\n", + "____________________________________________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "print(model.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%%time\n", + "history = model.fit(X_train, y_train, epochs = 10, batch_size = 100, verbose = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10000/10000 [==============================] - 74s \n" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14.50628567199707, 0.10000000000000001]\n" + ] + } + ], + "source": [ + "print('Test Accuracy: ', results[1])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03 - Day - 3 Reccurent NN/2. Advanced/3. GRU - CuDNN LSTM.ipynb b/03 - Day - 3 Reccurent NN/2. Advanced/3. GRU - CuDNN LSTM.ipynb new file mode 100644 index 0000000..48b4b71 --- /dev/null +++ b/03 - Day - 3 Reccurent NN/2. Advanced/3. GRU - CuDNN LSTM.ipynb @@ -0,0 +1,494 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GRU - CUDALSTM\n", + "\n", + "- CuDNNGRU & CuDNNLSTM implementation\n", + "- Note that you need to install Tensorflow > 1.4 & Keras > 2.08 to implement \n", + "- This source code is running on i5-7500 & GTX 1060 6GB" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras.datasets import imdb\n", + "from keras.layers import GRU, LSTM, CuDNNGRU, CuDNNLSTM, Activation\n", + "from keras.preprocessing.sequence import pad_sequences\n", + "from keras.models import Sequential" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import dataset\n", + "- IMDB dataset in Keras datasets\n", + "- doc: https://keras.io/datasets/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "num_words = 30000\n", + "maxlen = 300" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words = num_words)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25000,)\n", + "(25000,)\n", + "(25000,)\n", + "(25000,)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(y_train.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# pad the sequences with zeros \n", + "# padding parameter is set to 'post' => 0's are appended to end of sequences\n", + "X_train = pad_sequences(X_train, maxlen = maxlen, padding = 'post')\n", + "X_test = pad_sequences(X_test, maxlen = maxlen, padding = 'post')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train = X_train.reshape(X_train.shape + (1,))\n", + "X_test = X_test.reshape(X_test.shape + (1,))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25000, 300, 1)\n", + "(25000, 300, 1)\n", + "(25000,)\n", + "(25000,)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(y_train.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LSTM\n", + "- Naive LSTM model without CuDNN" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def lstm_model():\n", + " model = Sequential()\n", + " model.add(LSTM(50, input_shape = (300,1), return_sequences = True))\n", + " model.add(LSTM(1, return_sequences = False))\n", + " model.add(Activation('sigmoid'))\n", + " \n", + " model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = lstm_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 29min 40s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "model.fit(X_train, y_train, batch_size = 100, epochs = 10, verbose = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 50.84%\n" + ] + } + ], + "source": [ + "scores = model.evaluate(X_test, y_test, verbose=0)\n", + "print(\"Accuracy: %.2f%%\" % (scores[1]*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GRU\n", + "- Naive GRU model without CuDNN" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def gru_model():\n", + " model = Sequential()\n", + " model.add(GRU(50, input_shape = (300,1), return_sequences = True))\n", + " model.add(GRU(1, return_sequences = False))\n", + " model.add(Activation('sigmoid'))\n", + " \n", + " model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = gru_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 21min 46s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "model.fit(X_train, y_train, batch_size = 100, epochs = 10, verbose = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 52.40%\n" + ] + } + ], + "source": [ + "scores = model.evaluate(X_test, y_test, verbose=0)\n", + "print(\"Accuracy: %.2f%%\" % (scores[1]*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### CuDNN LSTM" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def cudnn_lstm_model():\n", + " model = Sequential()\n", + " model.add(CuDNNLSTM(50, input_shape = (300,1), return_sequences = True))\n", + " model.add(CuDNNLSTM(1, return_sequences = False))\n", + " model.add(Activation('sigmoid'))\n", + " \n", + " model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = cudnn_lstm_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 2min 53s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "model.fit(X_train, y_train, batch_size = 100, epochs = 10, verbose = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 51.70%\n" + ] + } + ], + "source": [ + "scores = model.evaluate(X_test, y_test, verbose=0)\n", + "print(\"Accuracy: %.2f%%\" % (scores[1]*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### CuDNN GRU" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def cudnn_gru_model():\n", + " model = Sequential()\n", + " model.add(CuDNNGRU(50, input_shape = (300,1), return_sequences = True))\n", + " model.add(CuDNNGRU(1, return_sequences = False))\n", + " model.add(Activation('sigmoid'))\n", + " \n", + " model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = cudnn_gru_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 1min 54s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "model.fit(X_train, y_train, batch_size = 100, epochs = 10, verbose = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 51.60%\n" + ] + } + ], + "source": [ + "scores = model.evaluate(X_test, y_test, verbose=0)\n", + "print(\"Accuracy: %.2f%%\" % (scores[1]*100))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Air Passenger forecasting using LSTM.ipynb b/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Air Passenger forecasting using LSTM.ipynb new file mode 100644 index 0000000..dfc671f --- /dev/null +++ b/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Air Passenger forecasting using LSTM.ipynb @@ -0,0 +1,661 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "# LSTM for international airline passengers problem with regression framing\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "from pandas import read_csv\n", + "import math\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Dropout\n", + "from keras.layers import LSTM, GRU\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_squared_error" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# convert an array of values into a dataset matrix\n", + "def create_dataset(dataset, look_back=1):\n", + "\tdataX, dataY = [], []\n", + "\tfor i in range(len(dataset)-look_back-1):\n", + "\t\ta = dataset[i:(i+look_back), 0]\n", + "\t\tdataX.append(a)\n", + "\t\tdataY.append(dataset[i + look_back, 0])\n", + "\treturn numpy.array(dataX), numpy.array(dataY)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# fix random seed for reproducibility\n", + "numpy.random.seed(7)\n", + "# load the dataset\n", + "dataframe = read_csv('AirPassengers.csv', usecols=[1], engine='python', skipfooter=3)\n", + "dataset = dataframe.values\n", + "dataset = dataset.astype('float32')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# normalize the dataset\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "dataset = scaler.fit_transform(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# split into train and test sets\n", + "train_size = int(len(dataset) * 0.67)\n", + "test_size = len(dataset) - train_size\n", + "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# reshape into X=t and Y=t+1\n", + "look_back = 5\n", + "trainX, trainY = create_dataset(train, look_back)\n", + "testX, testY = create_dataset(test, look_back)\n", + "# reshape input to be [samples, time steps, features]\n", + "trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n", + "testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/200\n", + " - 1s - loss: 0.0504\n", + "Epoch 2/200\n", + " - 0s - loss: 0.0246\n", + "Epoch 3/200\n", + " - 0s - loss: 0.0140\n", + "Epoch 4/200\n", + " - 0s - loss: 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190/200\n", + " - 0s - loss: 0.0021\n", + "Epoch 191/200\n", + " - 0s - loss: 0.0026\n", + "Epoch 192/200\n", + " - 0s - loss: 0.0023\n", + "Epoch 193/200\n", + " - 0s - loss: 0.0024\n", + "Epoch 194/200\n", + " - 0s - loss: 0.0022\n", + "Epoch 195/200\n", + " - 0s - loss: 0.0025\n", + "Epoch 196/200\n", + " - 0s - loss: 0.0023\n", + "Epoch 197/200\n", + " - 0s - loss: 0.0032\n", + "Epoch 198/200\n", + " - 0s - loss: 0.0023\n", + "Epoch 199/200\n", + " - 0s - loss: 0.0020\n", + "Epoch 200/200\n", + " - 0s - loss: 0.0023\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create and fit the LSTM network\n", + "model = Sequential()\n", + "model.add(GRU(40, input_shape=(1, look_back)))\n", + "model.add(Dropout(0.3))\n", + "model.add(Dense(1))\n", + "model.compile(loss='mean_squared_error', optimizer='adam')\n", + "model.fit(trainX, trainY, epochs=200, batch_size=10, verbose=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# make predictions\n", + "trainPredict = model.predict(trainX)\n", + "testPredict = model.predict(testX)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# invert predictions\n", + "trainPredict = scaler.inverse_transform(trainPredict)\n", + "trainY = scaler.inverse_transform([trainY])\n", + "testPredict = scaler.inverse_transform(testPredict)\n", + "testY = scaler.inverse_transform([testY])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Score: 21.86 RMSE\n", + "Test Score: 45.49 RMSE\n" + ] + } + ], + "source": [ + "# calculate root mean squared error\n", + "trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\n", + "print('Train Score: %.2f RMSE' % (trainScore))\n", + "testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\n", + "print('Test Score: %.2f RMSE' % (testScore))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# shift train predictions for plotting\n", + "trainPredictPlot = numpy.empty_like(dataset)\n", + "trainPredictPlot[:, :] = numpy.nan\n", + "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# shift test predictions for plotting\n", + "testPredictPlot = numpy.empty_like(dataset)\n", + "testPredictPlot[:, :] = numpy.nan\n", + "testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot baseline and predictions\n", + "plt.plot(scaler.inverse_transform(dataset))\n", + "plt.plot(trainPredictPlot)\n", + "plt.plot(testPredictPlot)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/AirPassengers.csv b/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/AirPassengers.csv new file mode 100644 index 0000000..7014d86 --- /dev/null +++ b/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/AirPassengers.csv @@ -0,0 +1,145 @@ +Month,#Passengers +1949-01,112 +1949-02,118 +1949-03,132 +1949-04,129 +1949-05,121 +1949-06,135 +1949-07,148 +1949-08,148 +1949-09,136 +1949-10,119 +1949-11,104 +1949-12,118 +1950-01,115 +1950-02,126 +1950-03,141 +1950-04,135 +1950-05,125 +1950-06,149 +1950-07,170 +1950-08,170 +1950-09,158 +1950-10,133 +1950-11,114 +1950-12,140 +1951-01,145 +1951-02,150 +1951-03,178 +1951-04,163 +1951-05,172 +1951-06,178 +1951-07,199 +1951-08,199 +1951-09,184 +1951-10,162 +1951-11,146 +1951-12,166 +1952-01,171 +1952-02,180 +1952-03,193 +1952-04,181 +1952-05,183 +1952-06,218 +1952-07,230 +1952-08,242 +1952-09,209 +1952-10,191 +1952-11,172 +1952-12,194 +1953-01,196 +1953-02,196 +1953-03,236 +1953-04,235 +1953-05,229 +1953-06,243 +1953-07,264 +1953-08,272 +1953-09,237 +1953-10,211 +1953-11,180 +1953-12,201 +1954-01,204 +1954-02,188 +1954-03,235 +1954-04,227 +1954-05,234 +1954-06,264 +1954-07,302 +1954-08,293 +1954-09,259 +1954-10,229 +1954-11,203 +1954-12,229 +1955-01,242 +1955-02,233 +1955-03,267 +1955-04,269 +1955-05,270 +1955-06,315 +1955-07,364 +1955-08,347 +1955-09,312 +1955-10,274 +1955-11,237 +1955-12,278 +1956-01,284 +1956-02,277 +1956-03,317 +1956-04,313 +1956-05,318 +1956-06,374 +1956-07,413 +1956-08,405 +1956-09,355 +1956-10,306 +1956-11,271 +1956-12,306 +1957-01,315 +1957-02,301 +1957-03,356 +1957-04,348 +1957-05,355 +1957-06,422 +1957-07,465 +1957-08,467 +1957-09,404 +1957-10,347 +1957-11,305 +1957-12,336 +1958-01,340 +1958-02,318 +1958-03,362 +1958-04,348 +1958-05,363 +1958-06,435 +1958-07,491 +1958-08,505 +1958-09,404 +1958-10,359 +1958-11,310 +1958-12,337 +1959-01,360 +1959-02,342 +1959-03,406 +1959-04,396 +1959-05,420 +1959-06,472 +1959-07,548 +1959-08,559 +1959-09,463 +1959-10,407 +1959-11,362 +1959-12,405 +1960-01,417 +1960-02,391 +1960-03,419 +1960-04,461 +1960-05,472 +1960-06,535 +1960-07,622 +1960-08,606 +1960-09,508 +1960-10,461 +1960-11,390 +1960-12,432 diff --git a/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Bitcoin Price Prediction.ipynb b/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Bitcoin Price Prediction.ipynb new file mode 100644 index 0000000..32909a2 --- /dev/null +++ b/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Bitcoin Price Prediction.ipynb @@ -0,0 +1,1276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Link to medium post: https://medium.com/@zhiwei_zhang/bitcoin-time-series-prediction-with-lstm-d3f49b1b4d00" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import datetime\n", + "import time\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from sklearn.metrics import mean_squared_error\n", + "from math import sqrt\n", + "\n", + "import keras\n", + "from keras.models import Sequential\n", + "from keras.layers import Activation, Dense\n", + "from keras.layers import LSTM\n", + "from keras.layers import Dropout" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data by reading table from html" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateOpen*HighLowClose**VolumeMarket Cap
02019-09-019630.599796.769582.949757.971.144536e+10174768292883
12019-08-319597.549673.229531.809630.661.145481e+10172470743516
22019-08-309514.849656.129428.309598.171.359526e+10171869216524
32019-08-299756.799756.799421.639510.201.704588e+10170274910132
42019-08-2810203.4310279.379716.669754.421.760379e+10174627460400
52019-08-2710372.8310381.3310087.3010185.501.476261e+10182325540955
62019-08-2610126.3010512.3310126.3010370.821.843865e+10185625235889
72019-08-2510160.7410304.6210008.7910138.521.415386e+10181450186164
82019-08-2410407.6410418.029982.3010159.961.545103e+10181813631752
92019-08-2310136.3110442.4410078.1910407.971.562702e+10186231409722
\n", + "
" + ], + "text/plain": [ + " Date Open* High Low Close** Volume \\\n", + "0 2019-09-01 9630.59 9796.76 9582.94 9757.97 1.144536e+10 \n", + "1 2019-08-31 9597.54 9673.22 9531.80 9630.66 1.145481e+10 \n", + "2 2019-08-30 9514.84 9656.12 9428.30 9598.17 1.359526e+10 \n", + "3 2019-08-29 9756.79 9756.79 9421.63 9510.20 1.704588e+10 \n", + "4 2019-08-28 10203.43 10279.37 9716.66 9754.42 1.760379e+10 \n", + "5 2019-08-27 10372.83 10381.33 10087.30 10185.50 1.476261e+10 \n", + "6 2019-08-26 10126.30 10512.33 10126.30 10370.82 1.843865e+10 \n", + "7 2019-08-25 10160.74 10304.62 10008.79 10138.52 1.415386e+10 \n", + "8 2019-08-24 10407.64 10418.02 9982.30 10159.96 1.545103e+10 \n", + "9 2019-08-23 10136.31 10442.44 10078.19 10407.97 1.562702e+10 \n", + "\n", + " Market Cap \n", + "0 174768292883 \n", + "1 172470743516 \n", + "2 171869216524 \n", + "3 170274910132 \n", + "4 174627460400 \n", + "5 182325540955 \n", + "6 185625235889 \n", + "7 181450186164 \n", + "8 181813631752 \n", + "9 186231409722 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_market_data(market, tag=True):\n", + " market_data = pd.read_html(\"https://coinmarketcap.com/currencies/\" + market + \n", + " \"/historical-data/?start=20130428&end=\"+time.strftime(\"%Y%m%d\"), flavor='html5lib')[0]\n", + " \n", + " market_data = market_data.assign(Date=pd.to_datetime(market_data['Date'])) \n", + " market_data['Volume'] = (pd.to_numeric(market_data['Volume'], errors='coerce').fillna(0))\n", + " return market_data\n", + "\n", + "btc_data = get_market_data(\"bitcoin\", tag='BTC')\n", + "btc_data.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateClose**
23172013-04-28134.21
23162013-04-29144.54
23152013-04-30139.00
23142013-05-01116.99
23132013-05-02105.21
\n", + "
" + ], + "text/plain": [ + " Date Close**\n", + "2317 2013-04-28 134.21\n", + "2316 2013-04-29 144.54\n", + "2315 2013-04-30 139.00\n", + "2314 2013-05-01 116.99\n", + "2313 2013-05-02 105.21" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# only keeps date and close price\n", + "data = btc_data[['Date']+['Close**']]\n", + "data = data.sort_values(by='Date')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(range(len(data['Close**'])), data['Close**'])\n", + "plt.title('Close Price vs Time')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(range(len(data['Close**'])), data['Close**'])\n", + "plt.title('Close Price vs Time')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.00338567],\n", + " [0.00391735],\n", + " [0.0036322 ],\n", + " ...,\n", + " [0.49049124],\n", + " [0.4921635 ],\n", + " [0.4987161 ]], dtype=float32)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Normalize close price\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "values = data['Close**'].values.reshape(-1,1)\n", + "values = values.astype('float32')\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled_price = scaler.fit_transform(values)\n", + "scaled_price" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1622 696\n" + ] + } + ], + "source": [ + "# split data for training, testing\n", + "train_size = int(len(scaled_price) * 0.7)\n", + "test_size = len(scaled_price) - train_size\n", + "\n", + "train, test = scaled_price[0:train_size,:], scaled_price[train_size:len(scaled_price),:]\n", + "print(len(train), len(test))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_train shape (1617, 5)\n", + "x_test shape (691, 5)\n", + "y_train shape (1617,)\n", + "y_test shape (691,)\n" + ] + } + ], + "source": [ + "def create_input(data,lookback):\n", + " inputs = []\n", + " for i in range(len(data) - lookback):\n", + " temp_set = data[i:(i + lookback),0].copy()\n", + " inputs.append(temp_set)\n", + " return np.array(inputs)\n", + "\n", + "def create_output(data,lookback):\n", + " output = []\n", + " for i in range(len(data)-lookback):\n", + " output.append(data[i + lookback, 0])\n", + " return np.array(output)\n", + "\n", + "# use lookback = 5\n", + "x_train = create_input(train,5)\n", + "x_test = create_input(test,5)\n", + "\n", + "y_train = create_output(train,5)\n", + "y_test = create_output(test,5)\n", + "\n", + "print('x_train shape', x_train.shape)\n", + "print('x_test shape', x_test.shape)\n", + "\n", + "print('y_train shape', y_train.shape)\n", + "print('y_test shape', y_test.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.00338567, 0.00391735, 0.0036322 , 0.00249936, 0.00189305],\n", + " [0.00391735, 0.0036322 , 0.00249936, 0.00189305, 0.00150909],\n", + " [0.0036322 , 0.00249936, 0.00189305, 0.00150909, 0.00226826],\n", + " ...,\n", + " [0.21134934, 0.2107492 , 0.21978931, 0.2231364 , 0.22342357],\n", + " [0.2107492 , 0.21978931, 0.2231364 , 0.22342357, 0.21869662],\n", + " [0.21978931, 0.2231364 , 0.22342357, 0.21869662, 0.21416111]],\n", + " dtype=float32)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00150909, 0.00226826, 0.00244377, ..., 0.21869662, 0.21416111,\n", + " 0.21925919], dtype=float32)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_input: [[0.00338567 0.00391735 0.0036322 0.00249936 0.00189305]\n", + " [0.00391735 0.0036322 0.00249936 0.00189305 0.00150909]\n", + " [0.0036322 0.00249936 0.00189305 0.00150909 0.00226826]\n", + " [0.00249936 0.00189305 0.00150909 0.00226826 0.00244377]]\n", + "y: [0.00150909 0.00226826 0.00244377]\n" + ] + } + ], + "source": [ + "print(\"x_input:\",x_train[0:4])\n", + "print(\"y:\",y_train[0:3])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_train shape (1617, 1, 5)\n", + "x_test shape (691, 1, 5)\n" + ] + } + ], + "source": [ + "x_train = np.reshape(x_train,(x_train.shape[0],1,x_train.shape[1]))\n", + "x_test = np.reshape(x_test,(x_test.shape[0],1,x_test.shape[1]))\n", + "print('x_train shape', x_train.shape)\n", + "print('x_test shape', x_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build Model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "lstm_1 (LSTM) (None, 100) 42400 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 1) 101 \n", + "=================================================================\n", + "Total params: 42,501\n", + "Trainable params: 42,501\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense,LSTM,Dropout, Flatten,Conv2D, MaxPooling2D, Activation\n", + "from keras.layers.normalization import BatchNormalization\n", + "from keras import optimizers\n", + "import keras\n", + "\n", + "model_1 = Sequential()\n", + "model_1.add(LSTM(100, input_shape=(1,x_train.shape[2])))\n", + "model_1.add(Dense(1))\n", + "model_1.compile(loss='mae', optimizer='adam')\n", + "model_1.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 1617 samples, validate on 691 samples\n", + "Epoch 1/100\n", + "1617/1617 [==============================] - 1s 612us/step - loss: 0.0228 - val_loss: 0.3423\n", + "Epoch 2/100\n", + "1617/1617 [==============================] - 0s 34us/step - loss: 0.0222 - val_loss: 0.3064\n", + "Epoch 3/100\n", + "1617/1617 [==============================] - 0s 34us/step - loss: 0.0193 - val_loss: 0.2720\n", + "Epoch 4/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0161 - val_loss: 0.2286\n", + "Epoch 5/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0140 - val_loss: 0.1860\n", + "Epoch 6/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0110 - val_loss: 0.1369\n", + "Epoch 7/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0084 - val_loss: 0.0843\n", + "Epoch 8/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0053 - val_loss: 0.0328\n", + "Epoch 9/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0036 - val_loss: 0.0322\n", + "Epoch 10/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0026 - val_loss: 0.0437\n", + "Epoch 11/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0019 - val_loss: 0.0258\n", + "Epoch 12/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0017 - val_loss: 0.0261\n", + "Epoch 13/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0022 - val_loss: 0.0263\n", + "Epoch 14/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0021 - val_loss: 0.0251\n", + "Epoch 15/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0018 - val_loss: 0.0287\n", + "Epoch 16/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0019 - val_loss: 0.0297\n", + "Epoch 17/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0020 - val_loss: 0.0295\n", + "Epoch 18/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0021 - val_loss: 0.0244\n", + "Epoch 19/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0018 - val_loss: 0.0273\n", + "Epoch 20/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0020 - val_loss: 0.0279\n", + "Epoch 21/100\n", + "1617/1617 [==============================] - 0s 35us/step - loss: 0.0019 - val_loss: 0.0262\n", + "Epoch 22/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0020 - val_loss: 0.0252\n", + "Epoch 23/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0017 - val_loss: 0.0248\n", + "Epoch 24/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0017 - val_loss: 0.0297\n", + "Epoch 25/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0019 - val_loss: 0.0255\n", + "Epoch 26/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0017 - val_loss: 0.0267\n", + "Epoch 27/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0282\n", + "Epoch 28/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0019 - val_loss: 0.0255\n", + "Epoch 29/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0017 - val_loss: 0.0285\n", + "Epoch 30/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0267\n", + "Epoch 31/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0019 - val_loss: 0.0251\n", + "Epoch 32/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0018 - val_loss: 0.0281\n", + "Epoch 33/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0019 - val_loss: 0.0243\n", + "Epoch 34/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0018 - val_loss: 0.0224\n", + "Epoch 35/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0281\n", + "Epoch 36/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0019 - val_loss: 0.0229\n", + "Epoch 37/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0259\n", + "Epoch 38/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0020 - val_loss: 0.0250\n", + "Epoch 39/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0234\n", + "Epoch 40/100\n", + "1617/1617 [==============================] - 0s 34us/step - loss: 0.0018 - val_loss: 0.0273\n", + "Epoch 41/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0019 - val_loss: 0.0239\n", + "Epoch 42/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0019 - val_loss: 0.0239\n", + "Epoch 43/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0019 - val_loss: 0.0232\n", + "Epoch 44/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0017 - val_loss: 0.0258\n", + "Epoch 45/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0019 - val_loss: 0.0243\n", + "Epoch 46/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0023 - val_loss: 0.0269\n", + "Epoch 47/100\n", + "1617/1617 [==============================] - 0s 34us/step - loss: 0.0017 - val_loss: 0.0218\n", + "Epoch 48/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0220\n", + "Epoch 49/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0018 - val_loss: 0.0210\n", + "Epoch 50/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0016 - val_loss: 0.0249\n", + "Epoch 51/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0019 - val_loss: 0.0226\n", + "Epoch 52/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0016 - val_loss: 0.0248\n", + "Epoch 53/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0234\n", + "Epoch 54/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0018 - val_loss: 0.0224\n", + "Epoch 55/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0016 - val_loss: 0.0241\n", + "Epoch 56/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0018 - val_loss: 0.0199\n", + "Epoch 57/100\n", + "1617/1617 [==============================] - 0s 26us/step - loss: 0.0016 - val_loss: 0.0262\n", + "Epoch 58/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0018 - val_loss: 0.0200\n", + "Epoch 59/100\n", + "1617/1617 [==============================] - 0s 25us/step - loss: 0.0019 - val_loss: 0.0214\n", + "Epoch 60/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0018 - val_loss: 0.0218\n", + "Epoch 61/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0015 - val_loss: 0.0231\n", + "Epoch 62/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0018 - val_loss: 0.0225\n", + "Epoch 63/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0021 - val_loss: 0.0240\n", + "Epoch 64/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0016 - val_loss: 0.0192\n", + "Epoch 65/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0024 - val_loss: 0.0198\n", + "Epoch 66/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0018 - val_loss: 0.0208\n", + "Epoch 67/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0022 - val_loss: 0.0206\n", + "Epoch 68/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0021 - val_loss: 0.0190\n", + "Epoch 69/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0040 - val_loss: 0.0262\n", + "Epoch 70/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0021 - val_loss: 0.0192\n", + "Epoch 71/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0017 - val_loss: 0.0184\n", + "Epoch 72/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0019 - val_loss: 0.0207\n", + "Epoch 73/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0020 - val_loss: 0.0216\n", + "Epoch 74/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0019 - val_loss: 0.0183\n", + "Epoch 75/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0016 - val_loss: 0.0229\n", + "Epoch 76/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0181\n", + "Epoch 77/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0019 - val_loss: 0.0187\n", + "Epoch 78/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0017 - val_loss: 0.0183\n", + "Epoch 79/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0018 - val_loss: 0.0203\n", + "Epoch 80/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0021 - val_loss: 0.0231\n", + "Epoch 81/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0018 - val_loss: 0.0183\n", + "Epoch 82/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0022 - val_loss: 0.0190\n", + "Epoch 83/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0016 - val_loss: 0.0188\n", + "Epoch 84/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0024 - val_loss: 0.0192\n", + "Epoch 85/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0017 - val_loss: 0.0184\n", + "Epoch 86/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0024 - val_loss: 0.0198\n", + "Epoch 87/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0017 - val_loss: 0.0176\n", + "Epoch 88/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0038 - val_loss: 0.0226\n", + "Epoch 89/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0020 - val_loss: 0.0180\n", + "Epoch 90/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0016 - val_loss: 0.0177\n", + "Epoch 91/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0187\n", + "Epoch 92/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0019 - val_loss: 0.0192\n", + "Epoch 93/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0021 - val_loss: 0.0176\n", + "Epoch 94/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0017 - val_loss: 0.0179\n", + "Epoch 95/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0188\n", + "Epoch 96/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0027 - val_loss: 0.0174\n", + "Epoch 97/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0018 - val_loss: 0.0194\n", + "Epoch 98/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0019 - val_loss: 0.0173\n", + "Epoch 99/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0035 - val_loss: 0.0225\n", + "Epoch 100/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0019 - val_loss: 0.0172\n" + ] + } + ], + "source": [ + "history = model_1.fit(x_train, y_train, epochs=100, batch_size=128, \n", + " validation_data=(x_test, y_test), verbose=1, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "epochs = range(len(loss))\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(epochs, loss, 'bo', label='Training loss')\n", + "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "yhat = model_1.predict(x_test)\n", + "plt.plot(yhat, label='predict')\n", + "plt.plot(y_test, label='true')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test RMSE: 522.289\n" + ] + } + ], + "source": [ + "yhat_inverse = scaler.inverse_transform(yhat.reshape(-1, 1))\n", + "testY_inverse = scaler.inverse_transform(y_test.reshape(-1, 1))\n", + "rmse = sqrt(mean_squared_error(testY_inverse, yhat_inverse))\n", + "print('Test RMSE: %.3f' % rmse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add a dropout layer" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from keras import backend as K\n", + "K.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "lstm_1 (LSTM) (None, 100) 42400 \n", + "_________________________________________________________________\n", + "dropout_1 (Dropout) (None, 100) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 1) 101 \n", + "=================================================================\n", + "Total params: 42,501\n", + "Trainable params: 42,501\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model_2 = Sequential()\n", + "model_2.add(LSTM(100, input_shape=(1, x_train.shape[2])))\n", + "model_2.add(Dropout(0.2))\n", + "model_2.add(Dense(1))\n", + "model_2.compile(loss='mae', optimizer='adam')\n", + "model_2.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 1617 samples, validate on 691 samples\n", + "Epoch 1/100\n", + "1617/1617 [==============================] - 1s 558us/step - loss: 0.0195 - val_loss: 0.2814\n", + "Epoch 2/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0180 - val_loss: 0.2411\n", + "Epoch 3/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0145 - val_loss: 0.1945\n", + "Epoch 4/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0116 - val_loss: 0.1450\n", + "Epoch 5/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0088 - val_loss: 0.0919\n", + "Epoch 6/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0056 - val_loss: 0.0395\n", + "Epoch 7/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0037 - val_loss: 0.0264\n", + "Epoch 8/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0030 - val_loss: 0.0371\n", + "Epoch 9/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0026 - val_loss: 0.0272\n", + "Epoch 10/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0024 - val_loss: 0.0243\n", + "Epoch 11/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0027 - val_loss: 0.0239\n", + "Epoch 12/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0026 - val_loss: 0.0256\n", + "Epoch 13/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0025 - val_loss: 0.0236\n", + "Epoch 14/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0030 - val_loss: 0.0241\n", + "Epoch 15/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0029 - val_loss: 0.0228\n", + "Epoch 16/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0029 - val_loss: 0.0225\n", + "Epoch 17/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0028 - val_loss: 0.0223\n", + "Epoch 18/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0030 - val_loss: 0.0228\n", + "Epoch 19/100\n", + "1617/1617 [==============================] - 0s 35us/step - loss: 0.0035 - val_loss: 0.0243\n", + "Epoch 20/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0049 - val_loss: 0.0263\n", + "Epoch 21/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0032 - val_loss: 0.0334\n", + "Epoch 22/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0025 - val_loss: 0.0223\n", + "Epoch 23/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0024 - val_loss: 0.0242\n", + "Epoch 24/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0025 - val_loss: 0.0243\n", + "Epoch 25/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0024 - val_loss: 0.0230\n", + "Epoch 26/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0027 - val_loss: 0.0239\n", + "Epoch 27/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0025 - val_loss: 0.0238\n", + "Epoch 28/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0024 - val_loss: 0.0261\n", + "Epoch 29/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0024 - val_loss: 0.0236\n", + "Epoch 30/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0027 - val_loss: 0.0226\n", + "Epoch 31/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0025 - val_loss: 0.0237\n", + "Epoch 32/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0022 - val_loss: 0.0235\n", + "Epoch 33/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0031 - val_loss: 0.0263\n", + "Epoch 34/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0027 - val_loss: 0.0218\n", + "Epoch 35/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0026 - val_loss: 0.0256\n", + "Epoch 36/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0023 - val_loss: 0.0238\n", + "Epoch 37/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0022 - val_loss: 0.0242\n", + "Epoch 38/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0025 - val_loss: 0.0227\n", + "Epoch 39/100\n", + "1617/1617 [==============================] - 0s 33us/step - loss: 0.0022 - val_loss: 0.0235\n", + "Epoch 40/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0022 - val_loss: 0.0246\n", + "Epoch 41/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0023 - val_loss: 0.0249\n", + "Epoch 42/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0022 - val_loss: 0.0214\n", + "Epoch 43/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0026 - val_loss: 0.0218\n", + "Epoch 44/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0025 - val_loss: 0.0224\n", + "Epoch 45/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0021 - val_loss: 0.0242\n", + "Epoch 46/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0023 - val_loss: 0.0248\n", + "Epoch 47/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0023 - val_loss: 0.0217\n", + "Epoch 48/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0025 - val_loss: 0.0213\n", + "Epoch 49/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0024 - val_loss: 0.0245\n", + "Epoch 50/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0026 - val_loss: 0.0245\n", + "Epoch 51/100\n", + "1617/1617 [==============================] - 0s 33us/step - loss: 0.0031 - val_loss: 0.0237\n", + "Epoch 52/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0026 - val_loss: 0.0210\n", + "Epoch 53/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0027 - val_loss: 0.0213\n", + "Epoch 54/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0023 - val_loss: 0.0212\n", + "Epoch 55/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0022 - val_loss: 0.0246\n", + "Epoch 56/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0022 - val_loss: 0.0221\n", + "Epoch 57/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0024 - val_loss: 0.0217\n", + "Epoch 58/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0020 - val_loss: 0.0265\n", + "Epoch 59/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0022 - val_loss: 0.0229\n", + "Epoch 60/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0021 - val_loss: 0.0239\n", + "Epoch 61/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0024 - val_loss: 0.0237\n", + "Epoch 62/100\n", + "1617/1617 [==============================] - 0s 26us/step - loss: 0.0028 - val_loss: 0.0221\n", + "Epoch 63/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0026 - val_loss: 0.0238\n", + "Epoch 64/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0021 - val_loss: 0.0206\n", + "Epoch 65/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0028 - val_loss: 0.0218\n", + "Epoch 66/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0026 - val_loss: 0.0202\n", + "Epoch 67/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0023 - val_loss: 0.0214\n", + "Epoch 68/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0020 - val_loss: 0.0230\n", + "Epoch 69/100\n", + "1617/1617 [==============================] - 0s 26us/step - loss: 0.0027 - val_loss: 0.0235\n", + "Epoch 70/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0022 - val_loss: 0.0209\n", + "Epoch 71/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0029 - val_loss: 0.0231\n", + "Epoch 72/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0025 - val_loss: 0.0207\n", + "Epoch 73/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0033 - val_loss: 0.0262\n", + "Epoch 74/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0031 - val_loss: 0.0198\n", + "Epoch 75/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0053 - val_loss: 0.0204\n", + "Epoch 76/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0031 - val_loss: 0.0336\n", + "Epoch 77/100\n", + "1617/1617 [==============================] - 0s 32us/step - loss: 0.0022 - val_loss: 0.0203\n", + "Epoch 78/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0047 - val_loss: 0.0247\n", + "Epoch 79/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0030 - val_loss: 0.0309\n", + "Epoch 80/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0028 - val_loss: 0.0196\n", + "Epoch 81/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0028 - val_loss: 0.0254\n", + "Epoch 82/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0024 - val_loss: 0.0193\n", + "Epoch 83/100\n", + "1617/1617 [==============================] - 0s 29us/step - loss: 0.0023 - val_loss: 0.0204\n", + "Epoch 84/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0020 - val_loss: 0.0209\n", + "Epoch 85/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0020 - val_loss: 0.0210\n", + "Epoch 86/100\n", + "1617/1617 [==============================] - 0s 26us/step - loss: 0.0020 - val_loss: 0.0214\n", + "Epoch 87/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0020 - val_loss: 0.0205\n", + "Epoch 88/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0021 - val_loss: 0.0197\n", + "Epoch 89/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0022 - val_loss: 0.0214\n", + "Epoch 90/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0023 - val_loss: 0.0199\n", + "Epoch 91/100\n", + "1617/1617 [==============================] - 0s 27us/step - loss: 0.0029 - val_loss: 0.0199\n", + "Epoch 92/100\n", + "1617/1617 [==============================] - 0s 28us/step - loss: 0.0036 - val_loss: 0.0187\n", + "Epoch 93/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0051 - val_loss: 0.0235\n", + "Epoch 94/100\n", + "1617/1617 [==============================] - 0s 31us/step - loss: 0.0026 - val_loss: 0.0277\n", + "Epoch 95/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0021 - val_loss: 0.0187\n", + "Epoch 96/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0029 - val_loss: 0.0215\n", + "Epoch 97/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0032 - val_loss: 0.0186\n", + "Epoch 98/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0045 - val_loss: 0.0195\n", + "Epoch 99/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0026 - val_loss: 0.0242\n", + "Epoch 100/100\n", + "1617/1617 [==============================] - 0s 30us/step - loss: 0.0020 - val_loss: 0.0189\n" + ] + } + ], + "source": [ + "model_2.compile(loss='mae', optimizer='adam')\n", + "history = model_2.fit(x_train, y_train, epochs=100, batch_size=128, \n", + " validation_data=(x_test, y_test), verbose=1, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "epochs = range(len(loss))\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(epochs, loss, 'bo', label='Training loss')\n", + "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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MREEQhI6IoMdChwiELbGzXKLw4mzbbgxaKM51t3noACGra8BMFARB6IgIeizCQQLagsPa/ceTmZnZtu3XdqaMyGgn6EHljHWZIAjCgCCCHotwkAAWbD0I+tIZ49u2/diYNCKjXcilOSxp/oIgDB4i6LEIG1kudqvqdpitYGLbth87RZnOdh56Y8BCeb13wMwUBEGIRgQ9FuEgAW3FZunm47nskfbx8laPPupYieUIH2zeNZCWCoIgtCGCHgszhh7TQz/9R7DoqzD70nbi7cZcSGRpP5Fq3/z0QFoqCILQhgR5Y2EKui2WoJ/8/ch2VLw8TcVeGXqgXmq6CIIwOIiHHgMdDhLUlu5DLtDOQ7/xi9fFHOL1+WMeFwRBSDQi6LEIhwhi7XFStLXHKBNPo2TKnJhDQlJ1URCEQUIEPRahIOE40hbbPHTV9TgpoysIwmAhgh4DHQ4SxILN0oOH3irkHQXdmQ3TzzdOhUNSpEsQhEFBJkVjEQ4Swoq9Jw9dm0LdIbOF2/ZDKAg/ycdKiGZfEIfN0fl6QRCEBCIeeiy0EUOPmeXSbpwp6LFCLqbI21SIJl8wwQYKgiB0RgQ9BiocIoQFe09ZLt0JulKElRUrYRF0QRAGBRH0WISDvfTQuxinrNjMkIsgCEOMna/CmoeSbUVCkRh6R7RG6VBkKX932NOM1/TC2Ley2LASxl+zH0bawJWVYGMFQegzj15mvC66Prl2JBDx0DsSDgEQ1FYcPXnoU86Gc++Gs++Kfd5iw0aIJS+cAitOTaydgiAIHRAPvSNhIzwSoofiXGCEWo6/oevzFhtWjD8Q1OxOkIGCIAixEQ+9I6agB+milksvUFYbNiQHXRCEwUEEvSNRHnqPeeg9oMyQiyAIw4iqneCpSbYVfUIEvSNmDD0Uz0rRHlAWG+kWKc4lCMOK3y2Eh89OthV9QgS9I20eehxZLj1hsZJpFUEXhCFN45HIdkud8Vq9Mzm29BMR9I60xdDjqLbYExYbGUoEXRCGNP87NbJdfyB5diQAEfSORHno/Y2hY7GR3kXjC0EQhiB1+5NtQb8QQe+IjuShp9mtPQzuARF0QRheDNPJ0FZE0DvSNilqJc3RX0G3dtmaThCEIUTYTC8OeCLHgsPvuyuC3pGoPHRXAjx0l/YmwChBEBKK1u33ffXGq785cix6e5gggt6RqDz0RIRcHGERdEEYcoQ7FMx77//gz+e3E/FdB8vjvFcInrwGnl2eQAP7RlyCrpRappTarpTapZS6tYsxn1dKbVFKbVZKPZZYMweRQIvxohwJyXJxhFsSYJQgCAkl2MHRWnUflL7bLuRy9z8/jO9e1bth6wuw8UmoS26WTI+CrpSyAvcD5wIzgSuVUjM7jJkC3AYs1VrPAm4eAFsHBzNtqcZWiOqqLG68dOhk1OANGP/5/7wRgpLOKAhJo6v4eGseOrDC8x0O1/fgkGkN9y+K7FdsSYBxfSceD/04YJfWeo/W2g88AVzUYcxXgfu11rUAWuuKxJo5iNS1CvrI/t/L0r722YsbD8Pz34BPHoND6/t/f0EQ+kb05Gc0ze2la+WGg726z8trt/bHqn4Tj6CPAaKfI8rMY9FMBaYqpVYppT5USi2LdSOl1HKl1Fql1NrKysq+WTzQ1B/AY8lEO9P7f68Ogr71YA34m4wd3ceiXeHwsE+tEoSk09WEZ1N7Qd9b1oOg+9sL+kebd/XHqn6TqElRGzAFOBW4EvijUiqn4yCt9Qqt9UKt9cLCwthNIZJO3X6q7EX9nxCFToJ+sLI28qjXF1HWGv5yPtw9IZJmJQhC7+lK0JsraVQZbbs1lT156O3vc4vtKT4ureqvdX0mHkE/CIyN2i82j0VTBrygtQ5orfcCOzAEfvhRd4BKS4IE3Wpvt1tZXR2ZjGmJQ9A7xtm3vwT7VhnbXT0yCoLQM1166EeoJrtt1+qp7uE+7b+H6crH6/9+vL/W9Zl4BH0NMEUpNUEp5QCuAF7oMOY5DO8cpVQBRghmTwLtHBy0hvoDlKvC/uegA2SacXirA4Arm/+Gri8DoKG2h2mGfe/DXYWw/6PIscptkW0RdEHoO10Jug5TGipo23X5ehB083v4StZlbYcc5etp8SenbHaPgq61DgLfBF4BtgJPaa03K6XuVEpdaA57BahWSm0B3gS+r7Xu4ZMYgrTUgr+JPYFcirJc/b9fxgjjNbsYgCtsb6HM2Pmjb25Ad1zcEM2GR43Xw59EjjVH5h0OHBmicxCCMBzoZtHQJ+FJbdsq6CEQ6ia8ad7nP8H5bHAvAWAMFeytSs6ipLhi6FrrF7XWU7XWk7TWPzWP3a61fsHc1lrr72qtZ2qt52itnxhIoweMaqNN3KeefIpz0xJwQzPt0dm5ObQbL42+YKfjHW3BmRk5FjVh8/Pn1ibAPkE4SmlNTojBjnAxn16xGgAnARpaAl3fx/TQ9zXCypn/i7dgDrk0sauy6/sPJLJSNBqz7+eu8EiKc939v9+MC4zX4zqvIHMSoLqpi1z0yh2w/wNjO/oXLyqlqqmxof/2CcLRSjce+j41mokj8wHje1rXnaCb96kN2hmb58aemU+uamJ/9RD20I8ajmxCKysH9IjEeOgFU+COeihZ2nboYt+PKQ0X4VQBqpu6WNzwh8WRbV+UcNftp8lu/KKlIStQBaHPmEK8QD2BVu1l0D1qKunpRqaLkwC1zd0sAjTv06KdTB+ZiTU9nzzVRE1zN38EBhAR9Gi2PM+RwiUEsCUo5GISFXI5/ZTTcLvTcRKgqisPPbrOhM/00OsOQG0pnzgWGPuBHmJ7giB0TdMRvBY3LqcTZWs/X3bzsnlgcwLgUn52HOkmfGIKulc5mT0mG9LyyFVN1DT7wNfYVkpksBBBbyUchroD7HNNB2B0zsAI+rfOmUtudiYOAlR15aFH8a+1O/AGQlBhrEB7KzADALf2UtOd5yAIQteUrWaXYzr5GY428T6YPR+u/gdLJheAWfbj27ZnObi3m9WfFZtptmaRlplPutMG7jwyaKa22Qs/Hwv3HzcYP00bIuitBDyApspnozDTmZi0xVasNjj3bpjzOVAKm8OFSwV6rhMB+Jvr2VbeCF6jxsSnTcYfB7fyUdk4/Oo1C0LS8dbDkc18zHRGZDrB9NDHzD0dJp/ZaXjxkddh/4ex4+77P2S7YxZ5maYD6MzEgqalqRHQRgek7rLZEowIeivmf9YRX4LDLa0cfwNc+hAAyuYkyxaitLrnXPJ0vFRVV8GzXwXgkM4DjCwZEXRB6ANla0CHWeWfzIgsF7TG0DNj129yeavgkXPguRvbnwiHoLaU3bqYggxjrQl2I5mipbk+Mq6hh9WmCUQEvRUzm+RQizUxGS7dYXORYQuxPy5Bb0Htf79t/4jOBcCNj2oJuQhC76naCcCHnjGGh252KaNweszhF7f8w9jY/nL7E01HIBxkbzCP/AwjbIPDqAHV0lQbGRdVwXGgEUFvxRT0g82WgfHQo7E5cVuCcYVcMpSXxqaI8AeUA21Lw628xsSLIAi9w2t4zw2kMyLTBRNOMo4XTuv+ulCH75tZmXWHL8eIxUObh15EpLRH2Dt4KcYi6K2Y2ST1YRdjEjkhGgubC4f2U98SiL1aNHscAJvCJeRYvVQfiXQiH5+fDg43mUo8dEHoE956QvZMwlgMD/2C38D1b0RWdnfDwYqowltm7fPdwRGRp3qH8TqSiIf+5HubE2d7D4igt2LG0D3aNSgeup0ggZDGE6vmQ8jHuvwL2alKKLD78VRHYnCTR2SgHOnk2HvIjxUEITbeevw2I8+8KMtliHDxgm4v8RQeA8DP/v5i5ODO/+BNL2avHsmkQrPctt14/Znj4bZhV+66BZ7/Jux9J4E/RGxE0FvxNwLQhGtQYuh2bYhxfaxVaH4PdUE72pFBpr+Cz1iMAl3LA7cweUQG2NPJtvolbVEQ+oK3Hq/VEPQRWc64Lvk0YLSACFbtIdi6/qNyO+WZMwHFpEKz5K7poTvp8L3++G/wlwv6bXpPiKC3EuWhD3jIxWLD5a9hmtrfWdADXgg0Uxe0YXEavySTLIepdY3lP6H5TBmRAY50Mi0i6ILQJ7z1NFvSUQry0x1xXfJ2peF556sGSqubjVTEhoMcDOeT4bQZoRto89C7pJuSA4lABL0Vs/BVKC2PNEcCc9BjYdY0/77tyc6CvvlZ0GHeDc4ixx5ZMVrmsWK1KBaMzwVHOhnKJ4IuCH3BW0+DTqcgw4nN2o0EfivSJvKwNkpu5NHAlsON4DF6G+zx5zCpMD3Sf9gRebr/hv+mTrd8+/1VifkZukAEHaDxCLzxE8IosrM6V0ZMOFPPBWCPHt1Z0BsOAfCGZxJ5lshf8ybt5qwZReakaDppSlaKCkKf8NZTF06LeNVdkTOubbMBN2FHJgWWRrYebgCzr8HmpsxIuAXA3vp0r7j1lttomXcdOm8iZdqosf7+hoFtIi2CDvDRAwBY0BT29J+cCE69DYAQls6C7m9GW+w0BBT+zEijqCZcTClqjdOlk6a9NHiDUs9FEHqLt57qoKtnQY9qIXn6nPFY0guYkNbCmr014DGyXXY2pzFpRJSgO7Nh7uXwpRcZm+cm7eLfoG76GH3dSgCaasoJhwdu5agIOrQrhjUiMwGNLXrCYkE7s3Hh71xrOeAhbOayls35ettih2ZcTBtp1kZ3ZOAKm2U7B9JL99RAVXKb3gpCQtEafA1UBFw9f9dbwyjAVUunQ3oBp/jf5uJD91BfUw5ALZmRDBcAiwUuWQHjl7S71dgxhnOWEapj7wCW1hVBB6jdC8D3Qt/s+a92orCn4VL+GB56E0GrIegFWZkww2gKVZSXy7JZ5tJkVzaOQBOgBzYX/eGz4HcLBrUWhSAMKP4m0GHK/U6K4sxwAYxQSm4JAFdZX2fPpjUA1OoMJkaHXLrCkU7Y5iJPNVC67eMB+06JoAM0HsE79mT+EVjCxMIeZqkThLI5ybQGY4Zc/BYjDleY6YT8yQAsLmiJTOC4srHoAC781HoSJOj+ZiiL6oK0/SWoNr3zQaxFIQgDirlKtF67GZndi2w2exqc+WNwGQ2krVVb0SgaVQbj8+NIc1YKlV7IbOsBznj9fPjg/r5Y3yMi6ADeOhowhHxSPH9tE4E9jQxrkDpPZ0H3WoxHwRGZThh3gnE8ugh/Wg4AWXioTVQh/X/eAA+dAc1mK9id/4mcOzJ4K90EYUAxBb1RuxmZ3QsP3ZkJ2WPg6mcBKPDsodmSSXFeBk5bfFlxKreEpWojAC3FS3oY3TdE0AFaaqkOG39lB03QbS7SLYGYHroHF3arIjvNDrnj4dKH4cLfRcaYXsKTjjsZt+GexNhzwHiEbO2RGP1I+NLb73Xf0FoQhgttdVzcjMzqhYeeUWS+GuUBRlPJkWB6ZF4rrnsY124PF/NiRUH81/UCEXStoaWWcn8a+ekOcuNcaNBv7Gm4LcHOqYf+JprDLgoynFgs5qTMnMsga1RkjCnoEyxHmLPnoQQZZAp20CxA1FKDN3sSTdpF+b7trN5b0/WlgjBc8BhPoHU6g5HZvUiAaJ0gTY/Ue3k2dBInTSmM/x6Zxnd47ZzbmV2cG/91vcDW85AUx98E4SAHvM7B887BKKFrbeRQXYeKi/5m6sO53U/Ougbgl6HVA9/3Hvz5PABqLEXU60KKVSXv7Kzk+In5iX9fQRhMavYAUG4dSa7b3vP4M+9oJ+LYXbDgi9SljcOtzuPiY8fE/96n3gZTzuKqiaf2wuDeIYJu1ioua3EyduwA13CJxubCbQlQ3ezH4w/idpj/FX4P9UFH9/nw+ZOMfFef8fiotY6sVOszpqD/69ttR/Zax2JNG89ZvvdY8+FDNJ/6C6PNliAMR7a8AK/eTqM1m3R3fnzfmRO/0/nYBfeRA3yjt+/vzICJp/b2ql4hIZcWo8xlmdcVd6GehGB34cIIt7Tz0v3N1ATs3Qt6Wg4UL2zb3bI3AVkoMWLkh/1puMYZVeb+S/+RtftqO40RhGHDU9cAcNgyqnfhlmGECLop6NWh9MHLQQewpZHZtJcnHXdSXm/GrbVG+5tMQe/hF84aifXveu6uBBjUWdB3h0eTteQrAPi0nXWlEkcXhj/VwUEowJckRD8LIGUAACAASURBVNBNQa8jfXCW/beSbsSjj7dso6LRaxwL+lA6RLN29WyLLSLoNTU1eAMx6qr3Bt25hEBV8RmUjJ8Ap96GXQUpqxy8VlqCMFDsDeSyqCQv2WYMCCLorYKuMyjMGERBzxzdttnW7NksrdmMs2dbrJHzafgor/f2y5zotMR3Q7Pxf28vd3/tc0amTXYxFjTNNbLASBimmNlbYWXlF8ErOWnKwKQNJhsRdK/hddaTbnQAHyzcEQ+holXQ/2RUYfQQh4ceFXJJU37KG7zw3DfgzgLY9XrvbNnyAsob8b7HX/sHHJl5kUkjM02yub66d/cVhKHCYWNBz0/Tvs+kccWMzRvEBIhBRAS9pZagcuDFMcgx9MgfjzYPvWo7AM06jkpwE05u20zDx5H6FtjwdwgH4O+XQCjYzcVR1O5rmywC8GRNZNyUue3HmIIeaK6nyRfnfQVhMNn2Yptox8TsQfBczXgunDe663HDHBH0llpabFlkOu2Dm5I37TNtmx6vH17574hJOHv20OddDjdvIjRmkeGhH9rf7vSmzZ/EZ4dZmAzgtsBXqLz02c5jnEaN+K/ZXqBszcr47isIg0XAC09cCQ+eZFQIfebLkRIWrex7n6bMiVSTnbLxcxBBh5ZaGlUGhYOZsgjGpOYptwKQ3lIGH0SW9ue6bbjscdSHyBmL1eEm1x6kumxnu1P3PflvQvHUXTbz8B+a/Tee4UyKRo/rPMZlCPpp1k+Y/tp1Un1RGFqUrY5sP3QGbPoHfPJYhzFrKHXPRalBLO+RBETQW+qo0xkUDUYd9I6Y3U3SvJXtDudk9KLio91Nji1Ic0UpADen/xKAyRxkbTxphmbs/LW9Po6fkB/7D4krp93u+xs2xW+fIAw09VGT9eZK0LAjqvNYKAAtNewN5lGcmzbwLSaTiAh6Sx3V4bTBXVTUitnIIstf3nbokM6nfuTSXtwjjQxrALvX6KDydnU2TY4RTLaUseFAHGmGZrGijdUWzpgxIvYYZ/sCRB9+8DasOG3ASoAKQq/wNXQ69PzqbZEds35LqcfJ1BG9KKY1DDnqBV231FIRcFM0mBkurZgNZXMCR9oOPRE8jWuWlMR/D7ubNHwUqHpCWKgjA2vRdGbayvn0YH3P17fUEVY2PDg5ZWoXhYaskZoXIWVl5KHX4NB6eOW/4rezv1Tvht8ugIbDg/eewvAghqAfOHQoEnI0BX1nk5PJRakbbgERdGippSY8yKtEWzFDLgXBirZDLkuQucU5XV3RGZsTp+cw37Q9T7XOIjfdhWv0TCZQxs7ybgS9aies/C54qmmxZpDusFGS302o5yuvwS27CI88hsus70SOB/qX/x43ax8xGm50jI0KgreBkCXy/Q1ZnOTQzK6KJuOAKeiVoXSmiIeeovia4NkbUIFm6nV6cjx0M+QyIhyJoVt1AKulF4W2ogoMVelsZozKRBVOw6W9tFQfINhVE+l/fg3WPgw7XqGGLGaMyoqU643F2EWQUYi9aAYOFVmV+ua6QYqnt9ajbjgEO/4D3s5emXCUUHfAmJj3m7X7fQ20WNP5Tugm9IyLCGaOJkc1sbOi0ThvCnqNzmSqeOgpys/HwMYnACNuPb03heoThc3wKvJVxJOekBNHSc9olt4M+VMAqNJZzBmTAwVTARinD1HaVUPa1sbYTeWsCkxn5uis2OM6kjmy3e7K9z/unb19xWJOZK15CB77HDz71cF5X2FoUbYO7p0NK06Bn42CxnLwNtCk3WzNPwt1+V+xp+eRo5oiHvq+9wkpO4d0AZNHiKCnHr7Gdrul1vHxNXpNNNZWQY94m0vPvKh398gZC8cZ4taMi+Mn5rUV0i+ilrWlXVRItEUeUT8ITGbmqDgF3VyhemjCZQC01Bzq+ikgkfia2u3W7/qAgx1ryQupz4EPjdfD5jqLg+vB10BtOK0tHdHizmOErYWK0i0Q9MP2l9iYvpicvPxImeoUJS5BV0otU0ptV0rtUkrd2s24S5VSWim1sKsxQ4JDG9rtTp+9oHdhjkTR6qHTyF5LCUv1I7jnXdz7+ziMX+S5JUWcOrWwLTwx13GQT8q6yHQJRVrf7dMjWTIpztoWJ9wIp9zK6AtvByBP13KgdhCE1R/5I7whPInMUANPfbRn4N9XGFrUl7Xf3/s2+vBG9geymdTa4D0tl2JVxc/KroO7CqH+AB8HxqV8hgvEIehKKStwP3AuMBO4Uik1M8a4TODbwEeJNjKhhAJGhgZwkbqPa9L/wI8vW5QcW0xv16kCVAUd5BaM6FujCm3EtMcUmkX7nYbAX8e/8FXv7zy+bF3bZwAwbeZcxsXTuRyMRUan3db2FJBDE9sOD0I8O8pD35N+LBalWb1518C/rzC0KP+0/f5HD6CaK/hTaBmTWsMpablkhNo7Muubcjl2XC+SDYYp8XjoxwG7tNZ7tNZ+4AkgVlzgJ8AvgUFKe+gjzy6HV28naHGwsSWfH1x5HnZrkiJPUWGPJp3GtKI4wx4dmXUxzL4UTuucRuivr+g8/k2jfvoLaZ9lo57Mjy8/qffvaXOgHZkUWJtYPRh10v1NNDlGcJHvTmYuPBWAusqDVDX5Bv69haGB1p0F3eTD8IxIBkta5xaNa+yLuOaEkgE0bmgQj5KNAQ5E7ZeZx9pQSs0Hxmqt/93djZRSy5VSa5VSaysrK7sbOnBsNmqVhLViZLabOcXZybED2lVM9OLo+8SsMxMue6T9hOXkMwHwNDd2Hu9tQJecxK3NV/Lsgr/i6mNcUblzmZTub99AurbUyERJNL4manQGzYXHMH3yZACKVA3v75YKkEcN3rq2lc3RVOksLl84jhmjWr8/7UtTvBpawI8uXkR2PD1Ehzn9dk2VUhbg18D3ehqrtV6htV6otV5YWNiLbtmJJG8SAP+yn8OseDM7BoooD/0EyzYunt+LhrM9YdaJCfuaOje/CLTQYsnA4w/1L7snLY9iVwtbDjdQ3xIwKjzeNw9+PQP+dgnseKUfP0AHfA1UB53MK86BolloZeE4x17e31WVuPcQhjZmv4BQB9lqShvDLy+bGwlXppvacuJ3AThmxhQuSOEKi9HE45odBMZG7Rebx1rJBGYDb5kf6EjgBaXUhVrrtYkyNGFkjkQ3V/LDhs/z9XgzOwaKqCYV2ROOxZLIBhsOY4LIjZcjDV7GRy8aCjRTFzT+66f2R9B1iAm175Ovr2FtaQ1nWCJxeXa/Tsv+9QRu3kxWei9q03T1VnUH2BcsZny+G1xZqKLZXFS9ji/sPNLzxUJqYOadV5DPKCrZHi5Gz7iQ6efe2H7cgi/B2ONh5BzIHEXhvCuSYGxyiMdDXwNMUUpNUEo5gCuAF1pPaq3rtdYFWusSrXUJ8CEwtMS8pS5SIdDbQEPRcQS1hZmjkxhugXZt5Cyf/0ti722WFXArH+W1Te3rowdaqPWbgl7UD0EvmgPAF+xvsX/T+/DaHW2n/hlaSlqglof/8kjf7w/GStTq3dB4iDJdGOkFufibjAnsZUzD+kg9eSG1CRge+sGwUf5Wo3Cc+d+Q06FCqNUGo+Yai+6OX95WLfRooEdB11oHgW8CrwBbgae01puVUncqpS4caAP7RdUuWHUf/HJ8pJCUr56aoLEqNOkhlygPPbqDUUIwUxndeJn5/DK4e2LkXKCFCq+FsXlpZPSnBvyFv4URM7nEuZovbf4iVG7j9/Yv8kDwfDLO/hEADZX9bFv3zxvgt/NR4aAh6LmmoE8/j7DFzomWTeypbOr+HkJqYHroh7Ux6Tk+25qc9SNDmLhi6FrrF7XWU7XWk7TWPzWP3a61fiHG2FOHjHf+uwXwqpEvzadPG6/eBsp9TjJdNopzk9z52zqAkzRmyCUdH5mNe8AXVdcl4KG8RTGtP945GJ7QnMsoCUaaZNzTeCajLrubsxbOAED5m2j0Brq6Q/c0VcCW59p2N1hmM7d1EtuZQSh7PCWqnD1VXayG7Yq/Xwov39Y3m4TkETAE/ZA2Gqyn4U+mNUOS1F0p2tg+thrU2gi7+Bop9zuYMiKjbznfiWQg39/mAmWhwNFBTEMBCAc53KyYlohyBwu+1LZ5rf+HnDytiPPmjGr3hLCv2tO3e28zkqbKR5/FKb5fc8qSE9qt9LNlFVFkaWBjV4unYvHmz2DXa/Dh7/tmk5A8zEnRNeHpxn6gj79XKUzqCvrB9g8JWw41UFVbAzpEhc/BqJwke+cDjVLgyGCMu0MPUPNL0Kwd/Yuft+LOQ9uNp4FLTlnEH65agM1qAaudsNVJhvKysSyOMr6xaDHKFtxh/y7ezPH84Jzp7U6rjBEUOxpZtSvO1EVvA7x3LwAe0iSHfbjx9HUAbNfFVJ95H1z5eJINGnqkrqDvfqPdrhsfa7ftA+Cg18GoZFRXHGzScphqiYphB7wQMJbpe3EmrFCRmvs5AD57ynHtusEoRzp5dj/r9nVRT6YnvPVoq5PXdtZx4bzRncszpI+gKHCQ2poq9vf0FPD23bDyOxDysV+PoEk7eX2rZMgMR1q0i/Tjr4HxS5JtypAjdQX94HrqCiMlZXJUEwfLjc5ANUEXI7OPAkHPLWFiY+RJZc2/HjTix4BHOyMZI/3lM/fAtz8BV/usIeXMYFRaKFLGtLd46wk6MgmGNcdPyO983ny//7H/lVW7u8lHD4fgzZ/CpmcAeDs0lyya+WjPIKxwFRJDMBIvt7jS4+u5exSSuoJeX8ZB21h+EriawKSzyFHNHDFXpzbiPjoEXbX/pV+08XY4YtQvD1jTyU5L0KSs1Q65JZ2P29I4yfMaeyoa0H1pLO2tx2MxniJixvsXfhmAk6ybeW9nN4Jevbtt85/6FLKKxuNSAbYfTNJqZaH3BCIT35kZqV9kq6+kpqAHfdBcwV5/Dq9kXYp94knYCNFSbVRqa9RpjBoqgn796/Ct9T2P6wtj5nd5qjxr1sBPCldtB2BxcA1HGuKIV7/xU1j1m8i+t55G0kmzW2NnJGWNgrN/ShHV7Duwr+v7lm8E4OrQHfzC8S1OmWvWj6+q7LyKVhiamCmLT6V/gcKsFJ//6gepKegNRtx4U3OWUefbLNbj9hglaRpwMzJ7iPxSFC+E/EkDc+9TboVzfgZzL+/8tmMnDMx7xiBbNbPrSCO89mN48BSo3NF5kNbwzt3w6v8z2uMBeOupC7sZn+/u+o9P0SwAchu24w/GqMveVAn/+AoAq4MTefKGJeTkGUvDM3Uj28o7hIPCYSMLpllqxAwpzMn87cEiRmQOEWdsCJKagl5vCPrGhgyjE48p6GMwHsubcCenh+hgY3PA4m/AJSvaVtM1ZU/hgel/5rbPzBj49//SywB8z/Y0asPf4L1fw+ENHPz3zzuPjQqLrPr7j40Gvy01VAZdjM3rprSv2Z1prKqgrDbGxOgbd7ZtzhpbQElBOmQXA1CsqtjUsZH2rteMeYZfTYRPnzEWpoWj/lBU7TSaKgiDi99YPFbusVJ4NHx3+0hqtu8wi+Af0nnMGp3dJuhzLYZoONJzk1cyN1lM+wx89AAZ1z7J1wbqiaAj4xejXTmM8tYwasuPAWjRDrbu3kuo2mPUYA+H4a8XQum7BC0ufCGNp/oQnx6sZ15TBft80xjfnaCnG405Tres57Ut5Sw/ZbJRYjXoM55+9n9IwJXPpfU3c/5ssxplrvF0Mt1RxeZDZi13rY1UTzM8A7R59rx6O3WLf0jOlseg3njKO3LB3yhyW2DHy3DG7ZAxInGfm9AZM+RSG3IwVwS9S1JT1RoMQT+s85kyIgOyjEprx1j28GZoHkUFCV5mPxw4+y64/o2BC+90gbK1//Jt0iVkqhY2HjQXAzWUQem7AHw3/G222aaTrxp4cd1OlL+J8lB29803zPufaf2Y+rVPGcceOBEeOgPeuQeqdvCoXsaRzJl8boFZYy5jBNjTWeguZ8uhevhoBfxqEjx1LVRsxZM2ioO6fVZNzge/hPoDHLQYjT1Wv/AggVW/g4//Bo9fAZufgw2PwdZ/wc5X23LoAdjwODx+pZFtI/QNM+TSohOXbpuKpKag15fhseUSUA6j9kf2OLTFeBjZoYu7f4RPVax2KF4w+O9rj3zWB3U+WXkjyVItkdz07S8BsNq5mLfVQqZPnsRcaymr1huecqXOYVyc/1+Wun3t+5u+8RMAXmscx81nTiU33SyGphRMW8Yp3tcpPVyJfvMuozP8ludh0zOsC07kiM0Iy/zcdTO/nv8qe10z+H/6a/xg9J/Z455Loa6OdIM6uM5Y9PLcjfDk1fDoZWx+9IeEvY1GKOm5r8H2F2l85GL0kS19/SSPbsxVoh6czEh2ldQhTIqGXA5SbStkVHaaGVqxoDJGQkMZjdod6WwiDDxmO7zmY7/K466v8q36e2ho3MHrWyu4/bwZqJd+AMDX6q/j+5+dRvoeD+gAt+mHAaggfkGfzh4+XfsOx3Y4vtcxhc8e06HW/PTzsW/6Bz+2rEB563ndspQzwqsAeKZ5HstOPRUcG7j15FtQFitc+CE/MS/Vz0zFveVdnC3VPG6/hI/1VCaOLuLT8mZ00Msdod8xq+xJ+MWTAPisGViDHjLL3sb3h5PZuPAXLDr/+l5/lEc1Zgw9aHUPnQy1IUhqCnrDQQ6F8xlbEJXJ4sqGhjI+f+JsRiwtSZppRx0Xr4A3f0r6eT/lFpsT/p1NlsXDzLq3KN3qZgKwPvssfOFcLpk/BlyfhR0vc6J1Mz5t47BrCiX58dVTP8+6Gl4yuiOuCU9lkWUHv7Vcxb3XntZuBSsABUbq4met71Ols7jft4wz7IagO4/5PKefPhds5xArt0ZljWZk2FhlusmTw9lXfZkzZxa1nddr8uDf38WvrTwbOolHfWcyZ+okPpNfzonrv0PZR/9k4qnXkJ/I+vepTtVOgtgIZY5Kfg2mIUzqCbrfAxVb2KWXtY+1mbXHx40eCbLKbPAomglXPBrZd2XhDDTwgONeeNqoq3JP7cmcO2eUUXjrmCth3yr4+G88HPoMi+ZNw9JxyX9HvrvV6JJksis8ml+N/F+e+mwW3xp9TOxrzM5V3oyxfN31G06eNgbsHph1MXcXTuv+/UpOQm99gXXO48gecVk7MQdQi76CnnUJG/ZWkh7M4I6cNBaMNybmm/euIKPKw5bDDZw0JUldu4YjRzZTZh9HfpbEz7sj9QR9lSESe4P57YtPtdYed0r8Lak0d17Ruc1fyH8tKYkcOPsuwgu+wgLfWJaXxDGBnTUaTvsRODM5WH6Y1bYT+eMZi6G7lbAON9zwLq78yTzlaA3p3BrfzzD1bNTUs1kILOxiiHLnctyszs2KHek5ZFXXsuGQCHrcaA3lG9nBTArlqaZbhr+g1+6DV/4LppwNC64zmhQDT4VO5eHoyZPWbIuoLkFCEph6Dqz/C95pn8W13ah1PnfqRGaPiaoDk5aDpfhYju/NfU/5PmB0L/9CvNeMmtubd0gIdncOudbDbDncMOjvPWxpOAhNR1ijz2NElgh6dwx/QV/3J9i2Er39JTy500iv2cth9zRaAhnMaScSprfUl5oiQuKYfh7cXovLYoE7jP+fn186+MKaNFxZ5Fq9kfx3oWcOrgNgtb+Ea4pzkmzM0Gb4Cvred2DsCVBnpI4pHSL9r+cYpyzzOWFifvuKbJ/5lbFCcOKpg2+r0B5LVLaszcWooVKGYTBwZpGJhz2VTTT5gv1rAXg0cGAN7H6DkLKxVY9nyaQYVTeFNobnb9PhT+AvF8Cir0LdAcrSZlDcsrXtdE3AzmULittfkzECzvnpIBsqdMsP9oJKzaUQXeLKwhFqJqw1G8vqWDKpINkWDV3K1sLDZwKwW01g9rhCRqd6Y5p+Mjy/Ta2LM9b8EcpWs9bTPsvgw/BM+aIMB9x5kHaUPUK7C7DoEBMsld2X/BVg28q2zd/5zuN7Z/eQfSQMV0Hf1G53bTBSOfCTazZz7MXfkwI+wtBk5kVgsfGm42aK1t5DOCxzOm00V7WtCAWgZi+ejBJmeB+hcMlVLJ0sTlpPDE9BP/wJ/qJj+EvwLOq125hoM5k3qZhLF45NonGC0A3ZY2D2ZQBcF3yadfv72J4vFfnVJHhkWWS/Zg+7Q4XkZOdw67nTu75OaGP4CbrWUP4pe2yT+J/gl6j51i7uuuYsOPdXRtU7QRjqnHcPAE3axfaO9diPVhoOGa/lG9EBL3gb0FU7WNc8grNnFh191VH7yPD7lPzN4K1jY3Muk0dkMKHAXBZ+/HI46XvJtU0Q4sGZiT5uOSGs7Kls7nl8qhFoAU9UP9dwCF79n7bdh//1Bqx9GBX08kJgEadNl9LE8TL8slx8Rv7uvmYbMybKqk9heKIcGWSoFnb3tYH2cGXPW/BXo94Od9RDzR547HKoinSx+tInXwClWe9YyB41kxMmSqpivAw/D91rCPp+jy3inQvCcMOZgZUwZVVHWQz975e1be7dvBp+v5hwzV4e1edwRcioZ2lVxkTxfU1n8OOLZrdfTyJ0y7D10Bu1m0mFIujCMMVhFJmqravFGwgdHaLVVAnhQNvuhKfPAuAnoS/xft4lPPKlRbChBT3lLF7eb+HanFGcMaOoq7sJMRi2HnqDdjOxQCqvCcMUh+GMuPFSWn2UxNHf+z8AfhT4UrvDH1qO5e/XH8+YnDQ49YeoMfM5d/ExIuZ9YBh66EZT30bclBQchZ2HhNTA9NDT8bKnspnpI1N8PuiV/4YP72edYxFvp11A8IIzCRVMZ/2GdTy44AJZN5Ighp+gmx66Mz2HTFc35VEFYSgTJei7K5qSbMwA422AD34HwE8bz+OqZSXYZk7CBiw+e3JybUsxhl/IxYyhFxTIqjFhGOMyKk1OTPezpyrFQy71BwDYMOdHrNdTOWmKfHcHimEn6HrqMv5L3cSoQmkOIAxjMozf36mZPnZXpriH7jN+vtV1WeSlO5iR6uGlJDLsBP2DhnweazmBY8YdZUWdhNQi3RD0c/yvsqeyGZ3KdfrNBs+rDwVYMim/55aCQp8ZdoK+r9rD+Hw3F3Xs4i4Iwwkzy2V880bG+XdxoKYlyQYNIKagl3msnCgFtgaUYTcpeuVx4/jcgmJsUttBSBHGqQo2HqxjXH6KZm2ZIZcmXMwf37nPqpA4hqUqipgLKcECIx97iXULW1K5JZ3poftUGiX5shhwIBFlFIRkccG9MGIWl1vfYl9FHWz9F1Rs7fm64YbPqFeTl5uHwyaSM5DIpysIyWThl3Dip/DQ6/Dk1fD7E2DP28m2KrH4mwhiZdwICbcMNHEJulJqmVJqu1Jql1Lq1hjnv6uU2qKU2qiUel0pNT7xpgpCCmJmu4xq2hw59tcLWVNaE3v8nrfgzZ9DOBz7/EBky6z8Dmz6R3xjPTUQ9Bnb/mY4sBq9/SUadBqTizITb5vQjh4FXSllBe4HzgVmAlcqpWZ2GPYxsFBrPRd4Brg70YYKQkriNkrDnmBpH2q5/9+rY6cy/vUiePsXcGcujU/e0P6crwnungiPnAul7yXGvrr9sPYReObL3Y/b9iI8cRXhe6ZS+eslbHzqLvwPngkPn4Wq3MbfQmcxd0x2YmwSuiQeD/04YJfWeo/W2g88AVwUPUBr/abW2mPufggUJ9ZMQUhRTEGfZ9nT7vDJh//EW9sru700c+sTbN652/DWD66HHS9DSw3sf59P/3Jz/xcsbX8J7p0DgE/b2XyoPnJOa9j4FKFX78BfsRv93I2wbSWWcIBCzy7mbvkVjuotHNT5XGBfQeEFd7Js9sj+2SP0SDxpi2OAA1H7ZcDx3Yz/CvBSrBNKqeXAcoBx48bFaaIgpDDuvLbNZu3kmVn3c92W6/my7WWuW7W3x249sx6dj7bYUOFg27Et4fHkqVp+9+4efn7J3L7Z5amBp7/YtutUAV5YcTscM54xHCFr+1NYWmqwAtZV/0dIK74S+D6TFl/MTSeOwvvhH6na8RGfzP8JzyyejtN2FJQHHgIkNA9dKXU1sBA4JdZ5rfUKYAXAwoULU3hpnCDESXohWB0Q8pOufHzuootg500Q8LC3dA+B0KL2/TTT8qClhsfU+XxBrwRoJ+Ye7aRu5BKmVj7NP9cf4Jazp5Gf0YdKhhVbIOjlm+o2rna+ywne97iNP8OGyJAHghcQyJ3EvHzNJsccrph7UpsXnn3uDyk6F2b15TMR+kw8gn4QGBu1X2wea4dS6kzgv4FTtNa+xJgnCCmOxQrf/gTunQtn/g9upwOufAL+eiFjQ/vYWFbPgtbFOA2HoKWG+4KXkLHkeli9st2tVk77BZkT5rM4tB7bq49zSng1a17Yw7L5E2HdX+DSh8DVTR2Vqp0QCkDRTGMb+LhlJDde+N9Q8W98c77Aqh1HOP2ti9mSsZjFl/+OucXZKKU4eaA+H6FXxCPoa4ApSqkJGEJ+BfCF6AFKqWOBB4FlWuuKhFspCKlM1mj473Kwml/HETMAOFbt4tDaf7Fg/LXG8TUPEVZW/hlayr1zZsH2sUYlw6v/Qdjq4vwJJxrjdhix7gcd98IOjH/A8/d9i3GLLmDiqALSJi3B4XBEbPhoBbz0fQA2OI5lhGogFycjx05i1rFLgCU4gdNHz4K5G5iZXgBOyVoZavQo6FrroFLqm8ArgBV4RGu9WSl1J7BWa/0C8CsgA3haKQWwX2t94QDaLQiphTXqq5heCGm53MLT8OnT7J85k3EzFkLpKo6kz2CfdxRTizLgun/B3rdh8pntsxsmnwnn3g0v/QCA120nc0bwHS5qeQ7eeQ6Af3A6Y699iOPch2HETChbA8DK0PEsCWzHrn38LHwt3z1nRmdb8yYM0Icg9BeVrCpvCxcu1GvXrk3KewvCkOfP50Ppu227j899hCu23cS/rafzh7Tl/Pumk7q/PhyG574Gcz9vCHz5JoIfP8Z2+zQytj7N+OrIvXc6ZzPFt4lVoVn8auSvePqGE7AqRVAjKzuHIEqpdVrrhbHODbviorgcSAAABfFJREFUXIJwVDDlrHaCXvDx71DWJj4OZHHe8aN6vt5igUtWRPZHzsZ27s+MScpZx6AfXoYKGpnGU3ybANivR3DnRbOwmxkpDoThhgi6IAxFjv8aZBfD+KXwv9M42WqIbpku5No5cQh6d4yah7q11MiuUQoaj1D/n58zY/T5zC2WPgPDGXmeEoShiM0Jsy+FzJFw2o9w4gegZMpMxieiYqHNaYg5QGYR2ZfeyzGLz+z/fYWkIh66IAx1Fn8dvHW0OAv43tJLk22NMIQRQReEoY4jHc75KWnJtkMY8kjIRRAEIUUQQRcEQUgRRNAFQRBSBBF0QRCEFEEEXRAEIUUQQRcEQUgRRNAFQRBSBBF0QRCEFCFp1RaVUpXAvj5eXgBUJdCcgWa42QvDz2axd2AReweW3tg7XmtdGOtE0gS9Pyil1nZVPnIoMtzsheFns9g7sIi9A0ui7JWQiyAIQooggi4IgpAiDFdBX9HzkCHFcLMXhp/NYu/AIvYOLAmxd1jG0AVBEITODFcPXRAEQeiACLogCEKKMOwEXSm1TCm1XSm1Syl1a7LtAVBKPaKUqlBKbYo6lqeUelUptdN8zTWPK6XUb0z7Nyql5ifB3rFKqTeVUluUUpuVUt8eyjYrpVxKqdVKqU9Me39sHp+glPrItOtJpZTDPO4093eZ50sG094ou61KqY+VUiuHur1KqVKl1KdKqQ1KqbXmsSH5+2DakKOUekYptU0ptVUptXio2quUmmZ+rq3/GpRSNw+IvVrrYfMPsAK7gYkYTck/AWYOAbtOBuYDm6KO3Q3cam7fCvzS3P4M8BKggBOAj5Jg7yhgvrmdCewAZg5Vm833zTC37cBHph1PAVeYxx8AbjS3vw48YG5fATyZpN+L7wKPASvN/SFrL1AKFHQ4NiR/H0wb/gJcb247gJyhbG+U3VagHBg/EPYm5Yfqx4exGHglav824LZk22XaUtJB0LcDo8ztUcB2c/tB4MpY45Jo+/PAWcPBZsANrAeOx1hZZ+v4uwG8Aiw2t23mODXIdhYDrwOnAyvNL+dQtjeWoA/J3wcgG9jb8TMaqvZ2sPFsYNVA2TvcQi5jgANR+2XmsaFIkdb6sLldDhSZ20PqZzAf74/F8HqHrM1m+GIDUAG8ivGkVqe1Dsawqc1e83w9kD+Y9gL3Aj8AwuZ+PkPbXg38Rym1Tim13Dw2VH8fJgCVwJ/MkNZDSql0hq690VwBPG5uJ9ze4SbowxJt/JkdcvmhSqkM4B/AzVrrhuhzQ81mrXVIa30Mhud7HDA9ySZ1ifr/7Zs9axRRFIafA/GLIImCRSCCBMROVCwEgwipEiRVGhFMYekvEMGfIFiIlaUoKCqpNdZ+f0VTGEEwQbMgaGFl8VqcM8kgbiEuO3eH88Awc8/d4hk4e3buubNmp4COpOdNu/wDk5KOANPAeTM7UZ8sLB+G8BbnNUmHgZ94y2KDwnwBiD2TWeD2n3O98h20gr4G7K2NxyNWIutmNgYQ507Ei7gHM9uCF/Mbku5GuGhnAEnfgUd4y2LUzIb+4rThG/MjwLc+ah4HZs3sE3ALb7tcKdgXSWtx7gD38B/NUvNhFViV9DjGd/ACX6pvxTTwQtJ6jHvuO2gF/SmwP94W2IovXxYadurGAjAf1/N4n7qKn42d7GPAj9qyqy+YmQHXgWVJl2tTRTqb2R4zG43rHXi/fxkv7HNdfKv7mAMW4wmoL0i6IGlc0j48RxclnSnV18yGzWxndY33eZcoNB8kfQU+m9mBCE0B70v1rXGazXZL5dVb3yY2Bv5zU2EGfyvjI3CxaZ9wugl8AX7hTw/n8B7oQ+AD8ADYHZ814Gr4vwWONuA7iS/v3gCv4pgp1Rk4CLwM3yXgUsQngCfACr6M3Rbx7TFeifmJBnPjJJtvuRTpG16v43hXfa9KzYdwOAQ8i5y4D+wq3HcYX3WN1GI9982//idJkrSEQWu5JEmSJF3Igp4kSdISsqAnSZK0hCzoSZIkLSELepIkSUvIgp4kSdISsqAnSZK0hN/tU8rmTl5wpgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "yhat = model_2.predict(x_test)\n", + "plt.plot(yhat, label='predict')\n", + "plt.plot(y_test, label='true')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test RMSE: 595.991\n" + ] + } + ], + "source": [ + "yhat_inverse = scaler.inverse_transform(yhat.reshape(-1, 1))\n", + "testY_inverse = scaler.inverse_transform(y_test.reshape(-1, 1))\n", + "rmse = sqrt(mean_squared_error(testY_inverse, yhat_inverse))\n", + "print('Test RMSE: %.3f' % rmse)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Predict the next word [Sentence Completion] using LSTM.ipynb b/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Predict the next word [Sentence Completion] using LSTM.ipynb new file mode 100644 index 0000000..4570f8a --- /dev/null +++ b/03 - Day - 3 Reccurent NN/3. Sample LSTM Application/Predict the next word [Sentence Completion] using LSTM.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "corpus length: 600893\n", + "total chars: 57\n", + "nb sequences: 200285\n", + "Vectorization...\n", + "Build model...\n", + "WARNING:tensorflow:From /anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n", + "WARNING:tensorflow:From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1044: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "keep_dims is deprecated, use keepdims instead\n", + "WARNING:tensorflow:From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "Epoch 1/6\n", + "200192/200285 [============================>.] - ETA: 0s - loss: 2.0025\n", + "----- Generating text after Epoch: 0\n", + "----- diversity: 0.2\n", + "----- Generating with seed: \"ide, all self-confidence of spirit, it i\"\n", + "ide, all self-confidence of spirit, it is the mankind the strange the still and the still and the mankind and the such and the can of the most still and the science of the conscience of the still and the still and the science of the still and the disconting and the science of the such a philosophy and the straint of the straint and the still and the still the such the science of the stand and still and the strange of all the still and a\n", + "----- diversity: 0.5\n", + "----- Generating with seed: \"ide, all self-confidence of spirit, it i\"\n", + "ide, all self-confidence of spirit, it is not the present of the all the devirious and power the morality and light forge their spetior of the was in the man herely sufferent of the despinitions the procance, and not in the schonder of all the as the par in the morality of the surperable of a sind as the stromby will the still pochaps and right of the fact of and a that the man in the side of last heart and the sperious for his mase the\n", + "----- diversity: 1.0\n", + "----- Generating with seed: \"ide, all self-confidence of spirit, it i\"\n", + "ide, all self-confidence of spirit, it is new an imply, worker, the schepid and sympathore of the powaded as monglities, ok mankind and is of feelive\n", + "of (andian.\n", + "\"ereis of waleshic serval that they confriveing\n", + "gen\n", + "it find. creation\". to oht which met one more varieve: in other one morality nanking estimal\n", + "everythenbrute as the reficu any\n", + "gait quest are--peacering, the criemity, the\n", + "comparion, itself the reciail and physefuls: to the t\n", + "----- diversity: 1.2\n", + "----- Generating with seed: \"ide, all self-confidence of spirit, it i\"\n", + "ide, all self-confidence of spirit, it iundeally,\n", + "all leae gran onlyy--with dould those some us he was\n", + "now it--thal okly, consequently.\", for vanding amaanness, which ie?--thus w3le ny\n", + "penseplaint\n", + "will there, the gothing. a thingely. \n", + " fuble many but\n", + "\n", + "\n", + "subphilorcandis; -remaincie withcred convict reperifiyicule. allsh for\n", + "distinds? and oreedwance'sle chroacid and to mrom in and\n", + "inigrutuve tho cauraweaw enob. gow catine been straidse\n", + "h\n", + "200285/200285 [==============================] - 200s - loss: 2.0022 \n", + "Epoch 2/6\n", + "200192/200285 [============================>.] - ETA: 0s - loss: 1.6402\n", + "----- Generating text after Epoch: 1\n", + "----- diversity: 0.2\n", + "----- Generating with seed: \"ast that it is immoral to say that 'what\"\n", + "ast that it is immoral to say that 'what all the world and the paste of the fores of the spirit and the present and and the fored to the same in the world and the free in the sense of the sense of the such a perhaps and such a present of the power and the perion of the stand of the world and the string the world and the sense, and the morality of the sard and and the morality and the world and the world and the morality and the sense of\n", + "----- diversity: 0.5\n", + "----- Generating with seed: \"ast that it is immoral to say that 'what\"\n", + "ast that it is immoral to say that 'what in the world of still supposity and the way and and spirit, by the been to a man the is in the replection of the because a dessicial althought and morality for the think and the science and a denerst of the the books\n", + "ond itself and morality as a so the greature as a obtained to person the one's for in the soul to the have the for the to philosopher of also that a word and the\n", + "mankind and the cons\n", + "----- diversity: 1.0\n", + "----- Generating with seed: \"ast that it is immoral to say that 'what\"\n", + "ast that it is immoral to say that 'what not myself--perhapt to intelf\n", + "conwinctom if not us so an\n", + "prevace,\" not be themsnonatle boseed of firenerful other to the uncertaint aicy be to nake trusherity with it\n", + "is to\n", + "which itslengerate revolity or obsermand of the evenlely hn evence order indine) to the questive man\n", + "that the nature dislikening\n", + "consequently in\n", + "the abter dusined, it. wonte, and that a be for that the our ort in heade be poss\n", + "----- diversity: 1.2\n", + "----- Generating with seed: \"ast that it is immoral to say that 'what\"\n", + "ast that it is immoral to say that 'what-ncuds\"ions,ost about the\n", + "mositatorphess,\n", + "\n", + "them)s wonked ho\n", + "kumenticaed ovpow for infeel aln philosophing and is therefore orasmed iven\n", + "hrought ha very unbelopor, oo jay\n", + "massarly\n", + "is pessistion withey comes, formingly betayf will-liud unclar-great apilies pingrypthou pres\n", + "it centerenturs that this iu\n", + "science ears ise, may\n", + "but adogine, lakepre conception efolity and base do not and\n", + "his petitian and \n", + "200285/200285 [==============================] - 212s - loss: 1.6402 \n", + "Epoch 3/6\n", + "200192/200285 [============================>.] - ETA: 0s - loss: 1.5525\n", + "----- Generating text after Epoch: 2\n", + "----- diversity: 0.2\n", + "----- Generating with seed: \"ood, is the instinct of the herding huma\"\n", + "ood, is the instinct of the herding human is a subtle and and and the sees and and the man of the constitution of the consequently the constitution of the sense and it is all the sense of the sense of all the sense of the sense of the sense of the sense of all the subtle and the sense of the sense of the subtle and and and for the art in the sense of all the subtle and man of the sense of the possible and the sense of the sense and and \n", + "----- diversity: 0.5\n", + "----- Generating with seed: \"ood, is the instinct of the herding huma\"\n", + "ood, is the instinct of the herding human self the\n", + "constitution of the prosest a the act it is not also a destruction to he manure the make with such a the act the spirit\" with the self and for the constraint to be the contemptiousness and a philosopher with proscend to the person that the conceptions of the soul and and and actually many the restrust no man intelle to the partious of the sense is\n", + "he probated to find the through of the \n", + "----- diversity: 1.0\n", + "----- Generating with seed: \"ood, is the instinct of the herding huma\"\n", + "ood, is the instinct of the herding human\n", + "prompation it case (up\n", + "all our tames. they hores of which agaquir to theirshed of oursout didder act in that certuunly, to wit itse is\n", + "fight actunart--the usenty, they with constraintment for as them to\n", + "inveruon recequarally when womst threater.\n", + "\n", + "\n", + "104. the\n", + "baps for grafiman of mature and not our symb ink of the so must a upon philosophers eotricitation. a man is man: with felloped mojal mistrusi\n", + "----- diversity: 1.2\n", + "----- Generating with seed: \"ood, is the instinct of the herding huma\"\n", + "ood, is the instinct of the herding humanity his experiently regarded\n", + "as the staplivevations at a\n", + "xoustrh oual, to more almost \"have bas as othersuman!--existucates--kind of they causle, in,\n", + "just alonator noe us at rects\n", + "freedot in the\n", + "value.: yan not suchlxe represiung fictur\n", + "nature them, ikees ar\n", + "cistell once of libstann of gain out acilation, is by difficultely a worshed, delorrenessn, wail be poseinglies; this fracc\n", + "use us extraemar\n", + "200285/200285 [==============================] - 207s - loss: 1.5525 \n", + "Epoch 4/6\n", + "200192/200285 [============================>.] - ETA: 0s - loss: 1.5062\n", + "----- Generating text after Epoch: 3\n", + "----- diversity: 0.2\n", + "----- Generating with seed: \"nce for the bible has hitherto been main\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nce for the bible has hitherto been maintainess and and the sentiment of the subjection of the subwless of the sentiment of the soul and substanding and the sentiment of the subliment of the soul and the sentiment of the particulated the philosophing the strength\" the sentiment of the sense and it is the substance of the science of the probably in the strength of the sense--and the desire and and the power of the desire the subjection o\n", + "----- diversity: 0.5\n", + "----- Generating with seed: \"nce for the bible has hitherto been main\"\n", + "nce for the bible has hitherto been mainer and so the free as the srelless and has so the and the individual to ascise of the courden the subbles the ancourdess of this power the increased the conduct of and as to see, and important and so of the desire and among it is not and and man. a so and substally and which interestrated pancy be of the delight to man in the world and something of the masters and does and subkise and the provicat\n", + "----- diversity: 1.0\n", + "----- Generating with seed: \"nce for the bible has hitherto been main\"\n", + "nce for the bible has hitherto been mainnely to my this capmon of peateress, a philosopher bebutely and life.ly conscience blong\n", + "syntimedntwly as--the deleint when gowmoelesty and also\n", + "volued the ? the --a tlireung you dawging and developinc, the de-daccudusing sand to exceptions\n", + "a theild. the knows the percais power, and in so man ofthing menines areafiness betragnem, he are\n", + "stampthing has musuptiety, and pranct as\n", + "resils\n", + "sureml-passic\n", + "----- diversity: 1.2\n", + "----- Generating with seed: \"nce for the bible has hitherto been main\"\n", + "nce for the bible has hitherto been main of solve experienc? into woute dearer\n", + "individual catsking heattr-psysous: for the tol? he developenis easike out him\n", + "is foods\n", + "impragition.\n", + "theredy. \n", + "? human instated one certainty and i othering lifetooneopous\n", + "value cipments and eitoparitually\n", + "ofcessity, history or\n", + "gress for if life their\n", + "=obquitily the by\n", + "know man of\n", + "us\n", + "one, with ; a gal agtramd\n", + "a thingly many\n", + "re, olvand and, will fage\n", + "had forat\n", + "200285/200285 [==============================] - 202s - loss: 1.5063 \n", + "Epoch 5/6\n", + "200192/200285 [============================>.] - ETA: 0s - loss: 1.4759\n", + "----- Generating text after Epoch: 4\n", + "----- diversity: 0.2\n", + "----- Generating with seed: \" necessity. but this distinction is\n", + "a de\"\n", + " necessity. but this distinction is\n", + "a destinent of the strange and an an an an an an an an an an an an an an an an an an a probably and the continuent of the person of the supersess of the constitution of the struggle the continuations of the more strange and the fact that is a man of the strumentant and an an an an an an an an an an an interpretess of the interpretess of the more profound and the struggle with the language and an an an\n", + "----- diversity: 0.5\n", + "----- Generating with seed: \" necessity. but this distinction is\n", + "a de\"\n", + " necessity. but this distinction is\n", + "a delight and an exception\n", + "the distrust of such as it have women a religious demositions of the highest conditional the amotion, but the ancient and as a will connection of powerful of an an interpretation. the age there is not first the person of the rather all the origination and forter in the reality, the livery and the whole the soul and an any acciumned\" the case of the most means of the highest \n", + "----- diversity: 1.0\n", + "----- Generating with seed: \" necessity. but this distinction is\n", + "a de\"\n", + " necessity. but this distinction is\n", + "a destined to becomiliby impossible only stapudsian poses it is the experatipdrient--above\n", + "e plight insuveralies: the general wise time. but good acts sense and its grantity of conrend precisely age any knowledges giving, as hat distance of nay to but that is notively, of persistement and when behil gening unception, amoth in a truthful skiesse, and nare of all of contradianss--every wagnner at a reli\n", + "----- diversity: 1.2\n", + "----- Generating with seed: \" necessity. but this distinction is\n", + "a de\"\n", + " necessity. but this distinction is\n", + "a decews--it only moodhing too o\"ther\n", + "world?--kantly it of the exived such a sla-lithry of being wishnism, at thou alchatuling conbitit\n", + "refreenaly. and a bypultion to shonly and frammently upen taste of a sidemel soul phanethic to\n", + "him and duch losing ords\n", + "of unite\" of all foremen germany glard\n", + "candder sge the\n", + "no denginoty, only, ideeration, ourlty, weaker wan andt--a\n", + "nowperst\n", + "defe fol trains must ita\n", + "200285/200285 [==============================] - 192s - loss: 1.4759 \n", + "Epoch 6/6\n", + "200192/200285 [============================>.] - ETA: 0s - loss: 1.4546\n", + "----- Generating text after Epoch: 5\n", + "----- diversity: 0.2\n", + "----- Generating with seed: \"\n", + "according to balzac--i would venture to\"\n", + "\n", + "according to balzac--i would venture to the sense of the present in the will something to the substance of the soul to the morality of the sense of the more the spectance of the sense of the contiments of the life the sense of the present to the morality in the sense of the power of the present to the present to the spirit and all the sense of the sense of the sense and sense of the former and the present to the spirituality of the sen\n", + "----- diversity: 0.5\n", + "----- Generating with seed: \"\n", + "according to balzac--i would venture to\"\n", + "\n", + "according to balzac--i would venture to be a peating, and to in the any persons, it is not as the concenting to the spiritual, but and becomes neverthit is an its charanisticism, and the and concerned end spiritualizating contrae from the world and its consequently, and an all distrust of the means and against men of the egoost and the subjection and the freed calls of the suppose of society of the conception of the sense of old to a w\n", + "----- diversity: 1.0\n", + "----- Generating with seed: \"\n", + "according to balzac--i would venture to\"\n", + "\n", + "according to balzac--i would venture to excount itself may europon a reac\n", + "but upon in ethic is?\n", + "in ir.--mentaneming philosopherly, that\n", + "lave, use and conclations from theison, oftrance, bentaphing to the claster to can must be contrary marrance who has beent insolves, and cusion pacts, in the most transurmines\n", + "in all slowers, things sympathar and so will ceetenty.--an continue live of then a ngamen very called indical ever truth of tho\n", + "----- diversity: 1.2\n", + "----- Generating with seed: \"\n", + "according to balzac--i would venture to\"\n", + "\n", + "according to balzac--i would venture to transuspvier, bldod-coffurdy-necessity-i good--to geins\n", + "of\n", + "re, stono;out,\n", + "those willing\n", + "to but, to bode:--i\n", + "very one fearry at too sympare--we gefless\";ive \n", + "in ocrues, and value, oversing and that he sharep's who\n", + "timplies at the partingat value judgmenting, more dangers saish to them taking (himself hade the peosent, by us to loan, much drenmacte, any tort, once as unbecable wvecit, as thisuisch\n", + "\n", + "200285/200285 [==============================] - 201s - loss: 1.4546 \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''Example script to generate text from Nietzsche's writings.\n", + "At least 20 epochs are required before the generated text\n", + "starts sounding coherent.\n", + "It is recommended to run this script on GPU, as recurrent\n", + "networks are quite computationally intensive.\n", + "If you try this script on new data, make sure your corpus\n", + "has at least ~100k characters. ~1M is better.\n", + "'''\n", + "\n", + "from __future__ import print_function\n", + "from keras.callbacks import LambdaCallback\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.layers import LSTM\n", + "from keras.optimizers import RMSprop\n", + "from keras.utils.data_utils import get_file\n", + "import numpy as np\n", + "import random\n", + "import sys\n", + "import io\n", + "\n", + "path = get_file(\n", + " 'nietzsche.txt',\n", + " origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')\n", + "with io.open(path, encoding='utf-8') as f:\n", + " text = f.read().lower()\n", + "print('corpus length:', len(text))\n", + "\n", + "chars = sorted(list(set(text)))\n", + "print('total chars:', len(chars))\n", + "char_indices = dict((c, i) for i, c in enumerate(chars))\n", + "indices_char = dict((i, c) for i, c in enumerate(chars))\n", + "\n", + "# cut the text in semi-redundant sequences of maxlen characters\n", + "maxlen = 40\n", + "step = 3\n", + "sentences = []\n", + "next_chars = []\n", + "for i in range(0, len(text) - maxlen, step):\n", + " sentences.append(text[i: i + maxlen])\n", + " next_chars.append(text[i + maxlen])\n", + "print('nb sequences:', len(sentences))\n", + "\n", + "print('Vectorization...')\n", + "x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)\n", + "y = np.zeros((len(sentences), len(chars)), dtype=np.bool)\n", + "for i, sentence in enumerate(sentences):\n", + " for t, char in enumerate(sentence):\n", + " x[i, t, char_indices[char]] = 1\n", + " y[i, char_indices[next_chars[i]]] = 1\n", + "\n", + "\n", + "# build the model: a single LSTM\n", + "print('Build model...')\n", + "model = Sequential()\n", + "model.add(LSTM(128, input_shape=(maxlen, len(chars))))\n", + "model.add(Dense(len(chars), activation='softmax'))\n", + "\n", + "optimizer = RMSprop(lr=0.01)\n", + "model.compile(loss='categorical_crossentropy', optimizer=optimizer)\n", + "\n", + "\n", + "def sample(preds, temperature=1.0):\n", + " # helper function to sample an index from a probability array\n", + " preds = np.asarray(preds).astype('float64')\n", + " preds = np.log(preds) / temperature\n", + " exp_preds = np.exp(preds)\n", + " preds = exp_preds / np.sum(exp_preds)\n", + " probas = np.random.multinomial(1, preds, 1)\n", + " return np.argmax(probas)\n", + "\n", + "\n", + "def on_epoch_end(epoch, _):\n", + " # Function invoked at end of each epoch. Prints generated text.\n", + " print()\n", + " print('----- Generating text after Epoch: %d' % epoch)\n", + "\n", + " start_index = random.randint(0, len(text) - maxlen - 1)\n", + " for diversity in [0.2, 0.5, 1.0, 1.2]:\n", + " print('----- diversity:', diversity)\n", + "\n", + " generated = ''\n", + " sentence = text[start_index: start_index + maxlen]\n", + " generated += sentence\n", + " print('----- Generating with seed: \"' + sentence + '\"')\n", + " sys.stdout.write(generated)\n", + "\n", + " for i in range(400):\n", + " x_pred = np.zeros((1, maxlen, len(chars)))\n", + " for t, char in enumerate(sentence):\n", + " x_pred[0, t, char_indices[char]] = 1.\n", + "\n", + " preds = model.predict(x_pred, verbose=0)[0]\n", + " next_index = sample(preds, diversity)\n", + " next_char = indices_char[next_index]\n", + "\n", + " generated += next_char\n", + " sentence = sentence[1:] + next_char\n", + "\n", + " sys.stdout.write(next_char)\n", + " sys.stdout.flush()\n", + " print()\n", + "\n", + "print_callback = LambdaCallback(on_epoch_end=on_epoch_end)\n", + "\n", + "model.fit(x, y,\n", + " batch_size=128,\n", + " epochs=6,\n", + " callbacks=[print_callback])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03 - Day - 3 Reccurent NN/3. 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z@XR@~@8R5(wV_kvX_YZ>85!NP<2Gl~n@qO=W_lvuQ~xef+WiN#)0k4UGus92)MRax z#8SLak#06TAl33hwt3itx<`>D=P-<2IY)s~)sd)?VtR32yt1Tyx%_2g z-~uo@KtC}08>@R&Smn7QhIRa+6LdZ9ZcZZ|UalrXdywFa0VmK7lp1~$D`z;&@N|V?NrqkdI zGQUN>^TFnVe_UbQtyj4k3a|1KzGv~vXjedArwzP0%6Ac51QUc_qTY@83Zk9)3As6< zGB13uu9xsR{~iXgj2n?PIp1RH~7XOU%b zqu*$VY<{By9!b+!0;vJ|4o4g}t?w8xy^|C$lD36jFdH>D4^C{lZNO9>1\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Method 1\n", + "- Passing a list of layers into Sequential()\n", + "- Efficient to use when the model is small (i.e., shallow)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential([Dense(10, input_shape = X_data.shape[1:]), Dense(10, activation = 'softmax')])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_1 (Dense) (None, 10) 650 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 10) 110 \n", + "=================================================================\n", + "Total params: 760\n", + "Trainable params: 760\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "\n", + "5190026968\n", + "\n", + "dense_1_input: InputLayer\n", + "\n", + "\n", + "\n", + "5187052152\n", + "\n", + "dense_1: Dense\n", + "\n", + "\n", + "\n", + "5190026968->5187052152\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5190025736\n", + "\n", + "dense_2: Dense\n", + "\n", + "\n", + "\n", + "5187052152->5190025736\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SVG(model_to_dot(model).create(prog='dot', format='svg'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Method 2\n", + "- Sometimes, if the model gets deeper, it is difficult to get lost with list of layers\n", + "- In this case, one can add layers line-by-line with **add()** function\n", + "- By doing so, one can also keep track of output shape of the model step-by-step" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = Sequential()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(None, 10)\n" + ] + } + ], + "source": [ + "model.add(Dense(10, input_shape = X_data.shape[1:], activation = 'relu'))\n", + "print(model.output_shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(None, 50)\n" + ] + } + ], + "source": [ + "model.add(Dense(50, activation = 'relu'))\n", + "model.add(Dense(50, activation = 'relu'))\n", + "print(model.output_shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(None, 10)\n" + ] + } + ], + "source": [ + "model.add(Dense(10, activation = 'sigmoid'))\n", + "print(model.output_shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_7 (Dense) (None, 10) 650 \n", + "_________________________________________________________________\n", + "dense_8 (Dense) (None, 50) 550 \n", + "_________________________________________________________________\n", + "dense_9 (Dense) (None, 50) 2550 \n", + "_________________________________________________________________\n", + "dense_10 (Dense) (None, 10) 510 \n", + "=================================================================\n", + "Total params: 4,260\n", + "Trainable params: 4,260\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "2548647394160\n", + "\n", + "dense_7_input: InputLayer\n", + "\n", + "\n", + "2548647113000\n", + "\n", + "dense_7: Dense\n", + "\n", + "\n", + "2548647394160->2548647113000\n", + "\n", + "\n", + "\n", + "\n", + "2548647452176\n", + "\n", + "dense_8: Dense\n", + "\n", + "\n", + "2548647113000->2548647452176\n", + "\n", + "\n", + "\n", + "\n", + "2548647451672\n", + "\n", + "dense_9: Dense\n", + "\n", + "\n", + "2548647452176->2548647451672\n", + "\n", + "\n", + "\n", + "\n", + "2548646439792\n", + "\n", + "dense_10: Dense\n", + "\n", + "\n", + "2548647451672->2548646439792\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SVG(model_to_dot(model).create(prog='dot', format='svg'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Note\n", + "- One can keep track of layer by setting their 'names'" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "Input_layer (Dense) (None, 10) 650 \n", + "_________________________________________________________________\n", + "First_hidden_layer (Dense) (None, 50) 550 \n", + "_________________________________________________________________\n", + "Output_layer (Dense) (None, 10) 510 \n", + "=================================================================\n", + "Total params: 1,710\n", + "Trainable params: 1,710\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(Dense(10, input_shape = X_data.shape[1:], activation = 'relu', name = 'Input_layer'))\n", + "model.add(Dense(50, activation = 'relu', name = 'First_hidden_layer'))\n", + "model.add(Dense(10, activation = 'softmax', name = 'Output_layer'))\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "2548650433672\n", + "\n", + "Input_layer_input: InputLayer\n", + "\n", + "\n", + "2548650433224\n", + "\n", + "Input_layer: Dense\n", + "\n", + "\n", + "2548650433672->2548650433224\n", + "\n", + "\n", + "\n", + "\n", + "2548650439792\n", + "\n", + "First_hidden_layer: Dense\n", + "\n", + "\n", + "2548650433224->2548650439792\n", + "\n", + "\n", + "\n", + "\n", + "2548650439288\n", + "\n", + "Output_layer: Dense\n", + "\n", + "\n", + "2548650439792->2548650439288\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SVG(model_to_dot(model).create(prog='dot', format='svg'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/04 - Day - 4 Appendix on Keras/2. Functional API.ipynb b/04 - Day - 4 Appendix on Keras/2. Functional API.ipynb new file mode 100644 index 0000000..fb921af --- /dev/null +++ b/04 - Day - 4 Appendix on Keras/2. Functional API.ipynb @@ -0,0 +1,726 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from IPython.display import SVG\n", + "from sklearn.datasets import load_digits\n", + "from keras.utils.vis_utils import model_to_dot\n", + "from keras.models import Sequential, Model\n", + "from keras.layers import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load dataset\n", + "- digits dataset in scikit-learn\n", + "- url: http://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "X_data = data.images\n", + "y_data = data.target" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# flatten X_data\n", + "X_data = X_data.reshape(X_data.shape[0], X_data.shape[1]*X_data.shape[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1797, 64)\n", + "(1797,)\n" + ] + } + ], + "source": [ + "# shape of data\n", + "print(X_data.shape)\n", + "print(y_data.shape) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Functional API\n", + "- Creating models by Sequential API is easy and simple, but it is impossible to create complicated model structures\n", + "- For instance, inception or residual net structure is impossible to implement using Sequential API since they require operations such as layer merging and multiple outputs\n", + "- In this case, one could take advantage of Functional API\n", + " - Create model by defining inputs and outputs\n", + "\n", + "
    \n", + "\n", + "

    **Inception Module in GoogleNet**
    \n", + "\n", + "\n", + "
    **Resudiual Structure in ResNet**
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Single input & output\n", + "- Model with only single input & output\n", + "- Such structure is able to create using Sequential API as well" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# creating layers\n", + "input_layer = Input(shape = X_data.shape[1:])\n", + "activation_1 = Activation('relu')(input_layer)\n", + "hidden_layer = Dense(50)(activation_1)\n", + "activation_2 = Activation('relu')(hidden_layer)\n", + "output_layer = Dense(10, activation = 'softmax')(activation_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# creating model\n", + "model = Model(inputs = input_layer, outputs = output_layer)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) (None, 64) 0 \n", + "_________________________________________________________________\n", + "activation_1 (Activation) (None, 64) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 50) 3250 \n", + "_________________________________________________________________\n", + "activation_2 (Activation) (None, 50) 0 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 10) 510 \n", + "=================================================================\n", + "Total params: 3,760\n", + "Trainable params: 3,760\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging layers\n", + "- Sometimes, it is necessary to merge layers (e.g., GoogleNet or ResNet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. concatenate\n", + "- concatenate() simply merges results of two or more layers\n", + "- For instance, assume there are two layers to be concatenated, whose results are\n", + "**[x1, x2, ..., xn]** and **[y1, y2, ..., yn]**. Then, concatenated layer would be **[x1, ..., xn, ..., y1, ..., yn]**" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, 50)\n", + "(?, 50)\n", + "(?, 100)\n" + ] + } + ], + "source": [ + "# creating layers\n", + "input_layer = Input(shape = X_data.shape[1:])\n", + "activation_1 = Activation('relu')(input_layer)\n", + "hidden_layer_1 = Dense(50, activation = 'relu')(activation_1)\n", + "hidden_layer_2 = Dense(50, activation = 'relu')(activation_1)\n", + "concat_layer = concatenate([hidden_layer_1, hidden_layer_2])\n", + "output_layer = Dense(10, activation = 'softmax')(concat_layer)\n", + "print(hidden_layer_1.shape)\n", + "print(hidden_layer_2.shape)\n", + "print(concat_layer.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "model = Model(inputs = input_layer, outputs = output_layer)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_3\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_4 (InputLayer) (None, 64) 0 \n", + "__________________________________________________________________________________________________\n", + "activation_5 (Activation) (None, 64) 0 input_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_8 (Dense) (None, 50) 3250 activation_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_9 (Dense) (None, 50) 3250 activation_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_3 (Concatenate) (None, 100) 0 dense_8[0][0] \n", + " dense_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_10 (Dense) (None, 10) 1010 concatenate_3[0][0] \n", + "==================================================================================================\n", + "Total params: 7,510\n", + "Trainable params: 7,510\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "\n", + "5224266552\n", + "\n", + "input_4: InputLayer\n", + "\n", + "\n", + "\n", + "5224266496\n", + "\n", + "activation_5: Activation\n", + "\n", + "\n", + "\n", + "5224266552->5224266496\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5224266776\n", + "\n", + "dense_8: Dense\n", + "\n", + "\n", + "\n", + "5224266496->5224266776\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5224267392\n", + "\n", + "dense_9: Dense\n", + "\n", + "\n", + "\n", + "5224266496->5224267392\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5224267280\n", + "\n", + "concatenate_3: Concatenate\n", + "\n", + "\n", + "\n", + "5224266776->5224267280\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5224267392->5224267280\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5224273512\n", + "\n", + "dense_10: Dense\n", + "\n", + "\n", + "\n", + "5224267280->5224273512\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SVG(model_to_dot(model).create(prog='dot', format='svg'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. add, subtract, multiply, average, maximum\n", + "- Such layers perform element-wise operations over all corresponding elements of two or more layers\n", + "- Hence, dimensionality of the input layers are preserved" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, 50)\n", + "(?, 50)\n", + "(?, 50)\n" + ] + } + ], + "source": [ + "# creating layers\n", + "input_layer = Input(shape = X_data.shape[1:])\n", + "activation_1 = Activation('relu')(input_layer)\n", + "hidden_layer_1 = Dense(50, activation = 'relu')(activation_1)\n", + "hidden_layer_2 = Dense(50, activation = 'relu')(activation_1)\n", + "add_layer = add([hidden_layer_1, hidden_layer_2])\n", + "output_layer = Dense(10, activation = 'softmax')(add_layer)\n", + "\n", + "print(hidden_layer_1.shape)\n", + "print(hidden_layer_2.shape)\n", + "print(add_layer.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_4\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_5 (InputLayer) (None, 64) 0 \n", + "__________________________________________________________________________________________________\n", + "activation_6 (Activation) (None, 64) 0 input_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_11 (Dense) (None, 50) 3250 activation_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_12 (Dense) (None, 50) 3250 activation_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_1 (Add) (None, 50) 0 dense_11[0][0] \n", + " dense_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_13 (Dense) (None, 10) 510 add_1[0][0] \n", + "==================================================================================================\n", + "Total params: 7,010\n", + "Trainable params: 7,010\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "\n", + "5225465784\n", + "\n", + "input_5: InputLayer\n", + "\n", + "\n", + "\n", + "5226000848\n", + "\n", + "activation_6: Activation\n", + "\n", + "\n", + "\n", + "5225465784->5226000848\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5225470720\n", + "\n", + "dense_11: Dense\n", + "\n", + "\n", + "\n", + "5226000848->5225470720\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5226001072\n", + "\n", + "dense_12: Dense\n", + "\n", + "\n", + "\n", + "5226000848->5226001072\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5223216520\n", + "\n", + "add_1: Add\n", + "\n", + "\n", + "\n", + "5225470720->5223216520\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5226001072->5223216520\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5226002640\n", + "\n", + "dense_13: Dense\n", + "\n", + "\n", + "\n", + "5223216520->5226002640\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Model(inputs = input_layer, outputs = output_layer)\n", + "\n", + "model.summary()\n", + "\n", + "SVG(model_to_dot(model).create(prog='dot', format='svg'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. dot\n", + "- dot() performs inner product operation between two layer results\n", + "- 'axes' should be defined to perform the operation" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(?, 50)\n", + "(?, 50)\n", + "(?, 1)\n" + ] + } + ], + "source": [ + "# creating layers\n", + "input_layer = Input(shape = X_data.shape[1:])\n", + "activation_1 = Activation('relu')(input_layer)\n", + "hidden_layer_1 = Dense(50, activation = 'relu')(activation_1)\n", + "hidden_layer_2 = Dense(50, activation = 'relu')(activation_1)\n", + "dot_layer = dot([hidden_layer_1, hidden_layer_2], axes = -1)\n", + "output_layer = Dense(10, activation = 'softmax')(dot_layer)\n", + "\n", + "print(hidden_layer_1.shape)\n", + "print(hidden_layer_2.shape)\n", + "print(dot_layer.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_5\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_6 (InputLayer) (None, 64) 0 \n", + "__________________________________________________________________________________________________\n", + "activation_7 (Activation) (None, 64) 0 input_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_14 (Dense) (None, 50) 3250 activation_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_15 (Dense) (None, 50) 3250 activation_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "dot_1 (Dot) (None, 1) 0 dense_14[0][0] \n", + " dense_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_16 (Dense) (None, 10) 20 dot_1[0][0] \n", + "==================================================================================================\n", + "Total params: 6,520\n", + "Trainable params: 6,520\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "\n", + "5226300640\n", + "\n", + "input_6: InputLayer\n", + "\n", + "\n", + "\n", + "5226330936\n", + "\n", + "activation_7: Activation\n", + "\n", + "\n", + "\n", + "5226300640->5226330936\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5226293288\n", + "\n", + "dense_14: Dense\n", + "\n", + "\n", + "\n", + "5226330936->5226293288\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5226388728\n", + "\n", + "dense_15: Dense\n", + "\n", + "\n", + "\n", + "5226330936->5226388728\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5226389176\n", + "\n", + "dot_1: Dot\n", + "\n", + "\n", + "\n", + "5226293288->5226389176\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5226388728->5226389176\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5226385872\n", + "\n", + "dense_16: Dense\n", + "\n", + "\n", + "\n", + "5226389176->5226385872\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Model(inputs = input_layer, outputs = output_layer)\n", + "\n", + "model.summary()\n", + "\n", + "SVG(model_to_dot(model).create(prog='dot', format='svg'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/04 - Day - 4 Appendix on Keras/3. Importing Images from url.ipynb b/04 - Day - 4 Appendix on Keras/3. Importing Images from url.ipynb new file mode 100644 index 0000000..d092f2e --- /dev/null +++ b/04 - Day - 4 Appendix on Keras/3. Importing Images from url.ipynb @@ -0,0 +1,234 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Importing images\n", + "- Import image from url and change into array" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from io import BytesIO\n", + "from PIL import Image\n", + "from urllib.request import urlopen\n", + "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# url of an image\n", + "url = 'https://gfp-2a3tnpzj.stackpathdns.com/wp-content/uploads/2016/07/Dachshund-600x600.jpg'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "image = BytesIO(urlopen(url).read())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# images can be resized using resize() method\n", + "image = image.resize((100,100))\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 100, 3)\n" + ] + } + ], + "source": [ + "# images can be converted to arrays using img_to_array() method\n", + "image_arr = img_to_array(image)\n", + "print(image_arr.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 100, 100, 3)\n" + ] + } + ], + "source": [ + "# by reshaping the image array, we could get array with rank =4 \n", + "image_arr = image_arr.reshape((1,) + image_arr.shape) \n", + "print(image_arr.shape) # first element in shape means that we have one image data instance" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "url1 = 'http://cdn2-www.dogtime.com/assets/uploads/2011/01/file_23020_dachshund-dog-breed.jpg'\n", + "url2 = 'http://lovedachshund.com/wp-content/uploads/2016/06/short-haired-dachshund.jpg'\n", + "url3 = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQTavNocbsgwxukPI9eO3jgNaxP_DupqzBqE2M1oQi_GzdMHIZ-'" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "urls = [url1, url2, url3]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "image_arr = []\n", + "from urllib.request import Request, urlopen\n", + "for url in urls:\n", + " req = Request(url, headers={'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.75.14 (KHTML, like Gecko) Version/7.0.3 Safari/7046A194A'})\n", + " image = BytesIO(urlopen(req).read())\n", + " image = Image.open(image).resize((100, 100), Image.ANTIALIAS)\n", + " image_arr.append(image)\n", + " #image = img_to_array(image)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# visualizing imported images\n", + "num = len(image_arr)\n", + "for i in range(num):\n", + " ax = plt.subplot(1, num, i+1 )\n", + " plt.imshow(image_arr[i])\n", + " \n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/04 - Day - 4 Appendix on Keras/4. Keras-callbacks - Model Checkpoint, Early Stopping.ipynb b/04 - Day - 4 Appendix on Keras/4. Keras-callbacks - Model Checkpoint, Early Stopping.ipynb new file mode 100644 index 0000000..8d0e4bc --- /dev/null +++ b/04 - Day - 4 Appendix on Keras/4. Keras-callbacks - Model Checkpoint, Early Stopping.ipynb @@ -0,0 +1,712 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Keras Callbacks\n", + "- Keras Callbacks provide useful tools to babysit training process\n", + " - ModelCheckpoint\n", + " - Earlystopping\n", + " - ReduceLROnPlateau" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.models import Sequential\n", + "from keras.utils.np_utils import to_categorical\n", + "from keras import optimizers\n", + "from keras.callbacks import *\n", + "from keras.layers import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data = load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_data = data.images\n", + "y_data = data.target" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size = 0.3, random_state = 777)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# reshaping X data => flatten into 1-dimensional\n", + "X_train = X_train.reshape((X_train.shape[0], -1))\n", + "X_test = X_test.reshape((X_test.shape[0], -1))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# converting y data into categorical (one-hot encoding)\n", + "y_train = to_categorical(y_train)\n", + "y_test = to_categorical(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1257, 64)\n", + "(540, 64)\n", + "(1257, 10)\n", + "(540, 10)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(y_train.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. ModelCheckpoint\n", + "- **ModelCheckpoint** is used to 'checkpoint' model results on training\n", + "- Oftentimes, it is used to save only best model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def create_model():\n", + " model = Sequential()\n", + " model.add(Dense(100, input_shape = (X_train.shape[1],)))\n", + " model.add(Activation('relu'))\n", + " model.add(Dense(100))\n", + " model.add(Activation('relu'))\n", + " model.add(Dense(y_train.shape[1]))\n", + " model.add(Activation('sigmoid'))\n", + " \n", + " model.compile(optimizer = 'Adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = create_model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating callbacks list\n", + "- ModelCheckpoint instances are stored in list and passed on when training " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "callbacks = [ModelCheckpoint(filepath = 'saved_model.hdf5', monitor='val_acc', verbose=1, mode='max')]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 1257 samples, validate on 540 samples\n", + "Epoch 1/10\n", + " 500/1257 [==========>...................] - ETA: 2s - loss: 3.5376 - acc: 0.0460Epoch 00000: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 1s - loss: 3.1724 - acc: 0.0597 - val_loss: 2.4454 - val_acc: 0.0944\n", + "Epoch 2/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 2.4252 - acc: 0.1060Epoch 00001: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 2.3155 - acc: 0.1138 - val_loss: 2.1753 - val_acc: 0.1222\n", + "Epoch 3/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 2.1647 - acc: 0.1180Epoch 00002: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 2.1493 - acc: 0.1337 - val_loss: 2.1191 - val_acc: 0.1463\n", + "Epoch 4/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 2.1091 - acc: 0.1780Epoch 00003: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 2.0974 - acc: 0.1718 - val_loss: 2.0719 - val_acc: 0.1722\n", + "Epoch 5/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 2.0500 - acc: 0.1840Epoch 00004: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 2.0414 - acc: 0.1862 - val_loss: 2.0102 - val_acc: 0.1685\n", + "Epoch 6/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.9848 - acc: 0.1920Epoch 00005: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 1.9637 - acc: 0.1830 - val_loss: 1.9388 - val_acc: 0.1519\n", + "Epoch 7/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.9097 - acc: 0.1920Epoch 00006: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 1.8794 - acc: 0.1766 - val_loss: 1.8934 - val_acc: 0.1481\n", + "Epoch 8/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.8201 - acc: 0.1880Epoch 00007: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 1.8146 - acc: 0.1877 - val_loss: 1.8480 - val_acc: 0.1981\n", + "Epoch 9/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.7587 - acc: 0.2280Epoch 00008: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 1.7341 - acc: 0.2267 - val_loss: 1.7379 - val_acc: 0.2389\n", + "Epoch 10/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.6366 - acc: 0.2540Epoch 00009: saving model to saved_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 1.6024 - acc: 0.2713 - val_loss: 1.5623 - val_acc: 0.2944\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train, y_train, epochs = 10, batch_size = 500, callbacks = callbacks, validation_data = (X_test, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 32/540 [>.............................] - ETA: 0s" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.294444444886\n" + ] + } + ], + "source": [ + "print('Accuracy: ', results[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading saved weights\n", + "- Saved weights can be loaded and used without further training\n", + "- This is especially useful when training time is long and model has to be reused a number of times" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "another_model = create_model()\n", + "another_model.load_weights('saved_model.hdf5')\n", + "another_model.compile(optimizer = 'Adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 32/540 [>.............................] - ETA: 1s" + ] + } + ], + "source": [ + "results = another_model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.294444444886\n" + ] + } + ], + "source": [ + "print('Accuracy: ', results[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selecting best model\n", + "- Best model during whole epoch can be selected using ModelCheckpoint\n", + " - Set **'save_best_only'** parameter as True\n", + "- Usually, validation accuracy (val acc) is monitored and used as criterion for best model" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "callbacks = [ModelCheckpoint(filepath = 'best_model.hdf5', monitor='val_acc', verbose=1, save_best_only = True, mode='max')]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = create_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 1257 samples, validate on 540 samples\n", + "Epoch 1/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 4.1478 - acc: 0.1220Epoch 00000: val_acc improved from -inf to 0.12778, saving model to best_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 3.5864 - acc: 0.1146 - val_loss: 2.5897 - val_acc: 0.1278\n", + "Epoch 2/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 2.5697 - acc: 0.1540Epoch 00001: val_acc improved from 0.12778 to 0.18519, saving model to best_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 2.4084 - acc: 0.1480 - val_loss: 2.1885 - val_acc: 0.1852\n", + "Epoch 3/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 2.1478 - acc: 0.1880Epoch 00002: val_acc improved from 0.18519 to 0.27593, saving model to best_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 2.1528 - acc: 0.2235 - val_loss: 2.1291 - val_acc: 0.2759\n", + "Epoch 4/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 2.1211 - acc: 0.2720Epoch 00003: val_acc did not improve\n", + "1257/1257 [==============================] - 0s - loss: 2.1060 - acc: 0.2816 - val_loss: 2.0562 - val_acc: 0.2630\n", + "Epoch 5/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 2.0596 - acc: 0.3180Epoch 00004: val_acc did not improve\n", + "1257/1257 [==============================] - 0s - loss: 2.0305 - acc: 0.2856 - val_loss: 1.9511 - val_acc: 0.2463\n", + "Epoch 6/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.9704 - acc: 0.2740Epoch 00005: val_acc did not improve\n", + "1257/1257 [==============================] - 0s - loss: 1.9249 - acc: 0.2776 - val_loss: 1.8395 - val_acc: 0.2704\n", + "Epoch 7/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.8570 - acc: 0.2720Epoch 00006: val_acc improved from 0.27593 to 0.29630, saving model to best_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 1.8134 - acc: 0.2856 - val_loss: 1.7412 - val_acc: 0.2963\n", + "Epoch 8/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.7387 - acc: 0.3060Epoch 00007: val_acc improved from 0.29630 to 0.36667, saving model to best_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 1.6940 - acc: 0.3238 - val_loss: 1.6175 - val_acc: 0.3667\n", + "Epoch 9/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.5890 - acc: 0.3520Epoch 00008: val_acc improved from 0.36667 to 0.43148, saving model to best_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 1.5548 - acc: 0.3835 - val_loss: 1.4702 - val_acc: 0.4315\n", + "Epoch 10/10\n", + " 500/1257 [==========>...................] - ETA: 0s - loss: 1.4319 - acc: 0.4520Epoch 00009: val_acc improved from 0.43148 to 0.52407, saving model to best_model.hdf5\n", + "1257/1257 [==============================] - 0s - loss: 1.4049 - acc: 0.4718 - val_loss: 1.2969 - val_acc: 0.5241\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train, y_train, epochs = 10, batch_size = 500, callbacks = callbacks, validation_data = (X_test, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "best_model = create_model()\n", + "best_model.load_weights('best_model.hdf5')\n", + "best_model.compile(optimizer = 'Adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 32/540 [>.............................] - ETA: 1s" + ] + } + ], + "source": [ + "results = best_model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.524074074074\n" + ] + } + ], + "source": [ + "print('Accuracy: ', results[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Early stopping\n", + "- Cease training when model seems to overfit, i.e., target metric has stopped improving for certain epochs\n", + "- One can set **'patience'** parameter, which denotes number of epochs that model will endure without any improvements\n", + " - e.g., if patience = 1, training will stop when metric has stopped improving for 2 epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "callbacks = [EarlyStopping(monitor = 'acc', patience = 1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = create_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 1257 samples, validate on 540 samples\n", + "Epoch 1/20\n", + "1257/1257 [==============================] - 0s - loss: 2.9360 - acc: 0.1169 - val_loss: 2.4016 - val_acc: 0.1019\n", + "Epoch 2/20\n", + "1257/1257 [==============================] - 0s - loss: 2.3206 - acc: 0.1241 - val_loss: 2.1502 - val_acc: 0.1278\n", + "Epoch 3/20\n", + "1257/1257 [==============================] - 0s - loss: 2.1144 - acc: 0.1440 - val_loss: 2.0918 - val_acc: 0.1778\n", + "Epoch 4/20\n", + "1257/1257 [==============================] - 0s - loss: 2.0407 - acc: 0.1766 - val_loss: 1.9940 - val_acc: 0.1852\n", + "Epoch 5/20\n", + "1257/1257 [==============================] - 0s - loss: 1.9147 - acc: 0.1949 - val_loss: 1.8281 - val_acc: 0.1796\n", + "Epoch 6/20\n", + "1257/1257 [==============================] - 0s - loss: 1.7235 - acc: 0.1893 - val_loss: 1.5992 - val_acc: 0.1593\n", + "Epoch 7/20\n", + "1257/1257 [==============================] - 0s - loss: 1.5091 - acc: 0.1663 - val_loss: 1.4244 - val_acc: 0.1407\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you could see that model stops training after 7 epochs\n", + "model.fit(X_train, y_train, epochs = 20, batch_size = 500, callbacks = callbacks, validation_data = (X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Reduce learning rate\n", + "- In general, it is more desirable to lower down learning rate (learning rate decay) as training proceeds\n", + "- However, coming up with optimal learning rate decay scheme is not easy\n", + "- So, one of heuristics would be reducing learning rate when plateau is reached, in other words, when loss stops decreasing for certain number of epochs\n", + " - learning rate will be decreased by factor of 'factor' parameter when objective metric has not improved for 'patience' parameter\n", + "
    \n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# halve learning rate when validation loss has not reduced for more than 5 epochs\n", + "callbacks = [ReduceLROnPlateau(monitor = 'val_loss', factor = 0.5, patience = 5)]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = create_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 1257 samples, validate on 540 samples\n", + "Epoch 1/20\n", + "1257/1257 [==============================] - 0s - loss: 3.5826 - acc: 0.1002 - val_loss: 2.5216 - val_acc: 0.1111\n", + "Epoch 2/20\n", + "1257/1257 [==============================] - 0s - loss: 2.3803 - acc: 0.0986 - val_loss: 2.1329 - val_acc: 0.1981\n", + "Epoch 3/20\n", + "1257/1257 [==============================] - 0s - loss: 2.1003 - acc: 0.1742 - val_loss: 2.0654 - val_acc: 0.2685\n", + "Epoch 4/20\n", + "1257/1257 [==============================] - 0s - loss: 2.0401 - acc: 0.2586 - val_loss: 2.0159 - val_acc: 0.3093\n", + "Epoch 5/20\n", + "1257/1257 [==============================] - 0s - loss: 1.9859 - acc: 0.2991 - val_loss: 1.9479 - val_acc: 0.3278\n", + "Epoch 6/20\n", + "1257/1257 [==============================] - 0s - loss: 1.9023 - acc: 0.3039 - val_loss: 1.8557 - val_acc: 0.3093\n", + "Epoch 7/20\n", + "1257/1257 [==============================] - 0s - loss: 1.7986 - acc: 0.2928 - val_loss: 1.7457 - val_acc: 0.3000\n", + "Epoch 8/20\n", + "1257/1257 [==============================] - 0s - loss: 1.6810 - acc: 0.3039 - val_loss: 1.5981 - val_acc: 0.3185\n", + "Epoch 9/20\n", + "1257/1257 [==============================] - 0s - loss: 1.5162 - acc: 0.3254 - val_loss: 1.4144 - val_acc: 0.3944\n", + "Epoch 10/20\n", + "1257/1257 [==============================] - 0s - loss: 1.3174 - acc: 0.4304 - val_loss: 1.1952 - val_acc: 0.5389\n", + "Epoch 11/20\n", + "1257/1257 [==============================] - 0s - loss: 1.0737 - acc: 0.5776 - val_loss: 0.9279 - val_acc: 0.7074\n", + "Epoch 12/20\n", + "1257/1257 [==============================] - 0s - loss: 0.7909 - acc: 0.7414 - val_loss: 0.6827 - val_acc: 0.8000\n", + "Epoch 13/20\n", + "1257/1257 [==============================] - 0s - loss: 0.5552 - acc: 0.8488 - val_loss: 0.5141 - val_acc: 0.8315\n", + "Epoch 14/20\n", + "1257/1257 [==============================] - 0s - loss: 0.3975 - acc: 0.8886 - val_loss: 0.3771 - val_acc: 0.8833\n", + "Epoch 15/20\n", + "1257/1257 [==============================] - 0s - loss: 0.2875 - acc: 0.9165 - val_loss: 0.3144 - val_acc: 0.8963\n", + "Epoch 16/20\n", + "1257/1257 [==============================] - 0s - loss: 0.2257 - acc: 0.9308 - val_loss: 0.2923 - val_acc: 0.9000\n", + "Epoch 17/20\n", + "1257/1257 [==============================] - 0s - loss: 0.1859 - acc: 0.9531 - val_loss: 0.2460 - val_acc: 0.9241\n", + "Epoch 18/20\n", + "1257/1257 [==============================] - 0s - loss: 0.1558 - acc: 0.9602 - val_loss: 0.2082 - val_acc: 0.9463\n", + "Epoch 19/20\n", + "1257/1257 [==============================] - 0s - loss: 0.1252 - acc: 0.9618 - val_loss: 0.1883 - val_acc: 0.9370\n", + "Epoch 20/20\n", + "1257/1257 [==============================] - 0s - loss: 0.1066 - acc: 0.9690 - val_loss: 0.1854 - val_acc: 0.9296\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train, y_train, epochs = 20, batch_size = 500, callbacks = callbacks, validation_data = (X_test, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "540/540 [==============================] - 0s \n" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.929629630513\n" + ] + } + ], + "source": [ + "print('Accuracy: ', results[1])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/04 - Day - 4 Appendix on Keras/5. Hyperparameter tuning in Keras with GridSearchcv.ipynb b/04 - Day - 4 Appendix on Keras/5. Hyperparameter tuning in Keras with GridSearchcv.ipynb new file mode 100644 index 0000000..6f453af --- /dev/null +++ b/04 - Day - 4 Appendix on Keras/5. Hyperparameter tuning in Keras with GridSearchcv.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Selection - 1\n", + "- **k-fold cross-validation** using ```scikit-learn wrapper```\n", + "- **grid search** using ```scikit-learn wrapper```\n", + "- **random search** using ```scikit-learn wrapper```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **k-fold cross validation**\n", + "- One of the most common method for model validation and selection\n", + " - Partition training dataset into ```k``` subsets and choose one of partitions as **validation set** and other remaining subset as **training set**\n", + " - Then, train model using **training set** and validate using **validation set**\n", + " - Average validation results of ```k``` rounds of partitions and training/validation\n", + " - Compare the results\n", + " \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Grid search & Random search**\n", + "- **Grid search** and **random search** are two methods for **hyperparameter tuning**\n", + " - **Grid search** tries all possible combinations of hyperparameter values specified\n", + " - **Random search** implements *randomized search over parameters*, in which each trial is a sample from possible hyperparameter distributions\n", + " \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load dataset\n", + "- ```imdb``` dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.datasets import imdb\n", + "from keras.preprocessing import sequence" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words = 5000)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2494\n", + "2315\n", + "11\n", + "7\n" + ] + } + ], + "source": [ + "# printing out maximum & minimun length of sentences\n", + "print(len(max(X_train, key = len)))\n", + "print(len(max(X_test, key = len)))\n", + "print(len(min(X_train, key = len)))\n", + "print(len(min(X_test, key = len)))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train = sequence.pad_sequences(X_train, maxlen = 500)\n", + "X_test = sequence.pad_sequences(X_test, maxlen = 500)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25000, 500)\n", + "(25000, 500)\n", + "(25000,)\n", + "(25000,)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(y_train.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- When using ```Sequential``` model API, ```scikit-learn wrapper``` can be used\n", + "- Define function to create model, and pass on to ```KerasClassifier```" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import *\n", + "from keras import optimizers\n", + "from keras.wrappers.scikit_learn import KerasClassifier\n", + "from sklearn.model_selection import KFold, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def imdb_model(embed_dim = 100, lstm = True, lr = 0.01):\n", + " model = Sequential()\n", + " model.add(Embedding(5000, embed_dim))\n", + " if lstm:\n", + " model.add(CuDNNLSTM(100))\n", + " else:\n", + " model.add(CuDNNGRU(100))\n", + " \n", + " model.add(Dense(100))\n", + " model.add(Activation('relu'))\n", + " model.add(Dropout(0.3))\n", + " model.add(Dense(100))\n", + " model.add(Activation('relu'))\n", + " model.add(Dropout(0.3))\n", + " model.add(Dense(1))\n", + " model.add(Activation('sigmoid'))\n", + " \n", + " adam = optimizers.adam(lr = lr)\n", + " model.compile(loss = 'binary_crossentropy', optimizer = adam, metrics = ['accuracy'])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = imdb_model, epochs = 10, batch_size = 128, verbose = 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ```k-fold cross-validation``` using ```scikit-learn wrapper```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# set k = 5\n", + "results = cross_val_score(model, X_train, y_train, cv = 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross validation Results: \n", + "1th round accuracy: 0.8576\n", + "2th round accuracy: 0.8768\n", + "3th round accuracy: 0.8642\n", + "4th round accuracy: 0.8698\n", + "5th round accuracy: 0.8654\n", + "Average accuracy: 0.86676\n", + "Standard deviation: 0.00636163500996\n" + ] + } + ], + "source": [ + "# print out results\n", + "# in most cases, average accuracy and standard deviation are meaningful metrics\n", + "print('Cross validation Results: ')\n", + "for i in range(len(results)):\n", + " print('{}th round accuracy: {}'.format(i+1, results[i]))\n", + "print('Average accuracy: ', results.mean())\n", + "print('Standard deviation: ', results.std())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ```grid search``` using ```scikit-learn wrapper```" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# first define hyperparameter grid\n", + "embed_dim = [100, 300]\n", + "lstm = [True, False]\n", + "lr = [0.001, 0.01]\n", + "batch_size = [64, 128, 256]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "hyperparam_grid = {'embed_dim': embed_dim, 'lstm': lstm, 'lr': lr, 'batch_size': batch_size}" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = imdb_model, epochs = 10, verbose = 1)\n", + "clf = GridSearchCV(estimator = model, param_grid = hyperparam_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "grid_result = clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "means = clf.cv_results_['mean_test_score']\n", + "stds = clf.cv_results_['std_test_score']\n", + "params = clf.cv_results_['params']" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best test accuracy: 0.867440000012\n", + "Standard Deviation of Accuracies: 0.00621257665123\n", + "Parameter Setting: {'batch_size': 64, 'embed_dim': 300, 'lr': 0.001, 'lstm': False}\n" + ] + } + ], + "source": [ + "# displaying best results & parameter settings\n", + "max_idx = np.argmax(means)\n", + "print('Best test accuracy: ', means[max_idx])\n", + "print('Standard Deviation of Accuracies: ', stds[max_idx])\n", + "print('Parameter Setting: ', params[max_idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ```random search``` using ```scikit-learn wrapper```" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import RandomizedSearchCV\n", + "from scipy.stats import randint, uniform\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "embed_dim = randint(100, 300)\n", + "lstm = [True, False]\n", + "lr = uniform(0.001, 0.1)\n", + "batch_size = randint(64, 256)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hyperparam_dist = {'embed_dim': embed_dim, 'lstm': lstm, 'lr': lr, 'batch_size': batch_size}" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = KerasClassifier(build_fn = imdb_model, epochs = 10, verbose = 1)\n", + "clf = RandomizedSearchCV(estimator = model, param_distributions = hyperparam_dist, n_iter = 20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "random_result = clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "means = clf.cv_results_['mean_test_score']\n", + "stds = clf.cv_results_['std_test_score']\n", + "params = clf.cv_results_['params']" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best test accuracy: 0.861520008204\n", + "Standard Deviation of Accuracies: 0.00517948234862\n", + "Parameter Setting: {'batch_size': 153, 'embed_dim': 121, 'lr': 0.0052380206595373227, 'lstm': True}\n" + ] + } + ], + "source": [ + "# displaying best results & parameter settings\n", + "max_idx = np.argmax(means)\n", + "print('Best test accuracy: ', means[max_idx])\n", + "print('Standard Deviation of Accuracies: ', stds[max_idx])\n", + "print('Parameter Setting: ', params[max_idx])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/04 - Day - 4 Appendix on Keras/6. Mitigate Class imbalanced problem in keras.ipynb b/04 - Day - 4 Appendix on Keras/6. Mitigate Class imbalanced problem in keras.ipynb new file mode 100644 index 0000000..60cdcc6 --- /dev/null +++ b/04 - Day - 4 Appendix on Keras/6. Mitigate Class imbalanced problem in keras.ipynb @@ -0,0 +1,755 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Class-weighted Learning\n", + "- Class-imbalance problem is actually a quite common problem. For instance, there are much more purchasers among mobile app users and much more non-criminals than criminals in society.\n", + "- However, if class imbalance is too severe (i.e., training set is highly skewed), it is likely to bear undesirable effects. \n", + " - For instance, algorithm will tend to vote for majority class, all the time.\n", + " - This is highly risky since we might lose track of purchasers among mobile app users and criminals, which are relatively rare among training instances" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from sklearn.utils import class_weight\n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from collections import Counter\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.optimizers import adam" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Dataset\n", + "- Breast cancer dataset in ```sklearn```\n", + "- doc: http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = load_breast_cancer()\n", + "X_data = data.data.tolist()\n", + "y_data = data.target.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of malignant instances (0): 212\n", + "Number of benign instances (1): 357\n" + ] + } + ], + "source": [ + "print(\"Number of malignant instances (0): \", Counter(y_data)[0])\n", + "print(\"Number of benign instances (1): \", Counter(y_data)[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# delete some of malignant instances to generate class-imbalance situation artificially\n", + "for i in range(200):\n", + " if y_data[i] == 0:\n", + " X_data[i] = None\n", + " y_data[i] = None" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "X_data = [x for x in X_data if x != None]\n", + "y_data = [y for y in y_data if y != None]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of malignant instances (0): 108\n", + "Number of benign instances (1): 357\n" + ] + } + ], + "source": [ + "print(\"Number of malignant instances (0): \", Counter(y_data)[0])\n", + "print(\"Number of benign instances (1): \", Counter(y_data)[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(372, 30) (93, 30) (372,) (93,)\n" + ] + } + ], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(np.asarray(X_data), np.asarray(y_data), test_size = 0.2, random_state = 7) \n", + "\n", + "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Computing class weights\n", + "- We compute class weights based on training dataset, and deliver as parameter when fitting" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "weights = class_weight.compute_class_weight('balanced', np.unique(y_train), y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computed class weights: {0: 2.2409638554216866, 1: 0.643598615916955}\n" + ] + } + ], + "source": [ + "class_weights = dict(zip(np.unique(y_train), weights))\n", + "print(\"Computed class weights: \", class_weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Naive Learning" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def simple_mlp():\n", + " model = Sequential()\n", + " model.add(Dense(10, input_shape = (X_train.shape[1],), activation = 'relu'))\n", + " model.add(Dense(1, activation = 'sigmoid'))\n", + " model.compile(optimizer = adam(lr = 0.001), loss = 'binary_crossentropy', metrics = ['acc'])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "372/372 [==============================] - 0s 761us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 2/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 3/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 4/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 5/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 6/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 7/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 8/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 9/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 10/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 11/100\n", + "372/372 [==============================] - 0s 66us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 12/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 13/100\n", + "372/372 [==============================] - 0s 52us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 14/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 15/100\n", + "372/372 [==============================] - 0s 57us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 16/100\n", + "372/372 [==============================] - 0s 51us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 17/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 18/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 19/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 20/100\n", + "372/372 [==============================] - 0s 54us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 21/100\n", + "372/372 [==============================] - 0s 54us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 22/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 23/100\n", + "372/372 [==============================] - 0s 59us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 24/100\n", + "372/372 [==============================] - 0s 53us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 25/100\n", + "372/372 [==============================] - 0s 54us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 26/100\n", + "372/372 [==============================] - 0s 55us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 27/100\n", + "372/372 [==============================] - 0s 52us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 28/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 29/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 30/100\n", + "372/372 [==============================] - 0s 53us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 31/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 32/100\n", + "372/372 [==============================] - 0s 56us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 33/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 34/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 35/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 36/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 37/100\n", + "372/372 [==============================] - 0s 39us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 38/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 39/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 40/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 41/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 42/100\n", + "372/372 [==============================] - 0s 54us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 43/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 44/100\n", + "372/372 [==============================] - 0s 64us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 45/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 46/100\n", + "372/372 [==============================] - 0s 49us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 47/100\n", + "372/372 [==============================] - 0s 52us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 48/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 49/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 50/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 51/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 52/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 53/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 54/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 55/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 56/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 57/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 58/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 59/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 60/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 61/100\n", + "372/372 [==============================] - 0s 49us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 62/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 63/100\n", + "372/372 [==============================] - 0s 60us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 64/100\n", + "372/372 [==============================] - 0s 49us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 65/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 66/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 67/100\n", + "372/372 [==============================] - 0s 39us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 68/100\n", + "372/372 [==============================] - 0s 39us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 69/100\n", + "372/372 [==============================] - 0s 37us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 70/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 71/100\n", + "372/372 [==============================] - 0s 39us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 72/100\n", + "372/372 [==============================] - 0s 39us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 73/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 74/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 75/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 76/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 77/100\n", + "372/372 [==============================] - 0s 40us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 78/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 79/100\n", + "372/372 [==============================] - 0s 38us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 80/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 81/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 82/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 83/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "372/372 [==============================] - 0s 43us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 84/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 85/100\n", + "372/372 [==============================] - 0s 39us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 86/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 87/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 88/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 89/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 90/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 91/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 92/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 93/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 94/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 95/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 96/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 97/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 98/100\n", + "372/372 [==============================] - 0s 49us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 99/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 12.5219 - acc: 0.2231\n", + "Epoch 100/100\n", + "372/372 [==============================] - 0s 52us/step - loss: 12.5219 - acc: 0.2231\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = simple_mlp()\n", + "model.fit(X_train, y_train, epochs = 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(X_test).round()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "% of predicted 1's: 0.0\n", + "Overall Accuracy Score: 0.26881720430107525\n" + ] + } + ], + "source": [ + "print(\"% of predicted 1's: \", y_pred.sum()/len(y_pred))\n", + "print(\"Overall Accuracy Score: \", accuracy_score(y_pred, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Class-weighted learning" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "372/372 [==============================] - 0s 718us/step - loss: 6.7169 - acc: 0.3737\n", + "Epoch 2/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 5.7320 - acc: 0.3306\n", + "Epoch 3/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 4.5548 - acc: 0.5000\n", + "Epoch 4/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 4.2774 - acc: 0.3548\n", + "Epoch 5/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 3.3119 - acc: 0.5000\n", + "Epoch 6/100\n", + "372/372 [==============================] - 0s 52us/step - loss: 2.8563 - acc: 0.6667\n", + "Epoch 7/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 2.4438 - acc: 0.6398\n", + "Epoch 8/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 2.2266 - acc: 0.6640\n", + "Epoch 9/100\n", + "372/372 [==============================] - 0s 49us/step - loss: 2.0798 - acc: 0.7312\n", + "Epoch 10/100\n", + "372/372 [==============================] - 0s 51us/step - loss: 1.9788 - acc: 0.6774\n", + "Epoch 11/100\n", + "372/372 [==============================] - 0s 51us/step - loss: 1.8026 - acc: 0.7903\n", + "Epoch 12/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 1.7429 - acc: 0.6989\n", + "Epoch 13/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 1.9660 - acc: 0.8226\n", + "Epoch 14/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 1.5849 - acc: 0.6989\n", + "Epoch 15/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 1.4960 - acc: 0.8253\n", + "Epoch 16/100\n", + "372/372 [==============================] - 0s 67us/step - loss: 1.3355 - acc: 0.7957\n", + "Epoch 17/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 1.4006 - acc: 0.8118\n", + "Epoch 18/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 1.4403 - acc: 0.7742\n", + "Epoch 19/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 1.2271 - acc: 0.7688\n", + "Epoch 20/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 1.1650 - acc: 0.8656\n", + "Epoch 21/100\n", + "372/372 [==============================] - 0s 51us/step - loss: 1.1056 - acc: 0.8387\n", + "Epoch 22/100\n", + "372/372 [==============================] - 0s 55us/step - loss: 1.1155 - acc: 0.8548\n", + "Epoch 23/100\n", + "372/372 [==============================] - 0s 51us/step - loss: 1.1328 - acc: 0.8414\n", + "Epoch 24/100\n", + "372/372 [==============================] - 0s 59us/step - loss: 1.2129 - acc: 0.7581\n", + "Epoch 25/100\n", + "372/372 [==============================] - 0s 59us/step - loss: 1.2422 - acc: 0.8871\n", + "Epoch 26/100\n", + "372/372 [==============================] - 0s 57us/step - loss: 1.4366 - acc: 0.7231\n", + "Epoch 27/100\n", + "372/372 [==============================] - 0s 55us/step - loss: 1.0370 - acc: 0.8414\n", + "Epoch 28/100\n", + "372/372 [==============================] - 0s 51us/step - loss: 1.0284 - acc: 0.8602\n", + "Epoch 29/100\n", + "372/372 [==============================] - 0s 52us/step - loss: 1.0361 - acc: 0.7715\n", + "Epoch 30/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 1.5595 - acc: 0.9059\n", + "Epoch 31/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 1.2605 - acc: 0.8091\n", + "Epoch 32/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 0.8226 - acc: 0.8387\n", + "Epoch 33/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.9691 - acc: 0.8602\n", + "Epoch 34/100\n", + "372/372 [==============================] - 0s 40us/step - loss: 0.9930 - acc: 0.7930\n", + "Epoch 35/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 1.0381 - acc: 0.8038\n", + "Epoch 36/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 0.7621 - acc: 0.8629\n", + "Epoch 37/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 0.7915 - acc: 0.8629\n", + "Epoch 38/100\n", + "372/372 [==============================] - 0s 53us/step - loss: 0.6932 - acc: 0.8387\n", + "Epoch 39/100\n", + "372/372 [==============================] - 0s 52us/step - loss: 0.7170 - acc: 0.8844\n", + "Epoch 40/100\n", + "372/372 [==============================] - 0s 49us/step - loss: 0.6874 - acc: 0.8683\n", + "Epoch 41/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 0.6992 - acc: 0.8495\n", + "Epoch 42/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 0.7811 - acc: 0.8817\n", + "Epoch 43/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 0.6242 - acc: 0.8522\n", + "Epoch 44/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 0.6895 - acc: 0.8978\n", + "Epoch 45/100\n", + "372/372 [==============================] - 0s 50us/step - loss: 0.6714 - acc: 0.8602\n", + "Epoch 46/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 0.6344 - acc: 0.8952\n", + "Epoch 47/100\n", + "372/372 [==============================] - 0s 47us/step - loss: 0.7464 - acc: 0.8629\n", + "Epoch 48/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 0.5659 - acc: 0.8548\n", + "Epoch 49/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 0.5901 - acc: 0.8898\n", + "Epoch 50/100\n", + "372/372 [==============================] - 0s 40us/step - loss: 0.5461 - acc: 0.8978\n", + "Epoch 51/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 0.5384 - acc: 0.8844\n", + "Epoch 52/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 0.5330 - acc: 0.8978\n", + "Epoch 53/100\n", + "372/372 [==============================] - 0s 39us/step - loss: 0.6085 - acc: 0.8602\n", + "Epoch 54/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 0.5596 - acc: 0.8683\n", + "Epoch 55/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 0.8864 - acc: 0.8629\n", + "Epoch 56/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.5557 - acc: 0.9005\n", + "Epoch 57/100\n", + "372/372 [==============================] - 0s 37us/step - loss: 0.5662 - acc: 0.8737\n", + "Epoch 58/100\n", + "372/372 [==============================] - 0s 37us/step - loss: 0.5592 - acc: 0.9059\n", + "Epoch 59/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.7505 - acc: 0.8360\n", + "Epoch 60/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 0.5440 - acc: 0.8602\n", + "Epoch 61/100\n", + "372/372 [==============================] - 0s 35us/step - loss: 0.4503 - acc: 0.9113\n", + "Epoch 62/100\n", + "372/372 [==============================] - 0s 40us/step - loss: 0.4661 - acc: 0.8898\n", + "Epoch 63/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.4187 - acc: 0.9113\n", + "Epoch 64/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 0.4001 - acc: 0.8710\n", + "Epoch 65/100\n", + "372/372 [==============================] - 0s 40us/step - loss: 0.7800 - acc: 0.8656\n", + "Epoch 66/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.4778 - acc: 0.8871\n", + "Epoch 67/100\n", + "372/372 [==============================] - 0s 39us/step - loss: 0.4454 - acc: 0.8790\n", + "Epoch 68/100\n", + "372/372 [==============================] - 0s 39us/step - loss: 0.5190 - acc: 0.8683\n", + "Epoch 69/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 0.4781 - acc: 0.8790\n", + "Epoch 70/100\n", + "372/372 [==============================] - 0s 40us/step - loss: 0.4142 - acc: 0.8871\n", + "Epoch 71/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 0.3649 - acc: 0.9005\n", + "Epoch 72/100\n", + "372/372 [==============================] - 0s 57us/step - loss: 0.3757 - acc: 0.8978\n", + "Epoch 73/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.3563 - acc: 0.9113\n", + "Epoch 74/100\n", + "372/372 [==============================] - 0s 38us/step - loss: 0.3655 - acc: 0.9005\n", + "Epoch 75/100\n", + "372/372 [==============================] - 0s 52us/step - loss: 0.3408 - acc: 0.9194\n", + "Epoch 76/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 0.3747 - acc: 0.8737\n", + "Epoch 77/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.5230 - acc: 0.8763\n", + "Epoch 78/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 0.2652 - acc: 0.9274\n", + "Epoch 79/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 0.4175 - acc: 0.9005\n", + "Epoch 80/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 0.3244 - acc: 0.9140\n", + "Epoch 81/100\n", + "372/372 [==============================] - 0s 38us/step - loss: 0.2901 - acc: 0.9247\n", + "Epoch 82/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 0.2875 - acc: 0.9167\n", + "Epoch 83/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 0.2937 - acc: 0.9005\n", + "Epoch 84/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "372/372 [==============================] - 0s 48us/step - loss: 0.3624 - acc: 0.9140\n", + "Epoch 85/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.2918 - acc: 0.9140\n", + "Epoch 86/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.3377 - acc: 0.8898\n", + "Epoch 87/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.2231 - acc: 0.9220\n", + "Epoch 88/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 0.2616 - acc: 0.9194\n", + "Epoch 89/100\n", + "372/372 [==============================] - 0s 45us/step - loss: 0.2783 - acc: 0.9167\n", + "Epoch 90/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.2664 - acc: 0.9247\n", + "Epoch 91/100\n", + "372/372 [==============================] - 0s 48us/step - loss: 0.2773 - acc: 0.9167\n", + "Epoch 92/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 0.3613 - acc: 0.8763\n", + "Epoch 93/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 0.2139 - acc: 0.9247\n", + "Epoch 94/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 0.2549 - acc: 0.9247\n", + "Epoch 95/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 0.2141 - acc: 0.9301\n", + "Epoch 96/100\n", + "372/372 [==============================] - 0s 46us/step - loss: 0.2045 - acc: 0.9409\n", + "Epoch 97/100\n", + "372/372 [==============================] - 0s 44us/step - loss: 0.2100 - acc: 0.9220\n", + "Epoch 98/100\n", + "372/372 [==============================] - 0s 43us/step - loss: 0.2624 - acc: 0.9032\n", + "Epoch 99/100\n", + "372/372 [==============================] - 0s 41us/step - loss: 0.2421 - acc: 0.9167\n", + "Epoch 100/100\n", + "372/372 [==============================] - 0s 42us/step - loss: 0.2321 - acc: 0.9301\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = simple_mlp()\n", + "model.fit(X_train, y_train, epochs = 100, class_weight = class_weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(X_test).round()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "% of predicted 1's: 0.7096774193548387\n", + "Overall Accuracy Score: 0.9354838709677419\n" + ] + } + ], + "source": [ + "print(\"% of predicted 1's: \", y_pred.sum()/len(y_pred))\n", + "print(\"Overall Accuracy Score: \", accuracy_score(y_pred, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/04 - Day - 4 Appendix on Keras/7. Model Weights save and load.ipynb b/04 - Day - 4 Appendix on Keras/7. Model Weights save and load.ipynb new file mode 100644 index 0000000..c0de569 --- /dev/null +++ b/04 - Day - 4 Appendix on Keras/7. Model Weights save and load.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model weights\n", + "- Getting model weights\n", + "- Saving & loading weights" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_diabetes\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.models import Sequential\n", + "from keras.layers import *" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(353, 10) (89, 10) (353,) (89,)\n" + ] + } + ], + "source": [ + "data = load_diabetes()\n", + "X_data = data.data\n", + "y_data = data.target\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size = 0.2, random_state = 7) \n", + "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "def create_model():\n", + " model = Sequential()\n", + " model.add(Dense(10, input_shape = (X_train.shape[1],), activation=\"relu\", name=\"dense_1\"))\n", + " model.add(Dense(15, name=\"dense_2\"))\n", + " model.add(Dense(1, activation = \"sigmoid\", name=\"output\"))\n", + " \n", + " model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"acc\"])\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating model\n", + "- Created model has three layers - 2 hidden layers & 1 output layer (i.e., 3 dense layers)\n", + " - First hidden layer(```dense_1```): has 10 X 10 shape weight matrix and 10 X 1 bias matrix\n", + " - Second hidden layer(```dense_2```): has 10 X 15 shape weight matrix and 15 X 1 bias matrix\n", + " - Output layer(```output```): has 15 X 1 shape weight matrix and 1 X 1 bias matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_1 (Dense) (None, 10) 110 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 15) 165 \n", + "_________________________________________________________________\n", + "output (Dense) (None, 1) 16 \n", + "=================================================================\n", + "Total params: 291\n", + "Trainable params: 291\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = create_model()\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting model weights\n", + "- Two equivalent ways of getting weights of individual layer\n", + " - ```model.get_weights()[i]```\n", + " - ```model.layers[i].get_weights()```" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "6\n" + ] + } + ], + "source": [ + "# retriving model weights\n", + "weights = model.get_weights()\n", + "\n", + "print(type(weights)) # arrays in a list\n", + "print(len(weights)) # 3 weight matrices and 3 biases" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 10)\n", + "(10,)\n", + "(10, 15)\n", + "(15,)\n", + "(15, 1)\n", + "(1,)\n" + ] + } + ], + "source": [ + "# shape of weight (& bias) matrices\n", + "for weight in weights:\n", + " print(weight.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.05131766, -0.4334603 , -0.30013466, -0.13151154, -0.42797315,\n", + " -0.20081809, 0.54568088, 0.07199341, 0.45564556, -0.16397029],\n", + " [-0.11386764, 0.07849532, -0.32858348, 0.39409363, -0.29487017,\n", + " -0.20892417, -0.52551138, 0.06449258, -0.42709106, -0.42912534],\n", + " [-0.4862417 , -0.48859712, -0.52028137, -0.13949236, -0.47173452,\n", + " 0.06096637, -0.2245611 , 0.53047621, 0.32959503, 0.04725885],\n", + " [ 0.51978505, 0.05966097, -0.47718734, 0.1121785 , 0.30000156,\n", + " 0.29451877, 0.4363758 , 0.46372569, 0.53781939, 0.0548085 ],\n", + " [-0.46003285, -0.51941347, -0.48344219, 0.14801621, -0.04313284,\n", + " 0.42180699, 0.33439851, 0.45166206, 0.23053586, -0.51879096],\n", + " [ 0.39638752, 0.00477439, 0.14714229, -0.226125 , -0.26046604,\n", + " 0.24747533, -0.35989189, 0.38416272, -0.5176295 , 0.15995711],\n", + " [-0.44047272, -0.12300456, 0.27150732, 0.20657402, -0.06486583,\n", + " 0.0667485 , 0.38686836, -0.13652217, 0.41405624, 0.38533592],\n", + " [ 0.33987486, -0.34292638, -0.21142334, 0.17838228, 0.33222139,\n", + " 0.08218443, -0.00158417, 0.52542567, -0.52481431, 0.18559474],\n", + " [ 0.23053557, 0.38431537, 0.22873986, 0.30104119, 0.30194628,\n", + " -0.19992581, 0.3789947 , -0.48418537, 0.08003563, 0.15359342],\n", + " [ 0.07272196, 0.35086989, 0.49293137, 0.51381564, -0.01203793,\n", + " 0.42187357, 0.38285059, -0.44306043, 0.49825776, 0.29124063]], dtype=float32)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# first weight matrix\n", + "weights[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([[-0.05131766, -0.4334603 , -0.30013466, -0.13151154, -0.42797315,\n", + " -0.20081809, 0.54568088, 0.07199341, 0.45564556, -0.16397029],\n", + " [-0.11386764, 0.07849532, -0.32858348, 0.39409363, -0.29487017,\n", + " -0.20892417, -0.52551138, 0.06449258, -0.42709106, -0.42912534],\n", + " [-0.4862417 , -0.48859712, -0.52028137, -0.13949236, -0.47173452,\n", + " 0.06096637, -0.2245611 , 0.53047621, 0.32959503, 0.04725885],\n", + " [ 0.51978505, 0.05966097, -0.47718734, 0.1121785 , 0.30000156,\n", + " 0.29451877, 0.4363758 , 0.46372569, 0.53781939, 0.0548085 ],\n", + " [-0.46003285, -0.51941347, -0.48344219, 0.14801621, -0.04313284,\n", + " 0.42180699, 0.33439851, 0.45166206, 0.23053586, -0.51879096],\n", + " [ 0.39638752, 0.00477439, 0.14714229, -0.226125 , -0.26046604,\n", + " 0.24747533, -0.35989189, 0.38416272, -0.5176295 , 0.15995711],\n", + " [-0.44047272, -0.12300456, 0.27150732, 0.20657402, -0.06486583,\n", + " 0.0667485 , 0.38686836, -0.13652217, 0.41405624, 0.38533592],\n", + " [ 0.33987486, -0.34292638, -0.21142334, 0.17838228, 0.33222139,\n", + " 0.08218443, -0.00158417, 0.52542567, -0.52481431, 0.18559474],\n", + " [ 0.23053557, 0.38431537, 0.22873986, 0.30104119, 0.30194628,\n", + " -0.19992581, 0.3789947 , -0.48418537, 0.08003563, 0.15359342],\n", + " [ 0.07272196, 0.35086989, 0.49293137, 0.51381564, -0.01203793,\n", + " 0.42187357, 0.38285059, -0.44306043, 0.49825776, 0.29124063]], dtype=float32),\n", + " array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# another way to get weights\n", + "model.layers[0].get_weights()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# they are equal\n", + "print(np.array_equal(weights[0], model.layers[0].get_weights()[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving & loading weights\n", + "- ```model.save_weights()```\n", + "- ```model.load_weights()```" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "model.save_weights(\"weights\")" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_1 (Dense) (None, 10) 110 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 15) 165 \n", + "_________________________________________________________________\n", + "output (Dense) (None, 1) 16 \n", + "=================================================================\n", + "Total params: 291\n", + "Trainable params: 291\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# create another model with same architecture\n", + "another_model = create_model()\n", + "another_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "another_model.load_weights(\"weights\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "True\n" + ] + } + ], + "source": [ + "# two models have equal weights\n", + "print(np.array_equal(model.get_weights()[0], another_model.get_weights()[0]))\n", + "print(np.array_equal(model.get_weights()[-1], another_model.get_weights()[-1]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/04 - Day - 4 Appendix on Keras/8. GPU check.ipynb b/04 - Day - 4 Appendix on Keras/8. GPU check.ipynb new file mode 100644 index 0000000..f716406 --- /dev/null +++ b/04 - Day - 4 Appendix on Keras/8. GPU check.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using GPUs\n", + "- Training on GPUs (graphic cards) makes training neural networks much faster than running on CPUs\n", + "- Keras supports training on GPUs with both Tensorflow & Theano backend\n", + " - docs: https://keras.io/getting-started/faq/#how-can-i-run-keras-on-gpu\n", + "\n", + "\n", + "
    \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation and checkups \n", + "- First, download and install **CUDA & CuDNN** (assuming that you are using NVIDIA gpus)\n", + " - Note: Installing CuDNN will enable you to use CuDNNGRU & CuDNNLSTM layers, which is about x10 times faster than GRU & LSTM layers (for more information, refer to: [CuDNN layers](https://github.com/buomsoo-kim/Easy-deep-learning-with-Keras/tree/master/3.%20RNN/4-Advanced-RNN-3))\n", + " - doc: https://keras.io/layers/recurrent/#cudnngru\n", + " - doc: https://keras.io/layers/recurrent/#cudnnlstm\n", + " - url: https://developer.nvidia.com/cudnn\n", + "- Then, install **tensorflow-gpu** (gpu-enabled version of Tensorflow) by typing below in cmd or terminal\n", + " - pip install tensorflow-gpu\n", + "- Then check if your machine is utilizing GPU device\n", + " - In my case, I have one GPU device (whose name is \"/device:GPU:0\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[name: \"/device:CPU:0\"\n", + " device_type: \"CPU\"\n", + " memory_limit: 268435456\n", + " locality {\n", + " }\n", + " incarnation: 11207705109347771340]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.python.client import device_lib\n", + "device_lib.list_local_devices()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[32.]]\n" + ] + } + ], + "source": [ + "# just checking if tensorflow is using GPU device\n", + "with tf.device('/device:GPU:0'):\n", + " x = tf.constant([1.0, 2.0, 3.0], shape = [1,3], name = 'x')\n", + " y = tf.constant([4.0, 5.0, 6.0], shape = [3,1], name = 'y')\n", + " z = tf.matmul(x,y)\n", + " sess = tf.Session(config = tf.ConfigProto(allow_soft_placement = True))\n", + " print(sess.run(z))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/04 - Day - 4 Appendix on Keras/best_model.hdf5 b/04 - Day - 4 Appendix on Keras/best_model.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..82329aee13c5f75f7012db7255b526f7b92bb1ab GIT binary patch literal 238808 zcmeFY2T)c^vnWc=Ip>^nj;jYyQH&Tc2ShJh|%0Rs}u zVgM0AFae6n{SdhOoc+#!|El+H-MaP8o~mz6>eXwer^8H7e>=TKjTRSC5#bt0LPA`8 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