Crop_classification_DL_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import NumPy for some statistics\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../Dataset/final.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    col_0   col_1   col_2   col_3   col_4   col_5    col_6    col_7   col_8  \\\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.drop(df.columns[50],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500000, 50)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Conv1D, MaxPooling1D, Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode class values as integers\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(labels)\n",
    "encoded_Y = encoder.transform(labels)\n",
    "# convert integers to dummy variables (i.e. one hot encoded)\n",
    "dummy_y = keras.utils.to_categorical(encoded_Y,num_classes=5) #np_utils.to_categorical(encoded_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, dummy_y, test_size=0.05, random_state=42,shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 200)               10200     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 5)                 1005      \n",
      "=================================================================\n",
      "Total params: 11,205\n",
      "Trainable params: 11,205\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from keras import regularizers\n",
    "from keras import initializers\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "numpy.random.seed(seed)\n",
    "\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(200, input_shape=(50, ), activation='relu',kernel_regularizer=regularizers.l2(1e-5),kernel_initializer=keras.initializers.glorot_normal(seed=seed),bias_initializer='zeros'))\n",
    "model.add(Dropout(5))\n",
    "model.add(Dense(5, activation='softmax'))\n",
    "model.summary()\n",
    "\n",
    "# Compile model\n",
    "#model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = keras.optimizers.Adadelta()\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=sgd,\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Epoch 1/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.2349 - acc: 0.9170\n",
      "Epoch 2/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.1768 - acc: 0.9364\n",
      "Epoch 3/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.1667 - acc: 0.9398\n",
      "Epoch 4/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.1603 - acc: 0.9423\n",
      "Epoch 5/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.1559 - acc: 0.9440\n",
      "Epoch 6/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.1522 - acc: 0.9455\n",
      "Epoch 7/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.1496 - acc: 0.9464\n",
      "Epoch 8/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.1471 - acc: 0.9472\n",
      "Epoch 9/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.1450 - acc: 0.9481\n",
      "Epoch 10/10\n",
      "475000/475000 [==============================] - 4s 8us/step - loss: 0.1432 - acc: 0.9487\n",
      "25000/25000 [==============================] - 0s 4us/step\n",
      "\n",
      "acc: 94.45%\n"
     ]
    }
   ],
   "source": [
    "model.fit(X_train, y_train,\n",
    "          epochs=10,\n",
    "          batch_size=100,\n",
    "         shuffle=True)\n",
    "score = model.evaluate(X_test, y_test, batch_size=100)\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], score[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 50, 1)\n",
      "(None, 46, 32)\n",
      "WARNING:tensorflow:From /home/monster/anaconda3/envs/geo_py37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "(None, 23, 32)\n",
      "(None, 19, 64)\n",
      "(None, 9, 64)\n",
      "(None, 576)\n",
      "(None, 1000)\n",
      "(None, 5)\n"
     ]
    }
   ],
   "source": [
    "n_classes = 5\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv1D(kernel_size = 5, strides=1,filters = 32, activation='relu',input_shape=(50,1)))\n",
    "                    \n",
    "print(model.input_shape)\n",
    "print(model.output_shape)\n",
    "\n",
    "model.add(MaxPooling1D(pool_size = (2), strides=(2)))\n",
    "print(model.output_shape)\n",
    "\n",
    "model.add(Conv1D (kernel_size = 5, strides=1, filters = 64, activation='relu'))\n",
    "print(model.output_shape)\n",
    "\n",
    "model.add(MaxPooling1D(pool_size = (2), strides=(2)))\n",
    "print(model.output_shape)\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "print(model.output_shape)\n",
    "\n",
    "model.add(Dense (1000, activation='relu'))\n",
    "print(model.output_shape)\n",
    "\n",
    "model.add(Dense(n_classes, activation = 'softmax'))#,activity_regularizer=keras.regularizers.l2()))\n",
    "print(model.output_shape)\n",
    "\n",
    "#model.compile( loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(lr=0.01), metrics=[keras.metrics.categorical_accuracy])\n",
    "model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(lr=0.001),metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = np.expand_dims(X_train, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((475000, 50, 1), (475000, 5))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "475000/475000 [==============================] - 27s 57us/step - loss: 0.2011 - accuracy: 0.9261\n",
      "Epoch 2/10\n",
      "475000/475000 [==============================] - 26s 56us/step - loss: 0.1517 - accuracy: 0.9442\n",
      "Epoch 3/10\n",
      "475000/475000 [==============================] - 27s 56us/step - loss: 0.1372 - accuracy: 0.9495\n",
      "Epoch 4/10\n",
      "475000/475000 [==============================] - 27s 56us/step - loss: 0.1286 - accuracy: 0.9524\n",
      "Epoch 5/10\n",
      "475000/475000 [==============================] - 27s 56us/step - loss: 0.1209 - accuracy: 0.9554\n",
      "Epoch 6/10\n",
      "475000/475000 [==============================] - 26s 55us/step - loss: 0.1138 - accuracy: 0.9577\n",
      "Epoch 7/10\n",
      "475000/475000 [==============================] - 26s 56us/step - loss: 0.1064 - accuracy: 0.9607\n",
      "Epoch 8/10\n",
      "475000/475000 [==============================] - 26s 55us/step - loss: 0.0988 - accuracy: 0.9633\n",
      "Epoch 9/10\n",
      "475000/475000 [==============================] - 26s 56us/step - loss: 0.0899 - accuracy: 0.9665\n",
      "Epoch 10/10\n",
      "475000/475000 [==============================] - 26s 56us/step - loss: 0.0813 - accuracy: 0.9699\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f74a00d7110>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X1, y_train, epochs=10, batch_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = np.expand_dims(X_test, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000/25000 [==============================] - 1s 41us/step\n",
      "\n",
      "accuracy: 94.98%\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(X2, y_test)\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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