Added Mnist using CNN

Author: chawlaj100

Machine_Learning/src/MNIST_using_CNN/mnist.py

@@ -0,0 +1,58 @@
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import cv2
+
+# CNN Model 
+def create_model():
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.Conv2D(16, (3,3), activation="relu", input_shape=(28,28,1)),
+        tf.keras.layers.MaxPooling2D((2,2)),
+        tf.keras.layers.Conv2D(32, (2,2), activation="relu"),
+        tf.keras.layers.MaxPooling2D((2,2)),
+        tf.keras.layers.Flatten(),
+        tf.keras.layers.Dense(64, activation="relu"),
+        tf.keras.layers.Dropout(0.3),
+        tf.keras.layers.Dense(10, activation="softmax")
+    ])
+    return model
+
+# Plot images with labels
+def plot(subset_x, subset_y):
+    for i in range(len(subset_y)):
+    plt.figure(figsize=(10,20))
+    ax = plt.subplot(len(subset_y)/2,2,i+1)
+    img = subset_x[i]
+    ax.imshow(img)
+    plt.show()
+    print("LABEL : ",subset_y[i])
+    print()
+
+# Load Mnist dataset from tensorflow datasets
+(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
+
+# Normalize the pixel values and convert the lables to one-hot vectors
+x_train = x_train/255.
+x_test = x_test/255.
+x_train = x_train.reshape((-1,28,28,1))
+x_test = x_test.reshape((-1,28,28,1))
+y_train = tf.one_hot(y_train, depth=10)
+y_test = tf.one_hot(y_test, depth=10)
+
+# Create and compile the Model
+my_model = create_model()
+my_model.compile(optimizer = tf.keras.optimizers.Adam(lr=0.001), loss="categorical_crossentropy", metrics=["accuracy"])
+
+# Fit the created model and store the information for plotting of loss and accuracy curves
+history = my_model.fit(x_train, y_train, validation_split=0.1, epochs=5, batch_size=32, shuffle=True, verbose=1)
+
+# Evaluate the model using the test data
+loss, acc = my_model.evaluate(x_test, y_test)
+
+# Make Predictions
+y_predictions = my_model.predict_classes(x_test)
+
+subset_x = x_test[0:10]
+subset_y = y_predictions[0:10]
+plot(subset_x, subset_y)
+