Create perceptron_mnist_keras.py

Machine_Learning/src/MNIST_Classification_Using_Perceptron/perceptron_mnist_keras.py

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+# MNIST classification using multi layered perceptron in keras
+
+from __future__ import print_function
+import numpy as np
+import matplotlib.pyplot as plt
+plt.rcParams['figure.figsize'] = (7,7) # Make the figures a bit bigger
+
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation
+from keras.utils import np_utils
+
+nb_classes = 10
+
+(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
+print("X_train shape", X_train.shape)
+print("Y_train shape", Y_train.shape)
+
+for i in range(9):
+    plt.subplot(3,3, i+1)
+    plt.imshow(X_train[i], cmap='gray', interpolation='none')
+    plt.title("Class {}".format(Y_train[i]))
+plt.show()
+
+print ("Formatting data")
+X_train = X_train.reshape(60000, 784)
+X_test = X_test.reshape(10000, 784)
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+X_train /= 225
+X_test /= 255
+
+# Back up for later re-use
+y_test = Y_test
+y_train = Y_train
+
+Y_train = np_utils.to_categorical(Y_train, nb_classes)
+Y_test = np_utils.to_categorical(Y_test, nb_classes)
+
+print("Train matrix is", X_train.shape)
+
+print("Building ANN")
+
+model = Sequential()
+model.add(Dense(1024, input_shape=(784,)))
+model.add(Activation('relu'))
+model.add(Dropout(0.2))
+model.add(Dense(512))
+model.add(Activation('relu'))
+model.add(Dropout(0.2))
+model.add(Dense(10))
+model.add(Activation('softmax'))
+
+model.compile(loss='categorical_crossentropy', optimizer='adam')
+
+
+model.fit(X_train, Y_train,
+          batch_size=128, nb_epoch=20,
+          verbose=1,
+          validation_data=(X_test, Y_test))
+
+score = model.evaluate(X_test, Y_test,)
+print("Score:", score)
+
+
+predicted_classes = model.predict_classes(X_test)
+
+correct_indices = np.nonzero(predicted_classes == y_test)[0]
+incorrect_indices = np.nonzero(predicted_classes != y_test)[0]
+
+plt.figure()
+for i, correct in enumerate(correct_indices[:9]):
+    plt.subplot(3,3,i+1)
+    plt.imshow(X_test[correct].reshape(28,28), cmap='gray', interpolation='none')
+    plt.title("Predicted {}, Class {}".format(predicted_classes[correct], y_test[correct]))
+
+plt.show()
+
+plt.figure()
+for i, incorrect in enumerate(incorrect_indices[:9]):
+    plt.subplot(3,3,i+1)
+    plt.imshow(X_test[incorrect].reshape(28,28), cmap='gray', interpolation='none')
+    plt.title("Predicted {}, Class {}".format(predicted_classes[incorrect], y_test[incorrect]))
+
+plt.show()
+