ScriptsDump/Machine_Learning/src/MNIST_Classification_Using_Perceptron/perceptron_mnist_keras.py at 742a5e547392c1caca231090f647be00eb7000bd · codezoned/ScriptsDump · GitHub

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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()