keras.py

from __future__ import print_function
from keras.datasets import mnist
import matplotlib.pyplot  as plt
import keras
# input image dimensions

def plot_image(xTest,YRealValue,YPredic):
    plt.figure(3)
    fig, axes = plt.subplots(10, 10, figsize=(28, 28))
    test_images = xTest.reshape(-1, 28, 28)

    for i, ax in enumerate(axes.flat):
        ax.imshow(test_images[i*10+i],cmap=plt.get_cmap('gray'))
        # ax.text(0.05, 0.05, str(YPredic[i*10+i]),
        #         transform=ax.transAxes,
        #         color='green' if (YRealValue[i*10+i][0] == YPredic[i*10+i]) else 'red')


img_rows, img_cols = 28, 28

(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape)
plt.subplot(221)
plt.imshow(x_train[0], cmap=plt.get_cmap('gray'))
plt.subplot(222)
plt.imshow(x_train[1], cmap=plt.get_cmap('gray'))
plt.subplot(223)
plt.imshow(x_train[2], cmap=plt.get_cmap('gray'))
plt.subplot(224)
plt.imshow(x_train[3], cmap=plt.get_cmap('gray'))
# show the plot
plt.show()


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 /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

print("Y train sample = ",y_train[0])
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
print("Y train sample with one-hot = ",y_train[0])


from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout

model = Sequential()
model.add(Dense(512, activation='sigmoid', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))

model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train,y_train,batch_size=100,epochs=1000,validation_data=(x_test,y_test))

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])