digit_recognition_keras.py

from keras.datasets import mnist
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D

(x_train, y_train), (x_test, y_test) = mnist.load_data()


# save input image dimensions
img_rows, img_cols = 28, 28

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)

x_train = x_train/255
x_test  = x_test/255


num_classes = 10

y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)


model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
     activation='relu',
     input_shape=(img_rows, img_cols, 1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
      optimizer='adam',
      metrics=['accuracy'])
batch_size = 128
epochs = 10

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          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])
model.save("test_model.h5")

# load the model
from keras.models import load_model
model = load_model("test_model.h5")

# predict digit
prediction = model.predict(gray)
print(prediction.argmax())