ml-class/keras-cnn-2.py at master · joew1234/ml-class · GitHub

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

(X_train, y_train), (X_test, y_test) = mnist.load_data()

img_width=28
img_height=28

X_train = X_train.astype('float32')
X_train /= 255.
X_test = X_test.astype('float32')
X_test /= 255.

#reshape input data
X_train = X_train.reshape(X_train.shape[0], img_width, img_height, 1)
X_test = X_test.reshape(X_test.shape[0], img_width, img_height, 1)

# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]

# build model

model = Sequential()

model.add(Conv2D(8, (5, 5), input_shape=(img_width, img_height,1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train)