conv.py

[sam-ppke]

import numpy from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers import Flatten from keras.layers.convolutional import Conv2D from keras.layers.convolutional import MaxPooling2D from keras.utils import np_utils from keras import backend as K K.set_image_dim_ordering('th')

fix random seed for reproducibility

seed = 7 numpy.random.seed(seed)

load data

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

reshape to be [samples][pixels][width][height]

X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32') X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')

normalize inputs from 0-255 to 0-1

X_train = X_train / 255 X_test = X_test / 255

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]

def baseline_model():

create model

model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(1, 28, 28), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model

build the model

model = baseline_model()

Fit the model

model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)

Final evaluation of the model

scores = model.evaluate(X_test, y_test, verbose=0) print("CNN Error: %.2f%%" % (100-scores[1]*100))

[botcs]

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