def create(self):
x_train, x_test = get_dataSets()
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test),28, 28, 1))
input_img = Input(shape=(28, 28, 1))
x = Convolution2D(16, (3, 3), activation='relu', border_mode='same')(input_img)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(8, (3, 3), activation='relu', border_mode='same')(x)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(8, (3, 3), activation='relu', border_mode='same')(x)
encoded = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(8, (3, 3), activation='relu', border_mode='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(8, (3, 3), activation='relu', border_mode='same')(x)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(16, 3, 3, activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Convolution2D(1, (3, 3), activation='sigmoid', border_mode='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train, epochs=20, batch_size=256,
shuffle=True,
validation_data=(x_test, x_test))
return autoencoder
def create(self):
x_train, x_test = get_dataSets()
encoding_dim = 32
input_img = Input(shape=(784,))
encoded = Dense(encoding_dim, activation='relu')(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)
autoencoder = Model(input=input_img, output=decoded)
encoder = Model(input=input_img, output=encoded)
encoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-1]
decoder = Model(input=encoded_input, output=decoder_layer(encoded_input))
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=50,
batch_size=128,
shuffle=True,
validation_data=(x_test, x_test))
return autoencoder, encoder, decoder
def create(self):
x_train, x_test = get_dataSets()
input_img = Input(shape=(784,))
encoded = Dense(128, activation='relu')(input_img)
encoded = Dense(64, activation='relu')(encoded)
encoded = Dense(32, activation='relu')(encoded)
decoded = Dense(64, activation='relu')(encoded)
decoded = Dense(128, activation='relu')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded)
autoencoder = Model(input=input_img, output=decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=1,
batch_size=128,
shuffle=True,
validation_data=(x_test, x_test))
return autoencoder
x_train, x_test = get_dataSets()
encoding_dim = 32
input_img = Input(shape=(784, ))
encoded = Dense(encoding_dim,
activation='relu',
activity_regularizer=regularizers.l1(10e-5))(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)
autoencoder = Model(input=input_img, output=decoded)
encoder = Model(input=input_img, output=encoded)
encoded_input = Input(shape=(encoding_dim, ))
decoder_layer = autoencoder.layers[-1]
decoder = Model(input=encoded_input,
output=decoder_layer(encoded_input))
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.fit(x_train,
x_train,
epochs=1,
batch_size=256,
shuffle=True,
validation_data=(x_test, x_test))
return autoencoder, encoder, decoder
pass
if __name__ == '__main__':
x_train, x_test = get_dataSets()
singel = sparse_layer()
autoencode, encoder, decoder = singel.create()
decoded_imgs = autoencode.predict(x_test)
show(x_test, decoded_imgs)