def relu_deep_model1(input_shape, relu_max):
input_img = Input(shape=input_shape)
print 'input shape:', input_img._keras_shape
# 32, 32
x = Conv2D(64, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(input_img)
x = BatchNormalization(mode=2, axis=3)(x)
# 16, 16
x = Conv2D(128, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# 8, 8
x = Conv2D(256, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# 4, 4
# latent_dim = (1, 1, 1024)
x = Conv2D(1024, 4, 4, activation='linear',
border_mode='same', subsample=(4, 4))(x)
x = GaussianNoise(0.2)(x)
# encoded = Activation('relu')(x)
encoded = Activation(relu_n(relu_max))(x)
print 'encoded shape:', encoded.get_shape().as_list()
# in the origianl design, no BN as the first layer of decoder because of bug
x = encoded
# x = BatchNormalization(mode=2, axis=3)(encoded)
# batch_size, h, w, _ = tf.shape(x)
batch_size = tf.shape(x)[0]
# dim: (1, 1, 512)
x = Deconv2D(512, 4, 4, output_shape=[batch_size, 4, 4, 512],
activation='relu', border_mode='same', subsample=(4, 4))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# (4, 4, 512)
x = Deconv2D(256, 5, 5, output_shape=[batch_size, 8, 8, 256],
activation='relu', border_mode='same', subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# dim: (8, 8, 256)
x = Deconv2D(128, 5, 5, output_shape=(batch_size, 16, 16, 128),
activation='relu', border_mode='same', subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# dim: (16, 16, 256)
x = Deconv2D(64, 5, 5, output_shape=(batch_size, 32, 32, 64),
activation='relu', border_mode='same', subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# dim: (32, 32, 64)
x = Deconv2D(3, 5, 5, output_shape=(batch_size, 32, 32, 3),
activation='linear', border_mode='same', subsample=(1, 1))(x)
decoded = BatchNormalization(mode=2, axis=3)(x)
print 'decoded shape:', decoded.get_shape().as_list()
autoencoder = Model(input_img, decoded)
return autoencoder
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