Cat DCGAN

generator.py

def generator(z, output_channel_dim, is_train=True):
    ''' Build the generator network.
    
        Arguments
        ---------
        z : Input tensor for the generator
        output_channel_dim : Shape of the generator output
        n_units : Number of units in hidden layer
        reuse : Reuse the variables with tf.variable_scope
        alpha : leak parameter for leaky ReLU
        
        Returns
        -------
        out: 
    '''
    with tf.variable_scope("generator", reuse= not is_train):
        
        # First FC layer --> 8x8x1024
        fc1 = tf.layers.dense(z, 8*8*1024)
        
        # Reshape it
        fc1 = tf.reshape(fc1, (-1, 8, 8, 1024))
        
        # Leaky ReLU
        fc1 = tf.nn.leaky_relu(fc1, alpha=alpha)

        
        # Transposed conv 1 --> BatchNorm --> LeakyReLU
        # 8x8x1024 --> 16x16x512
        trans_conv1 = tf.layers.conv2d_transpose(inputs = fc1,
                                  filters = 512,
                                  kernel_size = [5,5],
                                  strides = [2,2],
                                  padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name="trans_conv1")
        
        batch_trans_conv1 = tf.layers.batch_normalization(inputs = trans_conv1, training=is_train, epsilon=1e-5, name="batch_trans_conv1")
       
        trans_conv1_out = tf.nn.leaky_relu(batch_trans_conv1, alpha=alpha, name="trans_conv1_out")
        
        
        # Transposed conv 2 --> BatchNorm --> LeakyReLU
        # 16x16x512 --> 32x32x256
        trans_conv2 = tf.layers.conv2d_transpose(inputs = trans_conv1_out,
                                  filters = 256,
                                  kernel_size = [5,5],
                                  strides = [2,2],
                                  padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name="trans_conv2")
        
        batch_trans_conv2 = tf.layers.batch_normalization(inputs = trans_conv2, training=is_train, epsilon=1e-5, name="batch_trans_conv2")
       
        trans_conv2_out = tf.nn.leaky_relu(batch_trans_conv2, alpha=alpha, name="trans_conv2_out")
        
        
        # Transposed conv 3 --> BatchNorm --> LeakyReLU
        # 32x32x256 --> 64x64x128
        trans_conv3 = tf.layers.conv2d_transpose(inputs = trans_conv2_out,
                                  filters = 128,
                                  kernel_size = [5,5],
                                  strides = [2,2],
                                  padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name="trans_conv3")
        
        batch_trans_conv3 = tf.layers.batch_normalization(inputs = trans_conv3, training=is_train, epsilon=1e-5, name="batch_trans_conv3")
       
        trans_conv3_out = tf.nn.leaky_relu(batch_trans_conv3, alpha=alpha, name="trans_conv3_out")

        
        # Transposed conv 4 --> BatchNorm --> LeakyReLU
        # 64x64x128 --> 128x128x64
        trans_conv4 = tf.layers.conv2d_transpose(inputs = trans_conv3_out,
                                  filters = 64,
                                  kernel_size = [5,5],
                                  strides = [2,2],
                                  padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name="trans_conv4")
        
        batch_trans_conv4 = tf.layers.batch_normalization(inputs = trans_conv4, training=is_train, epsilon=1e-5, name="batch_trans_conv4")
       
        trans_conv4_out = tf.nn.leaky_relu(batch_trans_conv4, alpha=alpha, name="trans_conv4_out")

        
        # Transposed conv 5 --> tanh
        # 128x128x64 --> 128x128x3
        logits = tf.layers.conv2d_transpose(inputs = trans_conv4_out,
                                  filters = 3,
                                  kernel_size = [5,5],
                                  strides = [1,1],
                                  padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name="logits")
         
        out = tf.tanh(logits, name="out")
        
        return out