input channels does not match filter's input channels (Tensorflow)

Question

I would like to use tf.nn.conv2d_transpose to build a deconvolution layer for a GAN network.

I would like to create a function deconv_layer. It generates a new layer, which outputs filter_num filters with expand_size times the resolution of the input.

My code is:

def deconv_layer(x, filter_num, kernel_size=5, expand_size=2):

    x_shape = x.get_shape().as_list()

    with tf.name_scope('deconv_'+str(filter_num)):

        size_in = x_shape[-1]
        size_out = filter_num

        w = tf.Variable(tf.random_normal([kernel_size, kernel_size, size_in, size_out], mean=0.0, stddev=0.125), name="W")
        b = tf.Variable(tf.random_normal([size_out], mean=0.0, stddev=0.125), name="B")

        conv = tf.nn.conv2d_transpose(x, w, output_shape=[-1, x_shape[-3]*expand_size, x_shape[-2]*expand_size, filter_num], strides=[1,expand_size,expand_size,1], padding="SAME")
        act = tf.nn.relu(tf.nn.bias_add(conv, b))

        tf.summary.histogram('weights', w)
        tf.summary.histogram('biases', b)
        tf.summary.histogram('activations', act)

    return act

The error message:

ValueError: input channels does not match filter's input channels
At conv = tf.nn.conv2d_transpose(...)

I am not sure if I use tf.nn.conv2d_transpose properly. I tried to create it based on a convolutional layer.

Answer 1

The filter dimension is wrong. According to the docs:

filter: A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value (input).

You need to change your w size to :

w = tf.Variable(tf.random_normal([kernel_size, kernel_size, size_out, size_in], mean=0.0, stddev=0.125), name="W")