Tensorflow conv2d_transpose output_shape

Question

I want to implement a Generative adversarial network (GAN) with unfixed input size, like 4-D Tensor (Batch_size, None, None, 3).

But when I use conv2d_transpose, there is a parameter output_shape, this parameter must pass the true size after deconvolution opeartion.

For example, if the size of batch_img is (64, 32, 32, 128), w is weight with (3, 3, 64, 128) , after

deconv = tf.nn.conv2d_transpose(batch_img, w, output_shape=[64, 64, 64, 64],stride=[1,2,2,1], padding='SAME')

So, I get deconv with size (64, 64, 64, 64), it's ok if I pass the true size of output_shape.

But, I want to use unfixed input size (64, None, None, 128), and get deconv with (64, None, None, 64).

But, it raises an error as below.

TypeError: Failed to convert object of type <type'list'> to Tensor...

So, what can I do to avoid this parameter in deconv? or is there another way to implement unfixed GAN?

Answer 1

• The output shape list does not accept to have None in the list because the None object can not be converted to a Tensor Object
• None is only allowed in shapes of tf.placeholder
• for varying size output_shape instead of None try -1 for example you want size(64, None, None, 128) so try [64, -1, -1, 128]... I am not exactly sure whether this will work... It worked for me for batch_size that is my first argument was not of fixed size so I used -1
• How ever there is also one high level api for transpose convolution tf.layers.conv2d_transpose()
• I am sure the high level api tf.layers.conv2d_transpose() will work for you because it takes tensors of varying inputs
• You do not even need to specify the output-shape you just need to specify the output_channel and the kernel to be used
• For more details : https://www.tensorflow.org/api_docs/python/tf/layers/conv2d_transpose... I hope this helps