Comments (2)
Hi,
I took a quick look and saw your place holder is a flattened image and with a batch of size one, defined as:
x = tf.placeholder(tf.float32, [1, 84843], name='InputData')
Note that x (placeholder) is the input of the train_network function.
while your batch_imges that you feed into the sess.run seems to depend on batch_size and have a tensor shape (B, W, H, C). Fix this and I think you will be fine. If I am right, a fast fix is:
x = tf.placeholder(tf.float32, [None, 84, 84, 3], name='InputData')
check if it solves the problem.
from deep-convolutional-autoencoder.
@arashsaber
Perfect, I was able to solve the issue per your suggestion.
Although I still received an out of memory warning every epoch, but this went away when I rescaled the input image to 28*28.
Thanks.
from deep-convolutional-autoencoder.
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