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I've been playing with the Tensorflow library doing the tutorials. Now I wanted to play with my own data, but I fail horribly. This is perhaps a noob question but I can't figure it out.

I'm using this example: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/convolutional_network.py

I want to use my own images, for converting my images to use with tensorflow i'm using this: https://github.com/HamedMP/ImageFlow/blob/master/ImageFlow.py

Now I change the parameters in the example from this:

 n_input = 784
 n_classes = 10

to this:

 n_input = 9216
 n_classes = 2

I did that because my images are 96 * 96 and there are only 2 classes of my images

I also change the weights and biases to the numbers I need.

I read the data like this:

batch_xs = imgReader.read_images(pathname);

imgReader being the ImageFlow file

but when I try to run it I gives me an error:

 ValueError: Cannot feed value of shape (104, 96, 96, 1) for Tensor
 u'Placeholder:0', which has shape (Dimension(None), Dimension(9216))

I feel like i'm overlooking something small but I don't see it.

2 Answers 2

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This error arises because the shape of the data that you're trying to feed (104 x 96 x 96 x 1) does not match the shape of the input placeholder (batch_size x 9216, where batch_size may be variable).

To make it work, add the following line before running a training step:

batch_xs = np.reshape(batch_xs, (-1, 9216))

This uses numpy to reshape the images read in, which are 4-D arrays of batch_size x h x w x channels, into a batch_size x 9216 element matrix as expected by the placeholder.

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2 Comments

Glad it worked! Some of the shape stuff can be quite subtle, so we'll try to work on the error messages.
By the way, can make reshaping part of your graph with tf.reshape()
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I solved this problem by upgrading tensorflow through pip.

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