ValueError: Tensor("buffer_size:0", shape=(), dtype=int64) must be from the same graph as Tensor("ParallelMapDataset:0", shape=(), dtype=variant). about mixmatch HOT 7 CLOSED

What TensorFlow version is it happening on?
During development we used 1.12.0

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My TensorFlow version is 1.14.0.
And I also want to ask how many iterations in training process? I am a bit confused about you code. It's seem to the number of iteration in your implement is not same to the paper "Realistic evaluation of deep semi-supervised learning algorithms" (500,000 iterations).

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So try

pip install tensorflow-gpu==1.12.0

As to the iteration, this code counts them in number of images seen. For standard experiments, the number is 64 million roughly speaking. So in terms of iterations, the number would be divided by the batch size (64).

Yes there are many difference with the paper you mentioned, amongst them we also do not use a learning rate schedule, we also use less augmentations (just mirror and shift) to keep things simpler.

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I know, I will try tensorflow with 1.12.0. Thank you for your reply.

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I also pushed a commit to the requirements.txt file so that it install 1.12.0 by default.

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Did you have any success with making pseudo_label.py run @zhangbin0917 ? I am having a similar error while running mixup.py, vat.py, and pseudo_label.py, however, I am able to successfully run mixmatch.py, ict.py, and mean_teacher.py .

ValueError: Cannot feed value of shape (64, 2, 32, 32, 3) for Tensor 'y:0', which has shape '(?, 32, 32, 3)'

This is using version 1.13.1 of tensorflow.

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That's a different issue - the problem there is the placeholder y_in (line 43 in mixup.py, 37 in vat.py, and 36 in pseudo_label.py) has the wrong shape:
[None] + hwc,
when the ClassifySemi code passes in
[None, 2] + hwc
A quick fix might be changing the y_in line to
y_in = tf.placeholder(tf.float32, [None, 2] + hwc, 'y')
y_stacked = tf.reshape(y_in, [-1] + hwc)
and then changing
logits_y = classifier(y_in, training=True)
to
logits_y = classifier(y_stacked, training=True)
in the relevant files, but I'm not sure that follows intended behavior of the respective algorithms.

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