How to debug Keras in TensorFlow 2.0?

Question

Actually, I find the problem already in TensorFlow 1.13.0. (tensorflow1.12.0 works well).

My code is listed as a simple example:

def Lambda layer(temp):
    print(temp)
    return temp

which is used as a lambda layer in my Keras model. In tensorflow1.12.0, the print(temp) can output the detail data like following

[<tf.Tensor: id=250, shape=(1024, 2, 32), dtype=complex64, numpy=
array([[[ 7.68014073e-01+0.95353246j,  7.01403618e-01+0.64385843j,
          8.30483198e-01+1.0340731j , ..., -8.88018191e-01+0.4751519j ,
         -1.20197642e+00+0.6313924j , -1.03787208e+00+0.22964947j],
        [-7.94382274e-01+0.56390345j, -4.73938555e-01+0.55901265j,
         -8.73749971e-01+0.67095983j, ..., -5.81580341e-01-0.91620034j,
         -7.04443693e-01-1.2709806j , -3.23135853e-01-1.0887597j ]],

It is because I use the 1024 as batch_size. But when I update to tensorflow1.13.0 or TensorFlow 2.0, the same code's output

Tensor("lambda_1/truediv:0", shape=(None, 1), dtype=float32)

This is terrible since I can not know the exact mistakes. So, any idea about how to solve it?

Answer 1

You see that output because the Keras model is being converted to its graph representation, and thus print printes the tf.Tensor graph description.

To see the content of a tf.Tensor when using Tensorflow 2.0 you should use tf.print instead of print since the former gets converted to its graph representation while the latter doesn't.