Category : tensorflow1.15

So guys, I am using tensorflow==1.15.0 and python==3.7.10. I can’t upgrade to tensorflow==2.X because I am working upon the code which has been written in 1.X version. So, here is the complete Error:- Type of the model_obj: <class ‘CustomAutoencoder_tf21s.AutoEncoderModel’> Type of test_norm: <class ‘numpy.ndarray’> The test_norm is [[-0.8249858 -1.89925946 -0.19963089 -0.72175069 -0.08146654 0.17114502 -0.78429501 -0.41983836 ..

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I am trying to train a model using tensorflow 1.15 with eager execution enabled. For train loss I am using train loss = mse_loss*args.lmbda + bits_per_pixel_loss I’ve defined the optimizer as below main_optimiser = tf.train.AdamOptimiser(learning_rate=1e-3) Upto here the code is working fine The error is coming in the minimizer part of the optimisation main_step = ..

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I’m trying to get the same results using tf 1.15 and tf 2.4.1. Here are 2 examples that I’m expecting to produce similar results. The first example using tf.layers.conv2d() and the second using tf.keras.layers.Conv2D(). I tried using kernel_initializer=some_tf_initializer(seed=seed) and I also tried without using a kernel initializer, still the same. ========================================================================== TF1 import tensorflow as ..

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