You can use tf.where and tf.gather_nd to do that: import numpy as np import tensorflow as tf # Make a tensor from a constant a = np.reshape(np.arange(24), (3, 4, 2)) a_t = tf.constant(a) # Find indices where the tensor is not zero idx = tf.where(tf.not_equal(a_t, 0)) # Make the sparse tensor # Use tf.shape(a_t, out_type=tf.int64) … Read more
I’ll try to answer the original question of why model.add_loss() is being used instead of specifying a custom loss function to model.compile(loss=…). All loss functions in Keras always take two parameters y_true and y_pred. Have a look at the definition of the various standard loss functions available in Keras, they all have these two parameters. … Read more
It’s a simple incompatibility between the output shape of the decoder and the shape of your training data. (Target means output). I see you’ve got 2 MaxPoolings (dividing your image size by 4), and three upsamplings (multiplying the decoder’s input by 8). The final output of the autoencoder is too big and doesn’t match your … Read more