last_layer | last layer of deep structures | Machine Learning library

You should redefine the input layer also in the Joint model

This is the solution I've come up with. I assigned the layers that I wanted to keep as output to variables:

If you are working on jupyter notebook. The problem is more likely to be num_worker. You should set num_worker=0. You can find here some solutions to follow. Because unfortunately, jupyter notebook has some issues with running multiprocessing.

Since you set the parameter return_sequences to True in the LSTM layer, you are getting a sequence with the same number of time steps as your input and an output space of 1 for each timestep, hence the shape (None, 129, 1). Afterwards, you apply a Dense layer to this tensor, but this layer is always applied to the last dimension of a tensor, which in your case is 1 and not 129. Therefore you get the output (None, 129, 64). Then, you use a final output layer, which is also applied to the last dimension of your tensor resulting in output with the shape (None, 129, 1). The Tensorflow docs also explain this behavior:

If the input to the layer has a rank greater than 2, then Dense computes the dot product between the inputs and the kernel along the last axis of the inputs and axis 0 of the kernel (using tf.tensordot).

You can set return_sequences to False if you want to work with a 2D output (batch_size, features) instead of 3D (batch_size, time_steps, features), or you can use the Flatten layer.

You can utilize Python's reduce function to chain .when statements. That would be like this

You are mixing tensorflow and keras libraries. Recommended to use only tensorflow.keras.* instead of keras.*.

Here is the modified code:

You are missing exactly the last step in Keras transformation.

There is also a Sequential() class in TensorFlow(Keras) that you can use to instantiate the model.

I haven't checked for the exact match between TF and PyTorch, but this should be your starting point to solve your problem.

Thanks for using PyGAD.

I see that you built the example correctly. You can easily use the best solution to make predictions using simple 3 steps.

Please note that after each generation, the population attribute is updated by the latest population. That means after PyGAD completes all the generations, the last population is saved in the population attribute.

After you load the saved model using the pygad.load() function, and as you did in the fitness function, you can use the population attribute to restore the weights of the networks as follows:

You have to feed mixed data: