units | Dimensional correctness

As you manually use the loss and metrics function in the train_step (not in the .compile) for the training set, you should also do the same for the validation set or by defining the test_step in the custom model in order to get the loss score and metrics score. Add the following function to your custom model.

you can create a scope that will check the condition before joins

Try this

Given the comments, the problem is due to the OP accessing a directory without permissions. Several answers might be appropriate in this case but likely require knowing more about the system (Linux, Windows, Mac, which directory, etc.). However, I feel like the easiest way to avoid such hassles is to simply move your code into a sub-directory that is in your HOME directory.

HOME directory might mean different things depending on your system, but it is usually the one that contains your "documents" folder.

If you do not want to move the codebase for some reason, you wil require assistance with changing permissions and are best off asking in system-specific forum or with system-specific tags.

An LSTM cell in Keras gives you three outputs:

• an output state o_t (1st output)
• a hidden state h_t (2nd output)
• a cell state c_t (3rd output)

and you can see an LSTM cell here:

The output state is generally passed to any upper layers, but not to any layers to the right. You would use this state when predicting your final output.

The cell state is information that is transported from previous LSTM cells to the current LSTM cell. When it arrives in the LSTM cell, the cell decides whether information from the cell state should be deleted, i.e. we will "forget" some states. This is done by a forget gate: This gate takes the current features x_t as an input and the hidden state from the previous cell h_{t-1}. It outputs a vector of probabilities that we multiply with the last cell state c_{t-1}. After determining what information we want to forget, we update the cell state with the input gate. This gate takes the current features x_t as an input and the hidden state from the previous cell h_{t-1} and produces an input which is added to the last cell state (from which we have already forgotten information). This sum is the new cell state c_t. To get the new hidden state, we combine the cell state with a hidden state vector, which is again a vector of probabilities that determines which information from the cell state should be kept and which should be discarded.

As you have correctly interpreted, the first tensor is the output of all hidden states.

The second tensor is the hidden output, i.e. $h_t$, which acts as the short-term memory of the neural network The third tensor is the cell output, i.e. $c_t$, which acts as the long-term memory of the neural network

In the keras-documentation it is written that

The data class you are generating for your JSON response is not correct. Many of the things are objects, but you have assigned it as a List item. Here is the correct data class response based on your JSON. So the JSON response is not being parsed properly.

Why do you think it has to do with the projection? Either way, it appears to work for me.

If you specify the width of a div as a percentage, it refers to the percentage of the divs parent's computed width, when you specify viewport it refers to percentage of the window screen. Pixels on other-hand are absolute unit they are not relative like percentage. That is the primary reason percentage acts differently with flexbox and not just flexbox but with everything. See some of this articles for reference: https://developer.mozilla.org/en-US/docs/Web/CSS/CSS_Values_and_Units https://developer.mozilla.org/en-US/docs/Web/CSS/percentage

It's because your javascript code is executed before DOM is fully loaded. So you have two choices, either move as the last item inside body (before )

or place all your javascript code inside: