tensor | The swiss-army-knife of monitoring agents

You can just use the tokenizer decode function:

I had the same problem, man. I want to transplant tflite to the development board of CEVA. There is no problem in compiling. In the process of running, there is also an error in AddBuiltin(full_connect). At present, the only possible situation I guess is that some devices can not support tflite.

TL;DR

• Add tf.compat.v1.disable_v2_behavior() at the top for TF 2.4+
• calculate shap values on numpy array, not on df

Full reproducible example:

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

I can't run your code so I created something similar and I get this error when pre_norms has values NaN.

I get pre_norms with NaN because predictors has columns No_Model,Technique which have strings and predictors-predictors.mean()/predictors.std()) convert them to NaN

Solution could be removing columns No_Model,Technique but this create empty data - so it is useless.

I don't know you full code but you should check what you have in variables and if you have NaN then you have wrong calculations.

I made a mistake in the code itself while executing the Model part of in the functional API version.

You can make the entire class inherit torch.nn.Module like so:

the problem is that you are receiving a tensor on the act function on the Network and then save it as a tensor just remove the tensor in the action like this

You can achieve this with the tf.gather function.