Translator | JavaScript library built top on Google Speech | Speech library

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

Hello I found out myself how to reslver I basically reinstalled the Telepot and it started working Thank you very much to all who viewed and Help :)

RZG

it seems I need a way to turn self-owned reference to 'static lifetime.

That's what Rc is for. Define the struct as EmailTemplateMessageBuilder(Rc), and define translator as self.0.clone(). The clone() only clones the pointer and increases the reference count, so it's cheap. Finally, make the closure move, so it owns all captured data.

It will be fixed with the upcoming Spring-data-jdbc 2.3.x. Relevant issue 974 has been closed.

I wrongly referencing the i18 scripts. This has worked after a correct reference.

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• The "powered by Google Translate" graphic must always be displayed adjacent any translation results.

There are some issues with the code. The code needs to require the needed libraries, to define agent, to include an intent map, and the function must be named dialogflowFirebaseFulfillment to use the Dialogflow fulfillment library.

You can see the Dialogflow fulfillment library docs and samples to see the required boilerplate elements 1 then add your code around them.

I reproduced your error.

{"code":"BadRequest","message":null,"target":"pipeline//runid/abd35329-3625-490b-85cf-f6d0de3dac86","details":null,"error":null}

It is because you didn't pass your baseURL to link service in Source Dataset. Please do this:

And Dataset JSON code should be like this:

I tried this