Encoder | Encoding , Decoding and Obfuscating strings | Base64 library

it looks like the variable that has value is amp;code; not code. Do you have 2 ampersands somewhere by any chance? Yes you do -

So I ran into a similar issue with Codables... I've made this little extension that's saved me. Maybe it works for you too :)

You are violating the JPA spec by accessing the persistence context in a lifecycle listener.

See the JPA Specification 4.2 Section 3.5.2

In general, the lifecycle method of a portable application should not invoke EntityManager or query operations, access other entity instances, or modify relationships within the same persistence context. A lifecycle callback method may modify the non-relationship state of the entity on which it is invoked.

"a portable application should not" is the specification way of saying: Don't do that, anything might happen. Maybe the world ends.

The fix is not to do that. Maybe be preloading the currently logged in user and reference it so you may access it in your entity listener and do not set a reference to the user, but simple store its id or similar.

do some query work with Spark SQL

You wouldn't use a ForEachWriter for that

yellowbrick documentation How can I change the title of a Yellowbrick plot?

If I use single space in title=" " then I get plot without title.
It doesn't work with empty string title="".

Minimal working 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

You are passing the parameters in the incorrect format to boolean matches = passwordEncoder.matches(encodedPassword, user.getPassword());

The documentation says:

boolean matches​(java.lang.CharSequence rawPassword, java.lang.String encodedPassword)

The first parameter must be the raw password, not the encoded one.

It should be:

Two things: first, you need to pass the transformers or the estimators themselves to the pipeline, not the result of fitting/transforming them (that would give the resultant arrays to the pipeline not the transformers, and it'd fail). Pipeline itself will be fitting/transforming. Second, since you have specific transformations to the specific columns, ColumnTransformer is needed.

Putting these together: