swivel | Strategy driven , segmented feature toggles | Access Management library

You should ideally remove punctuations from a series like this:

Finally figured out the way to link pre-trained embeddings to LSTM or other layers. Just post the steps here in case anyone feels helpful.

Embedding layer has to be the first layer in the model. (hub_layer is the same as Embedding layer.) The not very intuitive part is that any text input to the hub layer will be converted to only one vector of shape [embedding_dim]. You need to do sentence splitting and tokenization to make sure whatever input to the model is a sequence in the form of array of arrays. e.g., "Let us prepare the data." should be converted to [["let"],["us"],["prepare"], ["the"], ["data"]]. You will also need to pad the sequences if you are using batch mode.

In addition, you will need to convert your target tokens to int if your training labels are strings. The input to the model is array of strings with shape [batch, seq_length], the hub embedding layer converts it to [batch, seq_length, embed_dim]. (If you add a LSTM or other RNN layer, the output from the layer is [batch, seq_length, rnn_units]. ) The output dense layer will output index of text instead of actual text. The index of text is stored in the downloaded tfhub directory as "tokens.txt". You can load the file and convert text to the corresponding index. Otherwise you cannot compute the loss.

Its because

The problem seems to be two-fold.

First, binary targets should always be [0, 1] and not [1, 2]. So, I subtracted one from your targets. Tokenizer() isn't made to encode labels, you should use tfds.features.ClassLabel() for that. For now, I just subtracted 1 in the fit() call.

I tried out your code and you can do the following to achieve what you want.

The issues has now been resolved. The error was thrown because 'label' was getting passed as tf.string instead of tf.int32. Explicit casting resolved this.

You are receiving this error as you are trying to convert the numpy.ndarry to Tensor. In short, your arrays are of different length and that is not accepted while converting it to Tensor.

What you have to do is make your x's of the same length and the y's of the same length.

There are several ways to achieve this. Based on the code you provided you can use something like below: The below code is pseudo-code and it just to demonstrate that you need arrays of equal length.

One way to minimise your code would be to write a function that you call to get the colors. It seemed that you are always checking for the same ranges and colors so just use a variable for the score and make a reusable function.

You can use