Generate TV Scripts

All project files are included in the submission

The project submission contains the project notebook, called “dlnd_tv_script_generation.ipynb”.

All unit tests in the project have passed

All the unit tests in project have passed.

The function create_lookup_tables is implemented

The function create_lookup_tables create two dictionaries:

• Dictionary to go from the words to an id, we'll call vocab_to_int
• Dictionary to go from the id to word, we'll call int_to_vocab

The function create_lookup_tables return these dictionaries as a tuple (vocab_to_int, int_to_vocab).

A special token dictionary is created

The function token_lookup returns a dict that can correctly tokenizes the provided symbols.

Data is broken into sequences

The function batch_data breaks up word id's into the appropriate sequence lengths, such that only complete sequence lengths are constructed.

Data is created using TensorDataset

In the function batch_data, data is converted into Tensors and formatted with TensorDataset.

Data is batched correctly.

Finally, batch_data returns a DataLoader for the batched training data.

An RNN class has been defined

The RNN class has complete __init__, forward , and init_hidden functions.

The RNN includes at least one LSTM (or GRU) and fully-connected layer.

The RNN must include an LSTM or GRU and at least one fully-connected layer. The LSTM/GRU should be correctly initialized, where relevant.

Reasonable hyperparameters are selected for training

• Enough epochs to get near a minimum in the training loss, no real upper limit on this. Just need to make sure the training loss is low and not improving much with more training.
• Batch size is large enough to train efficiently, but small enough to fit the data in memory. No real “best” value here, depends on GPU memory usually.
• Embedding dimension, significantly smaller than the size of the vocabulary, if you choose to use word embeddings
• Hidden dimension (number of units in the hidden layers of the RNN) is large enough to fit the data well. Again, no real “best” value.
• n_layers (number of layers in a GRU/LSTM) is between 1-3.
• The sequence length (seq_length) here should be about the size of the length of sentences you want to look at before you generate the next word.
• The learning rate shouldn’t be too large because the training algorithm won’t converge. But needs to be large enough that training doesn’t take forever.

The model shows improvement during training

The printed loss should decrease during training. The loss should reach a value lower than 3.5.

Question about hyperparameter choices is answered.

There is a provided answer that justifies choices about model size, sequence length, and other parameters.

The generator code generates a script

The generated script can vary in length, and should look structurally similar to the TV script in the dataset.

It doesn’t have to be grammatically correct or make sense.