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Recurrent Neural Networks
texts = download_and_read([
"http://www.gutenberg.org/cache/epub/28885/pg28885.txt",
"https://www.gutenberg.org/files/12/12-0.txt"
])
Next, we will create our vocabulary. In our case, our vocabulary contains 90 unique
characters, composed of uppercase and lowercase alphabets, numbers, and special
characters. We also create some mapping dictionaries to convert each vocabulary
character to a unique integer and vice versa. As noted earlier, the input and output
of the network is a sequence of characters. However, the actual input and output of
the network are sequences of integers, and we will use these mapping dictionaries
to handle this conversion:
# create the vocabulary
vocab = sorted(set(texts))
print("vocab size: {:d}".format(len(vocab)))
# create mapping from vocab chars to ints
char2idx = {c:i for i, c in enumerate(vocab)}
idx2char = {i:c for c, i in char2idx.items()}
The next step is to use these mapping dictionaries to convert our character
sequence input into an integer sequence, and then into a TensorFlow dataset. Each
of our sequences is going to be 100 characters long, with the output being offset
from the input by 1 character position. We first batch the dataset into slices of 101
characters, then apply the split_train_labels() function to every element of the
dataset to create our sequences dataset, which is a dataset of tuples of two elements,
each element of the tuple being a vector of size 100 and type tf.int64. We then
shuffle these sequences and then create batches of 64 tuples each for input to our
network. Each element of the dataset is now a tuple consisting of a pair of matrices,
each of size (64, 100) and type tf.int64:
# numericize the texts
texts_as_ints = np.array([char2idx[c] for c in texts])
data = tf.data.Dataset.from_tensor_slices(texts_as_ints)
# number of characters to show before asking for prediction
# sequences: [None, 100]
seq_length = 100
sequences = data.batch(seq_length + 1, drop_remainder=True)
def split_train_labels(sequence):
input_seq = sequence[0:-1]
output_seq = sequence[1:]
return input_seq, output_seq
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