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Chapter 8
That is, if the input is the sequence [c 1
, c 2
, …, c n
], the output will be [c 2
, c 3
, …, c n+1
].
We will train the network for 50 epochs, and at the end of every 10 epochs, we will
generate a fixed size sequence of characters starting with a standard prefix. In the
following example, we have used the prefix "Alice", the name of the protagonist
in our novels.
As always, we will first import the necessary libraries and set up some constants.
Here the DATA_DIR points to a data folder under the location where you downloaded
the source code for this chapter. The CHECKPOINT_DIR is the location, a folder
checkpoints under the data folder, where we will save the weights of the model at
the end of every 10 epochs:
import os
import numpy as np
import re
import shutil
import tensorflow as tf
DATA_DIR = "./data"
CHECKPOINT_DIR = os.path.join(DATA_DIR, "checkpoints")
Next we download and prepare the data for our network to consume. The texts
of both books are publicly available from the Project Gutenberg website. The
tf.keras.utils.get_file() function will check to see whether the file is already
downloaded to your local drive, and if not, it will download to a datasets folder
under the location of the code. We also preprocess the input a little here, removing
newline and byte order mark characters from the text. This step will create the
texts variable, a flat list of characters for these two books:
def download_and_read(urls):
texts = []
for i, url in enumerate(urls):
p = tf.keras.utils.get_file("ex1-{:d}.txt".format(i), url,
cache_dir=".")
text = open(p, "r").read()
# remove byte order mark
text = text.replace("\ufeff", "")
# remove newlines
text = text.replace('\n', ' ')
text = re.sub(r'\s+', " ", text)
# add it to the list
texts.extend(text)
return texts
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