Generate_Text.py

import tensorflow as tf
tf.enable_eager_execution()

import numpy as np
import os
import time

import pickle
#path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')

dialogues = []

with open('all_d.list', 'rb') as d_file:
    dialogues = pickle.load(d_file)
text = ''.join(dialogues)
vocab = sorted(set(text))
# Creating a mapping from unique characters to indices
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)

text_as_int = np.array([char2idx[c] for c in text])

# The maximum length sentence we want for a single input in characters
seq_length = 100

# Create training examples / targets
chunks = tf.data.Dataset.from_tensor_slices(text_as_int).batch(seq_length+1, drop_remainder=True)

def split_input_target(chunk):
    input_text = chunk[:-1]
    target_text = chunk[1:]
    return input_text, target_text

dataset = chunks.map(split_input_target)

# Batch size
BATCH_SIZE = 64

# Buffer size to shuffle the dataset
# (TF data is designed to work with possibly infinite sequences,
# so it doesn't attempt to shuffle the entire sequence in memory. Instead,
# it maintains a buffer in which it shuffles elements).
BUFFER_SIZE = 10000

dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)


class Model(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, units):
        super(Model, self).__init__()
        self.units = units

        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)

        if tf.test.is_gpu_available():
            self.gru = tf.keras.layers.CuDNNGRU(self.units,
                                                return_sequences=True,
                                                recurrent_initializer='glorot_uniform',
                                                stateful=True)
        else:
            self.gru = tf.keras.layers.GRU(self.units,
                                           return_sequences=True,
                                           recurrent_activation='sigmoid',
                                           recurrent_initializer='glorot_uniform',
                                           stateful=True)

        self.fc = tf.keras.layers.Dense(vocab_size)

    def call(self, x):
        embedding = self.embedding(x)

        # output at every time step
        # output shape == (batch_size, seq_length, hidden_size)
        output = self.gru(embedding)

        # The dense layer will output predictions for every time_steps(seq_length)
        # output shape after the dense layer == (seq_length * batch_size, vocab_size)
        prediction = self.fc(output)

        # states will be used to pass at every step to the model while training
        return prediction


# Length of the vocabulary in chars
vocab_size = len(vocab)

# The embedding dimension
embedding_dim = 256

# Number of RNN units
units = 1024

model = Model(vocab_size, embedding_dim, units)

# Using adam optimizer with default arguments
optimizer = tf.train.AdamOptimizer()

# Using sparse_softmax_cross_entropy so that we don't have to create one-hot vectors
def loss_function(real, preds):
    return tf.losses.sparse_softmax_cross_entropy(labels=real, logits=preds)

model.build(tf.TensorShape([BATCH_SIZE, seq_length]))

#model.summary()

# Directory where the checkpoints will be saved
checkpoint_dir = './training_checkpoints'
# Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")



model = Model(vocab_size, embedding_dim, units)

model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))

model.build(tf.TensorShape([1, None]))

# Evaluation step (generating text using the learned model)

# Number of characters to generate
num_generate = 1000

# You can change the start string to experiment
start_string = 'G'

# Converting our start string to numbers (vectorizing)
input_eval = [char2idx[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0)

# Empty string to store our results
text_generated = []

# Low temperatures results in more predictable text.
# Higher temperatures results in more surprising text.
# Experiment to find the best setting.
temperature = 0.2

# Evaluation loop.

# Here batch size == 1
model.reset_states()
for i in range(num_generate):
    predictions = model(input_eval)
    # remove the batch dimension
    predictions = tf.squeeze(predictions, 0)

    # using a multinomial distribution to predict the word returned by the model
    predictions = predictions / temperature
    predicted_id = tf.multinomial(predictions, num_samples=1)[-1, 0].numpy()

    # We pass the predicted word as the next input to the model
    # along with the previous hidden state
    input_eval = tf.expand_dims([predicted_id], 0)

    text_generated.append(idx2char[predicted_id])

print(start_string + ''.join(text_generated))