Multiple Layer GRU¶

from __future__ import absolute_import, division, print_function, unicode_literals


import tensorflow_datasets as tfds
import tensorflow as tf
print(tf.__version__)

# If the tf.__version__ is 1.x, please run this cell
!pip install tensorflow==2.0.0-beta0

import tensorflow_datasets as tfds
import tensorflow as tf
print(tf.__version__)

# Get the data
dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True, as_supervised=True)
train_dataset, test_dataset = dataset['train'], dataset['test']

tokenizer = info.features['text'].encoder

BUFFER_SIZE = 10000
BATCH_SIZE = 64

train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.padded_batch(BATCH_SIZE, train_dataset.output_shapes)
test_dataset = test_dataset.padded_batch(BATCH_SIZE, test_dataset.output_shapes)

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(tokenizer.vocab_size, 64),
    tf.keras.layers.Conv1D(128, 5, activation='relu'),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.summary()

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

NUM_EPOCHS = 10
history = model.fit(train_dataset, epochs=NUM_EPOCHS, validation_data=test_dataset)

import matplotlib.pyplot as plt


def plot_graphs(history, string):
  plt.plot(history.history[string])
  plt.plot(history.history['val_'+string])
  plt.xlabel("Epochs")
  plt.ylabel(string)
  plt.legend([string, 'val_'+string])
  plt.show()

plot_graphs(history, 'accuracy')

plot_graphs(history, 'loss')