In [1]:
import keras
keras.__version__
Out[1]:
This notebook contains the code samples found in Chapter 6, Section 2 of Deep Learning with Python. Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.
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The process we just naively implemented in Numpy corresponds to an actual Keras layer: the SimpleRNN layer:
In [2]:
from keras.layers import SimpleRNN
There is just one minor difference: SimpleRNN processes batches of sequences, like all other Keras layers, not just a single sequence like
in our Numpy example. This means that it takes inputs of shape (batch_size, timesteps, input_features), rather than (timesteps,
input_features).
Like all recurrent layers in Keras, SimpleRNN can be run in two different modes: it can return either the full sequences of successive
outputs for each timestep (a 3D tensor of shape (batch_size, timesteps, output_features)), or it can return only the last output for each
input sequence (a 2D tensor of shape (batch_size, output_features)). These two modes are controlled by the return_sequences constructor
argument. Let's take a look at an example:
In [3]:
from keras.models import Sequential
from keras.layers import Embedding, SimpleRNN
model = Sequential()
model.add(Embedding(10000, 32))
model.add(SimpleRNN(32))
model.summary()
In [4]:
model = Sequential()
model.add(Embedding(10000, 32))
model.add(SimpleRNN(32, return_sequences=True))
model.summary()
It is sometimes useful to stack several recurrent layers one after the other in order to increase the representational power of a network. In such a setup, you have to get all intermediate layers to return full sequences:
In [5]:
model = Sequential()
model.add(Embedding(10000, 32))
model.add(SimpleRNN(32, return_sequences=True))
model.add(SimpleRNN(32, return_sequences=True))
model.add(SimpleRNN(32, return_sequences=True))
model.add(SimpleRNN(32)) # This last layer only returns the last outputs.
model.summary()
Now let's try to use such a model on the IMDB movie review classification problem. First, let's preprocess the data:
In [6]:
from keras.datasets import imdb
from keras.preprocessing import sequence
max_features = 10000 # number of words to consider as features
maxlen = 500 # cut texts after this number of words (among top max_features most common words)
batch_size = 32
print('Loading data...')
(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)
print(len(input_train), 'train sequences')
print(len(input_test), 'test sequences')
print('Pad sequences (samples x time)')
input_train = sequence.pad_sequences(input_train, maxlen=maxlen)
input_test = sequence.pad_sequences(input_test, maxlen=maxlen)
print('input_train shape:', input_train.shape)
print('input_test shape:', input_test.shape)
Let's train a simple recurrent network using an Embedding layer and a SimpleRNN layer:
In [7]:
from keras.layers import Dense
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(input_train, y_train,
epochs=10,
batch_size=128,
validation_split=0.2)
Let's display the training and validation loss and accuracy:
In [8]:
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
As a reminder, in chapter 3, our very first naive approach to this very dataset got us to 88% test accuracy. Unfortunately, our small
recurrent network doesn't perform very well at all compared to this baseline (only up to 85% validation accuracy). Part of the problem is
that our inputs only consider the first 500 words rather the full sequences --
hence our RNN has access to less information than our earlier baseline model. The remainder of the problem is simply that SimpleRNN isn't very good at processing long sequences, like text. Other types of recurrent layers perform much better. Let's take a look at some
more advanced layers.
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Now let's switch to more practical concerns: we will set up a model using a LSTM layer and train it on the IMDB data. Here's the network,
similar to the one with SimpleRNN that we just presented. We only specify the output dimensionality of the LSTM layer, and leave every
other argument (there are lots) to the Keras defaults. Keras has good defaults, and things will almost always "just work" without you
having to spend time tuning parameters by hand.
In [11]:
from keras.layers import LSTM
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(input_train, y_train,
epochs=10,
batch_size=128,
validation_split=0.2)
In [12]:
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()