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Advanced Convolutional Neural Networks
# Pad sequences with max_len
X_train = preprocessing.sequence.pad_sequences(X_train,
maxlen=max_len)
X_test = preprocessing.sequence.pad_sequences(X_test, maxlen=max_len)
return (X_train, y_train), (X_test, y_test)
Then we build a suitable CNN model. We use Embeddings (see Chapter 9,
Autoencoders) to map the sparse vocabulary typically observed in documents into a
dense feature space of dimension dim_embedding. Then we use a Conv1D, followed
by a GlobalMaxPooling1D for averaging, and two Dense layers – the last one has
only one neuron firing binary choices (positive or negative reviews):
def build_model():
model = models.Sequential()
# Input - Embedding Layer
# the model will take as input an integer matrix of size
# (batch, input_length)
# the model will output dimension (input_length, dim_embedding)
# the largest integer in the input should be no larger
# than n_words (vocabulary size).
model.add(layers.Embedding(n_words,
dim_embedding, input_length=max_len))
model.add(layers.Dropout(0.3))
model.add(layers.Conv1D(256, 3, padding='valid',
activation='relu'))
# takes the maximum value of either feature vector from each of
# the n_words features
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
return model
(X_train, y_train), (X_test, y_test) = load_data()
model=build_model()
model.summary()
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