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Supplemental material for https://austingwalters.com/convolutional-neural-networks-cnn-to-classify-sentences/
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max_words, batch_size, maxlen, epochs = 10000, 64, 500, 2 | |
embedding_dims, filters, kernel_size, hidden_dims = 50, 250, 5, 150 | |
# Determine the number of categories + default(i.e. sentence types) | |
num_classes = np.max(y_train) + 1 | |
# Vectorize the output sentence type classifcations to Keras readable format | |
y_train = keras.utils.to_categorical(y_train, num_classes) | |
y_test = keras.utils.to_categorical(y_test, num_classes) | |
# Pad the input vectors to ensure a consistent length | |
x_train = sequence.pad_sequences(x_train, maxlen=maxlen) | |
x_test = sequence.pad_sequences(x_test, maxlen=maxlen) | |
model = Sequential() | |
# Created Embedding (Input) Layer (max_words) --> Convolutional Layer | |
model.add(Embedding(max_words, embedding_dims, input_length=maxlen)) | |
model.add(Dropout(0.2)) # masks various input values | |
# Create the convolutional layer | |
model.add(Conv1D(filters, kernel_size,padding='valid', activation='relu', strides=1)) | |
# Create the pooling layer | |
model.add(GlobalMaxPooling1D()) | |
# Create the fully connected layer | |
model.add(Dense(hidden_dims)) | |
model.add(Dropout(0.2)) | |
model.add(Activation('relu')) | |
# Create the output layer (num_classes) | |
model.add(Dense(num_classes)) | |
model.add(Activation('softmax')) | |
# Add optimization method, loss function and optimization value | |
model.compile(loss='categorical_crossentropy', | |
optimizer='adam', metrics=['accuracy']) | |
# "Fit the model" (train model), using training data (80% of datset) | |
model.fit(x_train, y_train, batch_size=batch_size, | |
epochs=epochs, validation_data=(x_test, y_test)) | |
# Evaluate the trained model, using the test data (20% of the dataset) | |
score = model.evaluate(x_test, y_test, batch_size=batch_size) |
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