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import keras | |
from keras.preprocessing import sequence | |
from keras.models import Sequential | |
from keras.layers import Dense, Dropout, Activation | |
from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D | |
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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