def cnn_model():
"""
return : threshold_score,accuracy
"""
# Loading data
X_train, X_test, y_train, y_test = load_train_test_data()
# defining the hyper-parameters
max_input_length = 50
vocabulary_size = 20000
embedding_dim = 100
# Configuring the neural network
model = Sequential()
model.add(Embedding(vocabulary_size, 100, input_length=max_input_length))
model.add(ZeroPadding1D((49, 49)))
model.add(Conv1D(64, 50, padding="same"))
model.add(KMaxPooling(k=5, axis=1))
model.add(Activation("relu"))
model.add(ZeroPadding1D((24, 24)))
model.add(Conv1D(64, 25, padding="same"))
model.add(Folding())
model.add(KMaxPooling(k=5, axis=1))
model.add(Activation("relu"))
model.add(Flatten())
model.add(Dense(y_train.shape[1], activation="softmax"))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
print("model fitting - CNN network")
model.summary()
# Training the model
history = model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=10)
plot_accuracy_and_loss_curves(history, "categorical_crossentropy", "cnn")
# Saving the model
model.save("models/cnn.h5")
plot_model(model,
to_file='model_images/cnn_model.png',
show_shapes=True,
show_layer_names=True)
# Calculating accuracy and threshold score
test_accuracy = model.evaluate(X_test, y_test, verbose=0)
train_accuracy = model.evaluate(X_train, y_train, verbose=0)
# Making predictions
prediction = model.predict_classes(X_test, batch_size=10, verbose=0)
# Calculating accuracy and threshold score
threshold = threshold_score(y_test.argmax(axis=-1), prediction)
return train_accuracy[1] * 100, test_accuracy[1] * 100, threshold * 100
# which is the length of each text sequence.
####################### Importing libraries ####################################
from clean_and_split_data import load_train_test_data
from utils import plot_accuracy_and_loss_curves
from keras.layers import LSTM, Conv1D, Flatten, MaxPooling1D, Dropout, Activation, Input, Dense, concatenate
from keras.models import Model, load_model
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.utils.vis_utils import plot_model
################################################################################
if __name__ == '__main__':
# Loading data
X_train, X_test, y_train, y_test = load_train_test_data()
# defining the hyper-parameters
max_input_length = 50
vocabulary_size = 20000
embedding_dim = 100
model = Sequential()
model.add(Embedding(vocabulary_size, 100, input_length=max_input_length))
model.add(Dropout(0.2))
model.add(Conv1D(64, 5, activation='relu'))
model.add(MaxPooling1D(pool_size=4))
model.add(LSTM(100))
model.add(Dense(y_train.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print("model fitting - CNN-LSTM convolutional neural network")