from tensorflow.keras.optimizers import Adam
from cnn import cnn
from model_preprocessor import Preprocessor
model = cnn()
x_train, x_test, y_train, y_test = Preprocessor.load_data_binary(10000)
# Define Deep Learning Model
''' Training phrase '''
epochs = 15
batch_size = 64
adam = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train,
y_train,
epochs=epochs,
batch_size=batch_size,
validation_split=0.11)
model.add(Dropout(0.2))
model.add(LSTM(units=128, return_sequences=True))
model.add(Dropout(0.5))
model.add(SeqSelfAttention(attention_activation='relu'))
model.add(Flatten())
model.add(Dense(9344, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(21, activation='softmax'))
return model
with tf.device("/GPU:0"):
# Load data using model preprocessor
x_train, x_test, y_train, y_test = Preprocessor.load_data()
# Define Deep Learning Model
model_name = "LSTM_ATT"
model = lstm_att()
# Define early stopping
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=3)
mc = ModelCheckpoint(filepath='./trained_models/' + model_name + '.hdf5',
monitor='val_loss',
mode='min',
save_best_only=True,
verbose=1)
''' Training phrase '''
epochs = 10
batch_size = 64
hidden2 = BatchNormalization(mode=0)(hidden2)
hidden2 = Dropout(0.5)(hidden2)
# Output layer (last fully connected layer)
output = Dense(4, activation='softmax', name='output')(hidden2)
# Compile model and define optimizer
model = Model(input=[main_input], output=[output])
return model
with tf.device("/GPU:0"):
# Load data using model preprocessor
x_train, x_test, y_train, y_test = Preprocessor.load_data_multi()
# Define Deep Learning Model
model_name = "ENSEMBLE_CNN_BILSTM_MULTI"
model = cnn_bilstm_att()
# Define early stopping
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=3)
mc = ModelCheckpoint(filepath='./trained_models/' + model_name + '.hdf5',
monitor='val_loss',
mode='min',
save_best_only=True,
verbose=1)
''' Training phrase '''
epochs = 10
batch_size = 64
