def create_model(self):
print(CELLS)
print(DROPOUT_PROBABILITY)
print(EMBEDDING_DIM)
print(LEARNING_RATE)
print(DECAY)
model = Sequential()
model._name = "Practice_Model"
# defining layer 1 forward LSTM layer
lstm_fw_layer1 = LSTM(CELLS,
dropout=DROPOUT_PROBABILITY,
activation='relu',
return_sequences=True)
# defining full BLSTM layer
blstm_layer1 = Bidirectional(lstm_fw_layer1,
input_shape=(self.batch_size, 1),
name="LSTM_Layer_1")
# defining layer 2 forward LSTM layer
lstm_fw_layer2 = LSTM(CELLS,
dropout=DROPOUT_PROBABILITY,
activation='relu',
return_sequences=True)
# defining full BLSTM layer
blstm_layer2 = Bidirectional(lstm_fw_layer2,
input_shape=(self.batch_size, 1),
name="LSTM_Layer_2")
# dense layer
feedforward_layer = Dense(EMBEDDING_DIM,
activation='tanh',
name="Embedding_Layer")
# creating the model
model.add(blstm_layer1)
model.add(blstm_layer2)
model.add(feedforward_layer)
opt = tf.keras.optimizers.Adam(lr=LEARNING_RATE, decay=DECAY)
model.compile(loss=self.loss, optimizer=opt, metrics=['accuracy'])
print('Model compiled successfully')
return model
# Save model description
with open(os.path.join(save_dir, "model_description.txt"), "w") as file:
with redirect_stdout(file):
print(model_description)
############################################################################################
# MODEL CONFIGURATION
############################################################################################
# MODEL ARCHITECTURE
####################
# Add the layers of model to a new sequential model
model = Sequential()
for layer in loaded_model.layers[:-1]: # remove last layer
model.add(layer)
# Rename model
model._name = model_name
model.name
# Freeze the weights in first blocks
for layer in model.layers[:limit_layer]:
layer.trainable = False
for layer in model.layers[limit_layer:]:
layer.trainable = True
# Add last layer for categories
model.add(Dense(len(class_names), activation="softmax"))
# Save model summary
model.summary()
with open(os.path.join(save_dir, "model_summary.txt"), "w") as file:
with redirect_stdout(file):
model.summary()
def NN(feat_train, target_train, feat_val, target_val, params):
epoch = params['epoch']
batch = params['batch']
layer = params['layer']
nodes = params['nodes']
wl2 = params['wl2']
lr = params['lr']
flr = params['flr']
flrstep = params['flrstep']
in_weight = params['in_weight']
import_weights = params['import_weights']
silent = params['silent']
dim_in = len(feat_train[0])
dim_out = len(target_train[0])
## input layer
model = Sequential([
Dense(nodes,
input_shape=(dim_in, ),
kernel_regularizer=regularizers.l2(wl2),
activation='tanh'),
BatchNormalization()
])
model._name = "single"
## hidden layers
for hd in range(layer):
model.add(
Dense(nodes,
kernel_regularizer=regularizers.l2(wl2),
activation='tanh'))
model.add(BatchNormalization())
## output layer
model.add(
Dense(dim_out,
kernel_regularizer=regularizers.l2(wl2),
activation='linear'))
adam = ks.optimizers.Adam(learning_rate=lr,
beta_1=0.9,
beta_2=0.999,
amsgrad=False)
model.compile(
optimizer=adam,
loss='mean_squared_error',
metrics=['mae', rmse, dmax],
)
if silent == 0:
print(model.summary())
if in_weight == -1:
model.set_weights(import_weights)
print('Successfully load weights')
# model.load_weights('%s-%s' % (model_name,weights_h5))
history = model.predict(feat_train)
else:
if in_weight > 0:
model.set_weights(import_weights)
print('Successfully load weights')
# model.load_weights('%s-%s' % (model_name,weights_h5))
history = model.fit(feat_train,
target_train,
epochs=epoch,
batch_size=batch,
callbacks=[
ks.callbacks.LearningRateScheduler(
lr_scheduler, verbose=0)
],
validation_data=[feat_val, target_val],
shuffle=True)
return history, model
