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NN_Models.py
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import tensorflow as tf
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, AveragePooling1D, MaxPooling1D, Flatten, Input, concatenate, LSTM, Dropout, \
Conv1D, add
def create_lstm_base(input_shape=(240, 12), output_dim=24):
input_layer = Input(shape=input_shape)
lstm_layer1 = LSTM(32, dropout=0.4, return_sequences=True, activation='tanh')(input_layer)
lstm_layer2 = LSTM(16, dropout=0.2, activation='relu')(lstm_layer1)
hidden_layer = Dense(units=100, activation='relu')(lstm_layer2)
output_layer = Dense(units=output_dim)(hidden_layer)
# kernel_regularizer=regularizers.l2(0.01), bias_regularizer=regularizers.l2(0.01)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_lstm_with_pooling(input_shape=(240, 12), output_dim=24, pooling=1):
input_layer = Input(shape=input_shape)
if pooling > 1:
pooling_layer = AveragePooling1D(pool_size=pooling, strides=pooling, padding="valid",
data_format="channels_last")(input_layer)
lstm_layer = LSTM(32, dropout=0.2, activation='tanh')(pooling_layer)
else:
lstm_layer = LSTM(32, dropout=0.2, activation='tanh')(input_layer)
hidden_layer = Dense(units=100, activation='relu')(lstm_layer)
output_layer = Dense(units=output_dim)(hidden_layer)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_cnn_base(input_shape=(240, 12), output_dim=48):
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Conv1D(filters=100, kernel_size=2, strides=1, padding="valid"))
model.add(Dropout(rate=0.2))
model.add(MaxPooling1D(pool_size=2, strides=2, padding='valid'))
model.add(Flatten())
model.add(Dense(units=output_dim))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_cnn(input_shape=(240, 12), output_dim=48, kernel=4, stride=1):
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Conv1D(filters=100, kernel_size=kernel, strides=stride, padding="valid"))
model.add(Dropout(rate=0.2))
model.add(Flatten())
model.add(Dense(units=output_dim))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_cnn_lstm(input_shape=(240, 12), output_dim=48, kernel=4, stride=1):
input_layer = Input(shape=input_shape)
cnn_layer = Conv1D(100, kernel_size=kernel, padding='valid', strides=stride)(input_layer)
cnn_layer = Dropout(0.2)(cnn_layer)
lstm_layer = LSTM(32)(cnn_layer)
output_layer = Dense(units=output_dim)(lstm_layer)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_lstm_cnn(input_shape=(240, 12), output_dim=48, kernel=4, stride=1):
input_layer = Input(shape=input_shape)
lstm_layer = LSTM(32, return_sequences=True)(input_layer)
cnn_layer = Conv1D(100, kernel_size=kernel, padding='valid', strides=stride)(lstm_layer)
cnn_layer = Dropout(0.2)(cnn_layer)
pooling_layer = Flatten()(cnn_layer)
output_layer = Dense(units=output_dim)(pooling_layer)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_lstm_cnn2(input_shape=(240, 12), output_dim=48, kernel=4, stride=1):
input_layer = Input(shape=input_shape)
lstm_layer = LSTM(32, return_sequences=False)(input_layer)
lstm_dense = Dense(output_dim)(lstm_layer)
cnn_layer = Conv1D(100, kernel_size=kernel, padding='valid', strides=stride)(input_layer)
cnn_layer = Dropout(0.2)(cnn_layer)
cnn_layer = Flatten()(cnn_layer)
cnn_dense = Dense(output_dim)(cnn_layer)
output_layer = Dense(output_dim)(add([lstm_dense, cnn_dense]))
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_mlp(input_shape=(240, 12), output_dim=48):
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Flatten())
model.add(Dense(units=100))
model.add(Dense(units=output_dim))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_cnn_mlp_concat(input_shape=(240, 12), input_dim=1440, output_dim=48):
mlp_input = Input(shape=input_dim)
cnn_input = Input(shape=input_shape)
cnn_layer = Conv1D(100, kernel_size=4, padding='valid', strides=4)(cnn_input)
cnn_layer = Dropout(0.2)(cnn_layer)
cnn_layer = Flatten()(cnn_layer)
output_layer = Dense(output_dim)(concatenate([mlp_input, cnn_layer]))
model = Model(inputs={'serial_input': cnn_input, 'vector_input': mlp_input}, outputs=output_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_cnn_mlp_attention(input_shape=(240, 12), input_dim=1440, output_dim=48):
mlp_input = Input(shape=input_dim)
mlp_output = Dense(units=100)(mlp_input)
mlp_output = Dropout(.3)(mlp_output)
cnn_input = Input(shape=input_shape)
cnn_layer = Conv1D(100, kernel_size=4, padding='valid', strides=4)(cnn_input)
cnn_layer = Dropout(0.3)(cnn_layer)
cnn_layer = Flatten()(cnn_layer)
cnn_output = Dense(units=100)(cnn_layer)
output_layer = Dense(output_dim)(add([mlp_output, cnn_output]))
model = Model(inputs={'serial_input': cnn_input, 'vector_input': mlp_input}, outputs=output_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_lstm_mlp_concat(input_shape=(240, 12), input_dim=1440, output_dim=48):
mlp_input = Input(shape=input_dim)
mlp_output = Dense(units=100)(mlp_input)
mlp_output = Dropout(.3)(mlp_output)
lstm_input = Input(shape=input_shape)
lstm_output = LSTM(32, return_sequences=False)(lstm_input)
output_layer = Dense(output_dim)(concatenate([lstm_output, mlp_output]))
model = Model(inputs={'serial_input': lstm_input, 'vector_input': mlp_input}, outputs=output_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model
def create_lstm_mlp_atention(input_shape=(240, 12), input_dim=1440, output_dim=48):
mlp_input = Input(shape=input_dim)
mlp_output = Dense(units=100)(mlp_input)
mlp_output = Dropout(.3)(mlp_output)
lstm_input = Input(shape=input_shape)
lstm_output = LSTM(32, return_sequences=False)(lstm_input)
lstm_output = Dense(units=100)(lstm_output)
lstm_output = Dropout(0.3)(lstm_output)
output_layer = Dense(output_dim)(add([lstm_output, mlp_output]))
model = Model(inputs={'serial_input': lstm_input, 'vector_input': mlp_input}, outputs=output_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='mae', metrics=['mean_absolute_error', 'mean_absolute_percentage_error'])
print(model.summary())
return model