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models.py
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"""
Author: Jay Lux Ferro
Date: 12 Dec 2019
Models
"""
from keras.layers import Embedding, LSTM, Dense, Dropout, Lambda, Flatten, GRU, Activation, SimpleRNN
from keras.models import Sequential, load_model, model_from_config
import keras.backend as K
import my_layers as ll
def simpleRNN(num_features):
model = Sequential()
model.add(SimpleRNN(300, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(SimpleRNN(64, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def simpleRNN2(num_features):
model = Sequential()
model.add(SimpleRNN(500, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(SimpleRNN(400, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def lstm(num_features):
model = Sequential()
model.add(LSTM(300, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(LSTM(64, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def lstm2(num_features):
model = Sequential()
model.add(LSTM(500, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(LSTM(400, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def gru(num_features):
model = Sequential()
model.add(GRU(300, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(GRU(64, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def gru2(num_features):
model = Sequential()
model.add(GRU(500, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(GRU(400, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def lstm_gru(num_features):
model = Sequential()
model.add(LSTM(300, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(GRU(64, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def lstm_gru2(num_features):
model = Sequential()
model.add(LSTM(500, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(GRU(400, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def gru_lstm(num_features):
model = Sequential()
model.add(GRU(300, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(LSTM(64, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def gru_lstm2(num_features):
model = Sequential()
model.add(GRU(500, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(LSTM(400, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def simpleRNN_lstm(num_features):
model = Sequential()
model.add(SimpleRNN(300, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(LSTM(64, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def simpleRNN_lstm2(num_features):
model = Sequential()
model.add(SimpleRNN(500, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(LSTM(400, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def lstm_simpleRNN(num_features):
model = Sequential()
model.add(LSTM(300, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(SimpleRNN(64, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def lstm_simpleRNN2(num_features):
model = Sequential()
model.add(LSTM(500, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(SimpleRNN(400, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def simpleRNN_gru(num_features):
model = Sequential()
model.add(SimpleRNN(300, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(GRU(64, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def simpleRNN_gru2(num_features):
model = Sequential()
model.add(SimpleRNN(500, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(GRU(400, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def gru_simpleRNN(num_features):
model = Sequential()
model.add(GRU(300, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(SimpleRNN(64, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model
def gru_simpleRNN2(num_features):
model = Sequential()
model.add(GRU(500, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, num_features], return_sequences=True))
model.add(SimpleRNN(400, recurrent_dropout=0.4))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])
model.summary()
return model