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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GRU, LSTM, Dense, Dropout
from warnings import filterwarnings
filterwarnings('ignore')
""" GRU (Gated Recurrent Units) Model """
async def gru_model(input_shape):
cdef object model = Sequential([
GRU(50, return_sequences = True, input_shape = input_shape),
Dropout(0.2),
GRU(50, return_sequences = True),
Dropout(0.2),
GRU(50, return_sequences = True),
Dropout(0.2),
GRU(50, return_sequences = False),
Dropout(0.2),
Dense(units = 1)
])
model.compile(optimizer = 'nadam', loss = 'mean_squared_error')
return model
""" LSTM (Long Short-Term Memory) Model """
async def lstm_model(input_shape):
cdef object model = Sequential([
LSTM(50, return_sequences = True, input_shape = input_shape),
Dropout(0.2),
LSTM(50, return_sequences = True),
Dropout(0.2),
LSTM(50, return_sequences = True),
Dropout(0.2),
LSTM(50, return_sequences = False),
Dropout(0.2),
Dense(units = 1)
])
model.compile(optimizer = 'nadam', loss = 'mean_squared_error')
return model
"""
LSTM (Long Short-Term Memory) and
GRU (Gated Recurrent Units) Model
"""
async def lstm_gru_model(input_shape):
cdef object model = Sequential([
LSTM(50, return_sequences = True, input_shape = input_shape),
Dropout(0.2),
GRU(50, return_sequences = True),
Dropout(0.2),
LSTM(50, return_sequences = True),
Dropout(0.2),
GRU(50, return_sequences = False),
Dropout(0.2),
Dense(units = 1)
])
model.compile(optimizer = 'nadam', loss = 'mean_squared_error')
return model
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