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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GRU, Bidirectional, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.regularizers import L2
import tensorflow as tf
def model_train (x_train, y_train):
tf.random.set_seed(100)
tf.keras.backend.clear_session()
model = Sequential()
model.add(Bidirectional(GRU(units=256, activation='relu', return_sequences=True, input_shape=(10,1))))
model.add(Dropout(0.5))
model.add(Bidirectional(GRU(units=256, activation='relu', return_sequences=False, kernel_regularizer=L2(0.01))))
model.add(Dropout(0.1))
model.add(Dense(units=1, activation='linear')) # Prediction of the next value
model.compile(optimizer='adam', loss='mape', metrics='mape')
model.build((None, 10, 1)) # Specify input shape
# Define EarlyStopping callback
early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
# Train the model
history = model.fit(x_train, y_train, epochs=10, batch_size=20, validation_split=0.2, callbacks=[early_stopping])
return history, model
def model_predict(train,scaler,model):
data_inf = train[-10:]
scaled_data_inf = scaler.transform(data_inf.values.reshape(-1,1))
data_inf_final = tf.expand_dims(scaled_data_inf,0)
pred_inf = model.predict(data_inf_final)
return scaler.inverse_transform(pred_inf) , data_inf
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