def create_lstm_model(optimizer, neurons, activation): model = Sequential() model.add(LSTM(units=neurons, input_shape=(X_train.shape[1], X_train.shape[2]))) model.add(Dropout(0.2)) model.add(Dense(1, activation=activation)) model.compile(loss=’mean_squared_error’, optimizer=optimizer) return model param_grid = {‘neurons’: [8, 16, 32, 64, 128], ‘optimizer’: [SGD(), RMSprop(), Adam()], ‘activation’: [‘relu’, ‘tanh’, ‘sigmoid’, ‘linear’,’swish’]} model = KerasRegressor(model=create_lstm_model, neurons=8, activation=’relu’, epochs=10, batch_size=16, verbose=0) grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, refit=False, scoring=”neg_mean_squared_error”, n_jobs = -1, error_score=’raise’) grid_result = […]