daily_nn.py

HOURS_AHEAD = 24
s = all_X.shape[1]

model = tf.keras.Sequential()
model.add(layers.Dense(s, activation=tf.nn.relu, input_shape=(HOURS_AHEAD, all_X.shape[1])))
model.add(layers.Dense(s, activation=tf.nn.relu))
model.add(layers.Dense(s, activation=tf.nn.relu))
model.add(layers.Dense(s, activation=tf.nn.relu))
model.add(layers.Dense(s, activation=tf.nn.relu))
model.add(layers.Flatten())
model.add(layers.Dense(all_X.shape[1]*HOURS_AHEAD//2, activation=tf.nn.relu))
model.add(layers.Dense(HOURS_AHEAD))

nadam = tf.keras.optimizers.Nadam(learning_rate=0.002, beta_1=0.9, beta_2=0.999)
model.compile(optimizer=nadam, loss='mape')