RNN - Naive Forecasting

RNN - Recurrent Neural Networks

 

RNN은 임의길이를 가진 시퀀스를 다룰 수 있는 신경망이다.

 

RNN Naive Forecasting

def generate_time_series(batch_size, n_steps):
    freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)
    time = np.linspace(0, 1, n_steps)
    series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10))
    series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20))
    series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5)  
    return series[..., np.newaxis].astype(np.float32)

n_steps = 50
series = generate_time_series(10000, n_steps + 1)
X_train, y_train = series[:7000, :n_steps], series[:7000, -1]
X_valid, y_valid = series[7000:9000, :n_steps], series[7000:9000, -1]
X_test, y_test = series[9000:, :n_steps], series[9000:, -1]

y_pred = X_valid[:, -1]
np.mean(keras.losses.mean_squared_error(y_valid, y_pred))

model = keras.models.Sequential([
    keras.layers.Flatten(input_shape=[50,1]),
    keras.layers.Dense(1)
])
model.summary()