Forecasting with a CNN¶
In [1]:
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
keras = tf.keras
In [2]:
def plot_series(time, series, format="-", start=0, end=None, label=None):
plt.plot(time[start:end], series[start:end], format, label=label)
plt.xlabel("Time")
plt.ylabel("Value")
if label:
plt.legend(fontsize=14)
plt.grid(True)
def trend(time, slope=0):
return slope * time
def seasonal_pattern(season_time):
"""Just an arbitrary pattern, you can change it if you wish"""
return np.where(season_time < 0.4,
np.cos(season_time * 2 * np.pi),
1 / np.exp(3 * season_time))
def seasonality(time, period, amplitude=1, phase=0):
"""Repeats the same pattern at each period"""
season_time = ((time + phase) % period) / period
return amplitude * seasonal_pattern(season_time)
def white_noise(time, noise_level=1, seed=None):
rnd = np.random.RandomState(seed)
return rnd.randn(len(time)) * noise_level
def seq2seq_window_dataset(series, window_size, batch_size=32,
shuffle_buffer=1000):
series = tf.expand_dims(series, axis=-1)
ds = tf.data.Dataset.from_tensor_slices(series)
ds = ds.window(window_size + 1, shift=1, drop_remainder=True)
ds = ds.flat_map(lambda w: w.batch(window_size + 1))
ds = ds.shuffle(shuffle_buffer)
ds = ds.map(lambda w: (w[:-1], w[1:]))
return ds.batch(batch_size).prefetch(1)
def model_forecast(model, series, window_size):
ds = tf.data.Dataset.from_tensor_slices(series)
ds = ds.window(window_size, shift=1, drop_remainder=True)
ds = ds.flat_map(lambda w: w.batch(window_size))
ds = ds.batch(32).prefetch(1)
forecast = model.predict(ds)
return forecast
In [3]:
time = np.arange(4 * 365 + 1)
slope = 0.05
baseline = 10
amplitude = 40
series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)
noise_level = 5
noise = white_noise(time, noise_level, seed=42)
series += noise
plt.figure(figsize=(10, 6))
plot_series(time, series)
plt.show()
In [4]:
split_time = 1000
time_train = time[:split_time]
x_train = series[:split_time]
time_valid = time[split_time:]
x_valid = series[split_time:]
Preprocessing With 1D-Convolutional Layers¶
Compare Learning rate
In [5]:
keras.backend.clear_session()
tf.random.set_seed(42)
np.random.seed(42)
window_size = 30
train_set = seq2seq_window_dataset(x_train, window_size,
batch_size=128)
model = keras.models.Sequential([
keras.layers.Conv1D(filters=32, kernel_size=5,
strides=1, padding="causal",
activation="relu",
input_shape=[None, 1]),
keras.layers.LSTM(32, return_sequences=True),
keras.layers.LSTM(32, return_sequences=True),
keras.layers.Dense(1),
keras.layers.Lambda(lambda x: x * 200)
])
lr_schedule = keras.callbacks.LearningRateScheduler(
lambda epoch: 1e-8 * 10**(epoch / 20))
optimizer = keras.optimizers.SGD(lr=1e-8, momentum=0.9)
model.compile(loss=keras.losses.Huber(),
optimizer=optimizer,
metrics=["mae"])
history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])
Epoch 1/100 8/8 [==============================] - 0s 13ms/step - loss: 87.8346 - mae: 88.3341 Epoch 2/100 8/8 [==============================] - 0s 8ms/step - loss: 87.1197 - mae: 87.6191 Epoch 3/100 8/8 [==============================] - 0s 7ms/step - loss: 85.9241 - mae: 86.4234 Epoch 4/100 8/8 [==============================] - 0s 7ms/step - loss: 84.3720 - mae: 84.8713 Epoch 5/100 8/8 [==============================] - 0s 8ms/step - loss: 82.4702 - mae: 82.9696 Epoch 6/100 8/8 [==============================] - 0s 8ms/step - loss: 80.2434 - mae: 80.7427 Epoch 7/100 8/8 [==============================] - 0s 7ms/step - loss: 77.7459 - mae: 78.2453 Epoch 8/100 8/8 [==============================] - 0s 7ms/step - loss: 75.0957 - mae: 75.5952 Epoch 9/100 8/8 [==============================] - 0s 7ms/step - loss: 72.4508 - mae: 72.9503 Epoch 10/100 8/8 [==============================] - 0s 7ms/step - loss: 69.8938 - mae: 70.3934 Epoch 11/100 8/8 [==============================] - 0s 7ms/step - loss: 67.4776 - mae: 67.9771 Epoch 12/100 8/8 [==============================] - 0s 7ms/step - loss: 65.1459 - mae: 65.6454 Epoch 13/100 8/8 [==============================] - 0s 7ms/step - loss: 62.8461 - mae: 63.3456 Epoch 14/100 8/8 [==============================] - 0s 8ms/step - loss: 60.5182 - mae: 61.0178 Epoch 15/100 8/8 [==============================] - 0s 7ms/step - loss: 58.1030 - mae: 58.6026 Epoch 16/100 8/8 [==============================] - 0s 7ms/step - loss: 55.5455 - mae: 56.0449 Epoch 17/100 8/8 [==============================] - 0s 8ms/step - loss: 52.8068 - mae: 53.3063 Epoch 18/100 8/8 [==============================] - 0s 8ms/step - loss: 49.8502 - mae: 50.3498 Epoch 19/100 8/8 [==============================] - 0s 7ms/step - loss: 46.6214 - mae: 47.1210 Epoch 20/100 8/8 [==============================] - 0s 7ms/step - loss: 43.0741 - mae: 43.5739 Epoch 21/100 8/8 [==============================] - 0s 7ms/step - loss: 39.1589 - mae: 39.6586 Epoch 22/100 8/8 [==============================] - 0s 7ms/step - loss: 34.8436 - mae: 35.3432 Epoch 23/100 8/8 [==============================] - 0s 7ms/step - loss: 30.0388 - mae: 30.5379 Epoch 24/100 8/8 [==============================] - 0s 7ms/step - loss: 24.8078 - mae: 25.3056 Epoch 25/100 8/8 [==============================] - 0s 7ms/step - loss: 19.6406 - mae: 20.1337 Epoch 26/100 8/8 [==============================] - 0s 7ms/step - loss: 16.1664 - mae: 16.6553 Epoch 27/100 8/8 [==============================] - 0s 7ms/step - loss: 14.9181 - mae: 15.4089 Epoch 28/100 8/8 [==============================] - 0s 8ms/step - loss: 14.1021 - mae: 14.5931 Epoch 29/100 8/8 [==============================] - 0s 8ms/step - loss: 13.2322 - mae: 13.7225 Epoch 30/100 8/8 [==============================] - 0s 8ms/step - loss: 12.5448 - mae: 13.0341 Epoch 31/100 8/8 [==============================] - 0s 6ms/step - loss: 11.9937 - mae: 12.4821 Epoch 32/100 8/8 [==============================] - 0s 7ms/step - loss: 11.5103 - mae: 11.9977 Epoch 33/100 8/8 [==============================] - 0s 7ms/step - loss: 11.0496 - mae: 11.5364 Epoch 34/100 8/8 [==============================] - 0s 7ms/step - loss: 10.6071 - mae: 11.0936 Epoch 35/100 8/8 [==============================] - 0s 7ms/step - loss: 10.1819 - mae: 10.6685 Epoch 36/100 8/8 [==============================] - 0s 7ms/step - loss: 9.7739 - mae: 10.2601 Epoch 37/100 8/8 [==============================] - 0s 7ms/step - loss: 9.3846 - mae: 9.8696 Epoch 38/100 8/8 [==============================] - 0s 7ms/step - loss: 9.0259 - mae: 9.5109 Epoch 39/100 8/8 [==============================] - 0s 7ms/step - loss: 8.7051 - mae: 9.1895 Epoch 40/100 8/8 [==============================] - 0s 7ms/step - loss: 8.4318 - mae: 8.9151 Epoch 41/100 8/8 [==============================] - 0s 7ms/step - loss: 8.1988 - mae: 8.6816 Epoch 42/100 8/8 [==============================] - 0s 7ms/step - loss: 7.9957 - mae: 8.4782 Epoch 43/100 8/8 [==============================] - 0s 7ms/step - loss: 7.8225 - mae: 8.3055 Epoch 44/100 8/8 [==============================] - 0s 7ms/step - loss: 7.6675 - mae: 8.1509 Epoch 45/100 8/8 [==============================] - 0s 7ms/step - loss: 7.5168 - mae: 8.0004 Epoch 46/100 8/8 [==============================] - 0s 8ms/step - loss: 7.3867 - mae: 7.8698 Epoch 47/100 8/8 [==============================] - 0s 7ms/step - loss: 7.2602 - mae: 7.7434 Epoch 48/100 8/8 [==============================] - 0s 7ms/step - loss: 7.1382 - mae: 7.6206 Epoch 49/100 8/8 [==============================] - 0s 8ms/step - loss: 7.0202 - mae: 7.5027 Epoch 50/100 8/8 [==============================] - 0s 7ms/step - loss: 6.9011 - mae: 7.3830 Epoch 51/100 8/8 [==============================] - 0s 8ms/step - loss: 6.7802 - mae: 7.2625 Epoch 52/100 8/8 [==============================] - 0s 7ms/step - loss: 6.6635 - mae: 7.1450 Epoch 53/100 8/8 [==============================] - 0s 7ms/step - loss: 6.5250 - mae: 7.0062 Epoch 54/100 8/8 [==============================] - 0s 7ms/step - loss: 6.4039 - mae: 6.8851 Epoch 55/100 8/8 [==============================] - 0s 7ms/step - loss: 6.2752 - mae: 6.7566 Epoch 56/100 8/8 [==============================] - 0s 7ms/step - loss: 6.1671 - mae: 6.6482 Epoch 57/100 8/8 [==============================] - 0s 8ms/step - loss: 6.0477 - mae: 6.5293 Epoch 58/100 8/8 [==============================] - 0s 7ms/step - loss: 5.9321 - mae: 6.4126 Epoch 59/100 8/8 [==============================] - 0s 7ms/step - loss: 5.8226 - mae: 6.3036 Epoch 60/100 8/8 [==============================] - 0s 7ms/step - loss: 5.7015 - mae: 6.1820 Epoch 61/100 8/8 [==============================] - 0s 7ms/step - loss: 5.5789 - mae: 6.0593 Epoch 62/100 8/8 [==============================] - 0s 7ms/step - loss: 5.4950 - mae: 5.9745 Epoch 63/100 8/8 [==============================] - 0s 7ms/step - loss: 5.4265 - mae: 5.9069 Epoch 64/100 8/8 [==============================] - 0s 7ms/step - loss: 5.3869 - mae: 5.8673 Epoch 65/100 8/8 [==============================] - 0s 7ms/step - loss: 5.7756 - mae: 6.2565 Epoch 66/100 8/8 [==============================] - 0s 7ms/step - loss: 5.4883 - mae: 5.9688 Epoch 67/100 8/8 [==============================] - 0s 7ms/step - loss: 5.3681 - mae: 5.8486 Epoch 68/100 8/8 [==============================] - 0s 8ms/step - loss: 5.4192 - mae: 5.8989 Epoch 69/100 8/8 [==============================] - 0s 7ms/step - loss: 5.8857 - mae: 6.3671 Epoch 70/100 8/8 [==============================] - 0s 7ms/step - loss: 6.5149 - mae: 6.9996 Epoch 71/100 8/8 [==============================] - 0s 7ms/step - loss: 6.1195 - mae: 6.6020 Epoch 72/100 8/8 [==============================] - 0s 7ms/step - loss: 6.2246 - mae: 6.7071 Epoch 73/100 8/8 [==============================] - 0s 7ms/step - loss: 6.2215 - mae: 6.7052 Epoch 74/100 8/8 [==============================] - 0s 9ms/step - loss: 7.0861 - mae: 7.5722 Epoch 75/100 8/8 [==============================] - 0s 7ms/step - loss: 7.0630 - mae: 7.5505 Epoch 76/100 8/8 [==============================] - 0s 8ms/step - loss: 6.5899 - mae: 7.0740 Epoch 77/100 8/8 [==============================] - 0s 7ms/step - loss: 6.8809 - mae: 7.3659 Epoch 78/100 8/8 [==============================] - 0s 7ms/step - loss: 8.5862 - mae: 9.0740 Epoch 79/100 8/8 [==============================] - 0s 8ms/step - loss: 10.0649 - mae: 10.5553 Epoch 80/100 8/8 [==============================] - 0s 7ms/step - loss: 7.3267 - mae: 7.8131 Epoch 81/100 8/8 [==============================] - 0s 7ms/step - loss: 7.7800 - mae: 8.2676 Epoch 82/100 8/8 [==============================] - 0s 7ms/step - loss: 8.9022 - mae: 9.3911 Epoch 83/100 8/8 [==============================] - 0s 7ms/step - loss: 17.6441 - mae: 18.1386 Epoch 84/100 8/8 [==============================] - 0s 8ms/step - loss: 21.8658 - mae: 22.3624 Epoch 85/100 8/8 [==============================] - 0s 7ms/step - loss: 18.0278 - mae: 18.5220 Epoch 86/100 8/8 [==============================] - 0s 8ms/step - loss: 16.2827 - mae: 16.7769 Epoch 87/100 8/8 [==============================] - 0s 7ms/step - loss: 22.8431 - mae: 23.3386 Epoch 88/100 8/8 [==============================] - 0s 7ms/step - loss: 21.8162 - mae: 22.3116 Epoch 89/100 8/8 [==============================] - 0s 7ms/step - loss: 16.7766 - mae: 17.2700 Epoch 90/100 8/8 [==============================] - 0s 8ms/step - loss: 15.5704 - mae: 16.0635 Epoch 91/100 8/8 [==============================] - 0s 8ms/step - loss: 16.9380 - mae: 17.4332 Epoch 92/100 8/8 [==============================] - 0s 7ms/step - loss: 15.7321 - mae: 16.2260 Epoch 93/100 8/8 [==============================] - 0s 7ms/step - loss: 13.8858 - mae: 14.3786 Epoch 94/100 8/8 [==============================] - 0s 8ms/step - loss: 20.9751 - mae: 21.4698 Epoch 95/100 8/8 [==============================] - 0s 7ms/step - loss: 16.8033 - mae: 17.2973 Epoch 96/100 8/8 [==============================] - 0s 7ms/step - loss: 16.3828 - mae: 16.8764 Epoch 97/100 8/8 [==============================] - 0s 8ms/step - loss: 15.0495 - mae: 15.5433 Epoch 98/100 8/8 [==============================] - 0s 7ms/step - loss: 18.0837 - mae: 18.5782 Epoch 99/100 8/8 [==============================] - 0s 8ms/step - loss: 17.8635 - mae: 18.3582 Epoch 100/100 8/8 [==============================] - 0s 8ms/step - loss: 18.7676 - mae: 19.2622
In [6]:
plt.semilogx(history.history["lr"], history.history["loss"])
plt.axis([1e-8, 1e-4, 0, 30])
Out[6]:
(1e-08, 0.0001, 0.0, 30.0)
In [7]:
keras.backend.clear_session()
tf.random.set_seed(42)
np.random.seed(42)
window_size = 30
train_set = seq2seq_window_dataset(x_train, window_size,
batch_size=128)
valid_set = seq2seq_window_dataset(x_valid, window_size,
batch_size=128)
model = keras.models.Sequential([
keras.layers.Conv1D(filters=32, kernel_size=5,
strides=1, padding="causal",
activation="relu",
input_shape=[None, 1]),
keras.layers.LSTM(32, return_sequences=True),
keras.layers.LSTM(32, return_sequences=True),
keras.layers.Dense(1),
keras.layers.Lambda(lambda x: x * 200)
])
optimizer = keras.optimizers.SGD(lr=1e-5, momentum=0.9)
model.compile(loss=keras.losses.Huber(),
optimizer=optimizer,
metrics=["mae"])
model_checkpoint = keras.callbacks.ModelCheckpoint(
"my_checkpoint.h5", save_best_only=True)
early_stopping = keras.callbacks.EarlyStopping(patience=50)
model.fit(train_set, epochs=500,
validation_data=valid_set,
callbacks=[early_stopping, model_checkpoint])
Epoch 1/500 8/8 [==============================] - 1s 95ms/step - loss: 56.9010 - mae: 57.4001 - val_loss: 28.7748 - val_mae: 29.2719 Epoch 2/500 8/8 [==============================] - 0s 16ms/step - loss: 26.1449 - mae: 26.6407 - val_loss: 38.9232 - val_mae: 39.4212 Epoch 3/500 8/8 [==============================] - 0s 16ms/step - loss: 17.3629 - mae: 17.8584 - val_loss: 30.1023 - val_mae: 30.6010 Epoch 4/500 8/8 [==============================] - 0s 20ms/step - loss: 12.0091 - mae: 12.4990 - val_loss: 24.5627 - val_mae: 25.0602 Epoch 5/500 8/8 [==============================] - 0s 19ms/step - loss: 9.9288 - mae: 10.4167 - val_loss: 18.8857 - val_mae: 19.3824 Epoch 6/500 8/8 [==============================] - 0s 19ms/step - loss: 8.8391 - mae: 9.3253 - val_loss: 16.0114 - val_mae: 16.5063 Epoch 7/500 8/8 [==============================] - 0s 20ms/step - loss: 8.0840 - mae: 8.5699 - val_loss: 12.5160 - val_mae: 13.0055 Epoch 8/500 8/8 [==============================] - 0s 19ms/step - loss: 7.4687 - mae: 7.9524 - val_loss: 12.2673 - val_mae: 12.7563 Epoch 9/500 8/8 [==============================] - 0s 19ms/step - loss: 6.9998 - mae: 7.4830 - val_loss: 11.4736 - val_mae: 11.9603 Epoch 10/500 8/8 [==============================] - 0s 19ms/step - loss: 6.6586 - mae: 7.1401 - val_loss: 10.6516 - val_mae: 11.1374 Epoch 11/500 8/8 [==============================] - 0s 20ms/step - loss: 6.4171 - mae: 6.8985 - val_loss: 10.2580 - val_mae: 10.7438 Epoch 12/500 8/8 [==============================] - 0s 19ms/step - loss: 6.2239 - mae: 6.7048 - val_loss: 9.9909 - val_mae: 10.4763 Epoch 13/500 8/8 [==============================] - 0s 19ms/step - loss: 6.0595 - mae: 6.5409 - val_loss: 9.4370 - val_mae: 9.9212 Epoch 14/500 8/8 [==============================] - 0s 17ms/step - loss: 5.9534 - mae: 6.4342 - val_loss: 9.4598 - val_mae: 9.9443 Epoch 15/500 8/8 [==============================] - 0s 18ms/step - loss: 5.8310 - mae: 6.3122 - val_loss: 9.1601 - val_mae: 9.6447 Epoch 16/500 8/8 [==============================] - 0s 16ms/step - loss: 5.7474 - mae: 6.2277 - val_loss: 9.2801 - val_mae: 9.7657 Epoch 17/500 8/8 [==============================] - 0s 20ms/step - loss: 5.6558 - mae: 6.1367 - val_loss: 8.9617 - val_mae: 9.4463 Epoch 18/500 8/8 [==============================] - 0s 16ms/step - loss: 5.5709 - mae: 6.0521 - val_loss: 9.1471 - val_mae: 9.6314 Epoch 19/500 8/8 [==============================] - 0s 20ms/step - loss: 5.5045 - mae: 5.9850 - val_loss: 8.6069 - val_mae: 9.0923 Epoch 20/500 8/8 [==============================] - 0s 18ms/step - loss: 5.4353 - mae: 5.9158 - val_loss: 8.4997 - val_mae: 8.9851 Epoch 21/500 8/8 [==============================] - 0s 19ms/step - loss: 5.3746 - mae: 5.8549 - val_loss: 8.4597 - val_mae: 8.9447 Epoch 22/500 8/8 [==============================] - 0s 18ms/step - loss: 5.3205 - mae: 5.8007 - val_loss: 8.2000 - val_mae: 8.6857 Epoch 23/500 8/8 [==============================] - 0s 16ms/step - loss: 5.2688 - mae: 5.7488 - val_loss: 8.2203 - val_mae: 8.7053 Epoch 24/500 8/8 [==============================] - 0s 19ms/step - loss: 5.2204 - mae: 5.6999 - val_loss: 8.1946 - val_mae: 8.6787 Epoch 25/500 8/8 [==============================] - 0s 19ms/step - loss: 5.1607 - mae: 5.6399 - val_loss: 7.9833 - val_mae: 8.4683 Epoch 26/500 8/8 [==============================] - 0s 20ms/step - loss: 5.1148 - mae: 5.5937 - val_loss: 7.5631 - val_mae: 8.0470 Epoch 27/500 8/8 [==============================] - 0s 17ms/step - loss: 5.0781 - mae: 5.5574 - val_loss: 7.7228 - val_mae: 8.2065 Epoch 28/500 8/8 [==============================] - 0s 16ms/step - loss: 5.0560 - mae: 5.5353 - val_loss: 7.7263 - val_mae: 8.2099 Epoch 29/500 8/8 [==============================] - 0s 16ms/step - loss: 5.0292 - mae: 5.5074 - val_loss: 7.6608 - val_mae: 8.1444 Epoch 30/500 8/8 [==============================] - 0s 18ms/step - loss: 4.9737 - mae: 5.4526 - val_loss: 7.3858 - val_mae: 7.8698 Epoch 31/500 8/8 [==============================] - 0s 16ms/step - loss: 4.9508 - mae: 5.4288 - val_loss: 7.6948 - val_mae: 8.1809 Epoch 32/500 8/8 [==============================] - 0s 18ms/step - loss: 4.9197 - mae: 5.3985 - val_loss: 7.0806 - val_mae: 7.5640 Epoch 33/500 8/8 [==============================] - 0s 17ms/step - loss: 4.8875 - mae: 5.3658 - val_loss: 7.3538 - val_mae: 7.8382 Epoch 34/500 8/8 [==============================] - 0s 16ms/step - loss: 4.8574 - mae: 5.3356 - val_loss: 7.1059 - val_mae: 7.5893 Epoch 35/500 8/8 [==============================] - 0s 20ms/step - loss: 4.8378 - mae: 5.3163 - val_loss: 6.9634 - val_mae: 7.4460 Epoch 36/500 8/8 [==============================] - 0s 17ms/step - loss: 4.8351 - mae: 5.3137 - val_loss: 7.3317 - val_mae: 7.8181 Epoch 37/500 8/8 [==============================] - 0s 19ms/step - loss: 4.8040 - mae: 5.2820 - val_loss: 6.7391 - val_mae: 7.2226 Epoch 38/500 8/8 [==============================] - 0s 16ms/step - loss: 4.7916 - mae: 5.2698 - val_loss: 6.9005 - val_mae: 7.3821 Epoch 39/500 8/8 [==============================] - 0s 16ms/step - loss: 4.7634 - mae: 5.2417 - val_loss: 6.9630 - val_mae: 7.4474 Epoch 40/500 8/8 [==============================] - 0s 19ms/step - loss: 4.7618 - mae: 5.2399 - val_loss: 6.6359 - val_mae: 7.1188 Epoch 41/500 8/8 [==============================] - 0s 19ms/step - loss: 4.7459 - mae: 5.2239 - val_loss: 6.6180 - val_mae: 7.1002 Epoch 42/500 8/8 [==============================] - 0s 17ms/step - loss: 4.7427 - mae: 5.2216 - val_loss: 7.0516 - val_mae: 7.5371 Epoch 43/500 8/8 [==============================] - 0s 18ms/step - loss: 4.7203 - mae: 5.1978 - val_loss: 6.4535 - val_mae: 6.9373 Epoch 44/500 8/8 [==============================] - 0s 16ms/step - loss: 4.7001 - mae: 5.1783 - val_loss: 6.8852 - val_mae: 7.3695 Epoch 45/500 8/8 [==============================] - 0s 16ms/step - loss: 4.6915 - mae: 5.1686 - val_loss: 6.5483 - val_mae: 7.0299 Epoch 46/500 8/8 [==============================] - 0s 17ms/step - loss: 4.6862 - mae: 5.1637 - val_loss: 6.6454 - val_mae: 7.1277 Epoch 47/500 8/8 [==============================] - 0s 16ms/step - loss: 4.6630 - mae: 5.1406 - val_loss: 6.7584 - val_mae: 7.2422 Epoch 48/500 8/8 [==============================] - 0s 16ms/step - loss: 4.6472 - mae: 5.1238 - val_loss: 6.4582 - val_mae: 6.9404 Epoch 49/500 8/8 [==============================] - 0s 19ms/step - loss: 4.6411 - mae: 5.1178 - val_loss: 6.8431 - val_mae: 7.3271 Epoch 50/500 8/8 [==============================] - 0s 16ms/step - loss: 4.6343 - mae: 5.1113 - val_loss: 6.5276 - val_mae: 7.0104 Epoch 51/500 8/8 [==============================] - 0s 19ms/step - loss: 4.6240 - mae: 5.1012 - val_loss: 6.3081 - val_mae: 6.7905 Epoch 52/500 8/8 [==============================] - 0s 15ms/step - loss: 4.6217 - mae: 5.1001 - val_loss: 6.7627 - val_mae: 7.2461 Epoch 53/500 8/8 [==============================] - 0s 17ms/step - loss: 4.6149 - mae: 5.0922 - val_loss: 6.3504 - val_mae: 6.8319 Epoch 54/500 8/8 [==============================] - 0s 17ms/step - loss: 4.5917 - mae: 5.0683 - val_loss: 6.5269 - val_mae: 7.0100 Epoch 55/500 8/8 [==============================] - 0s 17ms/step - loss: 4.5863 - mae: 5.0632 - val_loss: 6.6091 - val_mae: 7.0928 Epoch 56/500 8/8 [==============================] - 0s 17ms/step - loss: 4.5860 - mae: 5.0626 - val_loss: 6.4079 - val_mae: 6.8907 Epoch 57/500 8/8 [==============================] - 0s 18ms/step - loss: 4.5769 - mae: 5.0538 - val_loss: 6.2570 - val_mae: 6.7390 Epoch 58/500 8/8 [==============================] - 0s 15ms/step - loss: 4.5635 - mae: 5.0404 - val_loss: 6.5512 - val_mae: 7.0354 Epoch 59/500 8/8 [==============================] - 0s 16ms/step - loss: 4.5501 - mae: 5.0264 - val_loss: 6.3882 - val_mae: 6.8715 Epoch 60/500 8/8 [==============================] - 0s 19ms/step - loss: 4.5475 - mae: 5.0236 - val_loss: 6.1782 - val_mae: 6.6608 Epoch 61/500 8/8 [==============================] - 0s 17ms/step - loss: 4.5383 - mae: 5.0149 - val_loss: 6.3854 - val_mae: 6.8690 Epoch 62/500 8/8 [==============================] - 0s 16ms/step - loss: 4.5294 - mae: 5.0055 - val_loss: 6.2851 - val_mae: 6.7683 Epoch 63/500 8/8 [==============================] - 0s 17ms/step - loss: 4.5189 - mae: 4.9948 - val_loss: 6.3868 - val_mae: 6.8709 Epoch 64/500 8/8 [==============================] - 0s 16ms/step - loss: 4.5204 - mae: 4.9968 - val_loss: 6.6535 - val_mae: 7.1369 Epoch 65/500 8/8 [==============================] - 0s 19ms/step - loss: 4.5505 - mae: 5.0275 - val_loss: 5.9733 - val_mae: 6.4554 Epoch 66/500 8/8 [==============================] - 0s 16ms/step - loss: 4.5614 - mae: 5.0384 - val_loss: 6.2287 - val_mae: 6.7116 Epoch 67/500 8/8 [==============================] - 0s 16ms/step - loss: 4.5188 - mae: 4.9957 - val_loss: 6.5301 - val_mae: 7.0143 Epoch 68/500 8/8 [==============================] - 0s 19ms/step - loss: 4.5129 - mae: 4.9895 - val_loss: 6.1016 - val_mae: 6.5840 Epoch 69/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4907 - mae: 4.9673 - val_loss: 6.2082 - val_mae: 6.6918 Epoch 70/500 8/8 [==============================] - 0s 15ms/step - loss: 4.4814 - mae: 4.9571 - val_loss: 6.1913 - val_mae: 6.6749 Epoch 71/500 8/8 [==============================] - 0s 17ms/step - loss: 4.4833 - mae: 4.9596 - val_loss: 6.3098 - val_mae: 6.7943 Epoch 72/500 8/8 [==============================] - 0s 17ms/step - loss: 4.4757 - mae: 4.9512 - val_loss: 5.9928 - val_mae: 6.4750 Epoch 73/500 8/8 [==============================] - 0s 17ms/step - loss: 4.4766 - mae: 4.9525 - val_loss: 6.0977 - val_mae: 6.5807 Epoch 74/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4676 - mae: 4.9434 - val_loss: 6.3761 - val_mae: 6.8612 Epoch 75/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4612 - mae: 4.9364 - val_loss: 6.3120 - val_mae: 6.7969 Epoch 76/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4576 - mae: 4.9330 - val_loss: 6.2456 - val_mae: 6.7298 Epoch 77/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4570 - mae: 4.9329 - val_loss: 6.1299 - val_mae: 6.6135 Epoch 78/500 8/8 [==============================] - 0s 17ms/step - loss: 4.4452 - mae: 4.9203 - val_loss: 6.0966 - val_mae: 6.5799 Epoch 79/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4409 - mae: 4.9166 - val_loss: 6.1497 - val_mae: 6.6333 Epoch 80/500 8/8 [==============================] - 0s 18ms/step - loss: 4.4369 - mae: 4.9126 - val_loss: 6.0989 - val_mae: 6.5823 Epoch 81/500 8/8 [==============================] - 0s 17ms/step - loss: 4.4340 - mae: 4.9092 - val_loss: 6.1304 - val_mae: 6.6142 Epoch 82/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4343 - mae: 4.9097 - val_loss: 6.1662 - val_mae: 6.6502 Epoch 83/500 8/8 [==============================] - 0s 18ms/step - loss: 4.4298 - mae: 4.9052 - val_loss: 5.9246 - val_mae: 6.4067 Epoch 84/500 8/8 [==============================] - 0s 17ms/step - loss: 4.4266 - mae: 4.9019 - val_loss: 6.0150 - val_mae: 6.4977 Epoch 85/500 8/8 [==============================] - 0s 15ms/step - loss: 4.4219 - mae: 4.8968 - val_loss: 5.9547 - val_mae: 6.4375 Epoch 86/500 8/8 [==============================] - 0s 17ms/step - loss: 4.4208 - mae: 4.8961 - val_loss: 6.0472 - val_mae: 6.5301 Epoch 87/500 8/8 [==============================] - 0s 19ms/step - loss: 4.4121 - mae: 4.8879 - val_loss: 6.0319 - val_mae: 6.5153 Epoch 88/500 8/8 [==============================] - 0s 20ms/step - loss: 4.4136 - mae: 4.8888 - val_loss: 5.8578 - val_mae: 6.3400 Epoch 89/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4038 - mae: 4.8791 - val_loss: 5.9275 - val_mae: 6.4102 Epoch 90/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4010 - mae: 4.8762 - val_loss: 5.8991 - val_mae: 6.3816 Epoch 91/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3958 - mae: 4.8715 - val_loss: 5.9978 - val_mae: 6.4807 Epoch 92/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3980 - mae: 4.8739 - val_loss: 6.1265 - val_mae: 6.6107 Epoch 93/500 8/8 [==============================] - 0s 19ms/step - loss: 4.3903 - mae: 4.8657 - val_loss: 5.7802 - val_mae: 6.2621 Epoch 94/500 8/8 [==============================] - 0s 18ms/step - loss: 4.4018 - mae: 4.8780 - val_loss: 5.7223 - val_mae: 6.2025 Epoch 95/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3943 - mae: 4.8697 - val_loss: 6.0235 - val_mae: 6.5068 Epoch 96/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3881 - mae: 4.8635 - val_loss: 6.3300 - val_mae: 6.8145 Epoch 97/500 8/8 [==============================] - 0s 16ms/step - loss: 4.4099 - mae: 4.8858 - val_loss: 5.8979 - val_mae: 6.3808 Epoch 98/500 8/8 [==============================] - 0s 20ms/step - loss: 4.4312 - mae: 4.9078 - val_loss: 5.6946 - val_mae: 6.1749 Epoch 99/500 8/8 [==============================] - 0s 19ms/step - loss: 4.4370 - mae: 4.9133 - val_loss: 5.6438 - val_mae: 6.1244 Epoch 100/500 8/8 [==============================] - 0s 17ms/step - loss: 4.4060 - mae: 4.8823 - val_loss: 5.8510 - val_mae: 6.3332 Epoch 101/500 8/8 [==============================] - 0s 18ms/step - loss: 4.3935 - mae: 4.8690 - val_loss: 5.9931 - val_mae: 6.4764 Epoch 102/500 8/8 [==============================] - 0s 17ms/step - loss: 4.4069 - mae: 4.8823 - val_loss: 6.2050 - val_mae: 6.6891 Epoch 103/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3670 - mae: 4.8419 - val_loss: 6.0152 - val_mae: 6.4988 Epoch 104/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3595 - mae: 4.8341 - val_loss: 5.7499 - val_mae: 6.2314 Epoch 105/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3687 - mae: 4.8438 - val_loss: 5.6648 - val_mae: 6.1448 Epoch 106/500 8/8 [==============================] - 0s 18ms/step - loss: 4.3714 - mae: 4.8469 - val_loss: 6.0955 - val_mae: 6.5793 Epoch 107/500 8/8 [==============================] - 0s 18ms/step - loss: 4.3795 - mae: 4.8547 - val_loss: 6.4321 - val_mae: 6.9156 Epoch 108/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3943 - mae: 4.8706 - val_loss: 6.0201 - val_mae: 6.5032 Epoch 109/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3679 - mae: 4.8436 - val_loss: 5.6765 - val_mae: 6.1580 Epoch 110/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3725 - mae: 4.8478 - val_loss: 5.6541 - val_mae: 6.1339 Epoch 111/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3465 - mae: 4.8215 - val_loss: 5.8687 - val_mae: 6.3513 Epoch 112/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3475 - mae: 4.8227 - val_loss: 5.9577 - val_mae: 6.4411 Epoch 113/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3416 - mae: 4.8167 - val_loss: 5.9153 - val_mae: 6.3984 Epoch 114/500 8/8 [==============================] - 0s 18ms/step - loss: 4.3363 - mae: 4.8111 - val_loss: 5.6070 - val_mae: 6.0871 Epoch 115/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3294 - mae: 4.8042 - val_loss: 5.8056 - val_mae: 6.2882 Epoch 116/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3206 - mae: 4.7951 - val_loss: 5.9200 - val_mae: 6.4031 Epoch 117/500 8/8 [==============================] - 0s 20ms/step - loss: 4.3327 - mae: 4.8077 - val_loss: 5.5999 - val_mae: 6.0794 Epoch 118/500 8/8 [==============================] - 0s 19ms/step - loss: 4.3320 - mae: 4.8069 - val_loss: 5.5180 - val_mae: 5.9972 Epoch 119/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3411 - mae: 4.8161 - val_loss: 5.7220 - val_mae: 6.2045 Epoch 120/500 8/8 [==============================] - 0s 18ms/step - loss: 4.3346 - mae: 4.8103 - val_loss: 6.0125 - val_mae: 6.4961 Epoch 121/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3205 - mae: 4.7956 - val_loss: 6.0693 - val_mae: 6.5532 Epoch 122/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3094 - mae: 4.7842 - val_loss: 5.6757 - val_mae: 6.1578 Epoch 123/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3036 - mae: 4.7786 - val_loss: 5.7370 - val_mae: 6.2191 Epoch 124/500 8/8 [==============================] - 0s 16ms/step - loss: 4.3027 - mae: 4.7775 - val_loss: 5.8081 - val_mae: 6.2911 Epoch 125/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2980 - mae: 4.7725 - val_loss: 5.6942 - val_mae: 6.1764 Epoch 126/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2996 - mae: 4.7740 - val_loss: 5.6647 - val_mae: 6.1462 Epoch 127/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2928 - mae: 4.7677 - val_loss: 5.7813 - val_mae: 6.2639 Epoch 128/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2914 - mae: 4.7662 - val_loss: 5.5425 - val_mae: 6.0220 Epoch 129/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2917 - mae: 4.7659 - val_loss: 5.6056 - val_mae: 6.0870 Epoch 130/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2934 - mae: 4.7674 - val_loss: 5.7374 - val_mae: 6.2198 Epoch 131/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2943 - mae: 4.7689 - val_loss: 5.6345 - val_mae: 6.1165 Epoch 132/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2889 - mae: 4.7630 - val_loss: 5.7602 - val_mae: 6.2427 Epoch 133/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2835 - mae: 4.7582 - val_loss: 5.6676 - val_mae: 6.1498 Epoch 134/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2768 - mae: 4.7512 - val_loss: 5.6046 - val_mae: 6.0863 Epoch 135/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2749 - mae: 4.7495 - val_loss: 5.7820 - val_mae: 6.2648 Epoch 136/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2857 - mae: 4.7595 - val_loss: 5.8363 - val_mae: 6.3192 Epoch 137/500 8/8 [==============================] - 0s 17ms/step - loss: 4.3105 - mae: 4.7861 - val_loss: 5.8549 - val_mae: 6.3380 Epoch 138/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2946 - mae: 4.7696 - val_loss: 6.2312 - val_mae: 6.7146 Epoch 139/500 8/8 [==============================] - 0s 18ms/step - loss: 4.2919 - mae: 4.7661 - val_loss: 5.8012 - val_mae: 6.2840 Epoch 140/500 8/8 [==============================] - 0s 18ms/step - loss: 4.2971 - mae: 4.7727 - val_loss: 5.9726 - val_mae: 6.4560 Epoch 141/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2925 - mae: 4.7670 - val_loss: 6.1144 - val_mae: 6.5979 Epoch 142/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2835 - mae: 4.7584 - val_loss: 5.8985 - val_mae: 6.3812 Epoch 143/500 8/8 [==============================] - 0s 15ms/step - loss: 4.2631 - mae: 4.7376 - val_loss: 5.6856 - val_mae: 6.1680 Epoch 144/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2743 - mae: 4.7488 - val_loss: 5.6897 - val_mae: 6.1720 Epoch 145/500 8/8 [==============================] - 0s 20ms/step - loss: 4.2602 - mae: 4.7340 - val_loss: 5.4644 - val_mae: 5.9441 Epoch 146/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2531 - mae: 4.7274 - val_loss: 5.8702 - val_mae: 6.3530 Epoch 147/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2530 - mae: 4.7267 - val_loss: 5.5385 - val_mae: 6.0200 Epoch 148/500 8/8 [==============================] - 0s 18ms/step - loss: 4.2452 - mae: 4.7192 - val_loss: 5.8615 - val_mae: 6.3440 Epoch 149/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2497 - mae: 4.7236 - val_loss: 5.7783 - val_mae: 6.2607 Epoch 150/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2545 - mae: 4.7284 - val_loss: 5.6958 - val_mae: 6.1783 Epoch 151/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2535 - mae: 4.7274 - val_loss: 5.7410 - val_mae: 6.2236 Epoch 152/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2402 - mae: 4.7139 - val_loss: 5.7848 - val_mae: 6.2676 Epoch 153/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2442 - mae: 4.7185 - val_loss: 5.8420 - val_mae: 6.3247 Epoch 154/500 8/8 [==============================] - 0s 18ms/step - loss: 4.2464 - mae: 4.7204 - val_loss: 5.6999 - val_mae: 6.1825 Epoch 155/500 8/8 [==============================] - 0s 20ms/step - loss: 4.2866 - mae: 4.7618 - val_loss: 5.3828 - val_mae: 5.8608 Epoch 156/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2597 - mae: 4.7348 - val_loss: 5.3887 - val_mae: 5.8671 Epoch 157/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2638 - mae: 4.7385 - val_loss: 5.4693 - val_mae: 5.9489 Epoch 158/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2325 - mae: 4.7068 - val_loss: 5.3987 - val_mae: 5.8772 Epoch 159/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2337 - mae: 4.7078 - val_loss: 5.6534 - val_mae: 6.1355 Epoch 160/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2382 - mae: 4.7127 - val_loss: 5.8745 - val_mae: 6.3570 Epoch 161/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2245 - mae: 4.6978 - val_loss: 5.5667 - val_mae: 6.0483 Epoch 162/500 8/8 [==============================] - 0s 15ms/step - loss: 4.2174 - mae: 4.6919 - val_loss: 5.6747 - val_mae: 6.1568 Epoch 163/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2255 - mae: 4.6993 - val_loss: 5.5269 - val_mae: 6.0087 Epoch 164/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2221 - mae: 4.6955 - val_loss: 5.5338 - val_mae: 6.0151 Epoch 165/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2150 - mae: 4.6886 - val_loss: 5.6447 - val_mae: 6.1270 Epoch 166/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2226 - mae: 4.6959 - val_loss: 5.5645 - val_mae: 6.0462 Epoch 167/500 8/8 [==============================] - 0s 18ms/step - loss: 4.2091 - mae: 4.6826 - val_loss: 5.4313 - val_mae: 5.9118 Epoch 168/500 8/8 [==============================] - 0s 19ms/step - loss: 4.2314 - mae: 4.7054 - val_loss: 5.2292 - val_mae: 5.7083 Epoch 169/500 8/8 [==============================] - 0s 18ms/step - loss: 4.2790 - mae: 4.7546 - val_loss: 5.3795 - val_mae: 5.8588 Epoch 170/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2272 - mae: 4.7020 - val_loss: 5.6016 - val_mae: 6.0835 Epoch 171/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2049 - mae: 4.6786 - val_loss: 5.5155 - val_mae: 5.9971 Epoch 172/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2046 - mae: 4.6784 - val_loss: 5.9753 - val_mae: 6.4580 Epoch 173/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2335 - mae: 4.7083 - val_loss: 5.8281 - val_mae: 6.3099 Epoch 174/500 8/8 [==============================] - 0s 19ms/step - loss: 4.2431 - mae: 4.7181 - val_loss: 5.9843 - val_mae: 6.4668 Epoch 175/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2367 - mae: 4.7115 - val_loss: 5.6249 - val_mae: 6.1065 Epoch 176/500 8/8 [==============================] - 0s 18ms/step - loss: 4.2073 - mae: 4.6813 - val_loss: 5.5682 - val_mae: 6.0501 Epoch 177/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1970 - mae: 4.6707 - val_loss: 5.6940 - val_mae: 6.1759 Epoch 178/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1922 - mae: 4.6657 - val_loss: 5.8627 - val_mae: 6.3447 Epoch 179/500 8/8 [==============================] - 0s 16ms/step - loss: 4.2373 - mae: 4.7114 - val_loss: 6.0298 - val_mae: 6.5124 Epoch 180/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2273 - mae: 4.7013 - val_loss: 5.7982 - val_mae: 6.2799 Epoch 181/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1966 - mae: 4.6702 - val_loss: 5.9184 - val_mae: 6.4003 Epoch 182/500 8/8 [==============================] - 0s 17ms/step - loss: 4.2046 - mae: 4.6784 - val_loss: 5.6198 - val_mae: 6.1017 Epoch 183/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1897 - mae: 4.6638 - val_loss: 5.4191 - val_mae: 5.8992 Epoch 184/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1840 - mae: 4.6574 - val_loss: 5.6650 - val_mae: 6.1469 Epoch 185/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1841 - mae: 4.6575 - val_loss: 5.4544 - val_mae: 5.9354 Epoch 186/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1839 - mae: 4.6572 - val_loss: 5.6436 - val_mae: 6.1257 Epoch 187/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1816 - mae: 4.6547 - val_loss: 5.5446 - val_mae: 6.0264 Epoch 188/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1780 - mae: 4.6511 - val_loss: 5.3177 - val_mae: 5.7972 Epoch 189/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1821 - mae: 4.6557 - val_loss: 5.4122 - val_mae: 5.8929 Epoch 190/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1826 - mae: 4.6560 - val_loss: 5.3715 - val_mae: 5.8518 Epoch 191/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1821 - mae: 4.6561 - val_loss: 5.6396 - val_mae: 6.1215 Epoch 192/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1799 - mae: 4.6537 - val_loss: 5.5041 - val_mae: 5.9858 Epoch 193/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1768 - mae: 4.6500 - val_loss: 5.6965 - val_mae: 6.1780 Epoch 194/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1734 - mae: 4.6466 - val_loss: 5.4163 - val_mae: 5.8971 Epoch 195/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1669 - mae: 4.6403 - val_loss: 5.7947 - val_mae: 6.2764 Epoch 196/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1810 - mae: 4.6539 - val_loss: 5.5546 - val_mae: 6.0365 Epoch 197/500 8/8 [==============================] - 0s 15ms/step - loss: 4.1671 - mae: 4.6398 - val_loss: 5.4504 - val_mae: 5.9315 Epoch 198/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1669 - mae: 4.6395 - val_loss: 5.4227 - val_mae: 5.9034 Epoch 199/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1689 - mae: 4.6416 - val_loss: 5.6424 - val_mae: 6.1239 Epoch 200/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1673 - mae: 4.6402 - val_loss: 5.4295 - val_mae: 5.9102 Epoch 201/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1596 - mae: 4.6324 - val_loss: 5.3754 - val_mae: 5.8552 Epoch 202/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1660 - mae: 4.6393 - val_loss: 5.3150 - val_mae: 5.7947 Epoch 203/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1570 - mae: 4.6302 - val_loss: 5.3407 - val_mae: 5.8204 Epoch 204/500 8/8 [==============================] - 0s 15ms/step - loss: 4.1557 - mae: 4.6282 - val_loss: 5.5160 - val_mae: 5.9976 Epoch 205/500 8/8 [==============================] - 0s 19ms/step - loss: 4.1540 - mae: 4.6266 - val_loss: 5.5316 - val_mae: 6.0134 Epoch 206/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1834 - mae: 4.6567 - val_loss: 5.9305 - val_mae: 6.4130 Epoch 207/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1987 - mae: 4.6724 - val_loss: 5.7712 - val_mae: 6.2527 Epoch 208/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1744 - mae: 4.6480 - val_loss: 5.4550 - val_mae: 5.9364 Epoch 209/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1601 - mae: 4.6327 - val_loss: 5.4770 - val_mae: 5.9585 Epoch 210/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1537 - mae: 4.6264 - val_loss: 5.3867 - val_mae: 5.8668 Epoch 211/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1568 - mae: 4.6293 - val_loss: 5.1929 - val_mae: 5.6717 Epoch 212/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1769 - mae: 4.6507 - val_loss: 5.3330 - val_mae: 5.8128 Epoch 213/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1672 - mae: 4.6403 - val_loss: 5.3066 - val_mae: 5.7864 Epoch 214/500 8/8 [==============================] - 0s 15ms/step - loss: 4.1524 - mae: 4.6251 - val_loss: 5.4097 - val_mae: 5.8904 Epoch 215/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1482 - mae: 4.6209 - val_loss: 5.4160 - val_mae: 5.8973 Epoch 216/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1539 - mae: 4.6270 - val_loss: 5.4206 - val_mae: 5.9016 Epoch 217/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1640 - mae: 4.6371 - val_loss: 5.2221 - val_mae: 5.7004 Epoch 218/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1689 - mae: 4.6418 - val_loss: 5.3469 - val_mae: 5.8277 Epoch 219/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1623 - mae: 4.6350 - val_loss: 5.2894 - val_mae: 5.7679 Epoch 220/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1560 - mae: 4.6291 - val_loss: 5.3742 - val_mae: 5.8552 Epoch 221/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1506 - mae: 4.6237 - val_loss: 5.3962 - val_mae: 5.8772 Epoch 222/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1379 - mae: 4.6097 - val_loss: 5.1948 - val_mae: 5.6734 Epoch 223/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1524 - mae: 4.6255 - val_loss: 5.2218 - val_mae: 5.7015 Epoch 224/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1527 - mae: 4.6256 - val_loss: 5.2705 - val_mae: 5.7502 Epoch 225/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1545 - mae: 4.6267 - val_loss: 5.2354 - val_mae: 5.7141 Epoch 226/500 8/8 [==============================] - 0s 19ms/step - loss: 4.1418 - mae: 4.6145 - val_loss: 5.3043 - val_mae: 5.7847 Epoch 227/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1618 - mae: 4.6343 - val_loss: 5.3611 - val_mae: 5.8414 Epoch 228/500 8/8 [==============================] - 0s 15ms/step - loss: 4.1444 - mae: 4.6167 - val_loss: 5.3929 - val_mae: 5.8738 Epoch 229/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1426 - mae: 4.6157 - val_loss: 5.7602 - val_mae: 6.2416 Epoch 230/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1605 - mae: 4.6341 - val_loss: 5.8033 - val_mae: 6.2847 Epoch 231/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1691 - mae: 4.6428 - val_loss: 5.6345 - val_mae: 6.1157 Epoch 232/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1348 - mae: 4.6072 - val_loss: 5.3839 - val_mae: 5.8646 Epoch 233/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1332 - mae: 4.6057 - val_loss: 5.5331 - val_mae: 6.0142 Epoch 234/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1386 - mae: 4.6113 - val_loss: 5.6766 - val_mae: 6.1575 Epoch 235/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1353 - mae: 4.6075 - val_loss: 5.3380 - val_mae: 5.8182 Epoch 236/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1250 - mae: 4.5971 - val_loss: 5.4252 - val_mae: 5.9062 Epoch 237/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1314 - mae: 4.6037 - val_loss: 5.2604 - val_mae: 5.7395 Epoch 238/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1533 - mae: 4.6257 - val_loss: 5.4537 - val_mae: 5.9345 Epoch 239/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1441 - mae: 4.6164 - val_loss: 5.2869 - val_mae: 5.7676 Epoch 240/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1285 - mae: 4.6006 - val_loss: 5.5919 - val_mae: 6.0732 Epoch 241/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1346 - mae: 4.6070 - val_loss: 5.4197 - val_mae: 5.9007 Epoch 242/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1214 - mae: 4.5936 - val_loss: 5.2009 - val_mae: 5.6798 Epoch 243/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1213 - mae: 4.5934 - val_loss: 5.4630 - val_mae: 5.9442 Epoch 244/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1186 - mae: 4.5902 - val_loss: 5.6384 - val_mae: 6.1191 Epoch 245/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1374 - mae: 4.6103 - val_loss: 5.3895 - val_mae: 5.8703 Epoch 246/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1182 - mae: 4.5897 - val_loss: 5.2593 - val_mae: 5.7396 Epoch 247/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1264 - mae: 4.5983 - val_loss: 5.1922 - val_mae: 5.6711 Epoch 248/500 8/8 [==============================] - 0s 19ms/step - loss: 4.1362 - mae: 4.6090 - val_loss: 5.3020 - val_mae: 5.7820 Epoch 249/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1207 - mae: 4.5923 - val_loss: 5.2885 - val_mae: 5.7691 Epoch 250/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1206 - mae: 4.5931 - val_loss: 5.3459 - val_mae: 5.8266 Epoch 251/500 8/8 [==============================] - 0s 15ms/step - loss: 4.1197 - mae: 4.5919 - val_loss: 5.2539 - val_mae: 5.7334 Epoch 252/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1180 - mae: 4.5904 - val_loss: 5.1683 - val_mae: 5.6474 Epoch 253/500 8/8 [==============================] - 0s 19ms/step - loss: 4.1136 - mae: 4.5853 - val_loss: 5.1594 - val_mae: 5.6379 Epoch 254/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1225 - mae: 4.5953 - val_loss: 5.1760 - val_mae: 5.6544 Epoch 255/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1132 - mae: 4.5852 - val_loss: 5.3379 - val_mae: 5.8190 Epoch 256/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1097 - mae: 4.5813 - val_loss: 5.5829 - val_mae: 6.0639 Epoch 257/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1245 - mae: 4.5968 - val_loss: 5.5416 - val_mae: 6.0226 Epoch 258/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1287 - mae: 4.6018 - val_loss: 5.3273 - val_mae: 5.8080 Epoch 259/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1348 - mae: 4.6078 - val_loss: 5.1891 - val_mae: 5.6687 Epoch 260/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1209 - mae: 4.5933 - val_loss: 5.1393 - val_mae: 5.6174 Epoch 261/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1085 - mae: 4.5808 - val_loss: 5.4357 - val_mae: 5.9165 Epoch 262/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1543 - mae: 4.6275 - val_loss: 5.5401 - val_mae: 6.0207 Epoch 263/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1704 - mae: 4.6451 - val_loss: 5.1412 - val_mae: 5.6197 Epoch 264/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1364 - mae: 4.6094 - val_loss: 5.3256 - val_mae: 5.8061 Epoch 265/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1068 - mae: 4.5789 - val_loss: 5.1415 - val_mae: 5.6204 Epoch 266/500 8/8 [==============================] - 0s 20ms/step - loss: 4.1194 - mae: 4.5919 - val_loss: 5.0642 - val_mae: 5.5431 Epoch 267/500 8/8 [==============================] - 0s 19ms/step - loss: 4.1310 - mae: 4.6042 - val_loss: 5.1716 - val_mae: 5.6513 Epoch 268/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1090 - mae: 4.5813 - val_loss: 5.0872 - val_mae: 5.5663 Epoch 269/500 8/8 [==============================] - 0s 15ms/step - loss: 4.1179 - mae: 4.5902 - val_loss: 5.2009 - val_mae: 5.6794 Epoch 270/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1140 - mae: 4.5867 - val_loss: 5.3632 - val_mae: 5.8440 Epoch 271/500 8/8 [==============================] - 0s 15ms/step - loss: 4.1098 - mae: 4.5818 - val_loss: 5.5342 - val_mae: 6.0148 Epoch 272/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1149 - mae: 4.5869 - val_loss: 5.4837 - val_mae: 5.9650 Epoch 273/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1236 - mae: 4.5964 - val_loss: 5.5448 - val_mae: 6.0255 Epoch 274/500 8/8 [==============================] - 0s 19ms/step - loss: 4.1051 - mae: 4.5768 - val_loss: 5.3122 - val_mae: 5.7926 Epoch 275/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0971 - mae: 4.5689 - val_loss: 5.2563 - val_mae: 5.7364 Epoch 276/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1135 - mae: 4.5859 - val_loss: 5.2758 - val_mae: 5.7565 Epoch 277/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1026 - mae: 4.5741 - val_loss: 5.1164 - val_mae: 5.5944 Epoch 278/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1064 - mae: 4.5792 - val_loss: 5.2281 - val_mae: 5.7082 Epoch 279/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1035 - mae: 4.5757 - val_loss: 5.3186 - val_mae: 5.7995 Epoch 280/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1083 - mae: 4.5803 - val_loss: 5.5574 - val_mae: 6.0388 Epoch 281/500 8/8 [==============================] - 0s 19ms/step - loss: 4.1223 - mae: 4.5950 - val_loss: 5.1830 - val_mae: 5.6635 Epoch 282/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1256 - mae: 4.5980 - val_loss: 5.0250 - val_mae: 5.5050 Epoch 283/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1531 - mae: 4.6270 - val_loss: 5.0339 - val_mae: 5.5134 Epoch 284/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1122 - mae: 4.5840 - val_loss: 5.1212 - val_mae: 5.6008 Epoch 285/500 8/8 [==============================] - 0s 15ms/step - loss: 4.0947 - mae: 4.5663 - val_loss: 5.0682 - val_mae: 5.5474 Epoch 286/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0990 - mae: 4.5709 - val_loss: 5.3323 - val_mae: 5.8129 Epoch 287/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1024 - mae: 4.5746 - val_loss: 5.5255 - val_mae: 6.0060 Epoch 288/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1057 - mae: 4.5772 - val_loss: 5.0812 - val_mae: 5.5591 Epoch 289/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1089 - mae: 4.5813 - val_loss: 5.0435 - val_mae: 5.5230 Epoch 290/500 8/8 [==============================] - 0s 18ms/step - loss: 4.1151 - mae: 4.5882 - val_loss: 5.2402 - val_mae: 5.7201 Epoch 291/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1105 - mae: 4.5824 - val_loss: 5.3653 - val_mae: 5.8459 Epoch 292/500 8/8 [==============================] - 0s 15ms/step - loss: 4.0908 - mae: 4.5621 - val_loss: 5.3685 - val_mae: 5.8494 Epoch 293/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0951 - mae: 4.5672 - val_loss: 5.5988 - val_mae: 6.0791 Epoch 294/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1059 - mae: 4.5784 - val_loss: 5.4337 - val_mae: 5.9146 Epoch 295/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0858 - mae: 4.5572 - val_loss: 5.2921 - val_mae: 5.7723 Epoch 296/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1022 - mae: 4.5739 - val_loss: 5.5788 - val_mae: 6.0594 Epoch 297/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1057 - mae: 4.5778 - val_loss: 5.4056 - val_mae: 5.8859 Epoch 298/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0897 - mae: 4.5614 - val_loss: 5.1899 - val_mae: 5.6688 Epoch 299/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0932 - mae: 4.5644 - val_loss: 5.0887 - val_mae: 5.5671 Epoch 300/500 8/8 [==============================] - 0s 20ms/step - loss: 4.1228 - mae: 4.5954 - val_loss: 4.9943 - val_mae: 5.4734 Epoch 301/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0979 - mae: 4.5700 - val_loss: 5.2102 - val_mae: 5.6892 Epoch 302/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0790 - mae: 4.5504 - val_loss: 5.0287 - val_mae: 5.5081 Epoch 303/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0886 - mae: 4.5608 - val_loss: 5.2943 - val_mae: 5.7747 Epoch 304/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0779 - mae: 4.5491 - val_loss: 5.2748 - val_mae: 5.7554 Epoch 305/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0807 - mae: 4.5517 - val_loss: 5.1633 - val_mae: 5.6436 Epoch 306/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0851 - mae: 4.5562 - val_loss: 5.2147 - val_mae: 5.6948 Epoch 307/500 8/8 [==============================] - 0s 15ms/step - loss: 4.0726 - mae: 4.5433 - val_loss: 5.3041 - val_mae: 5.7847 Epoch 308/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0723 - mae: 4.5429 - val_loss: 5.4097 - val_mae: 5.8904 Epoch 309/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0796 - mae: 4.5505 - val_loss: 5.1685 - val_mae: 5.6478 Epoch 310/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0750 - mae: 4.5451 - val_loss: 5.0878 - val_mae: 5.5662 Epoch 311/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0843 - mae: 4.5560 - val_loss: 5.3267 - val_mae: 5.8077 Epoch 312/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0889 - mae: 4.5604 - val_loss: 5.0733 - val_mae: 5.5524 Epoch 313/500 8/8 [==============================] - 0s 15ms/step - loss: 4.1019 - mae: 4.5743 - val_loss: 5.0704 - val_mae: 5.5478 Epoch 314/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0933 - mae: 4.5650 - val_loss: 5.1203 - val_mae: 5.5991 Epoch 315/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0764 - mae: 4.5475 - val_loss: 5.3248 - val_mae: 5.8057 Epoch 316/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0818 - mae: 4.5535 - val_loss: 5.3329 - val_mae: 5.8139 Epoch 317/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0877 - mae: 4.5600 - val_loss: 5.2709 - val_mae: 5.7515 Epoch 318/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0710 - mae: 4.5416 - val_loss: 5.4283 - val_mae: 5.9093 Epoch 319/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0808 - mae: 4.5514 - val_loss: 5.3292 - val_mae: 5.8099 Epoch 320/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0682 - mae: 4.5392 - val_loss: 5.1901 - val_mae: 5.6704 Epoch 321/500 8/8 [==============================] - 0s 15ms/step - loss: 4.0659 - mae: 4.5367 - val_loss: 5.3019 - val_mae: 5.7826 Epoch 322/500 8/8 [==============================] - 0s 19ms/step - loss: 4.0820 - mae: 4.5537 - val_loss: 5.0328 - val_mae: 5.5121 Epoch 323/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0731 - mae: 4.5441 - val_loss: 5.1120 - val_mae: 5.5914 Epoch 324/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0731 - mae: 4.5442 - val_loss: 5.3270 - val_mae: 5.8081 Epoch 325/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0672 - mae: 4.5376 - val_loss: 5.2087 - val_mae: 5.6893 Epoch 326/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0623 - mae: 4.5327 - val_loss: 5.2386 - val_mae: 5.7192 Epoch 327/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0718 - mae: 4.5425 - val_loss: 5.1866 - val_mae: 5.6668 Epoch 328/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0709 - mae: 4.5425 - val_loss: 5.1758 - val_mae: 5.6559 Epoch 329/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0684 - mae: 4.5393 - val_loss: 5.1798 - val_mae: 5.6601 Epoch 330/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0619 - mae: 4.5322 - val_loss: 4.9784 - val_mae: 5.4572 Epoch 331/500 8/8 [==============================] - 0s 15ms/step - loss: 4.0843 - mae: 4.5562 - val_loss: 5.2507 - val_mae: 5.7315 Epoch 332/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0591 - mae: 4.5295 - val_loss: 5.1672 - val_mae: 5.6466 Epoch 333/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0564 - mae: 4.5266 - val_loss: 5.0889 - val_mae: 5.5697 Epoch 334/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0656 - mae: 4.5364 - val_loss: 5.4166 - val_mae: 5.8983 Epoch 335/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0825 - mae: 4.5539 - val_loss: 5.3829 - val_mae: 5.8638 Epoch 336/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1000 - mae: 4.5724 - val_loss: 5.1810 - val_mae: 5.6615 Epoch 337/500 8/8 [==============================] - 0s 15ms/step - loss: 4.0758 - mae: 4.5477 - val_loss: 5.1430 - val_mae: 5.6226 Epoch 338/500 8/8 [==============================] - 0s 19ms/step - loss: 4.0686 - mae: 4.5398 - val_loss: 5.0853 - val_mae: 5.5644 Epoch 339/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0607 - mae: 4.5313 - val_loss: 5.2891 - val_mae: 5.7700 Epoch 340/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0692 - mae: 4.5408 - val_loss: 5.2567 - val_mae: 5.7375 Epoch 341/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0619 - mae: 4.5327 - val_loss: 5.0846 - val_mae: 5.5632 Epoch 342/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0580 - mae: 4.5285 - val_loss: 5.0827 - val_mae: 5.5605 Epoch 343/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0619 - mae: 4.5328 - val_loss: 5.1946 - val_mae: 5.6758 Epoch 344/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0804 - mae: 4.5521 - val_loss: 5.2604 - val_mae: 5.7415 Epoch 345/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0849 - mae: 4.5569 - val_loss: 5.4339 - val_mae: 5.9144 Epoch 346/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0643 - mae: 4.5355 - val_loss: 5.3100 - val_mae: 5.7906 Epoch 347/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0652 - mae: 4.5359 - val_loss: 4.9856 - val_mae: 5.4639 Epoch 348/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0850 - mae: 4.5566 - val_loss: 5.2061 - val_mae: 5.6866 Epoch 349/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0581 - mae: 4.5289 - val_loss: 5.3387 - val_mae: 5.8192 Epoch 350/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0614 - mae: 4.5317 - val_loss: 5.5121 - val_mae: 5.9925 Epoch 351/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0746 - mae: 4.5455 - val_loss: 4.9441 - val_mae: 5.4225 Epoch 352/500 8/8 [==============================] - 0s 20ms/step - loss: 4.0812 - mae: 4.5538 - val_loss: 4.9243 - val_mae: 5.4036 Epoch 353/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1108 - mae: 4.5839 - val_loss: 5.0444 - val_mae: 5.5246 Epoch 354/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0650 - mae: 4.5360 - val_loss: 5.2185 - val_mae: 5.6986 Epoch 355/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0658 - mae: 4.5368 - val_loss: 5.0721 - val_mae: 5.5522 Epoch 356/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0536 - mae: 4.5248 - val_loss: 5.1250 - val_mae: 5.6053 Epoch 357/500 8/8 [==============================] - 0s 20ms/step - loss: 4.0757 - mae: 4.5474 - val_loss: 4.9119 - val_mae: 5.3905 Epoch 358/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0694 - mae: 4.5404 - val_loss: 5.3610 - val_mae: 5.8423 Epoch 359/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0588 - mae: 4.5294 - val_loss: 5.1857 - val_mae: 5.6664 Epoch 360/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0611 - mae: 4.5326 - val_loss: 5.0495 - val_mae: 5.5284 Epoch 361/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0576 - mae: 4.5282 - val_loss: 5.0249 - val_mae: 5.5034 Epoch 362/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0434 - mae: 4.5133 - val_loss: 5.0519 - val_mae: 5.5320 Epoch 363/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0443 - mae: 4.5144 - val_loss: 5.0985 - val_mae: 5.5785 Epoch 364/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0417 - mae: 4.5116 - val_loss: 5.2890 - val_mae: 5.7696 Epoch 365/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0651 - mae: 4.5363 - val_loss: 4.9005 - val_mae: 5.3793 Epoch 366/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0688 - mae: 4.5403 - val_loss: 5.0521 - val_mae: 5.5326 Epoch 367/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0556 - mae: 4.5268 - val_loss: 5.4411 - val_mae: 5.9218 Epoch 368/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0793 - mae: 4.5512 - val_loss: 5.1295 - val_mae: 5.6090 Epoch 369/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0667 - mae: 4.5377 - val_loss: 4.9939 - val_mae: 5.4739 Epoch 370/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0705 - mae: 4.5421 - val_loss: 5.1462 - val_mae: 5.6266 Epoch 371/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0601 - mae: 4.5312 - val_loss: 5.1857 - val_mae: 5.6663 Epoch 372/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0531 - mae: 4.5231 - val_loss: 5.0485 - val_mae: 5.5291 Epoch 373/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0490 - mae: 4.5201 - val_loss: 5.0128 - val_mae: 5.4917 Epoch 374/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0529 - mae: 4.5234 - val_loss: 5.1370 - val_mae: 5.6168 Epoch 375/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0750 - mae: 4.5465 - val_loss: 5.5699 - val_mae: 6.0498 Epoch 376/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0958 - mae: 4.5683 - val_loss: 5.3619 - val_mae: 5.8428 Epoch 377/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0638 - mae: 4.5351 - val_loss: 5.2853 - val_mae: 5.7656 Epoch 378/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0520 - mae: 4.5229 - val_loss: 5.2588 - val_mae: 5.7395 Epoch 379/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0672 - mae: 4.5388 - val_loss: 5.3642 - val_mae: 5.8454 Epoch 380/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0653 - mae: 4.5376 - val_loss: 5.5115 - val_mae: 5.9913 Epoch 381/500 8/8 [==============================] - 0s 15ms/step - loss: 4.0590 - mae: 4.5296 - val_loss: 5.2941 - val_mae: 5.7748 Epoch 382/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0443 - mae: 4.5148 - val_loss: 4.9985 - val_mae: 5.4770 Epoch 383/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0526 - mae: 4.5235 - val_loss: 4.9986 - val_mae: 5.4778 Epoch 384/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0620 - mae: 4.5338 - val_loss: 5.1434 - val_mae: 5.6238 Epoch 385/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0523 - mae: 4.5230 - val_loss: 5.0148 - val_mae: 5.4930 Epoch 386/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0428 - mae: 4.5131 - val_loss: 5.5050 - val_mae: 5.9849 Epoch 387/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0805 - mae: 4.5533 - val_loss: 5.6902 - val_mae: 6.1708 Epoch 388/500 8/8 [==============================] - 0s 17ms/step - loss: 4.1426 - mae: 4.6158 - val_loss: 5.6957 - val_mae: 6.1770 Epoch 389/500 8/8 [==============================] - 0s 16ms/step - loss: 4.1137 - mae: 4.5862 - val_loss: 5.3944 - val_mae: 5.8748 Epoch 390/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0764 - mae: 4.5489 - val_loss: 5.0439 - val_mae: 5.5247 Epoch 391/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0439 - mae: 4.5144 - val_loss: 4.9488 - val_mae: 5.4282 Epoch 392/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0563 - mae: 4.5281 - val_loss: 5.0088 - val_mae: 5.4885 Epoch 393/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0335 - mae: 4.5034 - val_loss: 5.1045 - val_mae: 5.5846 Epoch 394/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0316 - mae: 4.5016 - val_loss: 5.2254 - val_mae: 5.7064 Epoch 395/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0309 - mae: 4.5007 - val_loss: 5.1707 - val_mae: 5.6510 Epoch 396/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0374 - mae: 4.5073 - val_loss: 5.3099 - val_mae: 5.7905 Epoch 397/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0376 - mae: 4.5080 - val_loss: 5.0881 - val_mae: 5.5690 Epoch 398/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0492 - mae: 4.5201 - val_loss: 5.1179 - val_mae: 5.5980 Epoch 399/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0436 - mae: 4.5140 - val_loss: 5.2814 - val_mae: 5.7623 Epoch 400/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0561 - mae: 4.5273 - val_loss: 5.2893 - val_mae: 5.7699 Epoch 401/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0689 - mae: 4.5408 - val_loss: 4.9025 - val_mae: 5.3812 Epoch 402/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0521 - mae: 4.5235 - val_loss: 4.9376 - val_mae: 5.4168 Epoch 403/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0353 - mae: 4.5057 - val_loss: 5.1474 - val_mae: 5.6276 Epoch 404/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0338 - mae: 4.5040 - val_loss: 4.9186 - val_mae: 5.3976 Epoch 405/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0475 - mae: 4.5186 - val_loss: 4.9323 - val_mae: 5.4115 Epoch 406/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0506 - mae: 4.5218 - val_loss: 5.1027 - val_mae: 5.5825 Epoch 407/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0353 - mae: 4.5063 - val_loss: 5.2799 - val_mae: 5.7600 Epoch 408/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0365 - mae: 4.5066 - val_loss: 5.0652 - val_mae: 5.5455 Epoch 409/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0546 - mae: 4.5265 - val_loss: 5.0042 - val_mae: 5.4848 Epoch 410/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0613 - mae: 4.5333 - val_loss: 5.2611 - val_mae: 5.7418 Epoch 411/500 8/8 [==============================] - 0s 15ms/step - loss: 4.0539 - mae: 4.5254 - val_loss: 5.2361 - val_mae: 5.7168 Epoch 412/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0302 - mae: 4.5003 - val_loss: 5.2427 - val_mae: 5.7230 Epoch 413/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0400 - mae: 4.5113 - val_loss: 5.1446 - val_mae: 5.6254 Epoch 414/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0260 - mae: 4.4961 - val_loss: 5.0869 - val_mae: 5.5672 Epoch 415/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0302 - mae: 4.5010 - val_loss: 5.1018 - val_mae: 5.5822
Out[7]:
<tensorflow.python.keras.callbacks.History at 0x7fdf75c6df60>
In [8]:
model = keras.models.load_model("my_checkpoint.h5")
In [9]:
rnn_forecast = model_forecast(model, series[:, np.newaxis], window_size)
rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]
In [10]:
plt.figure(figsize=(10, 6))
plot_series(time_valid, x_valid)
plot_series(time_valid, rnn_forecast)
In [11]:
keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()
Out[11]:
5.112804
In [12]:
keras.backend.clear_session()
tf.random.set_seed(42)
np.random.seed(42)
window_size = 64
train_set = seq2seq_window_dataset(x_train, window_size,
batch_size=128)
model = keras.models.Sequential()
model.add(keras.layers.InputLayer(input_shape=[None, 1]))
for dilation_rate in (1, 2, 4, 8, 16, 32):
model.add(
keras.layers.Conv1D(filters=32,
kernel_size=2,
strides=1,
dilation_rate=dilation_rate,
padding="causal",
activation="relu")
)
model.add(keras.layers.Conv1D(filters=1, kernel_size=1))
lr_schedule = keras.callbacks.LearningRateScheduler(
lambda epoch: 1e-4 * 10**(epoch / 30))
optimizer = keras.optimizers.Adam(lr=1e-4)
model.compile(loss=keras.losses.Huber(),
optimizer=optimizer,
metrics=["mae"])
history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])
Epoch 1/100 8/8 [==============================] - 0s 20ms/step - loss: 42.2341 - mae: 42.7330 Epoch 2/100 8/8 [==============================] - 0s 5ms/step - loss: 41.8293 - mae: 42.3284 Epoch 3/100 8/8 [==============================] - 0s 6ms/step - loss: 41.4367 - mae: 41.9357 Epoch 4/100 8/8 [==============================] - 0s 5ms/step - loss: 41.0108 - mae: 41.5098 Epoch 5/100 8/8 [==============================] - 0s 6ms/step - loss: 40.4787 - mae: 40.9776 Epoch 6/100 8/8 [==============================] - 0s 5ms/step - loss: 39.7341 - mae: 40.2328 Epoch 7/100 8/8 [==============================] - 0s 5ms/step - loss: 38.5883 - mae: 39.0871 Epoch 8/100 8/8 [==============================] - 0s 5ms/step - loss: 36.6836 - mae: 37.1822 Epoch 9/100 8/8 [==============================] - 0s 5ms/step - loss: 33.3644 - mae: 33.8629 Epoch 10/100 8/8 [==============================] - 0s 5ms/step - loss: 27.9449 - mae: 28.4427 Epoch 11/100 8/8 [==============================] - 0s 5ms/step - loss: 21.6391 - mae: 22.1338 Epoch 12/100 8/8 [==============================] - 0s 5ms/step - loss: 19.9229 - mae: 20.4188 Epoch 13/100 8/8 [==============================] - 0s 6ms/step - loss: 18.2388 - mae: 18.7342 Epoch 14/100 8/8 [==============================] - 0s 7ms/step - loss: 15.6447 - mae: 16.1377 Epoch 15/100 8/8 [==============================] - 0s 7ms/step - loss: 12.8448 - mae: 13.3356 Epoch 16/100 8/8 [==============================] - 0s 8ms/step - loss: 9.8191 - mae: 10.3053 Epoch 17/100 8/8 [==============================] - 0s 6ms/step - loss: 7.7458 - mae: 8.2288 Epoch 18/100 8/8 [==============================] - 0s 7ms/step - loss: 7.1583 - mae: 7.6414 Epoch 19/100 8/8 [==============================] - 0s 6ms/step - loss: 6.4592 - mae: 6.9388 Epoch 20/100 8/8 [==============================] - 0s 7ms/step - loss: 6.1250 - mae: 6.6043 Epoch 21/100 8/8 [==============================] - 0s 6ms/step - loss: 5.7776 - mae: 6.2556 Epoch 22/100 8/8 [==============================] - 0s 8ms/step - loss: 5.5025 - mae: 5.9809 Epoch 23/100 8/8 [==============================] - 0s 6ms/step - loss: 5.2352 - mae: 5.7131 Epoch 24/100 8/8 [==============================] - 0s 7ms/step - loss: 4.9809 - mae: 5.4583 Epoch 25/100 8/8 [==============================] - 0s 7ms/step - loss: 4.7679 - mae: 5.2445 Epoch 26/100 8/8 [==============================] - 0s 8ms/step - loss: 4.6012 - mae: 5.0786 Epoch 27/100 8/8 [==============================] - 0s 7ms/step - loss: 4.4480 - mae: 4.9252 Epoch 28/100 8/8 [==============================] - 0s 7ms/step - loss: 4.3099 - mae: 4.7862 Epoch 29/100 8/8 [==============================] - 0s 7ms/step - loss: 4.1904 - mae: 4.6665 Epoch 30/100 8/8 [==============================] - 0s 6ms/step - loss: 4.0947 - mae: 4.5691 Epoch 31/100 8/8 [==============================] - 0s 7ms/step - loss: 4.0568 - mae: 4.5306 Epoch 32/100 8/8 [==============================] - 0s 7ms/step - loss: 4.0023 - mae: 4.4745 Epoch 33/100 8/8 [==============================] - 0s 6ms/step - loss: 3.9770 - mae: 4.4496 Epoch 34/100 8/8 [==============================] - 0s 7ms/step - loss: 3.9585 - mae: 4.4305 Epoch 35/100 8/8 [==============================] - 0s 7ms/step - loss: 3.9190 - mae: 4.3900 Epoch 36/100 8/8 [==============================] - 0s 7ms/step - loss: 3.8858 - mae: 4.3560 Epoch 37/100 8/8 [==============================] - 0s 7ms/step - loss: 3.8547 - mae: 4.3248 Epoch 38/100 8/8 [==============================] - 0s 7ms/step - loss: 3.8405 - mae: 4.3110 Epoch 39/100 8/8 [==============================] - 0s 7ms/step - loss: 3.8231 - mae: 4.2932 Epoch 40/100 8/8 [==============================] - 0s 6ms/step - loss: 3.8200 - mae: 4.2893 Epoch 41/100 8/8 [==============================] - 0s 8ms/step - loss: 3.8604 - mae: 4.3308 Epoch 42/100 8/8 [==============================] - 0s 7ms/step - loss: 3.9552 - mae: 4.4272 Epoch 43/100 8/8 [==============================] - 0s 6ms/step - loss: 3.9073 - mae: 4.3786 Epoch 44/100 8/8 [==============================] - 0s 6ms/step - loss: 3.8029 - mae: 4.2736 Epoch 45/100 8/8 [==============================] - 0s 7ms/step - loss: 3.6869 - mae: 4.1571 Epoch 46/100 8/8 [==============================] - 0s 7ms/step - loss: 3.7583 - mae: 4.2280 Epoch 47/100 8/8 [==============================] - 0s 6ms/step - loss: 3.9610 - mae: 4.4323 Epoch 48/100 8/8 [==============================] - 0s 7ms/step - loss: 4.1521 - mae: 4.6251 Epoch 49/100 8/8 [==============================] - 0s 7ms/step - loss: 4.6194 - mae: 5.0959 Epoch 50/100 8/8 [==============================] - 0s 7ms/step - loss: 4.1253 - mae: 4.5994 Epoch 51/100 8/8 [==============================] - 0s 7ms/step - loss: 4.3309 - mae: 4.8063 Epoch 52/100 8/8 [==============================] - 0s 6ms/step - loss: 4.1315 - mae: 4.6039 Epoch 53/100 8/8 [==============================] - 0s 7ms/step - loss: 4.0019 - mae: 4.4746 Epoch 54/100 8/8 [==============================] - 0s 7ms/step - loss: 3.7714 - mae: 4.2425 Epoch 55/100 8/8 [==============================] - 0s 6ms/step - loss: 3.7705 - mae: 4.2427 Epoch 56/100 8/8 [==============================] - 0s 6ms/step - loss: 3.9186 - mae: 4.3905 Epoch 57/100 8/8 [==============================] - 0s 6ms/step - loss: 4.0952 - mae: 4.5694 Epoch 58/100 8/8 [==============================] - 0s 6ms/step - loss: 4.1542 - mae: 4.6278 Epoch 59/100 8/8 [==============================] - 0s 7ms/step - loss: 3.9481 - mae: 4.4213 Epoch 60/100 8/8 [==============================] - 0s 7ms/step - loss: 3.9419 - mae: 4.4138 Epoch 61/100 8/8 [==============================] - 0s 7ms/step - loss: 6.3350 - mae: 6.8173 Epoch 62/100 8/8 [==============================] - 0s 7ms/step - loss: 6.6553 - mae: 7.1365 Epoch 63/100 8/8 [==============================] - 0s 6ms/step - loss: 6.0076 - mae: 6.4887 Epoch 64/100 8/8 [==============================] - 0s 8ms/step - loss: 7.7099 - mae: 8.1971 Epoch 65/100 8/8 [==============================] - 0s 7ms/step - loss: 7.4864 - mae: 7.9722 Epoch 66/100 8/8 [==============================] - 0s 6ms/step - loss: 5.9514 - mae: 6.4331 Epoch 67/100 8/8 [==============================] - 0s 7ms/step - loss: 4.4027 - mae: 4.8780 Epoch 68/100 8/8 [==============================] - 0s 6ms/step - loss: 5.3838 - mae: 5.8635 Epoch 69/100 8/8 [==============================] - 0s 8ms/step - loss: 4.6161 - mae: 5.0923 Epoch 70/100 8/8 [==============================] - 0s 7ms/step - loss: 6.9420 - mae: 7.4259 Epoch 71/100 8/8 [==============================] - 0s 6ms/step - loss: 8.2706 - mae: 8.7564 Epoch 72/100 8/8 [==============================] - 0s 7ms/step - loss: 16.4785 - mae: 16.9721 Epoch 73/100 8/8 [==============================] - 0s 6ms/step - loss: 13.9699 - mae: 14.4606 Epoch 74/100 8/8 [==============================] - 0s 7ms/step - loss: 5.4335 - mae: 5.9139 Epoch 75/100 8/8 [==============================] - 0s 7ms/step - loss: 6.2614 - mae: 6.7443 Epoch 76/100 8/8 [==============================] - 0s 7ms/step - loss: 5.0804 - mae: 5.5594 Epoch 77/100 8/8 [==============================] - 0s 7ms/step - loss: 6.0175 - mae: 6.4990 Epoch 78/100 8/8 [==============================] - 0s 7ms/step - loss: 5.5995 - mae: 6.0803 Epoch 79/100 8/8 [==============================] - 0s 6ms/step - loss: 6.4536 - mae: 6.9386 Epoch 80/100 8/8 [==============================] - 0s 7ms/step - loss: 6.3354 - mae: 6.8167 Epoch 81/100 8/8 [==============================] - 0s 6ms/step - loss: 6.4449 - mae: 6.9283 Epoch 82/100 8/8 [==============================] - 0s 8ms/step - loss: 7.2485 - mae: 7.7332 Epoch 83/100 8/8 [==============================] - 0s 7ms/step - loss: 6.1304 - mae: 6.6110 Epoch 84/100 8/8 [==============================] - 0s 8ms/step - loss: 7.5218 - mae: 8.0061 Epoch 85/100 8/8 [==============================] - 0s 7ms/step - loss: 7.7926 - mae: 8.2779 Epoch 86/100 8/8 [==============================] - 0s 7ms/step - loss: 7.8389 - mae: 8.3246 Epoch 87/100 8/8 [==============================] - 0s 8ms/step - loss: 7.6732 - mae: 8.1584 Epoch 88/100 8/8 [==============================] - 0s 7ms/step - loss: 8.7216 - mae: 9.2107 Epoch 89/100 8/8 [==============================] - 0s 7ms/step - loss: 7.6367 - mae: 8.1233 Epoch 90/100 8/8 [==============================] - 0s 7ms/step - loss: 8.4313 - mae: 8.9183 Epoch 91/100 8/8 [==============================] - 0s 7ms/step - loss: 9.1493 - mae: 9.6384 Epoch 92/100 8/8 [==============================] - 0s 6ms/step - loss: 9.6126 - mae: 10.1024 Epoch 93/100 8/8 [==============================] - 0s 7ms/step - loss: 8.2350 - mae: 8.7213 Epoch 94/100 8/8 [==============================] - 0s 7ms/step - loss: 10.3230 - mae: 10.8119 Epoch 95/100 8/8 [==============================] - 0s 7ms/step - loss: 14.2564 - mae: 14.7480 Epoch 96/100 8/8 [==============================] - 0s 7ms/step - loss: 678.4048 - mae: 678.9036 Epoch 97/100 8/8 [==============================] - 0s 7ms/step - loss: 44.2968 - mae: 44.7953 Epoch 98/100 8/8 [==============================] - 0s 7ms/step - loss: 37.4787 - mae: 37.9769 Epoch 99/100 8/8 [==============================] - 0s 7ms/step - loss: 35.6419 - mae: 36.1411 Epoch 100/100 8/8 [==============================] - 0s 6ms/step - loss: 33.5805 - mae: 34.0795
In [13]:
plt.semilogx(history.history["lr"], history.history["loss"])
plt.axis([1e-4, 1e-1, 0, 30])
Out[13]:
(0.0001, 0.1, 0.0, 30.0)
In [14]:
keras.backend.clear_session()
tf.random.set_seed(42)
np.random.seed(42)
window_size = 64
train_set = seq2seq_window_dataset(x_train, window_size,
batch_size=128)
valid_set = seq2seq_window_dataset(x_valid, window_size,
batch_size=128)
model = keras.models.Sequential()
model.add(keras.layers.InputLayer(input_shape=[None, 1]))
for dilation_rate in (1, 2, 4, 8, 16, 32):
model.add(
keras.layers.Conv1D(filters=32,
kernel_size=2,
strides=1,
dilation_rate=dilation_rate,
padding="causal",
activation="relu")
)
model.add(keras.layers.Conv1D(filters=1, kernel_size=1))
optimizer = keras.optimizers.Adam(lr=3e-4)
model.compile(loss=keras.losses.Huber(),
optimizer=optimizer,
metrics=["mae"])
model_checkpoint = keras.callbacks.ModelCheckpoint(
"my_checkpoint.h5", save_best_only=True)
early_stopping = keras.callbacks.EarlyStopping(patience=50)
history = model.fit(train_set, epochs=500,
validation_data=valid_set,
callbacks=[early_stopping, model_checkpoint])
Epoch 1/500 8/8 [==============================] - 1s 63ms/step - loss: 40.1883 - mae: 40.6871 - val_loss: 72.9189 - val_mae: 73.4189 Epoch 2/500 8/8 [==============================] - 0s 20ms/step - loss: 38.1153 - mae: 38.6140 - val_loss: 67.9777 - val_mae: 68.4777 Epoch 3/500 8/8 [==============================] - 0s 19ms/step - loss: 35.1357 - mae: 35.6344 - val_loss: 60.8664 - val_mae: 61.3664 Epoch 4/500 8/8 [==============================] - 0s 20ms/step - loss: 30.8280 - mae: 31.3262 - val_loss: 50.2194 - val_mae: 50.7190 Epoch 5/500 8/8 [==============================] - 0s 22ms/step - loss: 24.9595 - mae: 25.4564 - val_loss: 36.6519 - val_mae: 37.1479 Epoch 6/500 8/8 [==============================] - 0s 19ms/step - loss: 20.1383 - mae: 20.6330 - val_loss: 32.0186 - val_mae: 32.5157 Epoch 7/500 8/8 [==============================] - 0s 19ms/step - loss: 18.8909 - mae: 19.3858 - val_loss: 30.0781 - val_mae: 30.5755 Epoch 8/500 8/8 [==============================] - 0s 19ms/step - loss: 17.4303 - mae: 17.9244 - val_loss: 26.6352 - val_mae: 27.1319 Epoch 9/500 8/8 [==============================] - 0s 21ms/step - loss: 15.6128 - mae: 16.1052 - val_loss: 23.6696 - val_mae: 24.1641 Epoch 10/500 8/8 [==============================] - 0s 20ms/step - loss: 13.8392 - mae: 14.3297 - val_loss: 19.6555 - val_mae: 20.1485 Epoch 11/500 8/8 [==============================] - 0s 20ms/step - loss: 11.8764 - mae: 12.3644 - val_loss: 15.8618 - val_mae: 16.3499 Epoch 12/500 8/8 [==============================] - 0s 20ms/step - loss: 10.0871 - mae: 10.5730 - val_loss: 13.1248 - val_mae: 13.6122 Epoch 13/500 8/8 [==============================] - 0s 20ms/step - loss: 8.7071 - mae: 9.1909 - val_loss: 11.4140 - val_mae: 11.9018 Epoch 14/500 8/8 [==============================] - 0s 19ms/step - loss: 7.7845 - mae: 8.2681 - val_loss: 9.9348 - val_mae: 10.4204 Epoch 15/500 8/8 [==============================] - 0s 20ms/step - loss: 7.1003 - mae: 7.5831 - val_loss: 8.8058 - val_mae: 9.2891 Epoch 16/500 8/8 [==============================] - 0s 19ms/step - loss: 6.5380 - mae: 7.0190 - val_loss: 8.0493 - val_mae: 8.5331 Epoch 17/500 8/8 [==============================] - 0s 20ms/step - loss: 6.1365 - mae: 6.6174 - val_loss: 7.3821 - val_mae: 7.8644 Epoch 18/500 8/8 [==============================] - 0s 20ms/step - loss: 5.7809 - mae: 6.2605 - val_loss: 6.7753 - val_mae: 7.2574 Epoch 19/500 8/8 [==============================] - 0s 19ms/step - loss: 5.4688 - mae: 5.9478 - val_loss: 6.2164 - val_mae: 6.6962 Epoch 20/500 8/8 [==============================] - 0s 22ms/step - loss: 5.1842 - mae: 5.6637 - val_loss: 5.7561 - val_mae: 6.2335 Epoch 21/500 8/8 [==============================] - 0s 20ms/step - loss: 4.9869 - mae: 5.4654 - val_loss: 5.5748 - val_mae: 6.0528 Epoch 22/500 8/8 [==============================] - 0s 22ms/step - loss: 4.8647 - mae: 5.3433 - val_loss: 5.3857 - val_mae: 5.8629 Epoch 23/500 8/8 [==============================] - 0s 21ms/step - loss: 4.7693 - mae: 5.2477 - val_loss: 5.2794 - val_mae: 5.7576 Epoch 24/500 8/8 [==============================] - 0s 22ms/step - loss: 4.6911 - mae: 5.1697 - val_loss: 5.0904 - val_mae: 5.5665 Epoch 25/500 8/8 [==============================] - 0s 21ms/step - loss: 4.6126 - mae: 5.0914 - val_loss: 5.0179 - val_mae: 5.4951 Epoch 26/500 8/8 [==============================] - 0s 22ms/step - loss: 4.5452 - mae: 5.0235 - val_loss: 4.8853 - val_mae: 5.3618 Epoch 27/500 8/8 [==============================] - 0s 19ms/step - loss: 4.4794 - mae: 4.9574 - val_loss: 4.7813 - val_mae: 5.2573 Epoch 28/500 8/8 [==============================] - 0s 19ms/step - loss: 4.4159 - mae: 4.8936 - val_loss: 4.6986 - val_mae: 5.1746 Epoch 29/500 8/8 [==============================] - 0s 19ms/step - loss: 4.3569 - mae: 4.8342 - val_loss: 4.6204 - val_mae: 5.0966 Epoch 30/500 8/8 [==============================] - 0s 20ms/step - loss: 4.3035 - mae: 4.7805 - val_loss: 4.5487 - val_mae: 5.0251 Epoch 31/500 8/8 [==============================] - 0s 19ms/step - loss: 4.2588 - mae: 4.7349 - val_loss: 4.5200 - val_mae: 4.9963 Epoch 32/500 8/8 [==============================] - 0s 21ms/step - loss: 4.2220 - mae: 4.6972 - val_loss: 4.4653 - val_mae: 4.9409 Epoch 33/500 8/8 [==============================] - 0s 20ms/step - loss: 4.1890 - mae: 4.6639 - val_loss: 4.4356 - val_mae: 4.9116 Epoch 34/500 8/8 [==============================] - 0s 20ms/step - loss: 4.1603 - mae: 4.6355 - val_loss: 4.4012 - val_mae: 4.8770 Epoch 35/500 8/8 [==============================] - 0s 21ms/step - loss: 4.1326 - mae: 4.6074 - val_loss: 4.3868 - val_mae: 4.8627 Epoch 36/500 8/8 [==============================] - 0s 15ms/step - loss: 4.1058 - mae: 4.5803 - val_loss: 4.4048 - val_mae: 4.8810 Epoch 37/500 8/8 [==============================] - 0s 16ms/step - loss: 4.0826 - mae: 4.5566 - val_loss: 4.3915 - val_mae: 4.8677 Epoch 38/500 8/8 [==============================] - 0s 19ms/step - loss: 4.0605 - mae: 4.5340 - val_loss: 4.3125 - val_mae: 4.7866 Epoch 39/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0409 - mae: 4.5147 - val_loss: 4.3499 - val_mae: 4.8256 Epoch 40/500 8/8 [==============================] - 0s 18ms/step - loss: 4.0225 - mae: 4.4957 - val_loss: 4.3153 - val_mae: 4.7902 Epoch 41/500 8/8 [==============================] - 0s 17ms/step - loss: 4.0071 - mae: 4.4798 - val_loss: 4.3148 - val_mae: 4.7901 Epoch 42/500 8/8 [==============================] - 0s 20ms/step - loss: 3.9919 - mae: 4.4641 - val_loss: 4.2986 - val_mae: 4.7735 Epoch 43/500 8/8 [==============================] - 0s 17ms/step - loss: 3.9829 - mae: 4.4550 - val_loss: 4.3393 - val_mae: 4.8158 Epoch 44/500 8/8 [==============================] - 0s 16ms/step - loss: 3.9688 - mae: 4.4402 - val_loss: 4.2999 - val_mae: 4.7751 Epoch 45/500 8/8 [==============================] - 0s 16ms/step - loss: 3.9560 - mae: 4.4272 - val_loss: 4.3233 - val_mae: 4.7994 Epoch 46/500 8/8 [==============================] - 0s 16ms/step - loss: 3.9460 - mae: 4.4169 - val_loss: 4.2998 - val_mae: 4.7754 Epoch 47/500 8/8 [==============================] - 0s 20ms/step - loss: 3.9332 - mae: 4.4040 - val_loss: 4.2936 - val_mae: 4.7691 Epoch 48/500 8/8 [==============================] - 0s 17ms/step - loss: 3.9223 - mae: 4.3934 - val_loss: 4.3254 - val_mae: 4.8017 Epoch 49/500 8/8 [==============================] - 0s 20ms/step - loss: 3.9152 - mae: 4.3864 - val_loss: 4.2532 - val_mae: 4.7275 Epoch 50/500 8/8 [==============================] - 0s 17ms/step - loss: 3.9060 - mae: 4.3770 - val_loss: 4.2618 - val_mae: 4.7369 Epoch 51/500 8/8 [==============================] - 0s 17ms/step - loss: 3.8949 - mae: 4.3659 - val_loss: 4.3457 - val_mae: 4.8224 Epoch 52/500 8/8 [==============================] - 0s 20ms/step - loss: 3.8870 - mae: 4.3580 - val_loss: 4.2427 - val_mae: 4.7166 Epoch 53/500 8/8 [==============================] - 0s 17ms/step - loss: 3.8750 - mae: 4.3454 - val_loss: 4.2739 - val_mae: 4.7495 Epoch 54/500 8/8 [==============================] - 0s 18ms/step - loss: 3.8646 - mae: 4.3352 - val_loss: 4.2915 - val_mae: 4.7674 Epoch 55/500 8/8 [==============================] - 0s 21ms/step - loss: 3.8583 - mae: 4.3285 - val_loss: 4.2357 - val_mae: 4.7099 Epoch 56/500 8/8 [==============================] - 0s 17ms/step - loss: 3.8515 - mae: 4.3222 - val_loss: 4.2768 - val_mae: 4.7526 Epoch 57/500 8/8 [==============================] - 0s 17ms/step - loss: 3.8402 - mae: 4.3101 - val_loss: 4.2676 - val_mae: 4.7432 Epoch 58/500 8/8 [==============================] - 0s 17ms/step - loss: 3.8322 - mae: 4.3022 - val_loss: 4.2913 - val_mae: 4.7678 Epoch 59/500 8/8 [==============================] - 0s 17ms/step - loss: 3.8321 - mae: 4.3027 - val_loss: 4.2535 - val_mae: 4.7286 Epoch 60/500 8/8 [==============================] - 0s 15ms/step - loss: 3.8194 - mae: 4.2894 - val_loss: 4.2363 - val_mae: 4.7112 Epoch 61/500 8/8 [==============================] - 0s 18ms/step - loss: 3.8100 - mae: 4.2798 - val_loss: 4.2372 - val_mae: 4.7121 Epoch 62/500 8/8 [==============================] - 0s 14ms/step - loss: 3.8030 - mae: 4.2725 - val_loss: 4.2673 - val_mae: 4.7431 Epoch 63/500 8/8 [==============================] - 0s 17ms/step - loss: 3.7943 - mae: 4.2639 - val_loss: 4.2417 - val_mae: 4.7170 Epoch 64/500 8/8 [==============================] - 0s 16ms/step - loss: 3.7875 - mae: 4.2571 - val_loss: 4.2367 - val_mae: 4.7118 Epoch 65/500 8/8 [==============================] - 0s 16ms/step - loss: 3.7810 - mae: 4.2507 - val_loss: 4.2447 - val_mae: 4.7196 Epoch 66/500 8/8 [==============================] - 0s 16ms/step - loss: 3.7746 - mae: 4.2441 - val_loss: 4.2920 - val_mae: 4.7690 Epoch 67/500 8/8 [==============================] - 0s 16ms/step - loss: 3.7744 - mae: 4.2440 - val_loss: 4.2519 - val_mae: 4.7268 Epoch 68/500 8/8 [==============================] - 0s 21ms/step - loss: 3.7623 - mae: 4.2317 - val_loss: 4.2027 - val_mae: 4.6765 Epoch 69/500 8/8 [==============================] - 0s 18ms/step - loss: 3.7541 - mae: 4.2234 - val_loss: 4.2937 - val_mae: 4.7709 Epoch 70/500 8/8 [==============================] - 0s 19ms/step - loss: 3.7521 - mae: 4.2215 - val_loss: 4.2014 - val_mae: 4.6750 Epoch 71/500 8/8 [==============================] - 0s 18ms/step - loss: 3.7440 - mae: 4.2131 - val_loss: 4.2636 - val_mae: 4.7395 Epoch 72/500 8/8 [==============================] - 0s 16ms/step - loss: 3.7345 - mae: 4.2033 - val_loss: 4.2663 - val_mae: 4.7424 Epoch 73/500 8/8 [==============================] - 0s 22ms/step - loss: 3.7334 - mae: 4.2021 - val_loss: 4.1901 - val_mae: 4.6639 Epoch 74/500 8/8 [==============================] - 0s 16ms/step - loss: 3.7262 - mae: 4.1954 - val_loss: 4.2830 - val_mae: 4.7599 Epoch 75/500 8/8 [==============================] - 0s 16ms/step - loss: 3.7170 - mae: 4.1858 - val_loss: 4.1971 - val_mae: 4.6707 Epoch 76/500 8/8 [==============================] - 0s 16ms/step - loss: 3.7140 - mae: 4.1829 - val_loss: 4.2747 - val_mae: 4.7514 Epoch 77/500 8/8 [==============================] - 0s 16ms/step - loss: 3.7083 - mae: 4.1771 - val_loss: 4.2135 - val_mae: 4.6877 Epoch 78/500 8/8 [==============================] - 0s 17ms/step - loss: 3.7014 - mae: 4.1700 - val_loss: 4.2225 - val_mae: 4.6971 Epoch 79/500 8/8 [==============================] - 0s 15ms/step - loss: 3.6924 - mae: 4.1606 - val_loss: 4.2584 - val_mae: 4.7345 Epoch 80/500 8/8 [==============================] - 0s 15ms/step - loss: 3.6886 - mae: 4.1571 - val_loss: 4.2065 - val_mae: 4.6809 Epoch 81/500 8/8 [==============================] - 0s 16ms/step - loss: 3.6839 - mae: 4.1522 - val_loss: 4.2369 - val_mae: 4.7124 Epoch 82/500 8/8 [==============================] - 0s 16ms/step - loss: 3.6779 - mae: 4.1461 - val_loss: 4.2188 - val_mae: 4.6941 Epoch 83/500 8/8 [==============================] - 0s 18ms/step - loss: 3.6708 - mae: 4.1390 - val_loss: 4.2157 - val_mae: 4.6911 Epoch 84/500 8/8 [==============================] - 0s 15ms/step - loss: 3.6647 - mae: 4.1330 - val_loss: 4.2176 - val_mae: 4.6928 Epoch 85/500 8/8 [==============================] - 0s 16ms/step - loss: 3.6602 - mae: 4.1281 - val_loss: 4.2122 - val_mae: 4.6875 Epoch 86/500 8/8 [==============================] - 0s 17ms/step - loss: 3.6559 - mae: 4.1238 - val_loss: 4.2140 - val_mae: 4.6893 Epoch 87/500 8/8 [==============================] - 0s 16ms/step - loss: 3.6486 - mae: 4.1168 - val_loss: 4.2083 - val_mae: 4.6833 Epoch 88/500 8/8 [==============================] - 0s 16ms/step - loss: 3.6454 - mae: 4.1131 - val_loss: 4.2513 - val_mae: 4.7281 Epoch 89/500 8/8 [==============================] - 0s 16ms/step - loss: 3.6443 - mae: 4.1122 - val_loss: 4.2371 - val_mae: 4.7131 Epoch 90/500 8/8 [==============================] - 0s 21ms/step - loss: 3.6381 - mae: 4.1057 - val_loss: 4.1900 - val_mae: 4.6646 Epoch 91/500 8/8 [==============================] - 0s 16ms/step - loss: 3.6339 - mae: 4.1013 - val_loss: 4.2171 - val_mae: 4.6928 Epoch 92/500 8/8 [==============================] - 0s 17ms/step - loss: 3.6285 - mae: 4.0961 - val_loss: 4.2174 - val_mae: 4.6931 Epoch 93/500 8/8 [==============================] - 0s 15ms/step - loss: 3.6219 - mae: 4.0893 - val_loss: 4.1933 - val_mae: 4.6684 Epoch 94/500 8/8 [==============================] - 0s 17ms/step - loss: 3.6159 - mae: 4.0836 - val_loss: 4.2175 - val_mae: 4.6932 Epoch 95/500 8/8 [==============================] - 0s 16ms/step - loss: 3.6141 - mae: 4.0819 - val_loss: 4.1948 - val_mae: 4.6698 Epoch 96/500 8/8 [==============================] - 0s 18ms/step - loss: 3.6111 - mae: 4.0790 - val_loss: 4.2208 - val_mae: 4.6969 Epoch 97/500 8/8 [==============================] - 0s 17ms/step - loss: 3.6032 - mae: 4.0704 - val_loss: 4.1941 - val_mae: 4.6693 Epoch 98/500 8/8 [==============================] - 0s 18ms/step - loss: 3.5968 - mae: 4.0634 - val_loss: 4.2229 - val_mae: 4.6993 Epoch 99/500 8/8 [==============================] - 0s 16ms/step - loss: 3.5932 - mae: 4.0594 - val_loss: 4.2541 - val_mae: 4.7306 Epoch 100/500 8/8 [==============================] - 0s 19ms/step - loss: 3.5888 - mae: 4.0549 - val_loss: 4.2108 - val_mae: 4.6867 Epoch 101/500 8/8 [==============================] - 0s 17ms/step - loss: 3.5857 - mae: 4.0520 - val_loss: 4.2653 - val_mae: 4.7420 Epoch 102/500 8/8 [==============================] - 0s 19ms/step - loss: 3.5809 - mae: 4.0469 - val_loss: 4.2041 - val_mae: 4.6798 Epoch 103/500 8/8 [==============================] - 0s 17ms/step - loss: 3.5759 - mae: 4.0417 - val_loss: 4.2166 - val_mae: 4.6928 Epoch 104/500 8/8 [==============================] - 0s 17ms/step - loss: 3.5714 - mae: 4.0372 - val_loss: 4.2431 - val_mae: 4.7196 Epoch 105/500 8/8 [==============================] - 0s 18ms/step - loss: 3.5670 - mae: 4.0325 - val_loss: 4.2003 - val_mae: 4.6761 Epoch 106/500 8/8 [==============================] - 0s 17ms/step - loss: 3.5658 - mae: 4.0308 - val_loss: 4.2672 - val_mae: 4.7436 Epoch 107/500 8/8 [==============================] - 0s 16ms/step - loss: 3.5613 - mae: 4.0261 - val_loss: 4.1930 - val_mae: 4.6690 Epoch 108/500 8/8 [==============================] - 0s 18ms/step - loss: 3.5570 - mae: 4.0220 - val_loss: 4.2605 - val_mae: 4.7367 Epoch 109/500 8/8 [==============================] - 0s 17ms/step - loss: 3.5525 - mae: 4.0175 - val_loss: 4.2040 - val_mae: 4.6802 Epoch 110/500 8/8 [==============================] - 0s 17ms/step - loss: 3.5478 - mae: 4.0124 - val_loss: 4.2437 - val_mae: 4.7201 Epoch 111/500 8/8 [==============================] - 0s 16ms/step - loss: 3.5445 - mae: 4.0089 - val_loss: 4.2428 - val_mae: 4.7195 Epoch 112/500 8/8 [==============================] - 0s 17ms/step - loss: 3.5410 - mae: 4.0053 - val_loss: 4.2054 - val_mae: 4.6814 Epoch 113/500 8/8 [==============================] - 0s 16ms/step - loss: 3.5382 - mae: 4.0022 - val_loss: 4.2689 - val_mae: 4.7456 Epoch 114/500 8/8 [==============================] - 0s 18ms/step - loss: 3.5332 - mae: 3.9969 - val_loss: 4.2011 - val_mae: 4.6770 Epoch 115/500 8/8 [==============================] - 0s 17ms/step - loss: 3.5298 - mae: 3.9933 - val_loss: 4.2139 - val_mae: 4.6902 Epoch 116/500 8/8 [==============================] - 0s 17ms/step - loss: 3.5251 - mae: 3.9887 - val_loss: 4.2481 - val_mae: 4.7247 Epoch 117/500 8/8 [==============================] - 0s 20ms/step - loss: 3.5212 - mae: 3.9845 - val_loss: 4.1858 - val_mae: 4.6611 Epoch 118/500 8/8 [==============================] - 0s 18ms/step - loss: 3.5219 - mae: 3.9849 - val_loss: 4.3102 - val_mae: 4.7869 Epoch 119/500 8/8 [==============================] - 0s 18ms/step - loss: 3.5166 - mae: 3.9797 - val_loss: 4.1923 - val_mae: 4.6681 Epoch 120/500 8/8 [==============================] - 0s 16ms/step - loss: 3.5151 - mae: 3.9778 - val_loss: 4.2232 - val_mae: 4.6997 Epoch 121/500 8/8 [==============================] - 0s 16ms/step - loss: 3.5100 - mae: 3.9723 - val_loss: 4.2738 - val_mae: 4.7498 Epoch 122/500 8/8 [==============================] - 0s 16ms/step - loss: 3.5041 - mae: 3.9666 - val_loss: 4.2074 - val_mae: 4.6836 Epoch 123/500 8/8 [==============================] - 0s 16ms/step - loss: 3.5010 - mae: 3.9630 - val_loss: 4.2341 - val_mae: 4.7102 Epoch 124/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4961 - mae: 3.9583 - val_loss: 4.2187 - val_mae: 4.6947 Epoch 125/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4944 - mae: 3.9573 - val_loss: 4.2088 - val_mae: 4.6849 Epoch 126/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4949 - mae: 3.9581 - val_loss: 4.2193 - val_mae: 4.6956 Epoch 127/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4896 - mae: 3.9522 - val_loss: 4.2841 - val_mae: 4.7608 Epoch 128/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4874 - mae: 3.9496 - val_loss: 4.2120 - val_mae: 4.6878 Epoch 129/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4805 - mae: 3.9425 - val_loss: 4.2058 - val_mae: 4.6817 Epoch 130/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4785 - mae: 3.9401 - val_loss: 4.2698 - val_mae: 4.7462 Epoch 131/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4741 - mae: 3.9353 - val_loss: 4.2577 - val_mae: 4.7336 Epoch 132/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4717 - mae: 3.9340 - val_loss: 4.2213 - val_mae: 4.6974 Epoch 133/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4676 - mae: 3.9291 - val_loss: 4.2154 - val_mae: 4.6912 Epoch 134/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4659 - mae: 3.9275 - val_loss: 4.2375 - val_mae: 4.7135 Epoch 135/500 8/8 [==============================] - 0s 18ms/step - loss: 3.4655 - mae: 3.9269 - val_loss: 4.2591 - val_mae: 4.7355 Epoch 136/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4579 - mae: 3.9189 - val_loss: 4.2195 - val_mae: 4.6952 Epoch 137/500 8/8 [==============================] - 0s 18ms/step - loss: 3.4542 - mae: 3.9147 - val_loss: 4.2617 - val_mae: 4.7382 Epoch 138/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4513 - mae: 3.9117 - val_loss: 4.3026 - val_mae: 4.7793 Epoch 139/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4520 - mae: 3.9126 - val_loss: 4.2026 - val_mae: 4.6779 Epoch 140/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4479 - mae: 3.9079 - val_loss: 4.2281 - val_mae: 4.7038 Epoch 141/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4427 - mae: 3.9027 - val_loss: 4.2877 - val_mae: 4.7639 Epoch 142/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4384 - mae: 3.8981 - val_loss: 4.2394 - val_mae: 4.7157 Epoch 143/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4362 - mae: 3.8961 - val_loss: 4.2011 - val_mae: 4.6763 Epoch 144/500 8/8 [==============================] - 0s 18ms/step - loss: 3.4340 - mae: 3.8932 - val_loss: 4.2319 - val_mae: 4.7078 Epoch 145/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4272 - mae: 3.8860 - val_loss: 4.2611 - val_mae: 4.7374 Epoch 146/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4254 - mae: 3.8844 - val_loss: 4.2221 - val_mae: 4.6978 Epoch 147/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4225 - mae: 3.8810 - val_loss: 4.2519 - val_mae: 4.7278 Epoch 148/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4199 - mae: 3.8786 - val_loss: 4.2689 - val_mae: 4.7454 Epoch 149/500 8/8 [==============================] - 0s 17ms/step - loss: 3.4152 - mae: 3.8735 - val_loss: 4.2622 - val_mae: 4.7384 Epoch 150/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4121 - mae: 3.8698 - val_loss: 4.2472 - val_mae: 4.7228 Epoch 151/500 8/8 [==============================] - 0s 15ms/step - loss: 3.4082 - mae: 3.8658 - val_loss: 4.2216 - val_mae: 4.6971 Epoch 152/500 8/8 [==============================] - 0s 16ms/step - loss: 3.4056 - mae: 3.8629 - val_loss: 4.2154 - val_mae: 4.6905 Epoch 153/500 8/8 [==============================] - 0s 19ms/step - loss: 3.4028 - mae: 3.8603 - val_loss: 4.2724 - val_mae: 4.7486 Epoch 154/500 8/8 [==============================] - 0s 16ms/step - loss: 3.3997 - mae: 3.8569 - val_loss: 4.2653 - val_mae: 4.7408 Epoch 155/500 8/8 [==============================] - 0s 16ms/step - loss: 3.3969 - mae: 3.8541 - val_loss: 4.2748 - val_mae: 4.7509 Epoch 156/500 8/8 [==============================] - 0s 17ms/step - loss: 3.3925 - mae: 3.8495 - val_loss: 4.2369 - val_mae: 4.7126 Epoch 157/500 8/8 [==============================] - 0s 17ms/step - loss: 3.3912 - mae: 3.8484 - val_loss: 4.2057 - val_mae: 4.6804 Epoch 158/500 8/8 [==============================] - 0s 14ms/step - loss: 3.3920 - mae: 3.8495 - val_loss: 4.2080 - val_mae: 4.6821 Epoch 159/500 8/8 [==============================] - 0s 16ms/step - loss: 3.3843 - mae: 3.8409 - val_loss: 4.2360 - val_mae: 4.7116 Epoch 160/500 8/8 [==============================] - 0s 16ms/step - loss: 3.3773 - mae: 3.8334 - val_loss: 4.2483 - val_mae: 4.7236 Epoch 161/500 8/8 [==============================] - 0s 18ms/step - loss: 3.3741 - mae: 3.8306 - val_loss: 4.2649 - val_mae: 4.7405 Epoch 162/500 8/8 [==============================] - 0s 18ms/step - loss: 3.3715 - mae: 3.8279 - val_loss: 4.3185 - val_mae: 4.7952 Epoch 163/500 8/8 [==============================] - 0s 18ms/step - loss: 3.3717 - mae: 3.8283 - val_loss: 4.3517 - val_mae: 4.8285 Epoch 164/500 8/8 [==============================] - 0s 16ms/step - loss: 3.3712 - mae: 3.8277 - val_loss: 4.2806 - val_mae: 4.7563 Epoch 165/500 8/8 [==============================] - 0s 17ms/step - loss: 3.3637 - mae: 3.8200 - val_loss: 4.2648 - val_mae: 4.7400 Epoch 166/500 8/8 [==============================] - 0s 17ms/step - loss: 3.3603 - mae: 3.8167 - val_loss: 4.2951 - val_mae: 4.7712 Epoch 167/500 8/8 [==============================] - 0s 19ms/step - loss: 3.3565 - mae: 3.8128 - val_loss: 4.2629 - val_mae: 4.7386
In [15]:
model = keras.models.load_model("my_checkpoint.h5")
In [16]:
cnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)
cnn_forecast = cnn_forecast[split_time - window_size:-1, -1, 0]
In [17]:
plt.figure(figsize=(10, 6))
plot_series(time_valid, x_valid)
plot_series(time_valid, cnn_forecast)
In [18]:
keras.metrics.mean_absolute_error(x_valid, cnn_forecast).numpy()
Out[18]:
4.5288043
In [18]: