Series temporales: manchas solares
(SPANISH) Se resolverá la competición Kaggle de manchas solares
• 46 min read
Introducción
Se resolverá la competición de Kaggle sobre manchas solares.
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
print(tf.__version__)
# If not, !pip3 install tensorflow==2.2.0
# Does not work with Colab. Manually download Sunspots.csv and place it in /tmp
import os
os.system('kaggle datasets download -d robervalt/sunspots')
import numpy as np
import matplotlib.pyplot as plt
def plot_series(time, series, format="-", start=0, end=None):
plt.plot(time[start:end], series[start:end], format)
plt.xlabel("Time")
plt.ylabel("Value")
plt.grid(True)
import csv
time_step = []
sunspots = []
with open('/tmp/Sunspots.csv') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
next(reader)
for row in reader:
sunspots.append(float(row[2]))
time_step.append(int(row[0]))
series = np.array(sunspots)
time = np.array(time_step)
plt.figure(figsize=(10, 6))
plot_series(time, series)
2.3.0
series = np.array(sunspots)
time = np.array(time_step)
plt.figure(figsize=(10, 6))
plot_series(time, series)
split_time = 3000
time_train = time[:split_time]
x_train = series[:split_time]
time_valid = time[split_time:]
x_valid = series[split_time:]
window_size = 30
batch_size = 32
shuffle_buffer_size = 1000
def windowed_dataset(series, window_size, batch_size, shuffle_buffer):
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
tf.keras.backend.clear_session()
tf.random.set_seed(51)
np.random.seed(51)
window_size = 64
batch_size = 256
train_set = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)
print(train_set)
print(x_train.shape)
<PrefetchDataset shapes: ((None, None, 1), (None, None, 1)), types: (tf.float64, tf.float64)> (3000,)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv1D(filters=32, kernel_size=5,
strides=1, padding="causal",
activation="relu",
input_shape=[None, 1]),
tf.keras.layers.LSTM(64, return_sequences=True),
tf.keras.layers.LSTM(64, return_sequences=True),
tf.keras.layers.Dense(30, activation="relu"),
tf.keras.layers.Dense(10, activation="relu"),
tf.keras.layers.Dense(1),
tf.keras.layers.Lambda(lambda x: x * 400)
])
lr_schedule = tf.keras.callbacks.LearningRateScheduler(
lambda epoch: 1e-8 * 10**(epoch / 20))
optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)
model.compile(loss=tf.keras.losses.Huber(),
optimizer=optimizer,
metrics=["mae"])
history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])
Epoch 1/100 12/12 [==============================] - 0s 22ms/step - loss: 79.8340 - mae: 80.3314 Epoch 2/100 12/12 [==============================] - 0s 25ms/step - loss: 78.0944 - mae: 78.5918 Epoch 3/100 12/12 [==============================] - 0s 26ms/step - loss: 75.4519 - mae: 75.9497 Epoch 4/100 12/12 [==============================] - 0s 25ms/step - loss: 72.2679 - mae: 72.7658 Epoch 5/100 12/12 [==============================] - 0s 25ms/step - loss: 68.7693 - mae: 69.2672 Epoch 6/100 12/12 [==============================] - 0s 30ms/step - loss: 65.1128 - mae: 65.6099 Epoch 7/100 12/12 [==============================] - 0s 27ms/step - loss: 61.5272 - mae: 62.0241 Epoch 8/100 12/12 [==============================] - 0s 27ms/step - loss: 58.1406 - mae: 58.6369 Epoch 9/100 12/12 [==============================] - 0s 29ms/step - loss: 55.0732 - mae: 55.5697 Epoch 10/100 12/12 [==============================] - 0s 35ms/step - loss: 52.3436 - mae: 52.8400 Epoch 11/100 12/12 [==============================] - 0s 25ms/step - loss: 49.9148 - mae: 50.4113 Epoch 12/100 12/12 [==============================] - 0s 28ms/step - loss: 47.8592 - mae: 48.3560 Epoch 13/100 12/12 [==============================] - 0s 27ms/step - loss: 46.0553 - mae: 46.5517 Epoch 14/100 12/12 [==============================] - 0s 27ms/step - loss: 44.5444 - mae: 45.0406 Epoch 15/100 12/12 [==============================] - 0s 26ms/step - loss: 43.3078 - mae: 43.8045 Epoch 16/100 12/12 [==============================] - 0s 27ms/step - loss: 42.2855 - mae: 42.7826 Epoch 17/100 12/12 [==============================] - 0s 29ms/step - loss: 41.3797 - mae: 41.8767 Epoch 18/100 12/12 [==============================] - 0s 37ms/step - loss: 40.5481 - mae: 41.0453 Epoch 19/100 12/12 [==============================] - 0s 25ms/step - loss: 39.7690 - mae: 40.2659 Epoch 20/100 12/12 [==============================] - 0s 27ms/step - loss: 39.0213 - mae: 39.5181 Epoch 21/100 12/12 [==============================] - 0s 26ms/step - loss: 38.2735 - mae: 38.7699 Epoch 22/100 12/12 [==============================] - 0s 26ms/step - loss: 37.4213 - mae: 37.9177 Epoch 23/100 12/12 [==============================] - 0s 25ms/step - loss: 36.3873 - mae: 36.8839 Epoch 24/100 12/12 [==============================] - 0s 26ms/step - loss: 35.3029 - mae: 35.7992 Epoch 25/100 12/12 [==============================] - 0s 26ms/step - loss: 34.0842 - mae: 34.5800 Epoch 26/100 12/12 [==============================] - 0s 36ms/step - loss: 32.9492 - mae: 33.4449 Epoch 27/100 12/12 [==============================] - 0s 30ms/step - loss: 31.8640 - mae: 32.3595 Epoch 28/100 12/12 [==============================] - 0s 36ms/step - loss: 31.1214 - mae: 31.6171 Epoch 29/100 12/12 [==============================] - 0s 26ms/step - loss: 30.3468 - mae: 30.8424 Epoch 30/100 12/12 [==============================] - 0s 26ms/step - loss: 29.6211 - mae: 30.1165 Epoch 31/100 12/12 [==============================] - 0s 26ms/step - loss: 29.0370 - mae: 29.5323 Epoch 32/100 12/12 [==============================] - 0s 31ms/step - loss: 28.5865 - mae: 29.0818 Epoch 33/100 12/12 [==============================] - 0s 26ms/step - loss: 28.1712 - mae: 28.6663 Epoch 34/100 12/12 [==============================] - 0s 35ms/step - loss: 27.9727 - mae: 28.4673 Epoch 35/100 12/12 [==============================] - 0s 33ms/step - loss: 27.7913 - mae: 28.2859 Epoch 36/100 12/12 [==============================] - 0s 34ms/step - loss: 27.3850 - mae: 27.8797 Epoch 37/100 12/12 [==============================] - 0s 25ms/step - loss: 26.9803 - mae: 27.4748 Epoch 38/100 12/12 [==============================] - 0s 26ms/step - loss: 26.7822 - mae: 27.2767 Epoch 39/100 12/12 [==============================] - 0s 35ms/step - loss: 26.2987 - mae: 26.7932 Epoch 40/100 12/12 [==============================] - 0s 35ms/step - loss: 25.7735 - mae: 26.2680 Epoch 41/100 12/12 [==============================] - 0s 26ms/step - loss: 25.4864 - mae: 25.9808 Epoch 42/100 12/12 [==============================] - 0s 27ms/step - loss: 25.1964 - mae: 25.6908 Epoch 43/100 12/12 [==============================] - 0s 25ms/step - loss: 24.8792 - mae: 25.3733 Epoch 44/100 12/12 [==============================] - 0s 26ms/step - loss: 24.2325 - mae: 24.7270 Epoch 45/100 12/12 [==============================] - 0s 25ms/step - loss: 23.8610 - mae: 24.3553 Epoch 46/100 12/12 [==============================] - 0s 30ms/step - loss: 23.8753 - mae: 24.3694 Epoch 47/100 12/12 [==============================] - 0s 29ms/step - loss: 23.2602 - mae: 23.7541 Epoch 48/100 12/12 [==============================] - 0s 24ms/step - loss: 22.8506 - mae: 23.3443 Epoch 49/100 12/12 [==============================] - 0s 25ms/step - loss: 22.4956 - mae: 22.9894 Epoch 50/100 12/12 [==============================] - 0s 26ms/step - loss: 22.0830 - mae: 22.5768 Epoch 51/100 12/12 [==============================] - 0s 31ms/step - loss: 22.2996 - mae: 22.7936 Epoch 52/100 12/12 [==============================] - 0s 25ms/step - loss: 22.0087 - mae: 22.5026 Epoch 53/100 12/12 [==============================] - 0s 36ms/step - loss: 22.0203 - mae: 22.5139 Epoch 54/100 12/12 [==============================] - 0s 33ms/step - loss: 21.2748 - mae: 21.7688 Epoch 55/100 12/12 [==============================] - 0s 35ms/step - loss: 21.1535 - mae: 21.6472 Epoch 56/100 12/12 [==============================] - 0s 34ms/step - loss: 20.5946 - mae: 21.0881 Epoch 57/100 12/12 [==============================] - 0s 35ms/step - loss: 20.8638 - mae: 21.3574 Epoch 58/100 12/12 [==============================] - 0s 38ms/step - loss: 20.6624 - mae: 21.1558 Epoch 59/100 12/12 [==============================] - 0s 33ms/step - loss: 20.4109 - mae: 20.9043 Epoch 60/100 12/12 [==============================] - 0s 31ms/step - loss: 20.0422 - mae: 20.5353 Epoch 61/100 12/12 [==============================] - 0s 31ms/step - loss: 19.9312 - mae: 20.4242 Epoch 62/100 12/12 [==============================] - 0s 37ms/step - loss: 20.7496 - mae: 21.2431 Epoch 63/100 12/12 [==============================] - 0s 26ms/step - loss: 19.8060 - mae: 20.2991 Epoch 64/100 12/12 [==============================] - 0s 25ms/step - loss: 20.7451 - mae: 21.2383 Epoch 65/100 12/12 [==============================] - 0s 26ms/step - loss: 19.6167 - mae: 20.1099 Epoch 66/100 12/12 [==============================] - 0s 35ms/step - loss: 20.5485 - mae: 21.0418 Epoch 67/100 12/12 [==============================] - 0s 29ms/step - loss: 22.2171 - mae: 22.7108 Epoch 68/100 12/12 [==============================] - 0s 25ms/step - loss: 19.4993 - mae: 19.9921 Epoch 69/100 12/12 [==============================] - 0s 27ms/step - loss: 22.1643 - mae: 22.6578 Epoch 70/100 12/12 [==============================] - 0s 25ms/step - loss: 21.3564 - mae: 21.8497 Epoch 71/100 12/12 [==============================] - 0s 28ms/step - loss: 20.4340 - mae: 20.9270 Epoch 72/100 12/12 [==============================] - 0s 35ms/step - loss: 21.4140 - mae: 21.9070 Epoch 73/100 12/12 [==============================] - 0s 29ms/step - loss: 20.9080 - mae: 21.4009 Epoch 74/100 12/12 [==============================] - 0s 25ms/step - loss: 21.9128 - mae: 22.4061 Epoch 75/100 12/12 [==============================] - 0s 27ms/step - loss: 19.8545 - mae: 20.3472 Epoch 76/100 12/12 [==============================] - 0s 35ms/step - loss: 23.4880 - mae: 23.9823 Epoch 77/100 12/12 [==============================] - 0s 31ms/step - loss: 21.4208 - mae: 21.9141 Epoch 78/100 12/12 [==============================] - 0s 26ms/step - loss: 21.8130 - mae: 22.3065 Epoch 79/100 12/12 [==============================] - 0s 28ms/step - loss: 22.6996 - mae: 23.1936 Epoch 80/100 12/12 [==============================] - 0s 26ms/step - loss: 23.4137 - mae: 23.9077 Epoch 81/100 12/12 [==============================] - 0s 26ms/step - loss: 36.0214 - mae: 36.5176 Epoch 82/100 12/12 [==============================] - 0s 26ms/step - loss: 37.9856 - mae: 38.4822 Epoch 83/100 12/12 [==============================] - 0s 26ms/step - loss: 46.1187 - mae: 46.6162 Epoch 84/100 12/12 [==============================] - 0s 28ms/step - loss: 36.3539 - mae: 36.8504 Epoch 85/100 12/12 [==============================] - 0s 27ms/step - loss: 38.0205 - mae: 38.5174 Epoch 86/100 12/12 [==============================] - 0s 35ms/step - loss: 34.9671 - mae: 35.4639 Epoch 87/100 12/12 [==============================] - 0s 26ms/step - loss: 35.3582 - mae: 35.8547 Epoch 88/100 12/12 [==============================] - 0s 34ms/step - loss: 36.7537 - mae: 37.2507 Epoch 89/100 12/12 [==============================] - 0s 31ms/step - loss: 33.7122 - mae: 34.2083 Epoch 90/100 12/12 [==============================] - 0s 26ms/step - loss: 60.4209 - mae: 60.9192 Epoch 91/100 12/12 [==============================] - 0s 26ms/step - loss: 52.6610 - mae: 53.1589 Epoch 92/100 12/12 [==============================] - 0s 25ms/step - loss: 49.7735 - mae: 50.2717 Epoch 93/100 12/12 [==============================] - 0s 25ms/step - loss: 54.1865 - mae: 54.6842 Epoch 94/100 12/12 [==============================] - 0s 26ms/step - loss: 58.2520 - mae: 58.7497 Epoch 95/100 12/12 [==============================] - 0s 25ms/step - loss: 56.2516 - mae: 56.7495 Epoch 96/100 12/12 [==============================] - 0s 26ms/step - loss: 50.3765 - mae: 50.8749 Epoch 97/100 12/12 [==============================] - 0s 30ms/step - loss: 60.5635 - mae: 61.0616 Epoch 98/100 12/12 [==============================] - 0s 27ms/step - loss: 56.9803 - mae: 57.4789 Epoch 99/100 12/12 [==============================] - 0s 26ms/step - loss: 55.6900 - mae: 56.1883 Epoch 100/100 12/12 [==============================] - 0s 33ms/step - loss: 55.0430 - mae: 55.5413
plt.semilogx(history.history["lr"], history.history["loss"])
plt.axis([1e-8, 1e-4, 0, 60])
(1e-08, 0.0001, 0.0, 60.0)
tf.keras.backend.clear_session()
tf.random.set_seed(51)
np.random.seed(51)
train_set = windowed_dataset(x_train, window_size=60, batch_size=100, shuffle_buffer=shuffle_buffer_size)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv1D(filters=60, kernel_size=5,
strides=1, padding="causal",
activation="relu",
input_shape=[None, 1]),
tf.keras.layers.LSTM(60, return_sequences=True),
tf.keras.layers.LSTM(60, return_sequences=True),
tf.keras.layers.Dense(30, activation="relu"),
tf.keras.layers.Dense(10, activation="relu"),
tf.keras.layers.Dense(1),
tf.keras.layers.Lambda(lambda x: x * 400)
])
optimizer = tf.keras.optimizers.SGD(lr=1e-5, momentum=0.9)
model.compile(loss=tf.keras.losses.Huber(),
optimizer=optimizer,
metrics=["mae"])
history = model.fit(train_set,epochs=500)
rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)
rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]
plt.figure(figsize=(10, 6))
plot_series(time_valid, x_valid)
plot_series(time_valid, rnn_forecast)
tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
#-----------------------------------------------------------
# Retrieve a list of list results on training and test data
# sets for each training epoch
#-----------------------------------------------------------
loss=history.history['loss']
epochs=range(len(loss)) # Get number of epochs
#------------------------------------------------
# Plot training and validation loss per epoch
#------------------------------------------------
plt.plot(epochs, loss, 'r')
plt.title('Training loss')
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend(["Loss"])
plt.figure()
zoomed_loss = loss[200:]
zoomed_epochs = range(200,500)
#------------------------------------------------
# Plot training and validation loss per epoch
#------------------------------------------------
plt.plot(zoomed_epochs, zoomed_loss, 'r')
plt.title('Training loss')
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend(["Loss"])
plt.figure()
Epoch 1/500 30/30 [==============================] - 0s 14ms/step - loss: 38.9198 - mae: 39.4161 Epoch 2/500 30/30 [==============================] - 0s 14ms/step - loss: 25.7735 - mae: 26.2680 Epoch 3/500 30/30 [==============================] - 0s 13ms/step - loss: 22.0767 - mae: 22.5705 Epoch 4/500 30/30 [==============================] - 0s 14ms/step - loss: 20.4675 - mae: 20.9605 Epoch 5/500 30/30 [==============================] - 0s 15ms/step - loss: 19.7721 - mae: 20.2645 Epoch 6/500 30/30 [==============================] - 0s 14ms/step - loss: 19.3017 - mae: 19.7939 Epoch 7/500 30/30 [==============================] - 0s 15ms/step - loss: 18.7228 - mae: 19.2149 Epoch 8/500 30/30 [==============================] - 0s 14ms/step - loss: 18.1242 - mae: 18.6159 Epoch 9/500 30/30 [==============================] - 0s 14ms/step - loss: 18.0721 - mae: 18.5636 Epoch 10/500 30/30 [==============================] - 0s 14ms/step - loss: 18.1061 - mae: 18.5977 Epoch 11/500 30/30 [==============================] - 1s 17ms/step - loss: 17.8688 - mae: 18.3600 Epoch 12/500 30/30 [==============================] - 0s 16ms/step - loss: 17.9055 - mae: 18.3960 Epoch 13/500 30/30 [==============================] - 0s 14ms/step - loss: 17.8534 - mae: 18.3441 Epoch 14/500 30/30 [==============================] - 0s 14ms/step - loss: 17.7943 - mae: 18.2851 Epoch 15/500 30/30 [==============================] - 0s 14ms/step - loss: 17.5729 - mae: 18.0639 Epoch 16/500 30/30 [==============================] - 0s 15ms/step - loss: 17.5833 - mae: 18.0738 Epoch 17/500 30/30 [==============================] - 0s 14ms/step - loss: 17.5072 - mae: 17.9979 Epoch 18/500 30/30 [==============================] - 0s 14ms/step - loss: 17.3512 - mae: 17.8416 Epoch 19/500 30/30 [==============================] - 0s 14ms/step - loss: 17.6318 - mae: 18.1227 Epoch 20/500 30/30 [==============================] - 0s 14ms/step - loss: 17.3520 - mae: 17.8426 Epoch 21/500 30/30 [==============================] - 0s 14ms/step - loss: 17.6473 - mae: 18.1381 Epoch 22/500 30/30 [==============================] - 1s 17ms/step - loss: 17.3582 - mae: 17.8488 Epoch 23/500 30/30 [==============================] - 0s 16ms/step - loss: 17.1359 - mae: 17.6265 Epoch 24/500 30/30 [==============================] - 1s 17ms/step - loss: 17.2582 - mae: 17.7486 Epoch 25/500 30/30 [==============================] - 0s 15ms/step - loss: 17.1194 - mae: 17.6098 Epoch 26/500 30/30 [==============================] - 0s 14ms/step - loss: 17.2270 - mae: 17.7175 Epoch 27/500 30/30 [==============================] - 0s 14ms/step - loss: 17.0160 - mae: 17.5065 Epoch 28/500 30/30 [==============================] - 0s 14ms/step - loss: 17.3924 - mae: 17.8832 Epoch 29/500 30/30 [==============================] - 0s 14ms/step - loss: 17.1180 - mae: 17.6085 Epoch 30/500 30/30 [==============================] - 0s 14ms/step - loss: 16.9777 - mae: 17.4680 Epoch 31/500 30/30 [==============================] - 0s 14ms/step - loss: 16.9133 - mae: 17.4035 Epoch 32/500 30/30 [==============================] - 0s 14ms/step - loss: 17.0628 - mae: 17.5530 Epoch 33/500 30/30 [==============================] - 0s 14ms/step - loss: 17.1168 - mae: 17.6070 Epoch 34/500 30/30 [==============================] - 0s 14ms/step - loss: 16.9634 - mae: 17.4536 Epoch 35/500 30/30 [==============================] - 0s 14ms/step - loss: 17.0348 - mae: 17.5251 Epoch 36/500 30/30 [==============================] - 0s 14ms/step - loss: 16.8702 - mae: 17.3601 Epoch 37/500 30/30 [==============================] - 0s 16ms/step - loss: 17.1608 - mae: 17.6511 Epoch 38/500 30/30 [==============================] - 1s 17ms/step - loss: 17.1744 - mae: 17.6645 Epoch 39/500 30/30 [==============================] - 0s 15ms/step - loss: 17.0314 - mae: 17.5214 Epoch 40/500 30/30 [==============================] - 0s 14ms/step - loss: 16.9324 - mae: 17.4222 Epoch 41/500 30/30 [==============================] - 0s 14ms/step - loss: 16.8663 - mae: 17.3561 Epoch 42/500 30/30 [==============================] - 0s 14ms/step - loss: 16.8897 - mae: 17.3795 Epoch 43/500 30/30 [==============================] - 0s 14ms/step - loss: 16.8672 - mae: 17.3569 Epoch 44/500 30/30 [==============================] - 0s 16ms/step - loss: 17.2954 - mae: 17.7853 Epoch 45/500 30/30 [==============================] - 0s 14ms/step - loss: 16.7839 - mae: 17.2739 Epoch 46/500 30/30 [==============================] - 0s 14ms/step - loss: 16.8649 - mae: 17.3543 Epoch 47/500 30/30 [==============================] - 0s 14ms/step - loss: 16.8154 - mae: 17.3053 Epoch 48/500 30/30 [==============================] - 0s 14ms/step - loss: 16.8541 - mae: 17.3438 Epoch 49/500 30/30 [==============================] - 0s 15ms/step - loss: 16.9587 - mae: 17.4486 Epoch 50/500 30/30 [==============================] - 1s 19ms/step - loss: 16.7717 - mae: 17.2614 Epoch 51/500 30/30 [==============================] - 0s 15ms/step - loss: 16.7268 - mae: 17.2164 Epoch 52/500 30/30 [==============================] - 0s 14ms/step - loss: 16.9362 - mae: 17.4257 Epoch 53/500 30/30 [==============================] - 0s 14ms/step - loss: 16.7150 - mae: 17.2048 Epoch 54/500 30/30 [==============================] - 0s 14ms/step - loss: 16.7991 - mae: 17.2883 Epoch 55/500 30/30 [==============================] - 0s 15ms/step - loss: 16.7678 - mae: 17.2573 Epoch 56/500 30/30 [==============================] - 0s 14ms/step - loss: 16.6731 - mae: 17.1628 Epoch 57/500 30/30 [==============================] - 0s 15ms/step - loss: 16.6024 - mae: 17.0919 Epoch 58/500 30/30 [==============================] - 0s 15ms/step - loss: 16.5811 - mae: 17.0704 Epoch 59/500 30/30 [==============================] - 0s 17ms/step - loss: 16.7307 - mae: 17.2201 Epoch 60/500 30/30 [==============================] - 0s 14ms/step - loss: 16.7199 - mae: 17.2093 Epoch 61/500 30/30 [==============================] - 0s 16ms/step - loss: 16.6808 - mae: 17.1704 Epoch 62/500 30/30 [==============================] - 1s 17ms/step - loss: 16.6156 - mae: 17.1049 Epoch 63/500 30/30 [==============================] - 0s 16ms/step - loss: 16.6709 - mae: 17.1601 Epoch 64/500 30/30 [==============================] - 1s 17ms/step - loss: 16.5756 - mae: 17.0649 Epoch 65/500 30/30 [==============================] - 0s 14ms/step - loss: 16.5314 - mae: 17.0202 Epoch 66/500 30/30 [==============================] - 0s 14ms/step - loss: 16.5359 - mae: 17.0251 Epoch 67/500 30/30 [==============================] - 0s 15ms/step - loss: 16.5913 - mae: 17.0804 Epoch 68/500 30/30 [==============================] - 0s 15ms/step - loss: 16.6478 - mae: 17.1372 Epoch 69/500 30/30 [==============================] - 0s 14ms/step - loss: 16.5077 - mae: 16.9968 Epoch 70/500 30/30 [==============================] - 0s 14ms/step - loss: 16.6393 - mae: 17.1285 Epoch 71/500 30/30 [==============================] - 0s 14ms/step - loss: 16.5727 - mae: 17.0621 Epoch 72/500 30/30 [==============================] - 0s 16ms/step - loss: 16.4809 - mae: 16.9702 Epoch 73/500 30/30 [==============================] - 0s 16ms/step - loss: 16.5381 - mae: 17.0273 Epoch 74/500 30/30 [==============================] - 0s 14ms/step - loss: 16.6619 - mae: 17.1511 Epoch 75/500 30/30 [==============================] - 0s 16ms/step - loss: 16.4973 - mae: 16.9863 Epoch 76/500 30/30 [==============================] - 0s 15ms/step - loss: 16.4755 - mae: 16.9643 Epoch 77/500 30/30 [==============================] - 0s 14ms/step - loss: 16.4491 - mae: 16.9380 Epoch 78/500 30/30 [==============================] - 0s 14ms/step - loss: 16.4661 - mae: 16.9550 Epoch 79/500 30/30 [==============================] - 0s 15ms/step - loss: 16.4554 - mae: 16.9441 Epoch 80/500 30/30 [==============================] - 0s 14ms/step - loss: 16.6619 - mae: 17.1510 Epoch 81/500 30/30 [==============================] - 1s 17ms/step - loss: 16.4701 - mae: 16.9590 Epoch 82/500 30/30 [==============================] - 1s 17ms/step - loss: 16.4889 - mae: 16.9778 Epoch 83/500 30/30 [==============================] - 1s 17ms/step - loss: 16.3861 - mae: 16.8747 Epoch 84/500 30/30 [==============================] - 0s 14ms/step - loss: 16.4110 - mae: 16.8994 Epoch 85/500 30/30 [==============================] - 0s 15ms/step - loss: 16.4080 - mae: 16.8968 Epoch 86/500 30/30 [==============================] - 0s 14ms/step - loss: 16.4727 - mae: 16.9617 Epoch 87/500 30/30 [==============================] - 0s 17ms/step - loss: 16.7928 - mae: 17.2820 Epoch 88/500 30/30 [==============================] - 0s 16ms/step - loss: 16.4995 - mae: 16.9887 Epoch 89/500 30/30 [==============================] - 0s 14ms/step - loss: 16.3739 - mae: 16.8624 Epoch 90/500 30/30 [==============================] - 0s 14ms/step - loss: 16.4305 - mae: 16.9193 Epoch 91/500 30/30 [==============================] - 0s 14ms/step - loss: 16.4828 - mae: 16.9718 Epoch 92/500 30/30 [==============================] - 0s 15ms/step - loss: 16.4846 - mae: 16.9732 Epoch 93/500 30/30 [==============================] - 0s 14ms/step - loss: 16.4738 - mae: 16.9629 Epoch 94/500 30/30 [==============================] - 0s 16ms/step - loss: 16.6700 - mae: 17.1590 Epoch 95/500 30/30 [==============================] - 0s 17ms/step - loss: 16.5721 - mae: 17.0614 Epoch 96/500 30/30 [==============================] - 0s 14ms/step - loss: 16.3160 - mae: 16.8046 Epoch 97/500 30/30 [==============================] - 0s 16ms/step - loss: 16.3488 - mae: 16.8376 Epoch 98/500 30/30 [==============================] - 1s 17ms/step - loss: 16.5817 - mae: 17.0709 Epoch 99/500 30/30 [==============================] - 1s 18ms/step - loss: 16.3842 - mae: 16.8730 Epoch 100/500 30/30 [==============================] - 0s 16ms/step - loss: 16.3685 - mae: 16.8573 Epoch 101/500 30/30 [==============================] - 1s 18ms/step - loss: 16.3120 - mae: 16.8007 Epoch 102/500 30/30 [==============================] - 0s 15ms/step - loss: 16.6005 - mae: 17.0894 Epoch 103/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2952 - mae: 16.7837 Epoch 104/500 30/30 [==============================] - 0s 15ms/step - loss: 16.4471 - mae: 16.9363 Epoch 105/500 30/30 [==============================] - 0s 15ms/step - loss: 16.3051 - mae: 16.7936 Epoch 106/500 30/30 [==============================] - 0s 13ms/step - loss: 16.2802 - mae: 16.7686 Epoch 107/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2607 - mae: 16.7493 Epoch 108/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2879 - mae: 16.7763 Epoch 109/500 30/30 [==============================] - 0s 14ms/step - loss: 16.4059 - mae: 16.8947 Epoch 110/500 30/30 [==============================] - 0s 14ms/step - loss: 16.3604 - mae: 16.8490 Epoch 111/500 30/30 [==============================] - 0s 15ms/step - loss: 16.2226 - mae: 16.7111 Epoch 112/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2326 - mae: 16.7211 Epoch 113/500 30/30 [==============================] - 0s 14ms/step - loss: 16.3609 - mae: 16.8502 Epoch 114/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2546 - mae: 16.7434 Epoch 115/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2950 - mae: 16.7835 Epoch 116/500 30/30 [==============================] - 0s 14ms/step - loss: 16.3529 - mae: 16.8417 Epoch 117/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2277 - mae: 16.7164 Epoch 118/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2188 - mae: 16.7074 Epoch 119/500 30/30 [==============================] - 0s 14ms/step - loss: 16.1949 - mae: 16.6836 Epoch 120/500 30/30 [==============================] - 0s 15ms/step - loss: 16.2557 - mae: 16.7442 Epoch 121/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2540 - mae: 16.7428 Epoch 122/500 30/30 [==============================] - 0s 14ms/step - loss: 16.1959 - mae: 16.6844 Epoch 123/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2733 - mae: 16.7620 Epoch 124/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2494 - mae: 16.7381 Epoch 125/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2215 - mae: 16.7106 Epoch 126/500 30/30 [==============================] - 0s 15ms/step - loss: 16.1868 - mae: 16.6756 Epoch 127/500 30/30 [==============================] - 0s 15ms/step - loss: 16.2651 - mae: 16.7540 Epoch 128/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2318 - mae: 16.7204 Epoch 129/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2032 - mae: 16.6919 Epoch 130/500 30/30 [==============================] - 0s 16ms/step - loss: 16.1210 - mae: 16.6095 Epoch 131/500 30/30 [==============================] - 1s 17ms/step - loss: 16.1581 - mae: 16.6466 Epoch 132/500 30/30 [==============================] - 0s 15ms/step - loss: 16.1799 - mae: 16.6685 Epoch 133/500 30/30 [==============================] - 1s 17ms/step - loss: 16.1099 - mae: 16.5982 Epoch 134/500 30/30 [==============================] - 0s 15ms/step - loss: 16.1523 - mae: 16.6412 Epoch 135/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2892 - mae: 16.7783 Epoch 136/500 30/30 [==============================] - 1s 18ms/step - loss: 16.1824 - mae: 16.6713 Epoch 137/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2042 - mae: 16.6929 Epoch 138/500 30/30 [==============================] - 0s 14ms/step - loss: 16.1300 - mae: 16.6188 Epoch 139/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0768 - mae: 16.5655 Epoch 140/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0720 - mae: 16.5608 Epoch 141/500 30/30 [==============================] - 0s 15ms/step - loss: 16.0639 - mae: 16.5527 Epoch 142/500 30/30 [==============================] - 0s 14ms/step - loss: 16.2686 - mae: 16.7576 Epoch 143/500 30/30 [==============================] - 0s 15ms/step - loss: 16.5888 - mae: 17.0781 Epoch 144/500 30/30 [==============================] - 0s 16ms/step - loss: 16.1501 - mae: 16.6389 Epoch 145/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0998 - mae: 16.5884 Epoch 146/500 30/30 [==============================] - 0s 13ms/step - loss: 16.0652 - mae: 16.5535 Epoch 147/500 30/30 [==============================] - 0s 16ms/step - loss: 16.0644 - mae: 16.5532 Epoch 148/500 30/30 [==============================] - 1s 18ms/step - loss: 16.0389 - mae: 16.5276 Epoch 149/500 30/30 [==============================] - 0s 14ms/step - loss: 16.1162 - mae: 16.6048 Epoch 150/500 30/30 [==============================] - 0s 16ms/step - loss: 16.0238 - mae: 16.5122 Epoch 151/500 30/30 [==============================] - 0s 14ms/step - loss: 16.3710 - mae: 16.8603 Epoch 152/500 30/30 [==============================] - 0s 15ms/step - loss: 16.2191 - mae: 16.7078 Epoch 153/500 30/30 [==============================] - 0s 15ms/step - loss: 15.9968 - mae: 16.4854 Epoch 154/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0367 - mae: 16.5250 Epoch 155/500 30/30 [==============================] - 0s 15ms/step - loss: 15.9479 - mae: 16.4360 Epoch 156/500 30/30 [==============================] - 0s 14ms/step - loss: 16.1149 - mae: 16.6034 Epoch 157/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0225 - mae: 16.5109 Epoch 158/500 30/30 [==============================] - 1s 17ms/step - loss: 15.9680 - mae: 16.4562 Epoch 159/500 30/30 [==============================] - 0s 17ms/step - loss: 16.4934 - mae: 16.9826 Epoch 160/500 30/30 [==============================] - 0s 14ms/step - loss: 15.9927 - mae: 16.4808 Epoch 161/500 30/30 [==============================] - 0s 15ms/step - loss: 15.9507 - mae: 16.4391 Epoch 162/500 30/30 [==============================] - 1s 17ms/step - loss: 16.0650 - mae: 16.5536 Epoch 163/500 30/30 [==============================] - 0s 16ms/step - loss: 16.0183 - mae: 16.5065 Epoch 164/500 30/30 [==============================] - 0s 16ms/step - loss: 15.9695 - mae: 16.4579 Epoch 165/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0168 - mae: 16.5052 Epoch 166/500 30/30 [==============================] - 0s 14ms/step - loss: 15.9412 - mae: 16.4294 Epoch 167/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0221 - mae: 16.5106 Epoch 168/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0135 - mae: 16.5020 Epoch 169/500 30/30 [==============================] - 0s 14ms/step - loss: 15.8922 - mae: 16.3804 Epoch 170/500 30/30 [==============================] - 0s 14ms/step - loss: 15.9840 - mae: 16.4722 Epoch 171/500 30/30 [==============================] - 0s 14ms/step - loss: 15.9054 - mae: 16.3934 Epoch 172/500 30/30 [==============================] - 0s 15ms/step - loss: 15.9427 - mae: 16.4310 Epoch 173/500 30/30 [==============================] - 0s 14ms/step - loss: 15.9119 - mae: 16.4002 Epoch 174/500 30/30 [==============================] - 0s 14ms/step - loss: 16.1226 - mae: 16.6110 Epoch 175/500 30/30 [==============================] - 0s 14ms/step - loss: 16.1785 - mae: 16.6673 Epoch 176/500 30/30 [==============================] - 0s 13ms/step - loss: 15.9484 - mae: 16.4367 Epoch 177/500 30/30 [==============================] - 0s 14ms/step - loss: 15.9025 - mae: 16.3905 Epoch 178/500 30/30 [==============================] - 0s 14ms/step - loss: 15.9317 - mae: 16.4202 Epoch 179/500 30/30 [==============================] - 0s 14ms/step - loss: 15.9629 - mae: 16.4511 Epoch 180/500 30/30 [==============================] - 0s 14ms/step - loss: 15.9972 - mae: 16.4859 Epoch 181/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0321 - mae: 16.5207 Epoch 182/500 30/30 [==============================] - 0s 15ms/step - loss: 16.1572 - mae: 16.6460 Epoch 183/500 30/30 [==============================] - 0s 14ms/step - loss: 15.8769 - mae: 16.3651 Epoch 184/500 30/30 [==============================] - 0s 15ms/step - loss: 15.8820 - mae: 16.3701 Epoch 185/500 30/30 [==============================] - 0s 14ms/step - loss: 16.3207 - mae: 16.8093 Epoch 186/500 30/30 [==============================] - 0s 14ms/step - loss: 15.8750 - mae: 16.3633 Epoch 187/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0028 - mae: 16.4915 Epoch 188/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0252 - mae: 16.5137 Epoch 189/500 30/30 [==============================] - 0s 14ms/step - loss: 15.8437 - mae: 16.3319 Epoch 190/500 30/30 [==============================] - 0s 14ms/step - loss: 15.8724 - mae: 16.3606 Epoch 191/500 30/30 [==============================] - 0s 14ms/step - loss: 15.8171 - mae: 16.3053 Epoch 192/500 30/30 [==============================] - 0s 16ms/step - loss: 15.9633 - mae: 16.4516 Epoch 193/500 30/30 [==============================] - 1s 18ms/step - loss: 15.8101 - mae: 16.2980 Epoch 194/500 30/30 [==============================] - 1s 18ms/step - loss: 15.7552 - mae: 16.2428 Epoch 195/500 30/30 [==============================] - 0s 15ms/step - loss: 15.7855 - mae: 16.2734 Epoch 196/500 30/30 [==============================] - 0s 15ms/step - loss: 15.9667 - mae: 16.4550 Epoch 197/500 30/30 [==============================] - 0s 15ms/step - loss: 15.7721 - mae: 16.2598 Epoch 198/500 30/30 [==============================] - 0s 16ms/step - loss: 15.7905 - mae: 16.2785 Epoch 199/500 30/30 [==============================] - 1s 17ms/step - loss: 15.7814 - mae: 16.2695 Epoch 200/500 30/30 [==============================] - 0s 16ms/step - loss: 15.8151 - mae: 16.3030 Epoch 201/500 30/30 [==============================] - 0s 15ms/step - loss: 16.0109 - mae: 16.4994 Epoch 202/500 30/30 [==============================] - 0s 13ms/step - loss: 16.2435 - mae: 16.7325 Epoch 203/500 30/30 [==============================] - 0s 15ms/step - loss: 15.9296 - mae: 16.4180 Epoch 204/500 30/30 [==============================] - 0s 15ms/step - loss: 15.7759 - mae: 16.2640 Epoch 205/500 30/30 [==============================] - 0s 15ms/step - loss: 15.8343 - mae: 16.3225 Epoch 206/500 30/30 [==============================] - 0s 15ms/step - loss: 15.7869 - mae: 16.2749 Epoch 207/500 30/30 [==============================] - 0s 15ms/step - loss: 15.8904 - mae: 16.3786 Epoch 208/500 30/30 [==============================] - 0s 16ms/step - loss: 15.7712 - mae: 16.2591 Epoch 209/500 30/30 [==============================] - 1s 20ms/step - loss: 15.7325 - mae: 16.2202 Epoch 210/500 30/30 [==============================] - 0s 16ms/step - loss: 16.0256 - mae: 16.5142 Epoch 211/500 30/30 [==============================] - 0s 14ms/step - loss: 16.0629 - mae: 16.5517 Epoch 212/500 30/30 [==============================] - 0s 14ms/step - loss: 15.7444 - mae: 16.2323 Epoch 213/500 30/30 [==============================] - 0s 14ms/step - loss: 15.8732 - mae: 16.3613 Epoch 214/500 30/30 [==============================] - 0s 14ms/step - loss: 15.7050 - mae: 16.1929 Epoch 215/500 30/30 [==============================] - 0s 16ms/step - loss: 15.7031 - mae: 16.1909 Epoch 216/500 30/30 [==============================] - 1s 18ms/step - loss: 15.8182 - mae: 16.3062 Epoch 217/500 30/30 [==============================] - 0s 15ms/step - loss: 15.6561 - mae: 16.1438 Epoch 218/500 30/30 [==============================] - 0s 16ms/step - loss: 15.6673 - mae: 16.1550 Epoch 219/500 30/30 [==============================] - 1s 19ms/step - loss: 15.7449 - mae: 16.2332 Epoch 220/500 30/30 [==============================] - 0s 14ms/step - loss: 15.7299 - mae: 16.2177 Epoch 221/500 30/30 [==============================] - 0s 16ms/step - loss: 15.8053 - mae: 16.2936 Epoch 222/500 30/30 [==============================] - 0s 15ms/step - loss: 15.7894 - mae: 16.2776 Epoch 223/500 30/30 [==============================] - 0s 15ms/step - loss: 15.8301 - mae: 16.3182 Epoch 224/500 30/30 [==============================] - 0s 14ms/step - loss: 15.7778 - mae: 16.2661 Epoch 225/500 30/30 [==============================] - 0s 15ms/step - loss: 15.6241 - mae: 16.1117 Epoch 226/500 30/30 [==============================] - 0s 14ms/step - loss: 15.6527 - mae: 16.1405 Epoch 227/500 30/30 [==============================] - 1s 17ms/step - loss: 15.6845 - mae: 16.1722 Epoch 228/500 30/30 [==============================] - 0s 15ms/step - loss: 15.7902 - mae: 16.2785 Epoch 229/500 30/30 [==============================] - 0s 15ms/step - loss: 15.8082 - mae: 16.2962 Epoch 230/500 30/30 [==============================] - 0s 15ms/step - loss: 15.6261 - mae: 16.1139 Epoch 231/500 30/30 [==============================] - 0s 14ms/step - loss: 15.6192 - mae: 16.1068 Epoch 232/500 30/30 [==============================] - 0s 16ms/step - loss: 15.6034 - mae: 16.0912 Epoch 233/500 30/30 [==============================] - 0s 16ms/step - loss: 15.5755 - mae: 16.0633 Epoch 234/500 30/30 [==============================] - 0s 14ms/step - loss: 15.7108 - mae: 16.1984 Epoch 235/500 30/30 [==============================] - 0s 15ms/step - loss: 15.6383 - mae: 16.1264 Epoch 236/500 30/30 [==============================] - 0s 14ms/step - loss: 15.7013 - mae: 16.1889 Epoch 237/500 30/30 [==============================] - 0s 15ms/step - loss: 15.9201 - mae: 16.4087 Epoch 238/500 30/30 [==============================] - 0s 14ms/step - loss: 15.6194 - mae: 16.1070 Epoch 239/500 30/30 [==============================] - 0s 16ms/step - loss: 16.1093 - mae: 16.5979 Epoch 240/500 30/30 [==============================] - 0s 15ms/step - loss: 16.0429 - mae: 16.5318 Epoch 241/500 30/30 [==============================] - 1s 17ms/step - loss: 15.5977 - mae: 16.0856 Epoch 242/500 30/30 [==============================] - 0s 14ms/step - loss: 15.6842 - mae: 16.1722 Epoch 243/500 30/30 [==============================] - 0s 14ms/step - loss: 15.6201 - mae: 16.1081 Epoch 244/500 30/30 [==============================] - 0s 14ms/step - loss: 15.5879 - mae: 16.0757 Epoch 245/500 30/30 [==============================] - 0s 14ms/step - loss: 15.6471 - mae: 16.1352 Epoch 246/500 30/30 [==============================] - 0s 13ms/step - loss: 15.5125 - mae: 15.9998 Epoch 247/500 30/30 [==============================] - 0s 14ms/step - loss: 15.5122 - mae: 15.9999 Epoch 248/500 30/30 [==============================] - 0s 14ms/step - loss: 15.6258 - mae: 16.1138 Epoch 249/500 30/30 [==============================] - 0s 14ms/step - loss: 16.3115 - mae: 16.8002 Epoch 250/500 30/30 [==============================] - 0s 14ms/step - loss: 15.6440 - mae: 16.1321 Epoch 251/500 30/30 [==============================] - 0s 14ms/step - loss: 15.5283 - mae: 16.0154 Epoch 252/500 30/30 [==============================] - 0s 14ms/step - loss: 15.7157 - mae: 16.2039 Epoch 253/500 30/30 [==============================] - 0s 14ms/step - loss: 15.5189 - mae: 16.0065 Epoch 254/500 30/30 [==============================] - 0s 16ms/step - loss: 15.5357 - mae: 16.0235 Epoch 255/500 30/30 [==============================] - 1s 17ms/step - loss: 15.6168 - mae: 16.1043 Epoch 256/500 30/30 [==============================] - 1s 17ms/step - loss: 15.5067 - mae: 15.9946 Epoch 257/500 30/30 [==============================] - 1s 17ms/step - loss: 15.4329 - mae: 15.9205 Epoch 258/500 30/30 [==============================] - 0s 15ms/step - loss: 15.4566 - mae: 15.9441 Epoch 259/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4590 - mae: 15.9463 Epoch 260/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4482 - mae: 15.9357 Epoch 261/500 30/30 [==============================] - 0s 13ms/step - loss: 15.4836 - mae: 15.9712 Epoch 262/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4226 - mae: 15.9103 Epoch 263/500 30/30 [==============================] - 0s 14ms/step - loss: 15.5171 - mae: 16.0046 Epoch 264/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4175 - mae: 15.9046 Epoch 265/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4536 - mae: 15.9416 Epoch 266/500 30/30 [==============================] - 0s 14ms/step - loss: 15.5049 - mae: 15.9927 Epoch 267/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4413 - mae: 15.9287 Epoch 268/500 30/30 [==============================] - 0s 16ms/step - loss: 15.4964 - mae: 15.9838 Epoch 269/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3675 - mae: 15.8547 Epoch 270/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4797 - mae: 15.9677 Epoch 271/500 30/30 [==============================] - 0s 16ms/step - loss: 15.4602 - mae: 15.9476 Epoch 272/500 30/30 [==============================] - 0s 15ms/step - loss: 15.3877 - mae: 15.8752 Epoch 273/500 30/30 [==============================] - 1s 18ms/step - loss: 15.3989 - mae: 15.8862 Epoch 274/500 30/30 [==============================] - 1s 17ms/step - loss: 15.3990 - mae: 15.8861 Epoch 275/500 30/30 [==============================] - 0s 15ms/step - loss: 15.3618 - mae: 15.8492 Epoch 276/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4279 - mae: 15.9153 Epoch 277/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3635 - mae: 15.8505 Epoch 278/500 30/30 [==============================] - 0s 14ms/step - loss: 15.5433 - mae: 16.0306 Epoch 279/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3391 - mae: 15.8264 Epoch 280/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4045 - mae: 15.8917 Epoch 281/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3967 - mae: 15.8840 Epoch 282/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4836 - mae: 15.9712 Epoch 283/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3408 - mae: 15.8279 Epoch 284/500 30/30 [==============================] - 0s 15ms/step - loss: 15.3785 - mae: 15.8656 Epoch 285/500 30/30 [==============================] - 1s 17ms/step - loss: 15.3433 - mae: 15.8301 Epoch 286/500 30/30 [==============================] - 0s 15ms/step - loss: 15.5490 - mae: 16.0366 Epoch 287/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3053 - mae: 15.7924 Epoch 288/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3722 - mae: 15.8593 Epoch 289/500 30/30 [==============================] - 0s 15ms/step - loss: 15.2896 - mae: 15.7763 Epoch 290/500 30/30 [==============================] - 1s 17ms/step - loss: 15.3352 - mae: 15.8223 Epoch 291/500 30/30 [==============================] - 1s 17ms/step - loss: 15.4112 - mae: 15.8982 Epoch 292/500 30/30 [==============================] - 0s 16ms/step - loss: 15.2988 - mae: 15.7858 Epoch 293/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3388 - mae: 15.8259 Epoch 294/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3062 - mae: 15.7929 Epoch 295/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4105 - mae: 15.8977 Epoch 296/500 30/30 [==============================] - 0s 16ms/step - loss: 15.2703 - mae: 15.7570 Epoch 297/500 30/30 [==============================] - 1s 17ms/step - loss: 15.4421 - mae: 15.9292 Epoch 298/500 30/30 [==============================] - 1s 17ms/step - loss: 15.9984 - mae: 16.4868 Epoch 299/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3209 - mae: 15.8076 Epoch 300/500 30/30 [==============================] - 1s 18ms/step - loss: 15.2969 - mae: 15.7837 Epoch 301/500 30/30 [==============================] - 0s 15ms/step - loss: 15.3053 - mae: 15.7922 Epoch 302/500 30/30 [==============================] - 0s 17ms/step - loss: 15.3504 - mae: 15.8375 Epoch 303/500 30/30 [==============================] - 0s 14ms/step - loss: 15.2900 - mae: 15.7768 Epoch 304/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3542 - mae: 15.8414 Epoch 305/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1981 - mae: 15.6845 Epoch 306/500 30/30 [==============================] - 0s 14ms/step - loss: 15.2415 - mae: 15.7282 Epoch 307/500 30/30 [==============================] - 0s 14ms/step - loss: 15.4262 - mae: 15.9137 Epoch 308/500 30/30 [==============================] - 0s 15ms/step - loss: 15.2325 - mae: 15.7189 Epoch 309/500 30/30 [==============================] - 0s 14ms/step - loss: 15.5058 - mae: 15.9931 Epoch 310/500 30/30 [==============================] - 1s 18ms/step - loss: 15.3972 - mae: 15.8843 Epoch 311/500 30/30 [==============================] - 0s 15ms/step - loss: 15.3948 - mae: 15.8822 Epoch 312/500 30/30 [==============================] - 1s 17ms/step - loss: 15.3015 - mae: 15.7884 Epoch 313/500 30/30 [==============================] - 0s 15ms/step - loss: 15.3304 - mae: 15.8171 Epoch 314/500 30/30 [==============================] - 0s 15ms/step - loss: 15.2263 - mae: 15.7129 Epoch 315/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1832 - mae: 15.6693 Epoch 316/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1867 - mae: 15.6732 Epoch 317/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1514 - mae: 15.6378 Epoch 318/500 30/30 [==============================] - 0s 16ms/step - loss: 15.3113 - mae: 15.7983 Epoch 319/500 30/30 [==============================] - 1s 17ms/step - loss: 15.6725 - mae: 16.1602 Epoch 320/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1930 - mae: 15.6793 Epoch 321/500 30/30 [==============================] - 0s 15ms/step - loss: 15.1704 - mae: 15.6564 Epoch 322/500 30/30 [==============================] - 0s 15ms/step - loss: 15.5024 - mae: 15.9902 Epoch 323/500 30/30 [==============================] - 0s 15ms/step - loss: 15.2420 - mae: 15.7288 Epoch 324/500 30/30 [==============================] - 0s 13ms/step - loss: 15.1451 - mae: 15.6314 Epoch 325/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3194 - mae: 15.8064 Epoch 326/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1986 - mae: 15.6848 Epoch 327/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1682 - mae: 15.6546 Epoch 328/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1894 - mae: 15.6759 Epoch 329/500 30/30 [==============================] - 0s 14ms/step - loss: 15.2311 - mae: 15.7175 Epoch 330/500 30/30 [==============================] - 0s 14ms/step - loss: 15.2767 - mae: 15.7634 Epoch 331/500 30/30 [==============================] - 0s 13ms/step - loss: 15.1838 - mae: 15.6702 Epoch 332/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3262 - mae: 15.8128 Epoch 333/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1611 - mae: 15.6471 Epoch 334/500 30/30 [==============================] - 0s 16ms/step - loss: 15.1194 - mae: 15.6057 Epoch 335/500 30/30 [==============================] - 1s 18ms/step - loss: 15.1417 - mae: 15.6280 Epoch 336/500 30/30 [==============================] - 1s 17ms/step - loss: 15.1291 - mae: 15.6155 Epoch 337/500 30/30 [==============================] - 1s 18ms/step - loss: 15.1139 - mae: 15.6003 Epoch 338/500 30/30 [==============================] - 1s 18ms/step - loss: 15.1558 - mae: 15.6418 Epoch 339/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0973 - mae: 15.5834 Epoch 340/500 30/30 [==============================] - 0s 16ms/step - loss: 15.0798 - mae: 15.5663 Epoch 341/500 30/30 [==============================] - 1s 18ms/step - loss: 15.1344 - mae: 15.6208 Epoch 342/500 30/30 [==============================] - 0s 16ms/step - loss: 15.0556 - mae: 15.5419 Epoch 343/500 30/30 [==============================] - 1s 17ms/step - loss: 15.1649 - mae: 15.6512 Epoch 344/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3677 - mae: 15.8548 Epoch 345/500 30/30 [==============================] - 0s 15ms/step - loss: 15.4797 - mae: 15.9666 Epoch 346/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0599 - mae: 15.5458 Epoch 347/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1366 - mae: 15.6227 Epoch 348/500 30/30 [==============================] - 0s 14ms/step - loss: 15.2901 - mae: 15.7771 Epoch 349/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0421 - mae: 15.5281 Epoch 350/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0406 - mae: 15.5271 Epoch 351/500 30/30 [==============================] - 0s 13ms/step - loss: 15.0782 - mae: 15.5643 Epoch 352/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0080 - mae: 15.4939 Epoch 353/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0709 - mae: 15.5567 Epoch 354/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0508 - mae: 15.5368 Epoch 355/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0239 - mae: 15.5098 Epoch 356/500 30/30 [==============================] - 0s 15ms/step - loss: 14.9594 - mae: 15.4451 Epoch 357/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0580 - mae: 15.5441 Epoch 358/500 30/30 [==============================] - 0s 14ms/step - loss: 15.2814 - mae: 15.7687 Epoch 359/500 30/30 [==============================] - 0s 13ms/step - loss: 15.0490 - mae: 15.5351 Epoch 360/500 30/30 [==============================] - 0s 15ms/step - loss: 15.0498 - mae: 15.5365 Epoch 361/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0915 - mae: 15.5779 Epoch 362/500 30/30 [==============================] - 0s 15ms/step - loss: 15.2306 - mae: 15.7175 Epoch 363/500 30/30 [==============================] - 0s 14ms/step - loss: 15.3104 - mae: 15.7972 Epoch 364/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0009 - mae: 15.4870 Epoch 365/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9399 - mae: 15.4251 Epoch 366/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9447 - mae: 15.4307 Epoch 367/500 30/30 [==============================] - 0s 16ms/step - loss: 15.1911 - mae: 15.6779 Epoch 368/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0051 - mae: 15.4914 Epoch 369/500 30/30 [==============================] - 0s 15ms/step - loss: 15.0552 - mae: 15.5416 Epoch 370/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0436 - mae: 15.5295 Epoch 371/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0175 - mae: 15.5037 Epoch 372/500 30/30 [==============================] - 0s 15ms/step - loss: 14.9911 - mae: 15.4772 Epoch 373/500 30/30 [==============================] - 0s 15ms/step - loss: 14.9347 - mae: 15.4203 Epoch 374/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0221 - mae: 15.5081 Epoch 375/500 30/30 [==============================] - 1s 18ms/step - loss: 14.9755 - mae: 15.4615 Epoch 376/500 30/30 [==============================] - 1s 17ms/step - loss: 14.9219 - mae: 15.4074 Epoch 377/500 30/30 [==============================] - 1s 17ms/step - loss: 14.9700 - mae: 15.4562 Epoch 378/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1166 - mae: 15.6031 Epoch 379/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1503 - mae: 15.6369 Epoch 380/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8959 - mae: 15.3815 Epoch 381/500 30/30 [==============================] - 0s 15ms/step - loss: 14.9333 - mae: 15.4189 Epoch 382/500 30/30 [==============================] - 0s 14ms/step - loss: 15.2071 - mae: 15.6942 Epoch 383/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8798 - mae: 15.3658 Epoch 384/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9031 - mae: 15.3885 Epoch 385/500 30/30 [==============================] - 1s 18ms/step - loss: 14.9718 - mae: 15.4579 Epoch 386/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0478 - mae: 15.5342 Epoch 387/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9848 - mae: 15.4712 Epoch 388/500 30/30 [==============================] - 0s 15ms/step - loss: 14.9270 - mae: 15.4128 Epoch 389/500 30/30 [==============================] - 0s 17ms/step - loss: 14.8695 - mae: 15.3551 Epoch 390/500 30/30 [==============================] - 1s 18ms/step - loss: 15.4171 - mae: 15.9042 Epoch 391/500 30/30 [==============================] - 0s 15ms/step - loss: 15.0925 - mae: 15.5791 Epoch 392/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9192 - mae: 15.4048 Epoch 393/500 30/30 [==============================] - 0s 14ms/step - loss: 15.5875 - mae: 16.0752 Epoch 394/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1156 - mae: 15.6018 Epoch 395/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8878 - mae: 15.3734 Epoch 396/500 30/30 [==============================] - 0s 14ms/step - loss: 15.1636 - mae: 15.6498 Epoch 397/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8613 - mae: 15.3466 Epoch 398/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0361 - mae: 15.5222 Epoch 399/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8893 - mae: 15.3750 Epoch 400/500 30/30 [==============================] - 0s 15ms/step - loss: 14.8472 - mae: 15.3328 Epoch 401/500 30/30 [==============================] - 0s 13ms/step - loss: 15.6436 - mae: 16.1307 Epoch 402/500 30/30 [==============================] - 0s 16ms/step - loss: 15.9384 - mae: 16.4261 Epoch 403/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9776 - mae: 15.4637 Epoch 404/500 30/30 [==============================] - 0s 14ms/step - loss: 15.2612 - mae: 15.7481 Epoch 405/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8092 - mae: 15.2946 Epoch 406/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8157 - mae: 15.3010 Epoch 407/500 30/30 [==============================] - 0s 15ms/step - loss: 14.8365 - mae: 15.3220 Epoch 408/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7986 - mae: 15.2840 Epoch 409/500 30/30 [==============================] - 0s 15ms/step - loss: 14.7763 - mae: 15.2610 Epoch 410/500 30/30 [==============================] - 0s 15ms/step - loss: 14.8700 - mae: 15.3556 Epoch 411/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0433 - mae: 15.5297 Epoch 412/500 30/30 [==============================] - 1s 17ms/step - loss: 14.8291 - mae: 15.3144 Epoch 413/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7543 - mae: 15.2395 Epoch 414/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8680 - mae: 15.3535 Epoch 415/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9279 - mae: 15.4138 Epoch 416/500 30/30 [==============================] - 0s 15ms/step - loss: 15.0915 - mae: 15.5782 Epoch 417/500 30/30 [==============================] - 0s 15ms/step - loss: 14.8199 - mae: 15.3061 Epoch 418/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8473 - mae: 15.3334 Epoch 419/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7691 - mae: 15.2549 Epoch 420/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8012 - mae: 15.2871 Epoch 421/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8548 - mae: 15.3404 Epoch 422/500 30/30 [==============================] - 0s 15ms/step - loss: 14.8743 - mae: 15.3599 Epoch 423/500 30/30 [==============================] - 0s 14ms/step - loss: 15.0132 - mae: 15.4996 Epoch 424/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7946 - mae: 15.2807 Epoch 425/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6989 - mae: 15.1838 Epoch 426/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7783 - mae: 15.2639 Epoch 427/500 30/30 [==============================] - 0s 16ms/step - loss: 14.8919 - mae: 15.3780 Epoch 428/500 30/30 [==============================] - 1s 18ms/step - loss: 14.9089 - mae: 15.3947 Epoch 429/500 30/30 [==============================] - 1s 18ms/step - loss: 14.8476 - mae: 15.3332 Epoch 430/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7627 - mae: 15.2484 Epoch 431/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8583 - mae: 15.3440 Epoch 432/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7538 - mae: 15.2391 Epoch 433/500 30/30 [==============================] - 0s 15ms/step - loss: 14.7444 - mae: 15.2301 Epoch 434/500 30/30 [==============================] - 0s 15ms/step - loss: 14.7706 - mae: 15.2559 Epoch 435/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9036 - mae: 15.3899 Epoch 436/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6807 - mae: 15.1661 Epoch 437/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9748 - mae: 15.4613 Epoch 438/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8981 - mae: 15.3843 Epoch 439/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7594 - mae: 15.2446 Epoch 440/500 30/30 [==============================] - 0s 15ms/step - loss: 14.7507 - mae: 15.2364 Epoch 441/500 30/30 [==============================] - 0s 16ms/step - loss: 14.7284 - mae: 15.2135 Epoch 442/500 30/30 [==============================] - 0s 17ms/step - loss: 14.7428 - mae: 15.2281 Epoch 443/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8573 - mae: 15.3436 Epoch 444/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8124 - mae: 15.2985 Epoch 445/500 30/30 [==============================] - 0s 15ms/step - loss: 14.8117 - mae: 15.2975 Epoch 446/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8139 - mae: 15.3003 Epoch 447/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7197 - mae: 15.2053 Epoch 448/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6914 - mae: 15.1769 Epoch 449/500 30/30 [==============================] - 0s 15ms/step - loss: 14.9118 - mae: 15.3977 Epoch 450/500 30/30 [==============================] - 0s 14ms/step - loss: 14.9488 - mae: 15.4352 Epoch 451/500 30/30 [==============================] - 0s 15ms/step - loss: 14.7782 - mae: 15.2642 Epoch 452/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7453 - mae: 15.2312 Epoch 453/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7248 - mae: 15.2105 Epoch 454/500 30/30 [==============================] - 1s 17ms/step - loss: 14.6106 - mae: 15.0958 Epoch 455/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6153 - mae: 15.1010 Epoch 456/500 30/30 [==============================] - 0s 14ms/step - loss: 14.7214 - mae: 15.2072 Epoch 457/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6813 - mae: 15.1670 Epoch 458/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6108 - mae: 15.0960 Epoch 459/500 30/30 [==============================] - 0s 16ms/step - loss: 14.6650 - mae: 15.1502 Epoch 460/500 30/30 [==============================] - 0s 15ms/step - loss: 14.5851 - mae: 15.0707 Epoch 461/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5661 - mae: 15.0515 Epoch 462/500 30/30 [==============================] - 0s 16ms/step - loss: 14.5763 - mae: 15.0615 Epoch 463/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5853 - mae: 15.0706 Epoch 464/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6161 - mae: 15.1017 Epoch 465/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5613 - mae: 15.0463 Epoch 466/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6468 - mae: 15.1325 Epoch 467/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5848 - mae: 15.0699 Epoch 468/500 30/30 [==============================] - 1s 18ms/step - loss: 14.6184 - mae: 15.1037 Epoch 469/500 30/30 [==============================] - 0s 15ms/step - loss: 14.5933 - mae: 15.0786 Epoch 470/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8568 - mae: 15.3430 Epoch 471/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6584 - mae: 15.1439 Epoch 472/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8504 - mae: 15.3366 Epoch 473/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6431 - mae: 15.1293 Epoch 474/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5289 - mae: 15.0141 Epoch 475/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5658 - mae: 15.0509 Epoch 476/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5842 - mae: 15.0696 Epoch 477/500 30/30 [==============================] - 0s 15ms/step - loss: 14.6515 - mae: 15.1372 Epoch 478/500 30/30 [==============================] - 0s 15ms/step - loss: 14.6158 - mae: 15.1013 Epoch 479/500 30/30 [==============================] - 0s 17ms/step - loss: 14.5865 - mae: 15.0718 Epoch 480/500 30/30 [==============================] - 0s 15ms/step - loss: 14.5492 - mae: 15.0345 Epoch 481/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5075 - mae: 14.9931 Epoch 482/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5184 - mae: 15.0038 Epoch 483/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5044 - mae: 14.9896 Epoch 484/500 30/30 [==============================] - 0s 14ms/step - loss: 14.4902 - mae: 14.9754 Epoch 485/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5195 - mae: 15.0049 Epoch 486/500 30/30 [==============================] - 0s 14ms/step - loss: 14.8027 - mae: 15.2887 Epoch 487/500 30/30 [==============================] - 0s 15ms/step - loss: 15.7255 - mae: 16.2135 Epoch 488/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5453 - mae: 15.0308 Epoch 489/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5053 - mae: 14.9908 Epoch 490/500 30/30 [==============================] - 0s 15ms/step - loss: 14.5601 - mae: 15.0457 Epoch 491/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5002 - mae: 14.9853 Epoch 492/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5193 - mae: 15.0045 Epoch 493/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5330 - mae: 15.0182 Epoch 494/500 30/30 [==============================] - 0s 14ms/step - loss: 14.4550 - mae: 14.9402 Epoch 495/500 30/30 [==============================] - 1s 17ms/step - loss: 14.5057 - mae: 14.9907 Epoch 496/500 30/30 [==============================] - 0s 15ms/step - loss: 14.4685 - mae: 14.9537 Epoch 497/500 30/30 [==============================] - 0s 14ms/step - loss: 14.5498 - mae: 15.0353 Epoch 498/500 30/30 [==============================] - 0s 14ms/step - loss: 14.6195 - mae: 15.1050 Epoch 499/500 30/30 [==============================] - 0s 15ms/step - loss: 14.5915 - mae: 15.0775 Epoch 500/500 30/30 [==============================] - 0s 15ms/step - loss: 14.5214 - mae: 15.0068
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)
rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]
plt.figure(figsize=(10, 6))
plot_series(time_valid, x_valid)
plot_series(time_valid, rnn_forecast)
tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()
14.773848
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
#-----------------------------------------------------------
# Retrieve a list of list results on training and test data
# sets for each training epoch
#-----------------------------------------------------------
loss=history.history['loss']
epochs=range(len(loss)) # Get number of epochs
#------------------------------------------------
# Plot training and validation loss per epoch
#------------------------------------------------
plt.plot(epochs, loss, 'r')
plt.title('Training loss')
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend(["Loss"])
plt.figure()
zoomed_loss = loss[200:]
zoomed_epochs = range(200,500)
#------------------------------------------------
# Plot training and validation loss per epoch
#------------------------------------------------
plt.plot(zoomed_epochs, zoomed_loss, 'r')
plt.title('Training loss')
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend(["Loss"])
plt.figure()
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>