Play with TensorBoard
TensorBoard provides the visualization and tooling needed for machine learning experimentation
• Austin Chen • 34 min read
TensorBoard is TensorFlow's visualization toolkit, enabling ones to track metrics like loss and accuracy, visualize the model graph, view histograms of weights, biases, or other tensors as they change over time, and much more.
%load_ext tensorboard
import os
import numpy as np
import pandas as pd
import datetime
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras import layers
pd.options.display.max_columns=25
df = pd.read_csv("kc_house_data.csv")
df.shape
(21613, 21)
df.head().T
| 0 | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| id | 7129300520 | 6414100192 | 5631500400 | 2487200875 | 1954400510 |
| date | 20141013T000000 | 20141209T000000 | 20150225T000000 | 20141209T000000 | 20150218T000000 |
| price | 221900 | 538000 | 180000 | 604000 | 510000 |
| bedrooms | 3 | 3 | 2 | 4 | 3 |
| bathrooms | 1 | 2.25 | 1 | 3 | 2 |
| sqft_living | 1180 | 2570 | 770 | 1960 | 1680 |
| sqft_lot | 5650 | 7242 | 10000 | 5000 | 8080 |
| floors | 1 | 2 | 1 | 1 | 1 |
| waterfront | 0 | 0 | 0 | 0 | 0 |
| view | 0 | 0 | 0 | 0 | 0 |
| condition | 3 | 3 | 3 | 5 | 3 |
| grade | 7 | 7 | 6 | 7 | 8 |
| sqft_above | 1180 | 2170 | 770 | 1050 | 1680 |
| sqft_basement | 0 | 400 | 0 | 910 | 0 |
| yr_built | 1955 | 1951 | 1933 | 1965 | 1987 |
| yr_renovated | 0 | 1991 | 0 | 0 | 0 |
| zipcode | 98178 | 98125 | 98028 | 98136 | 98074 |
| lat | 47.5112 | 47.721 | 47.7379 | 47.5208 | 47.6168 |
| long | -122.257 | -122.319 | -122.233 | -122.393 | -122.045 |
| sqft_living15 | 1340 | 1690 | 2720 | 1360 | 1800 |
| sqft_lot15 | 5650 | 7639 | 8062 | 5000 | 7503 |
df.dtypes
id int64 date object price float64 bedrooms int64 bathrooms float64 sqft_living int64 sqft_lot int64 floors float64 waterfront int64 view int64 condition int64 grade int64 sqft_above int64 sqft_basement int64 yr_built int64 yr_renovated int64 zipcode int64 lat float64 long float64 sqft_living15 int64 sqft_lot15 int64 dtype: object
df['year'] = pd.to_numeric(df['date'].str.slice(0,4))
df['month'] = pd.to_numeric(df['date'].str.slice(4,6))
df['day'] = pd.to_numeric(df['date'].str.slice(6,8))
df.drop(['id', 'date'], axis="columns", inplace=True)
df.head().T
| 0 | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| price | 221900.0000 | 538000.000 | 180000.0000 | 604000.0000 | 510000.0000 |
| bedrooms | 3.0000 | 3.000 | 2.0000 | 4.0000 | 3.0000 |
| bathrooms | 1.0000 | 2.250 | 1.0000 | 3.0000 | 2.0000 |
| sqft_living | 1180.0000 | 2570.000 | 770.0000 | 1960.0000 | 1680.0000 |
| sqft_lot | 5650.0000 | 7242.000 | 10000.0000 | 5000.0000 | 8080.0000 |
| floors | 1.0000 | 2.000 | 1.0000 | 1.0000 | 1.0000 |
| waterfront | 0.0000 | 0.000 | 0.0000 | 0.0000 | 0.0000 |
| view | 0.0000 | 0.000 | 0.0000 | 0.0000 | 0.0000 |
| condition | 3.0000 | 3.000 | 3.0000 | 5.0000 | 3.0000 |
| grade | 7.0000 | 7.000 | 6.0000 | 7.0000 | 8.0000 |
| sqft_above | 1180.0000 | 2170.000 | 770.0000 | 1050.0000 | 1680.0000 |
| sqft_basement | 0.0000 | 400.000 | 0.0000 | 910.0000 | 0.0000 |
| yr_built | 1955.0000 | 1951.000 | 1933.0000 | 1965.0000 | 1987.0000 |
| yr_renovated | 0.0000 | 1991.000 | 0.0000 | 0.0000 | 0.0000 |
| zipcode | 98178.0000 | 98125.000 | 98028.0000 | 98136.0000 | 98074.0000 |
| lat | 47.5112 | 47.721 | 47.7379 | 47.5208 | 47.6168 |
| long | -122.2570 | -122.319 | -122.2330 | -122.3930 | -122.0450 |
| sqft_living15 | 1340.0000 | 1690.000 | 2720.0000 | 1360.0000 | 1800.0000 |
| sqft_lot15 | 5650.0000 | 7639.000 | 8062.0000 | 5000.0000 | 7503.0000 |
| year | 2014.0000 | 2014.000 | 2015.0000 | 2014.0000 | 2015.0000 |
| month | 10.0000 | 12.000 | 2.0000 | 12.0000 | 2.0000 |
| day | 13.0000 | 9.000 | 25.0000 | 9.0000 | 18.0000 |
n = df.shape[0]
ids = np.random.permutation(n)
train_ids = ids[:int(n * .6)]
valid_ids = ids[int(n * .4) : int(n * .8)]
test_ids = ids[int(n * .8):]
train_data = df.loc[train_ids]
valid_data = df.loc[valid_ids]
test_data = df.loc[test_ids]
train_valid_data = pd.concat([train_data, valid_data])
mean = train_valid_data.mean()
std = train_valid_data.std()
train_data = (train_data - mean) / std
valid_data = (valid_data - mean) / std
train_x = np.array(train_data.drop('price', axis='columns')).astype('float32')
train_y = np.array(train_data['price']).astype('float32')
valid_x = np.array(valid_data.drop('price', axis='columns')).astype('float32')
valid_y = np.array(valid_data['price']).astype('float32')
train_x.shape, valid_x.shape
((12967, 21), (8645, 21))
model = tf.keras.Sequential(name='model-1')
model.add(tf.keras.layers.Dense(64, activation='relu', input_shape=(21,)))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(1))
model.summary()
Model: "model-1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 64) 1408 _________________________________________________________________ dense_1 (Dense) (None, 64) 4160 _________________________________________________________________ dense_2 (Dense) (None, 1) 65 ================================================================= Total params: 5,633 Trainable params: 5,633 Non-trainable params: 0 _________________________________________________________________
model.compile(tf.keras.optimizers.Adam(0.001),
loss = tf.keras.losses.MeanSquaredError(),
metrics=[tf.keras.metrics.MeanAbsoluteError()])
log_dir = "logs/model_1/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint('models/best-model-1.h5',
monitor='val_mean_absolute_error',
save_best_only=True,
mode='min')
history = model.fit(train_x, train_y,
batch_size=64,
epochs=300,
validation_data=(valid_x, valid_y),
callbacks=[tensorboard_callback,
checkpoint_callback])
Epoch 1/300 203/203 [==============================] - 4s 7ms/step - loss: 0.4115 - mean_absolute_error: 0.4102 - val_loss: 0.2153 - val_mean_absolute_error: 0.2894 Epoch 2/300 203/203 [==============================] - 1s 4ms/step - loss: 0.2147 - mean_absolute_error: 0.2880 - val_loss: 0.1866 - val_mean_absolute_error: 0.2655 Epoch 3/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1913 - mean_absolute_error: 0.2684 - val_loss: 0.1700 - val_mean_absolute_error: 0.2589 Epoch 4/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1584 - mean_absolute_error: 0.2487 - val_loss: 0.1553 - val_mean_absolute_error: 0.2431 Epoch 5/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1488 - mean_absolute_error: 0.2400 - val_loss: 0.1401 - val_mean_absolute_error: 0.2347 Epoch 6/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1440 - mean_absolute_error: 0.2348 - val_loss: 0.1443 - val_mean_absolute_error: 0.2353 Epoch 7/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1418 - mean_absolute_error: 0.2270 - val_loss: 0.1287 - val_mean_absolute_error: 0.2207 Epoch 8/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1164 - mean_absolute_error: 0.2130 - val_loss: 0.1201 - val_mean_absolute_error: 0.2155 Epoch 9/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1208 - mean_absolute_error: 0.2127 - val_loss: 0.1287 - val_mean_absolute_error: 0.2241 Epoch 10/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1097 - mean_absolute_error: 0.2055 - val_loss: 0.1197 - val_mean_absolute_error: 0.2123 Epoch 11/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1099 - mean_absolute_error: 0.2057 - val_loss: 0.1171 - val_mean_absolute_error: 0.2131 Epoch 12/300 203/203 [==============================] - 1s 4ms/step - loss: 0.1007 - mean_absolute_error: 0.2010 - val_loss: 0.1153 - val_mean_absolute_error: 0.2086 Epoch 13/300 203/203 [==============================] - 1s 5ms/step - loss: 0.1045 - mean_absolute_error: 0.2044 - val_loss: 0.1153 - val_mean_absolute_error: 0.2176 Epoch 14/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0975 - mean_absolute_error: 0.1985 - val_loss: 0.1068 - val_mean_absolute_error: 0.2065 Epoch 15/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0882 - mean_absolute_error: 0.1919 - val_loss: 0.1044 - val_mean_absolute_error: 0.1989 Epoch 16/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0924 - mean_absolute_error: 0.1943 - val_loss: 0.1387 - val_mean_absolute_error: 0.2235 Epoch 17/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0904 - mean_absolute_error: 0.1940 - val_loss: 0.1000 - val_mean_absolute_error: 0.1973 Epoch 18/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0886 - mean_absolute_error: 0.1901 - val_loss: 0.1022 - val_mean_absolute_error: 0.1961 Epoch 19/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0842 - mean_absolute_error: 0.1871 - val_loss: 0.1005 - val_mean_absolute_error: 0.1954 Epoch 20/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0804 - mean_absolute_error: 0.1856 - val_loss: 0.1010 - val_mean_absolute_error: 0.1984 Epoch 21/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0846 - mean_absolute_error: 0.1871 - val_loss: 0.0965 - val_mean_absolute_error: 0.1941 Epoch 22/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0823 - mean_absolute_error: 0.1865 - val_loss: 0.0976 - val_mean_absolute_error: 0.1930 Epoch 23/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0801 - mean_absolute_error: 0.1829 - val_loss: 0.1029 - val_mean_absolute_error: 0.2059 Epoch 24/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0787 - mean_absolute_error: 0.1814 - val_loss: 0.0983 - val_mean_absolute_error: 0.1972 Epoch 25/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0756 - mean_absolute_error: 0.1794 - val_loss: 0.0951 - val_mean_absolute_error: 0.1914 Epoch 26/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0760 - mean_absolute_error: 0.1808 - val_loss: 0.0976 - val_mean_absolute_error: 0.1945 Epoch 27/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0740 - mean_absolute_error: 0.1809 - val_loss: 0.0941 - val_mean_absolute_error: 0.1931 Epoch 28/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0749 - mean_absolute_error: 0.1797 - val_loss: 0.0966 - val_mean_absolute_error: 0.1933 Epoch 29/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0735 - mean_absolute_error: 0.1779 - val_loss: 0.0959 - val_mean_absolute_error: 0.1935 Epoch 30/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0719 - mean_absolute_error: 0.1772 - val_loss: 0.0948 - val_mean_absolute_error: 0.1901 Epoch 31/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0701 - mean_absolute_error: 0.1766 - val_loss: 0.1059 - val_mean_absolute_error: 0.2212 Epoch 32/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0748 - mean_absolute_error: 0.1811 - val_loss: 0.0953 - val_mean_absolute_error: 0.1907 Epoch 33/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0668 - mean_absolute_error: 0.1734 - val_loss: 0.1223 - val_mean_absolute_error: 0.2204 Epoch 34/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0712 - mean_absolute_error: 0.1792 - val_loss: 0.0973 - val_mean_absolute_error: 0.1917 Epoch 35/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0660 - mean_absolute_error: 0.1725 - val_loss: 0.0950 - val_mean_absolute_error: 0.2000 Epoch 36/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0672 - mean_absolute_error: 0.1740 - val_loss: 0.0948 - val_mean_absolute_error: 0.1881 Epoch 37/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0647 - mean_absolute_error: 0.1689 - val_loss: 0.0906 - val_mean_absolute_error: 0.1885 Epoch 38/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0628 - mean_absolute_error: 0.1711 - val_loss: 0.0994 - val_mean_absolute_error: 0.1946 Epoch 39/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0653 - mean_absolute_error: 0.1722 - val_loss: 0.0905 - val_mean_absolute_error: 0.1875 Epoch 40/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0612 - mean_absolute_error: 0.1680 - val_loss: 0.0950 - val_mean_absolute_error: 0.1879 Epoch 41/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0618 - mean_absolute_error: 0.1690 - val_loss: 0.0978 - val_mean_absolute_error: 0.1918 Epoch 42/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0577 - mean_absolute_error: 0.1645 - val_loss: 0.0930 - val_mean_absolute_error: 0.1845 Epoch 43/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0547 - mean_absolute_error: 0.1613 - val_loss: 0.0961 - val_mean_absolute_error: 0.1952 Epoch 44/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0588 - mean_absolute_error: 0.1659 - val_loss: 0.0941 - val_mean_absolute_error: 0.1881 Epoch 45/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0612 - mean_absolute_error: 0.1674 - val_loss: 0.0929 - val_mean_absolute_error: 0.1881 Epoch 46/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0589 - mean_absolute_error: 0.1658 - val_loss: 0.0880 - val_mean_absolute_error: 0.1822 Epoch 47/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0589 - mean_absolute_error: 0.1658 - val_loss: 0.0954 - val_mean_absolute_error: 0.1882 Epoch 48/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0554 - mean_absolute_error: 0.1620 - val_loss: 0.0921 - val_mean_absolute_error: 0.1864 Epoch 49/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0568 - mean_absolute_error: 0.1631 - val_loss: 0.0924 - val_mean_absolute_error: 0.1848 Epoch 50/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0547 - mean_absolute_error: 0.1628 - val_loss: 0.0952 - val_mean_absolute_error: 0.1853 Epoch 51/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0537 - mean_absolute_error: 0.1605 - val_loss: 0.0958 - val_mean_absolute_error: 0.1912 Epoch 52/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0524 - mean_absolute_error: 0.1596 - val_loss: 0.0950 - val_mean_absolute_error: 0.1911 Epoch 53/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0558 - mean_absolute_error: 0.1629 - val_loss: 0.0959 - val_mean_absolute_error: 0.1935 Epoch 54/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0570 - mean_absolute_error: 0.1647 - val_loss: 0.0886 - val_mean_absolute_error: 0.1840 Epoch 55/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0531 - mean_absolute_error: 0.1597 - val_loss: 0.0932 - val_mean_absolute_error: 0.1840 Epoch 56/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0505 - mean_absolute_error: 0.1571 - val_loss: 0.0895 - val_mean_absolute_error: 0.1848 Epoch 57/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0552 - mean_absolute_error: 0.1630 - val_loss: 0.0910 - val_mean_absolute_error: 0.1833 Epoch 58/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0515 - mean_absolute_error: 0.1572 - val_loss: 0.0894 - val_mean_absolute_error: 0.1814 Epoch 59/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0493 - mean_absolute_error: 0.1547 - val_loss: 0.0888 - val_mean_absolute_error: 0.1837 Epoch 60/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0501 - mean_absolute_error: 0.1572 - val_loss: 0.0905 - val_mean_absolute_error: 0.1849 Epoch 61/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0493 - mean_absolute_error: 0.1546 - val_loss: 0.0911 - val_mean_absolute_error: 0.1848 Epoch 62/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0502 - mean_absolute_error: 0.1563 - val_loss: 0.0882 - val_mean_absolute_error: 0.1840 Epoch 63/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0484 - mean_absolute_error: 0.1546 - val_loss: 0.0893 - val_mean_absolute_error: 0.1835 Epoch 64/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0490 - mean_absolute_error: 0.1564 - val_loss: 0.0882 - val_mean_absolute_error: 0.1824 Epoch 65/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0517 - mean_absolute_error: 0.1572 - val_loss: 0.0861 - val_mean_absolute_error: 0.1796 Epoch 66/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0453 - mean_absolute_error: 0.1503 - val_loss: 0.0911 - val_mean_absolute_error: 0.1854 Epoch 67/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0484 - mean_absolute_error: 0.1543 - val_loss: 0.0958 - val_mean_absolute_error: 0.1882 Epoch 68/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0488 - mean_absolute_error: 0.1576 - val_loss: 0.0878 - val_mean_absolute_error: 0.1813 Epoch 69/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0460 - mean_absolute_error: 0.1511 - val_loss: 0.0866 - val_mean_absolute_error: 0.1780 Epoch 70/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0475 - mean_absolute_error: 0.1541 - val_loss: 0.0881 - val_mean_absolute_error: 0.1816 Epoch 71/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0453 - mean_absolute_error: 0.1511 - val_loss: 0.1133 - val_mean_absolute_error: 0.1959 Epoch 72/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0474 - mean_absolute_error: 0.1529 - val_loss: 0.0965 - val_mean_absolute_error: 0.1914 Epoch 73/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0465 - mean_absolute_error: 0.1535 - val_loss: 0.0856 - val_mean_absolute_error: 0.1795 Epoch 74/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0452 - mean_absolute_error: 0.1494 - val_loss: 0.0911 - val_mean_absolute_error: 0.1838 Epoch 75/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0445 - mean_absolute_error: 0.1497 - val_loss: 0.0901 - val_mean_absolute_error: 0.1816 Epoch 76/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0455 - mean_absolute_error: 0.1502 - val_loss: 0.0987 - val_mean_absolute_error: 0.1849 Epoch 77/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0497 - mean_absolute_error: 0.1566 - val_loss: 0.0870 - val_mean_absolute_error: 0.1807 Epoch 78/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0430 - mean_absolute_error: 0.1481 - val_loss: 0.0880 - val_mean_absolute_error: 0.1818 Epoch 79/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0443 - mean_absolute_error: 0.1500 - val_loss: 0.0857 - val_mean_absolute_error: 0.1783 Epoch 80/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0426 - mean_absolute_error: 0.1478 - val_loss: 0.0905 - val_mean_absolute_error: 0.1808 Epoch 81/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0444 - mean_absolute_error: 0.1476 - val_loss: 0.0869 - val_mean_absolute_error: 0.1794 Epoch 82/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0462 - mean_absolute_error: 0.1511 - val_loss: 0.0871 - val_mean_absolute_error: 0.1777 Epoch 83/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0422 - mean_absolute_error: 0.1475 - val_loss: 0.0876 - val_mean_absolute_error: 0.1832 Epoch 84/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0477 - mean_absolute_error: 0.1522 - val_loss: 0.0859 - val_mean_absolute_error: 0.1831 Epoch 85/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0420 - mean_absolute_error: 0.1476 - val_loss: 0.0907 - val_mean_absolute_error: 0.1864 Epoch 86/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0435 - mean_absolute_error: 0.1489 - val_loss: 0.0839 - val_mean_absolute_error: 0.1785 Epoch 87/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0413 - mean_absolute_error: 0.1451 - val_loss: 0.0897 - val_mean_absolute_error: 0.1854 Epoch 88/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0409 - mean_absolute_error: 0.1454 - val_loss: 0.0856 - val_mean_absolute_error: 0.1772 Epoch 89/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0414 - mean_absolute_error: 0.1445 - val_loss: 0.0853 - val_mean_absolute_error: 0.1759 Epoch 90/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0410 - mean_absolute_error: 0.1452 - val_loss: 0.0874 - val_mean_absolute_error: 0.1770 Epoch 91/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0390 - mean_absolute_error: 0.1421 - val_loss: 0.0858 - val_mean_absolute_error: 0.1791 Epoch 92/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0393 - mean_absolute_error: 0.1426 - val_loss: 0.0852 - val_mean_absolute_error: 0.1783 Epoch 93/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0440 - mean_absolute_error: 0.1505 - val_loss: 0.0899 - val_mean_absolute_error: 0.1795 Epoch 94/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0384 - mean_absolute_error: 0.1407 - val_loss: 0.0847 - val_mean_absolute_error: 0.1742 Epoch 95/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0386 - mean_absolute_error: 0.1409 - val_loss: 0.0932 - val_mean_absolute_error: 0.1831 Epoch 96/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0495 - mean_absolute_error: 0.1501 - val_loss: 0.0947 - val_mean_absolute_error: 0.1827 Epoch 97/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0425 - mean_absolute_error: 0.1465 - val_loss: 0.0859 - val_mean_absolute_error: 0.1754 Epoch 98/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0373 - mean_absolute_error: 0.1396 - val_loss: 0.0862 - val_mean_absolute_error: 0.1813 Epoch 99/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0395 - mean_absolute_error: 0.1434 - val_loss: 0.0886 - val_mean_absolute_error: 0.1789 Epoch 100/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0382 - mean_absolute_error: 0.1412 - val_loss: 0.0915 - val_mean_absolute_error: 0.1858 Epoch 101/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0386 - mean_absolute_error: 0.1429 - val_loss: 0.0948 - val_mean_absolute_error: 0.1834 Epoch 102/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0401 - mean_absolute_error: 0.1433 - val_loss: 0.0945 - val_mean_absolute_error: 0.1830 Epoch 103/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0393 - mean_absolute_error: 0.1447 - val_loss: 0.0878 - val_mean_absolute_error: 0.1784 Epoch 104/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0385 - mean_absolute_error: 0.1425 - val_loss: 0.0856 - val_mean_absolute_error: 0.1756 Epoch 105/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0379 - mean_absolute_error: 0.1396 - val_loss: 0.0865 - val_mean_absolute_error: 0.1824 Epoch 106/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0409 - mean_absolute_error: 0.1448 - val_loss: 0.0889 - val_mean_absolute_error: 0.1791 Epoch 107/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0373 - mean_absolute_error: 0.1395 - val_loss: 0.0866 - val_mean_absolute_error: 0.1796 Epoch 108/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0369 - mean_absolute_error: 0.1390 - val_loss: 0.0878 - val_mean_absolute_error: 0.1756 Epoch 109/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0351 - mean_absolute_error: 0.1378 - val_loss: 0.0833 - val_mean_absolute_error: 0.1750 Epoch 110/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0365 - mean_absolute_error: 0.1395 - val_loss: 0.0873 - val_mean_absolute_error: 0.1756 Epoch 111/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0364 - mean_absolute_error: 0.1387 - val_loss: 0.0848 - val_mean_absolute_error: 0.1784 Epoch 112/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0356 - mean_absolute_error: 0.1388 - val_loss: 0.0872 - val_mean_absolute_error: 0.1762 Epoch 113/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0365 - mean_absolute_error: 0.1384 - val_loss: 0.0884 - val_mean_absolute_error: 0.1766 Epoch 114/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0368 - mean_absolute_error: 0.1388 - val_loss: 0.0858 - val_mean_absolute_error: 0.1764 Epoch 115/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0361 - mean_absolute_error: 0.1386 - val_loss: 0.0873 - val_mean_absolute_error: 0.1772 Epoch 116/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0397 - mean_absolute_error: 0.1426 - val_loss: 0.0838 - val_mean_absolute_error: 0.1747 Epoch 117/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0377 - mean_absolute_error: 0.1413 - val_loss: 0.0935 - val_mean_absolute_error: 0.1806 Epoch 118/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0382 - mean_absolute_error: 0.1417 - val_loss: 0.0870 - val_mean_absolute_error: 0.1754 Epoch 119/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0372 - mean_absolute_error: 0.1402 - val_loss: 0.0877 - val_mean_absolute_error: 0.1819 Epoch 120/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0361 - mean_absolute_error: 0.1389 - val_loss: 0.0841 - val_mean_absolute_error: 0.1735 Epoch 121/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0364 - mean_absolute_error: 0.1371 - val_loss: 0.0882 - val_mean_absolute_error: 0.1788 Epoch 122/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0333 - mean_absolute_error: 0.1342 - val_loss: 0.0853 - val_mean_absolute_error: 0.1756 Epoch 123/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0344 - mean_absolute_error: 0.1355 - val_loss: 0.0820 - val_mean_absolute_error: 0.1710 Epoch 124/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0347 - mean_absolute_error: 0.1350 - val_loss: 0.0858 - val_mean_absolute_error: 0.1748 Epoch 125/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0352 - mean_absolute_error: 0.1377 - val_loss: 0.0867 - val_mean_absolute_error: 0.1774 Epoch 126/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0336 - mean_absolute_error: 0.1343 - val_loss: 0.0875 - val_mean_absolute_error: 0.1802 Epoch 127/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0359 - mean_absolute_error: 0.1368 - val_loss: 0.0855 - val_mean_absolute_error: 0.1784 Epoch 128/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0367 - mean_absolute_error: 0.1400 - val_loss: 0.0896 - val_mean_absolute_error: 0.1769 Epoch 129/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0339 - mean_absolute_error: 0.1345 - val_loss: 0.0869 - val_mean_absolute_error: 0.1797 Epoch 130/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0358 - mean_absolute_error: 0.1375 - val_loss: 0.0916 - val_mean_absolute_error: 0.1810 Epoch 131/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0351 - mean_absolute_error: 0.1369 - val_loss: 0.0874 - val_mean_absolute_error: 0.1765 Epoch 132/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0338 - mean_absolute_error: 0.1341 - val_loss: 0.0838 - val_mean_absolute_error: 0.1734 Epoch 133/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0334 - mean_absolute_error: 0.1334 - val_loss: 0.0870 - val_mean_absolute_error: 0.1760 Epoch 134/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0357 - mean_absolute_error: 0.1387 - val_loss: 0.0825 - val_mean_absolute_error: 0.1775 Epoch 135/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0362 - mean_absolute_error: 0.1399 - val_loss: 0.0811 - val_mean_absolute_error: 0.1735 Epoch 136/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0339 - mean_absolute_error: 0.1355 - val_loss: 0.0869 - val_mean_absolute_error: 0.1772 Epoch 137/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0377 - mean_absolute_error: 0.1400 - val_loss: 0.0832 - val_mean_absolute_error: 0.1735 Epoch 138/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0348 - mean_absolute_error: 0.1357 - val_loss: 0.0837 - val_mean_absolute_error: 0.1729 Epoch 139/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0335 - mean_absolute_error: 0.1351 - val_loss: 0.0836 - val_mean_absolute_error: 0.1722 Epoch 140/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0341 - mean_absolute_error: 0.1347 - val_loss: 0.0863 - val_mean_absolute_error: 0.1781 Epoch 141/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0332 - mean_absolute_error: 0.1348 - val_loss: 0.0853 - val_mean_absolute_error: 0.1728 Epoch 142/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0325 - mean_absolute_error: 0.1326 - val_loss: 0.0833 - val_mean_absolute_error: 0.1714 Epoch 143/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0317 - mean_absolute_error: 0.1298 - val_loss: 0.0932 - val_mean_absolute_error: 0.1821 Epoch 144/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0334 - mean_absolute_error: 0.1341 - val_loss: 0.0877 - val_mean_absolute_error: 0.1778 Epoch 145/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0335 - mean_absolute_error: 0.1345 - val_loss: 0.0918 - val_mean_absolute_error: 0.1801 Epoch 146/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0343 - mean_absolute_error: 0.1356 - val_loss: 0.0865 - val_mean_absolute_error: 0.1724 Epoch 147/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0336 - mean_absolute_error: 0.1323 - val_loss: 0.0857 - val_mean_absolute_error: 0.1761 Epoch 148/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0319 - mean_absolute_error: 0.1317 - val_loss: 0.0849 - val_mean_absolute_error: 0.1723 Epoch 149/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0321 - mean_absolute_error: 0.1328 - val_loss: 0.0889 - val_mean_absolute_error: 0.1758 Epoch 150/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0324 - mean_absolute_error: 0.1319 - val_loss: 0.0887 - val_mean_absolute_error: 0.1754 Epoch 151/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0340 - mean_absolute_error: 0.1351 - val_loss: 0.0912 - val_mean_absolute_error: 0.1878 Epoch 152/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0419 - mean_absolute_error: 0.1455 - val_loss: 0.0940 - val_mean_absolute_error: 0.1804 Epoch 153/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0341 - mean_absolute_error: 0.1347 - val_loss: 0.0867 - val_mean_absolute_error: 0.1728 Epoch 154/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0317 - mean_absolute_error: 0.1304 - val_loss: 0.0845 - val_mean_absolute_error: 0.1743 Epoch 155/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0327 - mean_absolute_error: 0.1330 - val_loss: 0.0845 - val_mean_absolute_error: 0.1735 Epoch 156/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0313 - mean_absolute_error: 0.1307 - val_loss: 0.0805 - val_mean_absolute_error: 0.1701 Epoch 157/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0306 - mean_absolute_error: 0.1297 - val_loss: 0.0848 - val_mean_absolute_error: 0.1737 Epoch 158/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0299 - mean_absolute_error: 0.1279 - val_loss: 0.0875 - val_mean_absolute_error: 0.1771 Epoch 159/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0339 - mean_absolute_error: 0.1345 - val_loss: 0.0885 - val_mean_absolute_error: 0.1763 Epoch 160/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0355 - mean_absolute_error: 0.1369 - val_loss: 0.0844 - val_mean_absolute_error: 0.1734 Epoch 161/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0303 - mean_absolute_error: 0.1281 - val_loss: 0.0848 - val_mean_absolute_error: 0.1725 Epoch 162/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0314 - mean_absolute_error: 0.1294 - val_loss: 0.0852 - val_mean_absolute_error: 0.1742 Epoch 163/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0310 - mean_absolute_error: 0.1289 - val_loss: 0.0827 - val_mean_absolute_error: 0.1727 Epoch 164/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0308 - mean_absolute_error: 0.1294 - val_loss: 0.0865 - val_mean_absolute_error: 0.1743 Epoch 165/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0314 - mean_absolute_error: 0.1297 - val_loss: 0.0858 - val_mean_absolute_error: 0.1754 Epoch 166/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0378 - mean_absolute_error: 0.1383 - val_loss: 0.0866 - val_mean_absolute_error: 0.1780 Epoch 167/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0320 - mean_absolute_error: 0.1316 - val_loss: 0.0825 - val_mean_absolute_error: 0.1723 Epoch 168/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0307 - mean_absolute_error: 0.1297 - val_loss: 0.0837 - val_mean_absolute_error: 0.1804 Epoch 169/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0322 - mean_absolute_error: 0.1322 - val_loss: 0.0831 - val_mean_absolute_error: 0.1730 Epoch 170/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0332 - mean_absolute_error: 0.1336 - val_loss: 0.0834 - val_mean_absolute_error: 0.1714 Epoch 171/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0329 - mean_absolute_error: 0.1314 - val_loss: 0.0929 - val_mean_absolute_error: 0.1790 Epoch 172/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0335 - mean_absolute_error: 0.1332 - val_loss: 0.0900 - val_mean_absolute_error: 0.1782 Epoch 173/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0317 - mean_absolute_error: 0.1309 - val_loss: 0.0834 - val_mean_absolute_error: 0.1733 Epoch 174/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0300 - mean_absolute_error: 0.1286 - val_loss: 0.0862 - val_mean_absolute_error: 0.1791 Epoch 175/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0322 - mean_absolute_error: 0.1335 - val_loss: 0.0860 - val_mean_absolute_error: 0.1716 Epoch 176/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0300 - mean_absolute_error: 0.1280 - val_loss: 0.0863 - val_mean_absolute_error: 0.1748 Epoch 177/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0316 - mean_absolute_error: 0.1297 - val_loss: 0.0876 - val_mean_absolute_error: 0.1761 Epoch 178/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0300 - mean_absolute_error: 0.1283 - val_loss: 0.0850 - val_mean_absolute_error: 0.1724 Epoch 179/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0300 - mean_absolute_error: 0.1280 - val_loss: 0.0892 - val_mean_absolute_error: 0.1774 Epoch 180/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0338 - mean_absolute_error: 0.1330 - val_loss: 0.0807 - val_mean_absolute_error: 0.1716 Epoch 181/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0305 - mean_absolute_error: 0.1291 - val_loss: 0.0900 - val_mean_absolute_error: 0.1772 Epoch 182/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0292 - mean_absolute_error: 0.1276 - val_loss: 0.0837 - val_mean_absolute_error: 0.1746 Epoch 183/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0301 - mean_absolute_error: 0.1287 - val_loss: 0.0879 - val_mean_absolute_error: 0.1717 Epoch 184/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0287 - mean_absolute_error: 0.1252 - val_loss: 0.0851 - val_mean_absolute_error: 0.1752 Epoch 185/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0313 - mean_absolute_error: 0.1310 - val_loss: 0.0858 - val_mean_absolute_error: 0.1735 Epoch 186/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0295 - mean_absolute_error: 0.1283 - val_loss: 0.0849 - val_mean_absolute_error: 0.1729 Epoch 187/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0315 - mean_absolute_error: 0.1309 - val_loss: 0.0867 - val_mean_absolute_error: 0.1757 Epoch 188/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0356 - mean_absolute_error: 0.1363 - val_loss: 0.0915 - val_mean_absolute_error: 0.1815 Epoch 189/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0350 - mean_absolute_error: 0.1332 - val_loss: 0.0869 - val_mean_absolute_error: 0.1724 Epoch 190/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0299 - mean_absolute_error: 0.1281 - val_loss: 0.0841 - val_mean_absolute_error: 0.1726 Epoch 191/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0283 - mean_absolute_error: 0.1251 - val_loss: 0.0827 - val_mean_absolute_error: 0.1712 Epoch 192/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0293 - mean_absolute_error: 0.1271 - val_loss: 0.0860 - val_mean_absolute_error: 0.1757 Epoch 193/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0294 - mean_absolute_error: 0.1265 - val_loss: 0.0845 - val_mean_absolute_error: 0.1720 Epoch 194/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0304 - mean_absolute_error: 0.1278 - val_loss: 0.0904 - val_mean_absolute_error: 0.1800 Epoch 195/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0315 - mean_absolute_error: 0.1302 - val_loss: 0.0915 - val_mean_absolute_error: 0.1788 Epoch 196/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0299 - mean_absolute_error: 0.1280 - val_loss: 0.0837 - val_mean_absolute_error: 0.1729 Epoch 197/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0299 - mean_absolute_error: 0.1280 - val_loss: 0.0837 - val_mean_absolute_error: 0.1695 Epoch 198/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0309 - mean_absolute_error: 0.1286 - val_loss: 0.0877 - val_mean_absolute_error: 0.1749 Epoch 199/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0310 - mean_absolute_error: 0.1290 - val_loss: 0.0840 - val_mean_absolute_error: 0.1741 Epoch 200/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0287 - mean_absolute_error: 0.1249 - val_loss: 0.0895 - val_mean_absolute_error: 0.1775 Epoch 201/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0297 - mean_absolute_error: 0.1282 - val_loss: 0.0866 - val_mean_absolute_error: 0.1749 Epoch 202/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0295 - mean_absolute_error: 0.1262 - val_loss: 0.0903 - val_mean_absolute_error: 0.1857 Epoch 203/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0292 - mean_absolute_error: 0.1261 - val_loss: 0.0823 - val_mean_absolute_error: 0.1708 Epoch 204/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0288 - mean_absolute_error: 0.1259 - val_loss: 0.0829 - val_mean_absolute_error: 0.1725 Epoch 205/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0293 - mean_absolute_error: 0.1266 - val_loss: 0.0871 - val_mean_absolute_error: 0.1743 Epoch 206/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0290 - mean_absolute_error: 0.1252 - val_loss: 0.0938 - val_mean_absolute_error: 0.1870 Epoch 207/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0309 - mean_absolute_error: 0.1298 - val_loss: 0.0845 - val_mean_absolute_error: 0.1705 Epoch 208/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0341 - mean_absolute_error: 0.1333 - val_loss: 0.0937 - val_mean_absolute_error: 0.1822 Epoch 209/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0326 - mean_absolute_error: 0.1305 - val_loss: 0.0872 - val_mean_absolute_error: 0.1714 Epoch 210/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0306 - mean_absolute_error: 0.1298 - val_loss: 0.0835 - val_mean_absolute_error: 0.1696 Epoch 211/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0333 - mean_absolute_error: 0.1302 - val_loss: 0.0863 - val_mean_absolute_error: 0.1711 Epoch 212/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0284 - mean_absolute_error: 0.1245 - val_loss: 0.0880 - val_mean_absolute_error: 0.1709 Epoch 213/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0271 - mean_absolute_error: 0.1226 - val_loss: 0.0834 - val_mean_absolute_error: 0.1740 Epoch 214/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0286 - mean_absolute_error: 0.1269 - val_loss: 0.0866 - val_mean_absolute_error: 0.1724 Epoch 215/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0284 - mean_absolute_error: 0.1247 - val_loss: 0.0916 - val_mean_absolute_error: 0.1765 Epoch 216/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0292 - mean_absolute_error: 0.1267 - val_loss: 0.0890 - val_mean_absolute_error: 0.1759 Epoch 217/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0285 - mean_absolute_error: 0.1255 - val_loss: 0.0861 - val_mean_absolute_error: 0.1729 Epoch 218/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0277 - mean_absolute_error: 0.1247 - val_loss: 0.0844 - val_mean_absolute_error: 0.1714 Epoch 219/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0363 - mean_absolute_error: 0.1341 - val_loss: 0.1008 - val_mean_absolute_error: 0.1855 Epoch 220/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0384 - mean_absolute_error: 0.1372 - val_loss: 0.0863 - val_mean_absolute_error: 0.1727 Epoch 221/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0294 - mean_absolute_error: 0.1258 - val_loss: 0.0856 - val_mean_absolute_error: 0.1733 Epoch 222/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0266 - mean_absolute_error: 0.1217 - val_loss: 0.0854 - val_mean_absolute_error: 0.1713 Epoch 223/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0272 - mean_absolute_error: 0.1225 - val_loss: 0.0840 - val_mean_absolute_error: 0.1687 Epoch 224/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0291 - mean_absolute_error: 0.1256 - val_loss: 0.0874 - val_mean_absolute_error: 0.1713 Epoch 225/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0269 - mean_absolute_error: 0.1225 - val_loss: 0.0822 - val_mean_absolute_error: 0.1707 Epoch 226/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0265 - mean_absolute_error: 0.1206 - val_loss: 0.0836 - val_mean_absolute_error: 0.1729 Epoch 227/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0277 - mean_absolute_error: 0.1226 - val_loss: 0.0834 - val_mean_absolute_error: 0.1701 Epoch 228/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0284 - mean_absolute_error: 0.1242 - val_loss: 0.0854 - val_mean_absolute_error: 0.1704 Epoch 229/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0303 - mean_absolute_error: 0.1275 - val_loss: 0.0856 - val_mean_absolute_error: 0.1722 Epoch 230/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0285 - mean_absolute_error: 0.1254 - val_loss: 0.0868 - val_mean_absolute_error: 0.1732 Epoch 231/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0268 - mean_absolute_error: 0.1217 - val_loss: 0.0891 - val_mean_absolute_error: 0.1744 Epoch 232/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0343 - mean_absolute_error: 0.1327 - val_loss: 0.0867 - val_mean_absolute_error: 0.1721 Epoch 233/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0316 - mean_absolute_error: 0.1294 - val_loss: 0.0850 - val_mean_absolute_error: 0.1708 Epoch 234/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0279 - mean_absolute_error: 0.1245 - val_loss: 0.0837 - val_mean_absolute_error: 0.1701 Epoch 235/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0295 - mean_absolute_error: 0.1261 - val_loss: 0.0865 - val_mean_absolute_error: 0.1699 Epoch 236/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0266 - mean_absolute_error: 0.1219 - val_loss: 0.0902 - val_mean_absolute_error: 0.1753 Epoch 237/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0297 - mean_absolute_error: 0.1281 - val_loss: 0.0883 - val_mean_absolute_error: 0.1740 Epoch 238/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0276 - mean_absolute_error: 0.1223 - val_loss: 0.0853 - val_mean_absolute_error: 0.1778 Epoch 239/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0296 - mean_absolute_error: 0.1276 - val_loss: 0.0833 - val_mean_absolute_error: 0.1700 Epoch 240/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0272 - mean_absolute_error: 0.1224 - val_loss: 0.0906 - val_mean_absolute_error: 0.1745 Epoch 241/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0305 - mean_absolute_error: 0.1283 - val_loss: 0.0869 - val_mean_absolute_error: 0.1749 Epoch 242/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0290 - mean_absolute_error: 0.1253 - val_loss: 0.0889 - val_mean_absolute_error: 0.1729 Epoch 243/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0267 - mean_absolute_error: 0.1211 - val_loss: 0.0866 - val_mean_absolute_error: 0.1721 Epoch 244/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0280 - mean_absolute_error: 0.1245 - val_loss: 0.0854 - val_mean_absolute_error: 0.1723 Epoch 245/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0278 - mean_absolute_error: 0.1236 - val_loss: 0.0837 - val_mean_absolute_error: 0.1686 Epoch 246/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0273 - mean_absolute_error: 0.1229 - val_loss: 0.0838 - val_mean_absolute_error: 0.1712 Epoch 247/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0394 - mean_absolute_error: 0.1359 - val_loss: 0.0871 - val_mean_absolute_error: 0.1707 Epoch 248/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0316 - mean_absolute_error: 0.1273 - val_loss: 0.0845 - val_mean_absolute_error: 0.1698 Epoch 249/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0276 - mean_absolute_error: 0.1219 - val_loss: 0.0858 - val_mean_absolute_error: 0.1715 Epoch 250/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0253 - mean_absolute_error: 0.1195 - val_loss: 0.0864 - val_mean_absolute_error: 0.1706 Epoch 251/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0263 - mean_absolute_error: 0.1209 - val_loss: 0.0858 - val_mean_absolute_error: 0.1697 Epoch 252/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0248 - mean_absolute_error: 0.1179 - val_loss: 0.0823 - val_mean_absolute_error: 0.1684 Epoch 253/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0283 - mean_absolute_error: 0.1233 - val_loss: 0.0862 - val_mean_absolute_error: 0.1721 Epoch 254/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0276 - mean_absolute_error: 0.1236 - val_loss: 0.0911 - val_mean_absolute_error: 0.1849 Epoch 255/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0266 - mean_absolute_error: 0.1224 - val_loss: 0.0859 - val_mean_absolute_error: 0.1705 Epoch 256/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0253 - mean_absolute_error: 0.1191 - val_loss: 0.0848 - val_mean_absolute_error: 0.1702 Epoch 257/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0257 - mean_absolute_error: 0.1200 - val_loss: 0.0852 - val_mean_absolute_error: 0.1728 Epoch 258/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0282 - mean_absolute_error: 0.1248 - val_loss: 0.0899 - val_mean_absolute_error: 0.1742 Epoch 259/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0315 - mean_absolute_error: 0.1289 - val_loss: 0.0860 - val_mean_absolute_error: 0.1703 Epoch 260/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0344 - mean_absolute_error: 0.1312 - val_loss: 0.0849 - val_mean_absolute_error: 0.1732 Epoch 261/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0268 - mean_absolute_error: 0.1220 - val_loss: 0.0918 - val_mean_absolute_error: 0.1908 Epoch 262/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0288 - mean_absolute_error: 0.1268 - val_loss: 0.0839 - val_mean_absolute_error: 0.1686 Epoch 263/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0261 - mean_absolute_error: 0.1196 - val_loss: 0.0875 - val_mean_absolute_error: 0.1718 Epoch 264/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0278 - mean_absolute_error: 0.1229 - val_loss: 0.0846 - val_mean_absolute_error: 0.1745 Epoch 265/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0267 - mean_absolute_error: 0.1218 - val_loss: 0.0853 - val_mean_absolute_error: 0.1710 Epoch 266/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0258 - mean_absolute_error: 0.1203 - val_loss: 0.0866 - val_mean_absolute_error: 0.1720 Epoch 267/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0273 - mean_absolute_error: 0.1223 - val_loss: 0.0889 - val_mean_absolute_error: 0.1729 Epoch 268/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0316 - mean_absolute_error: 0.1300 - val_loss: 0.0859 - val_mean_absolute_error: 0.1704 Epoch 269/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0267 - mean_absolute_error: 0.1208 - val_loss: 0.0862 - val_mean_absolute_error: 0.1727 Epoch 270/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0260 - mean_absolute_error: 0.1208 - val_loss: 0.0893 - val_mean_absolute_error: 0.1735 Epoch 271/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0258 - mean_absolute_error: 0.1197 - val_loss: 0.0851 - val_mean_absolute_error: 0.1683 Epoch 272/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0251 - mean_absolute_error: 0.1181 - val_loss: 0.0871 - val_mean_absolute_error: 0.1710 Epoch 273/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0251 - mean_absolute_error: 0.1186 - val_loss: 0.0849 - val_mean_absolute_error: 0.1712 Epoch 274/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0306 - mean_absolute_error: 0.1275 - val_loss: 0.0848 - val_mean_absolute_error: 0.1699 Epoch 275/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0243 - mean_absolute_error: 0.1174 - val_loss: 0.0857 - val_mean_absolute_error: 0.1746 Epoch 276/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0264 - mean_absolute_error: 0.1214 - val_loss: 0.0895 - val_mean_absolute_error: 0.1772 Epoch 277/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0268 - mean_absolute_error: 0.1215 - val_loss: 0.0883 - val_mean_absolute_error: 0.1726 Epoch 278/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0278 - mean_absolute_error: 0.1231 - val_loss: 0.0865 - val_mean_absolute_error: 0.1743 Epoch 279/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0289 - mean_absolute_error: 0.1248 - val_loss: 0.0967 - val_mean_absolute_error: 0.1790 Epoch 280/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0275 - mean_absolute_error: 0.1218 - val_loss: 0.0856 - val_mean_absolute_error: 0.1712 Epoch 281/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0264 - mean_absolute_error: 0.1220 - val_loss: 0.0856 - val_mean_absolute_error: 0.1717 Epoch 282/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0272 - mean_absolute_error: 0.1220 - val_loss: 0.0855 - val_mean_absolute_error: 0.1697 Epoch 283/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0294 - mean_absolute_error: 0.1266 - val_loss: 0.0849 - val_mean_absolute_error: 0.1682 Epoch 284/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0252 - mean_absolute_error: 0.1188 - val_loss: 0.0847 - val_mean_absolute_error: 0.1683 Epoch 285/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0251 - mean_absolute_error: 0.1177 - val_loss: 0.0869 - val_mean_absolute_error: 0.1741 Epoch 286/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0287 - mean_absolute_error: 0.1243 - val_loss: 0.0864 - val_mean_absolute_error: 0.1723 Epoch 287/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0279 - mean_absolute_error: 0.1225 - val_loss: 0.0903 - val_mean_absolute_error: 0.1718 Epoch 288/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0255 - mean_absolute_error: 0.1188 - val_loss: 0.0863 - val_mean_absolute_error: 0.1709 Epoch 289/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0247 - mean_absolute_error: 0.1172 - val_loss: 0.0870 - val_mean_absolute_error: 0.1734 Epoch 290/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0249 - mean_absolute_error: 0.1187 - val_loss: 0.0858 - val_mean_absolute_error: 0.1686 Epoch 291/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0246 - mean_absolute_error: 0.1180 - val_loss: 0.0850 - val_mean_absolute_error: 0.1701 Epoch 292/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0260 - mean_absolute_error: 0.1207 - val_loss: 0.0917 - val_mean_absolute_error: 0.1748 Epoch 293/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0277 - mean_absolute_error: 0.1221 - val_loss: 0.0851 - val_mean_absolute_error: 0.1693 Epoch 294/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0239 - mean_absolute_error: 0.1156 - val_loss: 0.0847 - val_mean_absolute_error: 0.1727 Epoch 295/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0256 - mean_absolute_error: 0.1200 - val_loss: 0.0890 - val_mean_absolute_error: 0.1713 Epoch 296/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0277 - mean_absolute_error: 0.1216 - val_loss: 0.0839 - val_mean_absolute_error: 0.1700 Epoch 297/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0270 - mean_absolute_error: 0.1221 - val_loss: 0.0848 - val_mean_absolute_error: 0.1714 Epoch 298/300 203/203 [==============================] - 1s 4ms/step - loss: 0.0272 - mean_absolute_error: 0.1224 - val_loss: 0.0851 - val_mean_absolute_error: 0.1684 Epoch 299/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0254 - mean_absolute_error: 0.1199 - val_loss: 0.0870 - val_mean_absolute_error: 0.1718 Epoch 300/300 203/203 [==============================] - 1s 5ms/step - loss: 0.0284 - mean_absolute_error: 0.1246 - val_loss: 0.0953 - val_mean_absolute_error: 0.1797
from IPython import display
display.IFrame(
src="https://tensorboard.dev/experiment/MVQyms8BSVym5wG2ETdDyA/",
width = "100%",
height="800px")