Fuel consumption prediction¶
Credit: AITS Cainvas Community
Photo by Tim Constantinov on Dribbble
Taking into account multiple factors such as distance, speed, temperatures inside and outside, AC, and other weather conditions to predict the consumption of different types of fuels during drives.
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras import models, optimizers, losses, layers, callbacks
from sklearn.metrics import confusion_matrix, auc, roc_auc_score, r2_score
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter("ignore")
The dataset¶
The dataset is a CSV file which was recorded by Andreas on everyday rides by noting factors such as weather (raining or warm), temperatures inside and outside, average speed etc. in addition to the distance travelled and corresponding fuel consumed per 100 km. The values are recorded for two different ypes of fuels - E10 and SP98.
(A similar dataset used for consumption prediction for any type of fuel)
df = pd.read_csv('https://cainvas-static.s3.amazonaws.com/media/user_data/cainvas-admin/measurements.csv')
df
distance | consume | speed | temp_inside | temp_outside | specials | gas_type | AC | rain | sun | refill liters | refill gas | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 28 | 5 | 26 | 21,5 | 12 | NaN | E10 | 0 | 0 | 0 | 45 | E10 |
1 | 12 | 4,2 | 30 | 21,5 | 13 | NaN | E10 | 0 | 0 | 0 | NaN | NaN |
2 | 11,2 | 5,5 | 38 | 21,5 | 15 | NaN | E10 | 0 | 0 | 0 | NaN | NaN |
3 | 12,9 | 3,9 | 36 | 21,5 | 14 | NaN | E10 | 0 | 0 | 0 | NaN | NaN |
4 | 18,5 | 4,5 | 46 | 21,5 | 15 | NaN | E10 | 0 | 0 | 0 | NaN | NaN |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
383 | 16 | 3,7 | 39 | 24,5 | 18 | NaN | SP98 | 0 | 0 | 0 | NaN | NaN |
384 | 16,1 | 4,3 | 38 | 25 | 31 | AC | SP98 | 1 | 0 | 0 | NaN | NaN |
385 | 16 | 3,8 | 45 | 25 | 19 | NaN | SP98 | 0 | 0 | 0 | NaN | NaN |
386 | 15,4 | 4,6 | 42 | 25 | 31 | AC | SP98 | 1 | 0 | 0 | NaN | NaN |
387 | 14,7 | 5 | 25 | 25 | 30 | AC | SP98 | 1 | 0 | 0 | NaN | NaN |
388 rows × 12 columns
Data preprocessing¶
Checking for NA values
df.isna().sum()
distance 0 consume 0 speed 0 temp_inside 12 temp_outside 0 specials 295 gas_type 0 AC 0 rain 0 sun 0 refill liters 375 refill gas 375 dtype: int64
Filling NA temp_inside column values with mode corresponding to each category.
df = df.sort_values('gas_type')
temp_inside = []
for t in df['gas_type'].unique():
temp_inside.extend(df[df['gas_type']==t]['temp_inside'].fillna(df[df['gas_type']==t]['temp_inside'].mode()[0]))
df['temp_inside'] = temp_inside
# df.isna().sum()
Filling NA 'refill liters' column values with '0'.
df['refill liters'] = df['refill liters'].fillna('0')
# df.isna().sum()
One hot encoding the 'refill liters' and 'gas_type' columns.
The NA values of the 'refill liters' column are encoded with 0 in each column.
Dropping unnecessary columns
df = pd.get_dummies(df, columns = ['refill gas'], dummy_na = True)
df = pd.get_dummies(df, columns = ['gas_type'], drop_first=True)
df = df.drop(columns = ['specials','refill gas_nan'], axis = 1)
df
distance | consume | speed | temp_inside | temp_outside | AC | rain | sun | refill liters | refill gas_E10 | refill gas_SP98 | gas_type_SP98 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 28 | 5 | 26 | 21,5 | 12 | 0 | 0 | 0 | 45 | 1 | 0 | 0 |
159 | 39,4 | 5,3 | 60 | 21,5 | 9 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
160 | 5,1 | 8,1 | 39 | 21,5 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
161 | 26,6 | 4,8 | 38 | 21,5 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
162 | 53,2 | 5,1 | 71 | 21,5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
133 | 11,8 | 4,5 | 43 | 21,5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
132 | 16,1 | 4,5 | 33 | 21,5 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
131 | 5,1 | 6,4 | 39 | 21,5 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
137 | 11,8 | 4,5 | 38 | 21,5 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
387 | 14,7 | 5 | 25 | 25 | 30 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
388 rows × 12 columns
Checking the datatypes
df.dtypes
distance object consume object speed int64 temp_inside object temp_outside int64 AC int64 rain int64 sun int64 refill liters object refill gas_E10 uint8 refill gas_SP98 uint8 gas_type_SP98 uint8 dtype: object
Values in certain columns have ',' in numeric values. In some instances, the decimal point is represented using ','s.
Replacing such values.
numeric_columns = ['distance','consume','temp_inside','refill liters'] # columns with ','
for column in numeric_columns:
#print(column)
x = [x.replace(',','.') for x in df[column].values]
df[column] = list(map(np.float32, x))
Extend it to include all numeric columns.
numeric_columns.extend(['speed','temp_outside'])
Change datatypes of the columns.
for column in df.columns:
if column in numeric_columns:
df[column] = df[column].astype('float32')
else:
df[column] = df[column].astype('int64')
Train val split¶
# Splitting into train and val set -- 90-10 split
train_df, val_df = train_test_split(df, test_size = 0.1)
print("Number of samples in...")
print("Training set: ", len(train_df))
print("Validation set: ", len(val_df))
Number of samples in... Training set: 349 Validation set: 39
Scaling the values¶
The values in the feature columns are not of the same range.
numeric_columns.remove('consume')
ss = StandardScaler()
train_df[numeric_columns] = ss.fit_transform(train_df[numeric_columns])
val_df[numeric_columns] = ss.transform(val_df[numeric_columns])
Defining the input and output columns¶
input_columns = df.columns.tolist()
input_columns.remove('consume')
output_columns = ['consume']
# Splitting into X (input) and y (output)
Xtrain, ytrain = np.array(train_df[input_columns]), np.array(train_df[output_columns])
Xval, yval = np.array(val_df[input_columns]), np.array(val_df[output_columns])
The model¶
model = models.Sequential([
layers.Dense(1024, activation = 'relu', input_shape = Xtrain[0].shape),
layers.Dense(512, activation = 'relu'),
layers.Dense(256, activation = 'relu'),
layers.Dense(64, activation = 'relu'),
layers.Dense(16, activation = 'relu'),
layers.Dense(1)
])
cb = callbacks.EarlyStopping(patience = 20, restore_best_weights = True)
model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 1024) 12288 _________________________________________________________________ dense_1 (Dense) (None, 512) 524800 _________________________________________________________________ dense_2 (Dense) (None, 256) 131328 _________________________________________________________________ dense_3 (Dense) (None, 64) 16448 _________________________________________________________________ dense_4 (Dense) (None, 16) 1040 _________________________________________________________________ dense_5 (Dense) (None, 1) 17 ================================================================= Total params: 685,921 Trainable params: 685,921 Non-trainable params: 0 _________________________________________________________________
model.compile(optimizer = optimizers.Adam(0.00001), loss = losses.MeanSquaredError(), metrics = ['mae'])
history = model.fit(Xtrain, ytrain, validation_data = (Xval, yval), epochs = 512, callbacks = cb)
Epoch 1/512 11/11 [==============================] - 0s 17ms/step - loss: 25.4798 - mae: 4.9373 - val_loss: 23.0147 - val_mae: 4.7295 Epoch 2/512 11/11 [==============================] - 0s 3ms/step - loss: 25.1533 - mae: 4.9042 - val_loss: 22.7279 - val_mae: 4.6994 Epoch 3/512 11/11 [==============================] - 0s 6ms/step - loss: 24.8117 - mae: 4.8700 - val_loss: 22.4388 - val_mae: 4.6687 Epoch 4/512 11/11 [==============================] - 0s 3ms/step - loss: 24.4842 - mae: 4.8362 - val_loss: 22.1565 - val_mae: 4.6383 Epoch 5/512 11/11 [==============================] - 0s 6ms/step - loss: 24.1718 - mae: 4.8038 - val_loss: 21.8762 - val_mae: 4.6079 Epoch 6/512 11/11 [==============================] - 0s 3ms/step - loss: 23.8520 - mae: 4.7704 - val_loss: 21.5993 - val_mae: 4.5777 Epoch 7/512 11/11 [==============================] - 0s 5ms/step - loss: 23.5277 - mae: 4.7359 - val_loss: 21.3126 - val_mae: 4.5463 Epoch 8/512 11/11 [==============================] - 0s 3ms/step - loss: 23.1956 - mae: 4.7008 - val_loss: 21.0075 - val_mae: 4.5126 Epoch 9/512 11/11 [==============================] - 0s 5ms/step - loss: 22.8426 - mae: 4.6625 - val_loss: 20.6882 - val_mae: 4.4769 Epoch 10/512 11/11 [==============================] - 0s 3ms/step - loss: 22.4756 - mae: 4.6230 - val_loss: 20.3499 - val_mae: 4.4387 Epoch 11/512 11/11 [==============================] - 0s 3ms/step - loss: 22.0888 - mae: 4.5804 - val_loss: 19.9913 - val_mae: 4.3979 Epoch 12/512 11/11 [==============================] - 0s 7ms/step - loss: 21.6731 - mae: 4.5342 - val_loss: 19.6161 - val_mae: 4.3546 Epoch 13/512 11/11 [==============================] - 0s 5ms/step - loss: 21.2405 - mae: 4.4856 - val_loss: 19.2138 - val_mae: 4.3076 Epoch 14/512 11/11 [==============================] - 0s 3ms/step - loss: 20.7747 - mae: 4.4330 - val_loss: 18.7879 - val_mae: 4.2572 Epoch 15/512 11/11 [==============================] - 0s 5ms/step - loss: 20.2818 - mae: 4.3756 - val_loss: 18.3360 - val_mae: 4.2029 Epoch 16/512 11/11 [==============================] - 0s 3ms/step - loss: 19.7588 - mae: 4.3132 - val_loss: 17.8531 - val_mae: 4.1440 Epoch 17/512 11/11 [==============================] - 0s 5ms/step - loss: 19.2080 - mae: 4.2471 - val_loss: 17.3416 - val_mae: 4.0805 Epoch 18/512 11/11 [==============================] - 0s 3ms/step - loss: 18.6323 - mae: 4.1762 - val_loss: 16.7984 - val_mae: 4.0118 Epoch 19/512 11/11 [==============================] - 0s 3ms/step - loss: 18.0182 - mae: 4.0992 - val_loss: 16.2335 - val_mae: 3.9390 Epoch 20/512 11/11 [==============================] - 0s 3ms/step - loss: 17.3775 - mae: 4.0163 - val_loss: 15.6452 - val_mae: 3.8614 Epoch 21/512 11/11 [==============================] - 0s 5ms/step - loss: 16.7235 - mae: 3.9304 - val_loss: 15.0272 - val_mae: 3.7780 Epoch 22/512 11/11 [==============================] - 0s 3ms/step - loss: 16.0376 - mae: 3.8368 - val_loss: 14.3897 - val_mae: 3.6895 Epoch 23/512 11/11 [==============================] - 0s 6ms/step - loss: 15.3251 - mae: 3.7361 - val_loss: 13.7373 - val_mae: 3.5964 Epoch 24/512 11/11 [==============================] - 0s 5ms/step - loss: 14.6097 - mae: 3.6307 - val_loss: 13.0605 - val_mae: 3.4967 Epoch 25/512 11/11 [==============================] - 0s 3ms/step - loss: 13.8742 - mae: 3.5231 - val_loss: 12.3676 - val_mae: 3.3911 Epoch 26/512 11/11 [==============================] - 0s 7ms/step - loss: 13.1260 - mae: 3.4096 - val_loss: 11.6672 - val_mae: 3.2802 Epoch 27/512 11/11 [==============================] - 0s 3ms/step - loss: 12.3719 - mae: 3.2951 - val_loss: 10.9652 - val_mae: 3.1646 Epoch 28/512 11/11 [==============================] - 0s 5ms/step - loss: 11.6156 - mae: 3.1738 - val_loss: 10.2663 - val_mae: 3.0441 Epoch 29/512 11/11 [==============================] - 0s 3ms/step - loss: 10.8949 - mae: 3.0561 - val_loss: 9.5568 - val_mae: 2.9160 Epoch 30/512 11/11 [==============================] - 0s 5ms/step - loss: 10.1570 - mae: 2.9316 - val_loss: 8.8756 - val_mae: 2.7867 Epoch 31/512 11/11 [==============================] - 0s 3ms/step - loss: 9.4616 - mae: 2.8148 - val_loss: 8.2103 - val_mae: 2.6644 Epoch 32/512 11/11 [==============================] - 0s 3ms/step - loss: 8.7902 - mae: 2.6989 - val_loss: 7.5768 - val_mae: 2.5417 Epoch 33/512 11/11 [==============================] - 0s 5ms/step - loss: 8.1557 - mae: 2.5835 - val_loss: 6.9825 - val_mae: 2.4219 Epoch 34/512 11/11 [==============================] - 0s 3ms/step - loss: 7.5736 - mae: 2.4809 - val_loss: 6.4169 - val_mae: 2.3092 Epoch 35/512 11/11 [==============================] - 0s 5ms/step - loss: 7.0227 - mae: 2.3780 - val_loss: 5.9063 - val_mae: 2.2015 Epoch 36/512 11/11 [==============================] - 0s 6ms/step - loss: 6.5316 - mae: 2.2790 - val_loss: 5.4339 - val_mae: 2.1153 Epoch 37/512 11/11 [==============================] - 0s 3ms/step - loss: 6.0669 - mae: 2.1819 - val_loss: 5.0199 - val_mae: 2.0379 Epoch 38/512 11/11 [==============================] - 0s 3ms/step - loss: 5.6759 - mae: 2.0916 - val_loss: 4.6352 - val_mae: 1.9595 Epoch 39/512 11/11 [==============================] - 0s 5ms/step - loss: 5.3077 - mae: 2.0005 - val_loss: 4.3040 - val_mae: 1.8846 Epoch 40/512 11/11 [==============================] - 0s 3ms/step - loss: 4.9835 - mae: 1.9218 - val_loss: 4.0092 - val_mae: 1.8188 Epoch 41/512 11/11 [==============================] - 0s 7ms/step - loss: 4.6964 - mae: 1.8526 - val_loss: 3.7546 - val_mae: 1.7587 Epoch 42/512 11/11 [==============================] - 0s 5ms/step - loss: 4.4324 - mae: 1.7880 - val_loss: 3.5385 - val_mae: 1.7029 Epoch 43/512 11/11 [==============================] - 0s 3ms/step - loss: 4.2084 - mae: 1.7318 - val_loss: 3.3487 - val_mae: 1.6493 Epoch 44/512 11/11 [==============================] - 0s 5ms/step - loss: 4.0033 - mae: 1.6775 - val_loss: 3.1846 - val_mae: 1.5990 Epoch 45/512 11/11 [==============================] - 0s 3ms/step - loss: 3.8220 - mae: 1.6295 - val_loss: 3.0327 - val_mae: 1.5484 Epoch 46/512 11/11 [==============================] - 0s 3ms/step - loss: 3.6620 - mae: 1.5818 - val_loss: 2.9041 - val_mae: 1.5016 Epoch 47/512 11/11 [==============================] - 0s 5ms/step - loss: 3.5101 - mae: 1.5409 - val_loss: 2.7988 - val_mae: 1.4627 Epoch 48/512 11/11 [==============================] - 0s 3ms/step - loss: 3.3752 - mae: 1.5041 - val_loss: 2.7067 - val_mae: 1.4320 Epoch 49/512 11/11 [==============================] - 0s 3ms/step - loss: 3.2571 - mae: 1.4715 - val_loss: 2.6221 - val_mae: 1.4100 Epoch 50/512 11/11 [==============================] - 0s 5ms/step - loss: 3.1440 - mae: 1.4400 - val_loss: 2.5515 - val_mae: 1.3905 Epoch 51/512 11/11 [==============================] - 0s 3ms/step - loss: 3.0387 - mae: 1.4115 - val_loss: 2.4879 - val_mae: 1.3719 Epoch 52/512 11/11 [==============================] - 0s 5ms/step - loss: 2.9458 - mae: 1.3875 - val_loss: 2.4323 - val_mae: 1.3546 Epoch 53/512 11/11 [==============================] - 0s 6ms/step - loss: 2.8598 - mae: 1.3638 - val_loss: 2.3792 - val_mae: 1.3368 Epoch 54/512 11/11 [==============================] - 0s 6ms/step - loss: 2.7852 - mae: 1.3425 - val_loss: 2.3363 - val_mae: 1.3227 Epoch 55/512 11/11 [==============================] - 0s 3ms/step - loss: 2.7156 - mae: 1.3227 - val_loss: 2.2953 - val_mae: 1.3113 Epoch 56/512 11/11 [==============================] - 0s 5ms/step - loss: 2.6446 - mae: 1.3030 - val_loss: 2.2604 - val_mae: 1.3014 Epoch 57/512 11/11 [==============================] - 0s 3ms/step - loss: 2.5875 - mae: 1.2867 - val_loss: 2.2299 - val_mae: 1.2923 Epoch 58/512 11/11 [==============================] - 0s 5ms/step - loss: 2.5323 - mae: 1.2731 - val_loss: 2.2023 - val_mae: 1.2835 Epoch 59/512 11/11 [==============================] - 0s 3ms/step - loss: 2.4800 - mae: 1.2590 - val_loss: 2.1714 - val_mae: 1.2732 Epoch 60/512 11/11 [==============================] - 0s 7ms/step - loss: 2.4303 - mae: 1.2450 - val_loss: 2.1470 - val_mae: 1.2646 Epoch 61/512 11/11 [==============================] - 0s 3ms/step - loss: 2.3898 - mae: 1.2329 - val_loss: 2.1255 - val_mae: 1.2562 Epoch 62/512 11/11 [==============================] - 0s 7ms/step - loss: 2.3476 - mae: 1.2231 - val_loss: 2.1043 - val_mae: 1.2479 Epoch 63/512 11/11 [==============================] - 0s 5ms/step - loss: 2.3113 - mae: 1.2134 - val_loss: 2.0831 - val_mae: 1.2390 Epoch 64/512 11/11 [==============================] - 0s 3ms/step - loss: 2.2740 - mae: 1.2034 - val_loss: 2.0664 - val_mae: 1.2318 Epoch 65/512 11/11 [==============================] - 0s 5ms/step - loss: 2.2417 - mae: 1.1942 - val_loss: 2.0492 - val_mae: 1.2241 Epoch 66/512 11/11 [==============================] - 0s 3ms/step - loss: 2.2081 - mae: 1.1844 - val_loss: 2.0313 - val_mae: 1.2159 Epoch 67/512 11/11 [==============================] - 0s 7ms/step - loss: 2.1796 - mae: 1.1764 - val_loss: 2.0171 - val_mae: 1.2094 Epoch 68/512 11/11 [==============================] - 0s 3ms/step - loss: 2.1483 - mae: 1.1664 - val_loss: 1.9983 - val_mae: 1.2009 Epoch 69/512 11/11 [==============================] - 0s 5ms/step - loss: 2.1235 - mae: 1.1583 - val_loss: 1.9812 - val_mae: 1.1940 Epoch 70/512 11/11 [==============================] - 0s 3ms/step - loss: 2.0963 - mae: 1.1498 - val_loss: 1.9647 - val_mae: 1.1874 Epoch 71/512 11/11 [==============================] - 0s 3ms/step - loss: 2.0732 - mae: 1.1425 - val_loss: 1.9491 - val_mae: 1.1815 Epoch 72/512 11/11 [==============================] - 0s 7ms/step - loss: 2.0497 - mae: 1.1354 - val_loss: 1.9322 - val_mae: 1.1750 Epoch 73/512 11/11 [==============================] - 0s 3ms/step - loss: 2.0257 - mae: 1.1287 - val_loss: 1.9193 - val_mae: 1.1696 Epoch 74/512 11/11 [==============================] - 0s 5ms/step - loss: 2.0068 - mae: 1.1238 - val_loss: 1.9036 - val_mae: 1.1632 Epoch 75/512 11/11 [==============================] - 0s 7ms/step - loss: 1.9860 - mae: 1.1181 - val_loss: 1.8914 - val_mae: 1.1582 Epoch 76/512 11/11 [==============================] - 0s 5ms/step - loss: 1.9670 - mae: 1.1131 - val_loss: 1.8760 - val_mae: 1.1522 Epoch 77/512 11/11 [==============================] - 0s 3ms/step - loss: 1.9491 - mae: 1.1085 - val_loss: 1.8639 - val_mae: 1.1470 Epoch 78/512 11/11 [==============================] - 0s 3ms/step - loss: 1.9300 - mae: 1.1034 - val_loss: 1.8498 - val_mae: 1.1415 Epoch 79/512 11/11 [==============================] - 0s 6ms/step - loss: 1.9118 - mae: 1.0986 - val_loss: 1.8360 - val_mae: 1.1359 Epoch 80/512 11/11 [==============================] - 0s 3ms/step - loss: 1.8970 - mae: 1.0941 - val_loss: 1.8224 - val_mae: 1.1306 Epoch 81/512 11/11 [==============================] - 0s 3ms/step - loss: 1.8798 - mae: 1.0906 - val_loss: 1.8094 - val_mae: 1.1261 Epoch 82/512 11/11 [==============================] - 0s 6ms/step - loss: 1.8616 - mae: 1.0854 - val_loss: 1.7953 - val_mae: 1.1225 Epoch 83/512 11/11 [==============================] - 0s 3ms/step - loss: 1.8463 - mae: 1.0810 - val_loss: 1.7817 - val_mae: 1.1184 Epoch 84/512 11/11 [==============================] - 0s 5ms/step - loss: 1.8306 - mae: 1.0770 - val_loss: 1.7670 - val_mae: 1.1145 Epoch 85/512 11/11 [==============================] - 0s 3ms/step - loss: 1.8175 - mae: 1.0735 - val_loss: 1.7532 - val_mae: 1.1100 Epoch 86/512 11/11 [==============================] - 0s 3ms/step - loss: 1.7999 - mae: 1.0686 - val_loss: 1.7408 - val_mae: 1.1066 Epoch 87/512 11/11 [==============================] - 0s 5ms/step - loss: 1.7862 - mae: 1.0644 - val_loss: 1.7321 - val_mae: 1.1039 Epoch 88/512 11/11 [==============================] - 0s 3ms/step - loss: 1.7693 - mae: 1.0596 - val_loss: 1.7167 - val_mae: 1.0993 Epoch 89/512 11/11 [==============================] - 0s 5ms/step - loss: 1.7549 - mae: 1.0554 - val_loss: 1.7038 - val_mae: 1.0954 Epoch 90/512 11/11 [==============================] - 0s 5ms/step - loss: 1.7417 - mae: 1.0513 - val_loss: 1.6920 - val_mae: 1.0918 Epoch 91/512 11/11 [==============================] - 0s 3ms/step - loss: 1.7274 - mae: 1.0466 - val_loss: 1.6784 - val_mae: 1.0872 Epoch 92/512 11/11 [==============================] - 0s 5ms/step - loss: 1.7133 - mae: 1.0424 - val_loss: 1.6648 - val_mae: 1.0828 Epoch 93/512 11/11 [==============================] - 0s 3ms/step - loss: 1.6991 - mae: 1.0381 - val_loss: 1.6524 - val_mae: 1.0793 Epoch 94/512 11/11 [==============================] - 0s 5ms/step - loss: 1.6866 - mae: 1.0341 - val_loss: 1.6406 - val_mae: 1.0752 Epoch 95/512 11/11 [==============================] - 0s 3ms/step - loss: 1.6724 - mae: 1.0300 - val_loss: 1.6279 - val_mae: 1.0713 Epoch 96/512 11/11 [==============================] - 0s 3ms/step - loss: 1.6602 - mae: 1.0260 - val_loss: 1.6174 - val_mae: 1.0675 Epoch 97/512 11/11 [==============================] - 0s 3ms/step - loss: 1.6471 - mae: 1.0218 - val_loss: 1.6047 - val_mae: 1.0635 Epoch 98/512 11/11 [==============================] - 0s 3ms/step - loss: 1.6343 - mae: 1.0176 - val_loss: 1.5922 - val_mae: 1.0595 Epoch 99/512 11/11 [==============================] - 0s 6ms/step - loss: 1.6222 - mae: 1.0136 - val_loss: 1.5813 - val_mae: 1.0564 Epoch 100/512 11/11 [==============================] - 0s 3ms/step - loss: 1.6104 - mae: 1.0099 - val_loss: 1.5674 - val_mae: 1.0510 Epoch 101/512 11/11 [==============================] - 0s 5ms/step - loss: 1.5984 - mae: 1.0060 - val_loss: 1.5588 - val_mae: 1.0484 Epoch 102/512 11/11 [==============================] - 0s 7ms/step - loss: 1.5849 - mae: 1.0015 - val_loss: 1.5498 - val_mae: 1.0457 Epoch 103/512 11/11 [==============================] - 0s 7ms/step - loss: 1.5741 - mae: 0.9979 - val_loss: 1.5388 - val_mae: 1.0414 Epoch 104/512 11/11 [==============================] - 0s 7ms/step - loss: 1.5615 - mae: 0.9937 - val_loss: 1.5288 - val_mae: 1.0385 Epoch 105/512 11/11 [==============================] - 0s 5ms/step - loss: 1.5505 - mae: 0.9895 - val_loss: 1.5143 - val_mae: 1.0329 Epoch 106/512 11/11 [==============================] - 0s 3ms/step - loss: 1.5380 - mae: 0.9856 - val_loss: 1.5040 - val_mae: 1.0297 Epoch 107/512 11/11 [==============================] - 0s 3ms/step - loss: 1.5262 - mae: 0.9818 - val_loss: 1.4945 - val_mae: 1.0262 Epoch 108/512 11/11 [==============================] - 0s 8ms/step - loss: 1.5151 - mae: 0.9783 - val_loss: 1.4827 - val_mae: 1.0219 Epoch 109/512 11/11 [==============================] - 0s 3ms/step - loss: 1.5043 - mae: 0.9747 - val_loss: 1.4736 - val_mae: 1.0190 Epoch 110/512 11/11 [==============================] - 0s 3ms/step - loss: 1.4938 - mae: 0.9717 - val_loss: 1.4602 - val_mae: 1.0136 Epoch 111/512 11/11 [==============================] - 0s 6ms/step - loss: 1.4820 - mae: 0.9676 - val_loss: 1.4552 - val_mae: 1.0122 Epoch 112/512 11/11 [==============================] - 0s 5ms/step - loss: 1.4723 - mae: 0.9642 - val_loss: 1.4405 - val_mae: 1.0065 Epoch 113/512 11/11 [==============================] - 0s 3ms/step - loss: 1.4611 - mae: 0.9606 - val_loss: 1.4308 - val_mae: 1.0031 Epoch 114/512 11/11 [==============================] - 0s 11ms/step - loss: 1.4499 - mae: 0.9561 - val_loss: 1.4231 - val_mae: 1.0002 Epoch 115/512 11/11 [==============================] - 0s 4ms/step - loss: 1.4400 - mae: 0.9527 - val_loss: 1.4142 - val_mae: 0.9971 Epoch 116/512 11/11 [==============================] - 0s 3ms/step - loss: 1.4296 - mae: 0.9490 - val_loss: 1.4037 - val_mae: 0.9930 Epoch 117/512 11/11 [==============================] - 0s 3ms/step - loss: 1.4199 - mae: 0.9455 - val_loss: 1.3931 - val_mae: 0.9887 Epoch 118/512 11/11 [==============================] - 0s 3ms/step - loss: 1.4097 - mae: 0.9414 - val_loss: 1.3885 - val_mae: 0.9873 Epoch 119/512 11/11 [==============================] - 0s 7ms/step - loss: 1.4001 - mae: 0.9380 - val_loss: 1.3764 - val_mae: 0.9832 Epoch 120/512 11/11 [==============================] - 0s 3ms/step - loss: 1.3893 - mae: 0.9347 - val_loss: 1.3659 - val_mae: 0.9793 Epoch 121/512 11/11 [==============================] - 0s 3ms/step - loss: 1.3797 - mae: 0.9311 - val_loss: 1.3557 - val_mae: 0.9761 Epoch 122/512 11/11 [==============================] - 0s 3ms/step - loss: 1.3703 - mae: 0.9274 - val_loss: 1.3465 - val_mae: 0.9729 Epoch 123/512 11/11 [==============================] - 0s 3ms/step - loss: 1.3609 - mae: 0.9237 - val_loss: 1.3377 - val_mae: 0.9699 Epoch 124/512 11/11 [==============================] - 0s 11ms/step - loss: 1.3516 - mae: 0.9197 - val_loss: 1.3295 - val_mae: 0.9671 Epoch 125/512 11/11 [==============================] - 0s 9ms/step - loss: 1.3429 - mae: 0.9166 - val_loss: 1.3222 - val_mae: 0.9648 Epoch 126/512 11/11 [==============================] - 0s 5ms/step - loss: 1.3334 - mae: 0.9132 - val_loss: 1.3131 - val_mae: 0.9617 Epoch 127/512 11/11 [==============================] - 0s 8ms/step - loss: 1.3242 - mae: 0.9095 - val_loss: 1.3044 - val_mae: 0.9584 Epoch 128/512 11/11 [==============================] - 0s 11ms/step - loss: 1.3159 - mae: 0.9065 - val_loss: 1.2942 - val_mae: 0.9547 Epoch 129/512 11/11 [==============================] - 0s 3ms/step - loss: 1.3072 - mae: 0.9032 - val_loss: 1.2906 - val_mae: 0.9538 Epoch 130/512 11/11 [==============================] - 0s 5ms/step - loss: 1.2981 - mae: 0.8994 - val_loss: 1.2820 - val_mae: 0.9507 Epoch 131/512 11/11 [==============================] - 0s 7ms/step - loss: 1.2908 - mae: 0.8971 - val_loss: 1.2716 - val_mae: 0.9462 Epoch 132/512 11/11 [==============================] - 0s 5ms/step - loss: 1.2816 - mae: 0.8936 - val_loss: 1.2647 - val_mae: 0.9443 Epoch 133/512 11/11 [==============================] - 0s 5ms/step - loss: 1.2740 - mae: 0.8896 - val_loss: 1.2585 - val_mae: 0.9412 Epoch 134/512 11/11 [==============================] - 0s 3ms/step - loss: 1.2649 - mae: 0.8864 - val_loss: 1.2486 - val_mae: 0.9380 Epoch 135/512 11/11 [==============================] - 0s 3ms/step - loss: 1.2578 - mae: 0.8843 - val_loss: 1.2388 - val_mae: 0.9344 Epoch 136/512 11/11 [==============================] - 0s 7ms/step - loss: 1.2491 - mae: 0.8814 - val_loss: 1.2325 - val_mae: 0.9320 Epoch 137/512 11/11 [==============================] - 0s 3ms/step - loss: 1.2407 - mae: 0.8777 - val_loss: 1.2263 - val_mae: 0.9295 Epoch 138/512 11/11 [==============================] - 0s 3ms/step - loss: 1.2338 - mae: 0.8747 - val_loss: 1.2177 - val_mae: 0.9265 Epoch 139/512 11/11 [==============================] - 0s 9ms/step - loss: 1.2257 - mae: 0.8716 - val_loss: 1.2130 - val_mae: 0.9244 Epoch 140/512 11/11 [==============================] - 0s 4ms/step - loss: 1.2177 - mae: 0.8685 - val_loss: 1.2065 - val_mae: 0.9218 Epoch 141/512 11/11 [==============================] - 0s 3ms/step - loss: 1.2099 - mae: 0.8655 - val_loss: 1.1998 - val_mae: 0.9188 Epoch 142/512 11/11 [==============================] - 0s 7ms/step - loss: 1.2033 - mae: 0.8619 - val_loss: 1.1906 - val_mae: 0.9151 Epoch 143/512 11/11 [==============================] - 0s 9ms/step - loss: 1.1959 - mae: 0.8594 - val_loss: 1.1828 - val_mae: 0.9129 Epoch 144/512 11/11 [==============================] - 0s 5ms/step - loss: 1.1881 - mae: 0.8567 - val_loss: 1.1765 - val_mae: 0.9097 Epoch 145/512 11/11 [==============================] - 0s 7ms/step - loss: 1.1809 - mae: 0.8541 - val_loss: 1.1689 - val_mae: 0.9066 Epoch 146/512 11/11 [==============================] - 0s 5ms/step - loss: 1.1733 - mae: 0.8505 - val_loss: 1.1635 - val_mae: 0.9040 Epoch 147/512 11/11 [==============================] - 0s 5ms/step - loss: 1.1663 - mae: 0.8476 - val_loss: 1.1564 - val_mae: 0.9012 Epoch 148/512 11/11 [==============================] - 0s 3ms/step - loss: 1.1598 - mae: 0.8449 - val_loss: 1.1500 - val_mae: 0.8989 Epoch 149/512 11/11 [==============================] - 0s 3ms/step - loss: 1.1538 - mae: 0.8432 - val_loss: 1.1430 - val_mae: 0.8957 Epoch 150/512 11/11 [==============================] - 0s 6ms/step - loss: 1.1454 - mae: 0.8400 - val_loss: 1.1393 - val_mae: 0.8939 Epoch 151/512 11/11 [==============================] - 0s 3ms/step - loss: 1.1397 - mae: 0.8378 - val_loss: 1.1310 - val_mae: 0.8904 Epoch 152/512 11/11 [==============================] - 0s 11ms/step - loss: 1.1320 - mae: 0.8344 - val_loss: 1.1269 - val_mae: 0.8889 Epoch 153/512 11/11 [==============================] - 0s 3ms/step - loss: 1.1256 - mae: 0.8312 - val_loss: 1.1210 - val_mae: 0.8865 Epoch 154/512 11/11 [==============================] - 0s 3ms/step - loss: 1.1191 - mae: 0.8289 - val_loss: 1.1130 - val_mae: 0.8828 Epoch 155/512 11/11 [==============================] - 0s 3ms/step - loss: 1.1137 - mae: 0.8269 - val_loss: 1.1041 - val_mae: 0.8794 Epoch 156/512 11/11 [==============================] - 0s 3ms/step - loss: 1.1054 - mae: 0.8235 - val_loss: 1.1007 - val_mae: 0.8779 Epoch 157/512 11/11 [==============================] - 0s 6ms/step - loss: 1.1006 - mae: 0.8214 - val_loss: 1.0982 - val_mae: 0.8764 Epoch 158/512 11/11 [==============================] - 0s 3ms/step - loss: 1.0931 - mae: 0.8182 - val_loss: 1.0903 - val_mae: 0.8734 Epoch 159/512 11/11 [==============================] - 0s 3ms/step - loss: 1.0873 - mae: 0.8170 - val_loss: 1.0830 - val_mae: 0.8703 Epoch 160/512 11/11 [==============================] - 0s 5ms/step - loss: 1.0809 - mae: 0.8142 - val_loss: 1.0796 - val_mae: 0.8686 Epoch 161/512 11/11 [==============================] - 0s 7ms/step - loss: 1.0743 - mae: 0.8110 - val_loss: 1.0735 - val_mae: 0.8659 Epoch 162/512 11/11 [==============================] - 0s 7ms/step - loss: 1.0722 - mae: 0.8108 - val_loss: 1.0661 - val_mae: 0.8631 Epoch 163/512 11/11 [==============================] - 0s 5ms/step - loss: 1.0619 - mae: 0.8063 - val_loss: 1.0610 - val_mae: 0.8601 Epoch 164/512 11/11 [==============================] - 0s 3ms/step - loss: 1.0557 - mae: 0.8032 - val_loss: 1.0570 - val_mae: 0.8583 Epoch 165/512 11/11 [==============================] - 0s 3ms/step - loss: 1.0496 - mae: 0.8010 - val_loss: 1.0512 - val_mae: 0.8556 Epoch 166/512 11/11 [==============================] - 0s 8ms/step - loss: 1.0442 - mae: 0.7990 - val_loss: 1.0450 - val_mae: 0.8524 Epoch 167/512 11/11 [==============================] - 0s 7ms/step - loss: 1.0384 - mae: 0.7964 - val_loss: 1.0383 - val_mae: 0.8493 Epoch 168/512 11/11 [==============================] - 0s 3ms/step - loss: 1.0317 - mae: 0.7938 - val_loss: 1.0323 - val_mae: 0.8468 Epoch 169/512 11/11 [==============================] - 0s 3ms/step - loss: 1.0266 - mae: 0.7918 - val_loss: 1.0279 - val_mae: 0.8446 Epoch 170/512 11/11 [==============================] - 0s 6ms/step - loss: 1.0202 - mae: 0.7893 - val_loss: 1.0246 - val_mae: 0.8432 Epoch 171/512 11/11 [==============================] - 0s 7ms/step - loss: 1.0151 - mae: 0.7871 - val_loss: 1.0206 - val_mae: 0.8409 Epoch 172/512 11/11 [==============================] - 0s 7ms/step - loss: 1.0091 - mae: 0.7839 - val_loss: 1.0153 - val_mae: 0.8387 Epoch 173/512 11/11 [==============================] - 0s 3ms/step - loss: 1.0033 - mae: 0.7819 - val_loss: 1.0091 - val_mae: 0.8355 Epoch 174/512 11/11 [==============================] - 0s 5ms/step - loss: 0.9970 - mae: 0.7786 - val_loss: 1.0040 - val_mae: 0.8330 Epoch 175/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9915 - mae: 0.7766 - val_loss: 1.0006 - val_mae: 0.8310 Epoch 176/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9850 - mae: 0.7729 - val_loss: 0.9962 - val_mae: 0.8286 Epoch 177/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9793 - mae: 0.7706 - val_loss: 0.9879 - val_mae: 0.8247 Epoch 178/512 11/11 [==============================] - 0s 5ms/step - loss: 0.9740 - mae: 0.7688 - val_loss: 0.9833 - val_mae: 0.8225 Epoch 179/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9683 - mae: 0.7659 - val_loss: 0.9823 - val_mae: 0.8212 Epoch 180/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9625 - mae: 0.7628 - val_loss: 0.9765 - val_mae: 0.8184 Epoch 181/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9577 - mae: 0.7605 - val_loss: 0.9720 - val_mae: 0.8161 Epoch 182/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9531 - mae: 0.7579 - val_loss: 0.9683 - val_mae: 0.8134 Epoch 183/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9465 - mae: 0.7548 - val_loss: 0.9630 - val_mae: 0.8113 Epoch 184/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9416 - mae: 0.7535 - val_loss: 0.9570 - val_mae: 0.8082 Epoch 185/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9384 - mae: 0.7510 - val_loss: 0.9529 - val_mae: 0.8055 Epoch 186/512 11/11 [==============================] - 0s 5ms/step - loss: 0.9312 - mae: 0.7478 - val_loss: 0.9460 - val_mae: 0.8016 Epoch 187/512 11/11 [==============================] - 0s 7ms/step - loss: 0.9265 - mae: 0.7462 - val_loss: 0.9447 - val_mae: 0.8014 Epoch 188/512 11/11 [==============================] - 0s 7ms/step - loss: 0.9210 - mae: 0.7441 - val_loss: 0.9413 - val_mae: 0.7994 Epoch 189/512 11/11 [==============================] - 0s 5ms/step - loss: 0.9167 - mae: 0.7420 - val_loss: 0.9377 - val_mae: 0.7967 Epoch 190/512 11/11 [==============================] - 0s 5ms/step - loss: 0.9116 - mae: 0.7393 - val_loss: 0.9328 - val_mae: 0.7939 Epoch 191/512 11/11 [==============================] - 0s 3ms/step - loss: 0.9074 - mae: 0.7363 - val_loss: 0.9291 - val_mae: 0.7912 Epoch 192/512 11/11 [==============================] - 0s 5ms/step - loss: 0.9027 - mae: 0.7349 - val_loss: 0.9215 - val_mae: 0.7877 Epoch 193/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8975 - mae: 0.7328 - val_loss: 0.9234 - val_mae: 0.7881 Epoch 194/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8927 - mae: 0.7299 - val_loss: 0.9221 - val_mae: 0.7864 Epoch 195/512 11/11 [==============================] - 0s 7ms/step - loss: 0.8878 - mae: 0.7271 - val_loss: 0.9160 - val_mae: 0.7835 Epoch 196/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8834 - mae: 0.7256 - val_loss: 0.9106 - val_mae: 0.7804 Epoch 197/512 11/11 [==============================] - 0s 7ms/step - loss: 0.8781 - mae: 0.7233 - val_loss: 0.9086 - val_mae: 0.7789 Epoch 198/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8742 - mae: 0.7205 - val_loss: 0.9052 - val_mae: 0.7767 Epoch 199/512 11/11 [==============================] - 0s 5ms/step - loss: 0.8708 - mae: 0.7176 - val_loss: 0.9013 - val_mae: 0.7736 Epoch 200/512 11/11 [==============================] - 0s 5ms/step - loss: 0.8663 - mae: 0.7167 - val_loss: 0.8908 - val_mae: 0.7679 Epoch 201/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8603 - mae: 0.7145 - val_loss: 0.8928 - val_mae: 0.7687 Epoch 202/512 11/11 [==============================] - 0s 5ms/step - loss: 0.8563 - mae: 0.7116 - val_loss: 0.8906 - val_mae: 0.7665 Epoch 203/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8526 - mae: 0.7093 - val_loss: 0.8883 - val_mae: 0.7648 Epoch 204/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8485 - mae: 0.7083 - val_loss: 0.8821 - val_mae: 0.7618 Epoch 205/512 11/11 [==============================] - 0s 7ms/step - loss: 0.8438 - mae: 0.7062 - val_loss: 0.8841 - val_mae: 0.7621 Epoch 206/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8396 - mae: 0.7044 - val_loss: 0.8764 - val_mae: 0.7576 Epoch 207/512 11/11 [==============================] - 0s 7ms/step - loss: 0.8350 - mae: 0.7014 - val_loss: 0.8754 - val_mae: 0.7560 Epoch 208/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8308 - mae: 0.6991 - val_loss: 0.8736 - val_mae: 0.7542 Epoch 209/512 11/11 [==============================] - 0s 5ms/step - loss: 0.8265 - mae: 0.6966 - val_loss: 0.8683 - val_mae: 0.7514 Epoch 210/512 11/11 [==============================] - 0s 6ms/step - loss: 0.8242 - mae: 0.6954 - val_loss: 0.8684 - val_mae: 0.7498 Epoch 211/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8182 - mae: 0.6931 - val_loss: 0.8622 - val_mae: 0.7473 Epoch 212/512 11/11 [==============================] - 0s 5ms/step - loss: 0.8148 - mae: 0.6909 - val_loss: 0.8587 - val_mae: 0.7452 Epoch 213/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8096 - mae: 0.6886 - val_loss: 0.8560 - val_mae: 0.7433 Epoch 214/512 11/11 [==============================] - 0s 6ms/step - loss: 0.8071 - mae: 0.6866 - val_loss: 0.8554 - val_mae: 0.7416 Epoch 215/512 11/11 [==============================] - 0s 3ms/step - loss: 0.8037 - mae: 0.6858 - val_loss: 0.8471 - val_mae: 0.7385 Epoch 216/512 11/11 [==============================] - 0s 5ms/step - loss: 0.7976 - mae: 0.6829 - val_loss: 0.8503 - val_mae: 0.7389 Epoch 217/512 11/11 [==============================] - 0s 4ms/step - loss: 0.7945 - mae: 0.6802 - val_loss: 0.8477 - val_mae: 0.7367 Epoch 218/512 11/11 [==============================] - 0s 4ms/step - loss: 0.7910 - mae: 0.6789 - val_loss: 0.8405 - val_mae: 0.7331 Epoch 219/512 11/11 [==============================] - 0s 7ms/step - loss: 0.7866 - mae: 0.6760 - val_loss: 0.8376 - val_mae: 0.7311 Epoch 220/512 11/11 [==============================] - 0s 5ms/step - loss: 0.7829 - mae: 0.6736 - val_loss: 0.8363 - val_mae: 0.7295 Epoch 221/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7791 - mae: 0.6726 - val_loss: 0.8318 - val_mae: 0.7280 Epoch 222/512 11/11 [==============================] - 0s 7ms/step - loss: 0.7763 - mae: 0.6709 - val_loss: 0.8348 - val_mae: 0.7280 Epoch 223/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7717 - mae: 0.6691 - val_loss: 0.8281 - val_mae: 0.7252 Epoch 224/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7687 - mae: 0.6680 - val_loss: 0.8270 - val_mae: 0.7249 Epoch 225/512 11/11 [==============================] - 0s 5ms/step - loss: 0.7651 - mae: 0.6654 - val_loss: 0.8246 - val_mae: 0.7217 Epoch 226/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7608 - mae: 0.6632 - val_loss: 0.8212 - val_mae: 0.7212 Epoch 227/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7575 - mae: 0.6621 - val_loss: 0.8186 - val_mae: 0.7203 Epoch 228/512 11/11 [==============================] - 0s 6ms/step - loss: 0.7562 - mae: 0.6606 - val_loss: 0.8197 - val_mae: 0.7180 Epoch 229/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7567 - mae: 0.6614 - val_loss: 0.8108 - val_mae: 0.7189 Epoch 230/512 11/11 [==============================] - 0s 5ms/step - loss: 0.7499 - mae: 0.6565 - val_loss: 0.8167 - val_mae: 0.7140 Epoch 231/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7438 - mae: 0.6534 - val_loss: 0.8108 - val_mae: 0.7136 Epoch 232/512 11/11 [==============================] - 0s 5ms/step - loss: 0.7403 - mae: 0.6528 - val_loss: 0.8077 - val_mae: 0.7144 Epoch 233/512 11/11 [==============================] - 0s 4ms/step - loss: 0.7382 - mae: 0.6518 - val_loss: 0.8055 - val_mae: 0.7123 Epoch 234/512 11/11 [==============================] - 0s 6ms/step - loss: 0.7351 - mae: 0.6504 - val_loss: 0.7999 - val_mae: 0.7128 Epoch 235/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7302 - mae: 0.6485 - val_loss: 0.8000 - val_mae: 0.7097 Epoch 236/512 11/11 [==============================] - 0s 6ms/step - loss: 0.7279 - mae: 0.6471 - val_loss: 0.7960 - val_mae: 0.7093 Epoch 237/512 11/11 [==============================] - 0s 4ms/step - loss: 0.7246 - mae: 0.6441 - val_loss: 0.7981 - val_mae: 0.7061 Epoch 238/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7209 - mae: 0.6414 - val_loss: 0.7927 - val_mae: 0.7053 Epoch 239/512 11/11 [==============================] - 0s 6ms/step - loss: 0.7182 - mae: 0.6409 - val_loss: 0.7876 - val_mae: 0.7048 Epoch 240/512 11/11 [==============================] - 0s 4ms/step - loss: 0.7152 - mae: 0.6400 - val_loss: 0.7875 - val_mae: 0.7070 Epoch 241/512 11/11 [==============================] - 0s 4ms/step - loss: 0.7120 - mae: 0.6385 - val_loss: 0.7871 - val_mae: 0.7029 Epoch 242/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7102 - mae: 0.6371 - val_loss: 0.7840 - val_mae: 0.7051 Epoch 243/512 11/11 [==============================] - 0s 3ms/step - loss: 0.7059 - mae: 0.6346 - val_loss: 0.7878 - val_mae: 0.7004 Epoch 244/512 11/11 [==============================] - 0s 5ms/step - loss: 0.7023 - mae: 0.6323 - val_loss: 0.7824 - val_mae: 0.6996 Epoch 245/512 11/11 [==============================] - 0s 5ms/step - loss: 0.7009 - mae: 0.6328 - val_loss: 0.7769 - val_mae: 0.7024 Epoch 246/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6974 - mae: 0.6325 - val_loss: 0.7751 - val_mae: 0.7002 Epoch 247/512 11/11 [==============================] - 0s 7ms/step - loss: 0.6934 - mae: 0.6296 - val_loss: 0.7779 - val_mae: 0.6970 Epoch 248/512 11/11 [==============================] - 0s 5ms/step - loss: 0.6926 - mae: 0.6268 - val_loss: 0.7790 - val_mae: 0.6964 Epoch 249/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6906 - mae: 0.6274 - val_loss: 0.7660 - val_mae: 0.6953 Epoch 250/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6849 - mae: 0.6234 - val_loss: 0.7685 - val_mae: 0.6927 Epoch 251/512 11/11 [==============================] - 0s 2ms/step - loss: 0.6827 - mae: 0.6223 - val_loss: 0.7682 - val_mae: 0.6926 Epoch 252/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6796 - mae: 0.6214 - val_loss: 0.7644 - val_mae: 0.6919 Epoch 253/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6773 - mae: 0.6200 - val_loss: 0.7645 - val_mae: 0.6909 Epoch 254/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6740 - mae: 0.6179 - val_loss: 0.7615 - val_mae: 0.6902 Epoch 255/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6741 - mae: 0.6169 - val_loss: 0.7618 - val_mae: 0.6885 Epoch 256/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6709 - mae: 0.6155 - val_loss: 0.7517 - val_mae: 0.6894 Epoch 257/512 11/11 [==============================] - 0s 2ms/step - loss: 0.6671 - mae: 0.6163 - val_loss: 0.7601 - val_mae: 0.6878 Epoch 258/512 11/11 [==============================] - 0s 4ms/step - loss: 0.6631 - mae: 0.6123 - val_loss: 0.7587 - val_mae: 0.6859 Epoch 259/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6617 - mae: 0.6098 - val_loss: 0.7570 - val_mae: 0.6846 Epoch 260/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6578 - mae: 0.6085 - val_loss: 0.7499 - val_mae: 0.6845 Epoch 261/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6558 - mae: 0.6089 - val_loss: 0.7494 - val_mae: 0.6849 Epoch 262/512 11/11 [==============================] - 0s 5ms/step - loss: 0.6538 - mae: 0.6075 - val_loss: 0.7521 - val_mae: 0.6837 Epoch 263/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6521 - mae: 0.6063 - val_loss: 0.7517 - val_mae: 0.6828 Epoch 264/512 11/11 [==============================] - 0s 4ms/step - loss: 0.6492 - mae: 0.6038 - val_loss: 0.7503 - val_mae: 0.6805 Epoch 265/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6474 - mae: 0.6040 - val_loss: 0.7444 - val_mae: 0.6813 Epoch 266/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6441 - mae: 0.6022 - val_loss: 0.7408 - val_mae: 0.6807 Epoch 267/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6413 - mae: 0.6002 - val_loss: 0.7405 - val_mae: 0.6767 Epoch 268/512 11/11 [==============================] - 0s 5ms/step - loss: 0.6385 - mae: 0.5986 - val_loss: 0.7420 - val_mae: 0.6772 Epoch 269/512 11/11 [==============================] - 0s 7ms/step - loss: 0.6363 - mae: 0.5967 - val_loss: 0.7416 - val_mae: 0.6772 Epoch 270/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6367 - mae: 0.5978 - val_loss: 0.7350 - val_mae: 0.6783 Epoch 271/512 11/11 [==============================] - 0s 5ms/step - loss: 0.6331 - mae: 0.5951 - val_loss: 0.7406 - val_mae: 0.6740 Epoch 272/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6314 - mae: 0.5921 - val_loss: 0.7369 - val_mae: 0.6722 Epoch 273/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6303 - mae: 0.5946 - val_loss: 0.7311 - val_mae: 0.6784 Epoch 274/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6255 - mae: 0.5921 - val_loss: 0.7337 - val_mae: 0.6720 Epoch 275/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6229 - mae: 0.5900 - val_loss: 0.7312 - val_mae: 0.6697 Epoch 276/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6209 - mae: 0.5883 - val_loss: 0.7316 - val_mae: 0.6701 Epoch 277/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6198 - mae: 0.5872 - val_loss: 0.7340 - val_mae: 0.6702 Epoch 278/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6183 - mae: 0.5856 - val_loss: 0.7304 - val_mae: 0.6692 Epoch 279/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6144 - mae: 0.5841 - val_loss: 0.7236 - val_mae: 0.6691 Epoch 280/512 11/11 [==============================] - 0s 4ms/step - loss: 0.6135 - mae: 0.5861 - val_loss: 0.7227 - val_mae: 0.6714 Epoch 281/512 11/11 [==============================] - 0s 5ms/step - loss: 0.6109 - mae: 0.5855 - val_loss: 0.7278 - val_mae: 0.6691 Epoch 282/512 11/11 [==============================] - 0s 7ms/step - loss: 0.6082 - mae: 0.5825 - val_loss: 0.7282 - val_mae: 0.6662 Epoch 283/512 11/11 [==============================] - 0s 6ms/step - loss: 0.6076 - mae: 0.5814 - val_loss: 0.7235 - val_mae: 0.6684 Epoch 284/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6043 - mae: 0.5807 - val_loss: 0.7234 - val_mae: 0.6659 Epoch 285/512 11/11 [==============================] - 0s 4ms/step - loss: 0.6025 - mae: 0.5789 - val_loss: 0.7230 - val_mae: 0.6659 Epoch 286/512 11/11 [==============================] - 0s 3ms/step - loss: 0.6013 - mae: 0.5772 - val_loss: 0.7203 - val_mae: 0.6629 Epoch 287/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5997 - mae: 0.5770 - val_loss: 0.7177 - val_mae: 0.6648 Epoch 288/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5967 - mae: 0.5761 - val_loss: 0.7166 - val_mae: 0.6639 Epoch 289/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5953 - mae: 0.5739 - val_loss: 0.7166 - val_mae: 0.6613 Epoch 290/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5934 - mae: 0.5733 - val_loss: 0.7130 - val_mae: 0.6636 Epoch 291/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5914 - mae: 0.5733 - val_loss: 0.7159 - val_mae: 0.6634 Epoch 292/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5907 - mae: 0.5731 - val_loss: 0.7152 - val_mae: 0.6623 Epoch 293/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5880 - mae: 0.5699 - val_loss: 0.7148 - val_mae: 0.6592 Epoch 294/512 11/11 [==============================] - 0s 7ms/step - loss: 0.5854 - mae: 0.5685 - val_loss: 0.7097 - val_mae: 0.6625 Epoch 295/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5851 - mae: 0.5680 - val_loss: 0.7109 - val_mae: 0.6585 Epoch 296/512 11/11 [==============================] - 0s 7ms/step - loss: 0.5826 - mae: 0.5670 - val_loss: 0.7058 - val_mae: 0.6635 Epoch 297/512 11/11 [==============================] - 0s 4ms/step - loss: 0.5808 - mae: 0.5676 - val_loss: 0.7060 - val_mae: 0.6616 Epoch 298/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5781 - mae: 0.5656 - val_loss: 0.7089 - val_mae: 0.6589 Epoch 299/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5776 - mae: 0.5639 - val_loss: 0.7076 - val_mae: 0.6562 Epoch 300/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5790 - mae: 0.5628 - val_loss: 0.7073 - val_mae: 0.6552 Epoch 301/512 11/11 [==============================] - 0s 4ms/step - loss: 0.5759 - mae: 0.5624 - val_loss: 0.6996 - val_mae: 0.6620 Epoch 302/512 11/11 [==============================] - 0s 4ms/step - loss: 0.5768 - mae: 0.5622 - val_loss: 0.7046 - val_mae: 0.6544 Epoch 303/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5699 - mae: 0.5580 - val_loss: 0.6985 - val_mae: 0.6563 Epoch 304/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5690 - mae: 0.5599 - val_loss: 0.6989 - val_mae: 0.6589 Epoch 305/512 11/11 [==============================] - 0s 6ms/step - loss: 0.5671 - mae: 0.5586 - val_loss: 0.7014 - val_mae: 0.6554 Epoch 306/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5657 - mae: 0.5570 - val_loss: 0.6996 - val_mae: 0.6552 Epoch 307/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5647 - mae: 0.5576 - val_loss: 0.6949 - val_mae: 0.6569 Epoch 308/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5626 - mae: 0.5566 - val_loss: 0.6980 - val_mae: 0.6536 Epoch 309/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5608 - mae: 0.5538 - val_loss: 0.6967 - val_mae: 0.6529 Epoch 310/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5596 - mae: 0.5534 - val_loss: 0.6959 - val_mae: 0.6556 Epoch 311/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5584 - mae: 0.5538 - val_loss: 0.6959 - val_mae: 0.6538 Epoch 312/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5570 - mae: 0.5513 - val_loss: 0.6952 - val_mae: 0.6481 Epoch 313/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5574 - mae: 0.5517 - val_loss: 0.6918 - val_mae: 0.6533 Epoch 314/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5545 - mae: 0.5502 - val_loss: 0.6893 - val_mae: 0.6525 Epoch 315/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5513 - mae: 0.5494 - val_loss: 0.6920 - val_mae: 0.6497 Epoch 316/512 11/11 [==============================] - 0s 2ms/step - loss: 0.5524 - mae: 0.5479 - val_loss: 0.6906 - val_mae: 0.6498 Epoch 317/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5499 - mae: 0.5481 - val_loss: 0.6874 - val_mae: 0.6498 Epoch 318/512 11/11 [==============================] - 0s 9ms/step - loss: 0.5485 - mae: 0.5476 - val_loss: 0.6856 - val_mae: 0.6503 Epoch 319/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5485 - mae: 0.5451 - val_loss: 0.6926 - val_mae: 0.6461 Epoch 320/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5453 - mae: 0.5429 - val_loss: 0.6835 - val_mae: 0.6466 Epoch 321/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5437 - mae: 0.5436 - val_loss: 0.6811 - val_mae: 0.6510 Epoch 322/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5439 - mae: 0.5437 - val_loss: 0.6873 - val_mae: 0.6483 Epoch 323/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5410 - mae: 0.5432 - val_loss: 0.6863 - val_mae: 0.6482 Epoch 324/512 11/11 [==============================] - 0s 7ms/step - loss: 0.5393 - mae: 0.5423 - val_loss: 0.6822 - val_mae: 0.6455 Epoch 325/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5378 - mae: 0.5413 - val_loss: 0.6804 - val_mae: 0.6453 Epoch 326/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5381 - mae: 0.5413 - val_loss: 0.6803 - val_mae: 0.6473 Epoch 327/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5354 - mae: 0.5389 - val_loss: 0.6822 - val_mae: 0.6426 Epoch 328/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5349 - mae: 0.5374 - val_loss: 0.6792 - val_mae: 0.6419 Epoch 329/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5351 - mae: 0.5381 - val_loss: 0.6784 - val_mae: 0.6435 Epoch 330/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5322 - mae: 0.5364 - val_loss: 0.6782 - val_mae: 0.6426 Epoch 331/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5328 - mae: 0.5386 - val_loss: 0.6753 - val_mae: 0.6457 Epoch 332/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5307 - mae: 0.5360 - val_loss: 0.6776 - val_mae: 0.6400 Epoch 333/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5297 - mae: 0.5355 - val_loss: 0.6762 - val_mae: 0.6435 Epoch 334/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5268 - mae: 0.5337 - val_loss: 0.6762 - val_mae: 0.6423 Epoch 335/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5265 - mae: 0.5322 - val_loss: 0.6718 - val_mae: 0.6405 Epoch 336/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5253 - mae: 0.5321 - val_loss: 0.6746 - val_mae: 0.6389 Epoch 337/512 11/11 [==============================] - 0s 2ms/step - loss: 0.5231 - mae: 0.5317 - val_loss: 0.6728 - val_mae: 0.6417 Epoch 338/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5225 - mae: 0.5318 - val_loss: 0.6716 - val_mae: 0.6419 Epoch 339/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5241 - mae: 0.5304 - val_loss: 0.6721 - val_mae: 0.6341 Epoch 340/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5215 - mae: 0.5305 - val_loss: 0.6665 - val_mae: 0.6406 Epoch 341/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5194 - mae: 0.5299 - val_loss: 0.6712 - val_mae: 0.6398 Epoch 342/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5180 - mae: 0.5282 - val_loss: 0.6733 - val_mae: 0.6363 Epoch 343/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5164 - mae: 0.5273 - val_loss: 0.6704 - val_mae: 0.6394 Epoch 344/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5154 - mae: 0.5272 - val_loss: 0.6701 - val_mae: 0.6382 Epoch 345/512 11/11 [==============================] - 0s 6ms/step - loss: 0.5143 - mae: 0.5256 - val_loss: 0.6693 - val_mae: 0.6349 Epoch 346/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5136 - mae: 0.5243 - val_loss: 0.6661 - val_mae: 0.6351 Epoch 347/512 11/11 [==============================] - 0s 5ms/step - loss: 0.5124 - mae: 0.5260 - val_loss: 0.6648 - val_mae: 0.6373 Epoch 348/512 11/11 [==============================] - 0s 7ms/step - loss: 0.5119 - mae: 0.5239 - val_loss: 0.6674 - val_mae: 0.6324 Epoch 349/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5095 - mae: 0.5227 - val_loss: 0.6662 - val_mae: 0.6342 Epoch 350/512 11/11 [==============================] - 0s 6ms/step - loss: 0.5087 - mae: 0.5217 - val_loss: 0.6646 - val_mae: 0.6362 Epoch 351/512 11/11 [==============================] - 0s 7ms/step - loss: 0.5098 - mae: 0.5217 - val_loss: 0.6614 - val_mae: 0.6339 Epoch 352/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5071 - mae: 0.5199 - val_loss: 0.6654 - val_mae: 0.6319 Epoch 353/512 11/11 [==============================] - 0s 6ms/step - loss: 0.5067 - mae: 0.5215 - val_loss: 0.6607 - val_mae: 0.6339 Epoch 354/512 11/11 [==============================] - 0s 4ms/step - loss: 0.5058 - mae: 0.5196 - val_loss: 0.6661 - val_mae: 0.6303 Epoch 355/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5032 - mae: 0.5177 - val_loss: 0.6613 - val_mae: 0.6316 Epoch 356/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5037 - mae: 0.5202 - val_loss: 0.6594 - val_mae: 0.6339 Epoch 357/512 11/11 [==============================] - 0s 3ms/step - loss: 0.5033 - mae: 0.5192 - val_loss: 0.6633 - val_mae: 0.6280 Epoch 358/512 11/11 [==============================] - 0s 6ms/step - loss: 0.5012 - mae: 0.5176 - val_loss: 0.6608 - val_mae: 0.6319 Epoch 359/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4993 - mae: 0.5162 - val_loss: 0.6614 - val_mae: 0.6300 Epoch 360/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4992 - mae: 0.5145 - val_loss: 0.6589 - val_mae: 0.6288 Epoch 361/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4990 - mae: 0.5159 - val_loss: 0.6559 - val_mae: 0.6315 Epoch 362/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4964 - mae: 0.5131 - val_loss: 0.6571 - val_mae: 0.6261 Epoch 363/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4966 - mae: 0.5142 - val_loss: 0.6574 - val_mae: 0.6306 Epoch 364/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4968 - mae: 0.5130 - val_loss: 0.6604 - val_mae: 0.6258 Epoch 365/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4932 - mae: 0.5107 - val_loss: 0.6573 - val_mae: 0.6278 Epoch 366/512 11/11 [==============================] - 0s 2ms/step - loss: 0.4922 - mae: 0.5103 - val_loss: 0.6572 - val_mae: 0.6301 Epoch 367/512 11/11 [==============================] - 0s 2ms/step - loss: 0.4925 - mae: 0.5103 - val_loss: 0.6594 - val_mae: 0.6268 Epoch 368/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4914 - mae: 0.5101 - val_loss: 0.6547 - val_mae: 0.6301 Epoch 369/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4902 - mae: 0.5111 - val_loss: 0.6550 - val_mae: 0.6267 Epoch 370/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4900 - mae: 0.5088 - val_loss: 0.6584 - val_mae: 0.6226 Epoch 371/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4881 - mae: 0.5062 - val_loss: 0.6541 - val_mae: 0.6254 Epoch 372/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4873 - mae: 0.5079 - val_loss: 0.6540 - val_mae: 0.6276 Epoch 373/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4867 - mae: 0.5085 - val_loss: 0.6521 - val_mae: 0.6264 Epoch 374/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4886 - mae: 0.5062 - val_loss: 0.6558 - val_mae: 0.6194 Epoch 375/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4854 - mae: 0.5038 - val_loss: 0.6507 - val_mae: 0.6226 Epoch 376/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4838 - mae: 0.5050 - val_loss: 0.6487 - val_mae: 0.6280 Epoch 377/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4824 - mae: 0.5044 - val_loss: 0.6509 - val_mae: 0.6224 Epoch 378/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4816 - mae: 0.5036 - val_loss: 0.6524 - val_mae: 0.6250 Epoch 379/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4806 - mae: 0.5037 - val_loss: 0.6518 - val_mae: 0.6217 Epoch 380/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4814 - mae: 0.5025 - val_loss: 0.6549 - val_mae: 0.6239 Epoch 381/512 11/11 [==============================] - 0s 2ms/step - loss: 0.4806 - mae: 0.5044 - val_loss: 0.6489 - val_mae: 0.6249 Epoch 382/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4798 - mae: 0.5007 - val_loss: 0.6515 - val_mae: 0.6174 Epoch 383/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4778 - mae: 0.4993 - val_loss: 0.6497 - val_mae: 0.6205 Epoch 384/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4763 - mae: 0.4994 - val_loss: 0.6474 - val_mae: 0.6219 Epoch 385/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4763 - mae: 0.4997 - val_loss: 0.6474 - val_mae: 0.6222 Epoch 386/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4770 - mae: 0.4983 - val_loss: 0.6483 - val_mae: 0.6156 Epoch 387/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4736 - mae: 0.4962 - val_loss: 0.6454 - val_mae: 0.6200 Epoch 388/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4727 - mae: 0.4974 - val_loss: 0.6440 - val_mae: 0.6196 Epoch 389/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4731 - mae: 0.4986 - val_loss: 0.6470 - val_mae: 0.6202 Epoch 390/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4725 - mae: 0.4960 - val_loss: 0.6482 - val_mae: 0.6149 Epoch 391/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4713 - mae: 0.4963 - val_loss: 0.6433 - val_mae: 0.6222 Epoch 392/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4696 - mae: 0.4964 - val_loss: 0.6447 - val_mae: 0.6185 Epoch 393/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4682 - mae: 0.4949 - val_loss: 0.6459 - val_mae: 0.6157 Epoch 394/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4685 - mae: 0.4940 - val_loss: 0.6428 - val_mae: 0.6175 Epoch 395/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4674 - mae: 0.4929 - val_loss: 0.6448 - val_mae: 0.6143 Epoch 396/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4670 - mae: 0.4907 - val_loss: 0.6431 - val_mae: 0.6156 Epoch 397/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4673 - mae: 0.4931 - val_loss: 0.6414 - val_mae: 0.6198 Epoch 398/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4649 - mae: 0.4920 - val_loss: 0.6439 - val_mae: 0.6132 Epoch 399/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4637 - mae: 0.4898 - val_loss: 0.6406 - val_mae: 0.6145 Epoch 400/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4633 - mae: 0.4893 - val_loss: 0.6424 - val_mae: 0.6157 Epoch 401/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4632 - mae: 0.4885 - val_loss: 0.6407 - val_mae: 0.6148 Epoch 402/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4635 - mae: 0.4913 - val_loss: 0.6392 - val_mae: 0.6182 Epoch 403/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4617 - mae: 0.4892 - val_loss: 0.6433 - val_mae: 0.6121 Epoch 404/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4608 - mae: 0.4900 - val_loss: 0.6394 - val_mae: 0.6192 Epoch 405/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4585 - mae: 0.4877 - val_loss: 0.6394 - val_mae: 0.6119 Epoch 406/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4585 - mae: 0.4862 - val_loss: 0.6396 - val_mae: 0.6127 Epoch 407/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4569 - mae: 0.4858 - val_loss: 0.6376 - val_mae: 0.6128 Epoch 408/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4567 - mae: 0.4855 - val_loss: 0.6370 - val_mae: 0.6124 Epoch 409/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4563 - mae: 0.4850 - val_loss: 0.6377 - val_mae: 0.6129 Epoch 410/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4556 - mae: 0.4850 - val_loss: 0.6387 - val_mae: 0.6145 Epoch 411/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4543 - mae: 0.4838 - val_loss: 0.6367 - val_mae: 0.6104 Epoch 412/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4558 - mae: 0.4857 - val_loss: 0.6389 - val_mae: 0.6152 Epoch 413/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4547 - mae: 0.4829 - val_loss: 0.6387 - val_mae: 0.6080 Epoch 414/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4521 - mae: 0.4803 - val_loss: 0.6347 - val_mae: 0.6141 Epoch 415/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4516 - mae: 0.4810 - val_loss: 0.6335 - val_mae: 0.6141 Epoch 416/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4512 - mae: 0.4813 - val_loss: 0.6339 - val_mae: 0.6090 Epoch 417/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4499 - mae: 0.4809 - val_loss: 0.6348 - val_mae: 0.6121 Epoch 418/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4498 - mae: 0.4814 - val_loss: 0.6320 - val_mae: 0.6126 Epoch 419/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4499 - mae: 0.4810 - val_loss: 0.6387 - val_mae: 0.6073 Epoch 420/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4482 - mae: 0.4791 - val_loss: 0.6336 - val_mae: 0.6144 Epoch 421/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4478 - mae: 0.4792 - val_loss: 0.6347 - val_mae: 0.6084 Epoch 422/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4469 - mae: 0.4771 - val_loss: 0.6318 - val_mae: 0.6089 Epoch 423/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4462 - mae: 0.4772 - val_loss: 0.6295 - val_mae: 0.6120 Epoch 424/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4448 - mae: 0.4777 - val_loss: 0.6296 - val_mae: 0.6105 Epoch 425/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4452 - mae: 0.4765 - val_loss: 0.6324 - val_mae: 0.6059 Epoch 426/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4433 - mae: 0.4761 - val_loss: 0.6328 - val_mae: 0.6105 Epoch 427/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4440 - mae: 0.4765 - val_loss: 0.6305 - val_mae: 0.6090 Epoch 428/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4423 - mae: 0.4750 - val_loss: 0.6312 - val_mae: 0.6101 Epoch 429/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4430 - mae: 0.4751 - val_loss: 0.6326 - val_mae: 0.6078 Epoch 430/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4419 - mae: 0.4747 - val_loss: 0.6271 - val_mae: 0.6114 Epoch 431/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4404 - mae: 0.4743 - val_loss: 0.6294 - val_mae: 0.6094 Epoch 432/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4397 - mae: 0.4739 - val_loss: 0.6304 - val_mae: 0.6058 Epoch 433/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4414 - mae: 0.4768 - val_loss: 0.6299 - val_mae: 0.6114 Epoch 434/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4393 - mae: 0.4730 - val_loss: 0.6345 - val_mae: 0.6075 Epoch 435/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4381 - mae: 0.4725 - val_loss: 0.6260 - val_mae: 0.6104 Epoch 436/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4368 - mae: 0.4727 - val_loss: 0.6265 - val_mae: 0.6092 Epoch 437/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4367 - mae: 0.4721 - val_loss: 0.6316 - val_mae: 0.6050 Epoch 438/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4349 - mae: 0.4705 - val_loss: 0.6271 - val_mae: 0.6118 Epoch 439/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4354 - mae: 0.4727 - val_loss: 0.6265 - val_mae: 0.6106 Epoch 440/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4349 - mae: 0.4694 - val_loss: 0.6301 - val_mae: 0.6024 Epoch 441/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4326 - mae: 0.4671 - val_loss: 0.6268 - val_mae: 0.6122 Epoch 442/512 11/11 [==============================] - 0s 2ms/step - loss: 0.4328 - mae: 0.4695 - val_loss: 0.6279 - val_mae: 0.6096 Epoch 443/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4333 - mae: 0.4684 - val_loss: 0.6269 - val_mae: 0.6029 Epoch 444/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4304 - mae: 0.4669 - val_loss: 0.6221 - val_mae: 0.6091 Epoch 445/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4309 - mae: 0.4689 - val_loss: 0.6242 - val_mae: 0.6062 Epoch 446/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4305 - mae: 0.4659 - val_loss: 0.6276 - val_mae: 0.6048 Epoch 447/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4295 - mae: 0.4668 - val_loss: 0.6222 - val_mae: 0.6075 Epoch 448/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4282 - mae: 0.4662 - val_loss: 0.6263 - val_mae: 0.6062 Epoch 449/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4275 - mae: 0.4663 - val_loss: 0.6256 - val_mae: 0.6061 Epoch 450/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4272 - mae: 0.4659 - val_loss: 0.6248 - val_mae: 0.6069 Epoch 451/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4268 - mae: 0.4651 - val_loss: 0.6254 - val_mae: 0.6080 Epoch 452/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4265 - mae: 0.4638 - val_loss: 0.6230 - val_mae: 0.6032 Epoch 453/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4263 - mae: 0.4628 - val_loss: 0.6248 - val_mae: 0.6047 Epoch 454/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4277 - mae: 0.4674 - val_loss: 0.6204 - val_mae: 0.6131 Epoch 455/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4251 - mae: 0.4655 - val_loss: 0.6241 - val_mae: 0.6021 Epoch 456/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4232 - mae: 0.4623 - val_loss: 0.6210 - val_mae: 0.6072 Epoch 457/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4238 - mae: 0.4613 - val_loss: 0.6205 - val_mae: 0.6010 Epoch 458/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4215 - mae: 0.4610 - val_loss: 0.6219 - val_mae: 0.6071 Epoch 459/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4214 - mae: 0.4602 - val_loss: 0.6231 - val_mae: 0.6054 Epoch 460/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4208 - mae: 0.4608 - val_loss: 0.6232 - val_mae: 0.6106 Epoch 461/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4197 - mae: 0.4602 - val_loss: 0.6236 - val_mae: 0.6057 Epoch 462/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4185 - mae: 0.4581 - val_loss: 0.6198 - val_mae: 0.6056 Epoch 463/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4179 - mae: 0.4585 - val_loss: 0.6191 - val_mae: 0.6011 Epoch 464/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4196 - mae: 0.4566 - val_loss: 0.6209 - val_mae: 0.6017 Epoch 465/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4170 - mae: 0.4591 - val_loss: 0.6187 - val_mae: 0.6087 Epoch 466/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4159 - mae: 0.4583 - val_loss: 0.6226 - val_mae: 0.6060 Epoch 467/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4151 - mae: 0.4575 - val_loss: 0.6218 - val_mae: 0.6038 Epoch 468/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4183 - mae: 0.4604 - val_loss: 0.6185 - val_mae: 0.6092 Epoch 469/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4136 - mae: 0.4566 - val_loss: 0.6214 - val_mae: 0.6025 Epoch 470/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4135 - mae: 0.4533 - val_loss: 0.6220 - val_mae: 0.6003 Epoch 471/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4133 - mae: 0.4532 - val_loss: 0.6178 - val_mae: 0.6051 Epoch 472/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4125 - mae: 0.4553 - val_loss: 0.6162 - val_mae: 0.6083 Epoch 473/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4125 - mae: 0.4543 - val_loss: 0.6173 - val_mae: 0.5999 Epoch 474/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4105 - mae: 0.4530 - val_loss: 0.6164 - val_mae: 0.6058 Epoch 475/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4105 - mae: 0.4540 - val_loss: 0.6194 - val_mae: 0.6029 Epoch 476/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4111 - mae: 0.4521 - val_loss: 0.6199 - val_mae: 0.6020 Epoch 477/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4086 - mae: 0.4516 - val_loss: 0.6166 - val_mae: 0.6067 Epoch 478/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4097 - mae: 0.4543 - val_loss: 0.6160 - val_mae: 0.6076 Epoch 479/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4085 - mae: 0.4551 - val_loss: 0.6225 - val_mae: 0.6052 Epoch 480/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4083 - mae: 0.4507 - val_loss: 0.6203 - val_mae: 0.6003 Epoch 481/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4067 - mae: 0.4495 - val_loss: 0.6153 - val_mae: 0.6037 Epoch 482/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4057 - mae: 0.4487 - val_loss: 0.6159 - val_mae: 0.6022 Epoch 483/512 11/11 [==============================] - 0s 4ms/step - loss: 0.4055 - mae: 0.4496 - val_loss: 0.6135 - val_mae: 0.6062 Epoch 484/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4044 - mae: 0.4492 - val_loss: 0.6187 - val_mae: 0.6019 Epoch 485/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4044 - mae: 0.4493 - val_loss: 0.6211 - val_mae: 0.6049 Epoch 486/512 11/11 [==============================] - 0s 7ms/step - loss: 0.4042 - mae: 0.4489 - val_loss: 0.6166 - val_mae: 0.6052 Epoch 487/512 11/11 [==============================] - 0s 5ms/step - loss: 0.4028 - mae: 0.4485 - val_loss: 0.6145 - val_mae: 0.6011 Epoch 488/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4042 - mae: 0.4469 - val_loss: 0.6168 - val_mae: 0.5971 Epoch 489/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4014 - mae: 0.4456 - val_loss: 0.6123 - val_mae: 0.6055 Epoch 490/512 11/11 [==============================] - 0s 3ms/step - loss: 0.4025 - mae: 0.4491 - val_loss: 0.6161 - val_mae: 0.6055 Epoch 491/512 11/11 [==============================] - 0s 6ms/step - loss: 0.4008 - mae: 0.4465 - val_loss: 0.6154 - val_mae: 0.6034 Epoch 492/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3995 - mae: 0.4457 - val_loss: 0.6159 - val_mae: 0.5982 Epoch 493/512 11/11 [==============================] - 0s 4ms/step - loss: 0.3992 - mae: 0.4440 - val_loss: 0.6147 - val_mae: 0.6039 Epoch 494/512 11/11 [==============================] - 0s 4ms/step - loss: 0.3982 - mae: 0.4448 - val_loss: 0.6137 - val_mae: 0.6029 Epoch 495/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3974 - mae: 0.4438 - val_loss: 0.6144 - val_mae: 0.6021 Epoch 496/512 11/11 [==============================] - 0s 6ms/step - loss: 0.3978 - mae: 0.4423 - val_loss: 0.6137 - val_mae: 0.5974 Epoch 497/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3974 - mae: 0.4444 - val_loss: 0.6135 - val_mae: 0.6052 Epoch 498/512 11/11 [==============================] - 0s 6ms/step - loss: 0.3995 - mae: 0.4434 - val_loss: 0.6160 - val_mae: 0.5973 Epoch 499/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3951 - mae: 0.4418 - val_loss: 0.6122 - val_mae: 0.6076 Epoch 500/512 11/11 [==============================] - 0s 7ms/step - loss: 0.3959 - mae: 0.4438 - val_loss: 0.6137 - val_mae: 0.6002 Epoch 501/512 11/11 [==============================] - 0s 2ms/step - loss: 0.3939 - mae: 0.4413 - val_loss: 0.6135 - val_mae: 0.6005 Epoch 502/512 11/11 [==============================] - 0s 2ms/step - loss: 0.3938 - mae: 0.4413 - val_loss: 0.6173 - val_mae: 0.6031 Epoch 503/512 11/11 [==============================] - 0s 7ms/step - loss: 0.3937 - mae: 0.4430 - val_loss: 0.6161 - val_mae: 0.6045 Epoch 504/512 11/11 [==============================] - 0s 6ms/step - loss: 0.3913 - mae: 0.4402 - val_loss: 0.6157 - val_mae: 0.5999 Epoch 505/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3920 - mae: 0.4407 - val_loss: 0.6157 - val_mae: 0.6024 Epoch 506/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3910 - mae: 0.4393 - val_loss: 0.6161 - val_mae: 0.6006 Epoch 507/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3902 - mae: 0.4378 - val_loss: 0.6135 - val_mae: 0.5994 Epoch 508/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3896 - mae: 0.4372 - val_loss: 0.6115 - val_mae: 0.6020 Epoch 509/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3893 - mae: 0.4386 - val_loss: 0.6111 - val_mae: 0.5993 Epoch 510/512 11/11 [==============================] - 0s 6ms/step - loss: 0.3892 - mae: 0.4395 - val_loss: 0.6143 - val_mae: 0.6022 Epoch 511/512 11/11 [==============================] - 0s 3ms/step - loss: 0.3888 - mae: 0.4384 - val_loss: 0.6153 - val_mae: 0.5992 Epoch 512/512 11/11 [==============================] - 0s 5ms/step - loss: 0.3917 - mae: 0.4357 - val_loss: 0.6142 - val_mae: 0.5979
# Predictions
n = 5
print("Actual - Predicted")
for i in range(n):
print(yval[i][0],'\t', model.predict(np.expand_dims(Xval[i],0))[0][0])
Actual - Predicted 5.7 4.6674523 4.8 5.2382164 3.7 3.7035098 3.9 3.9793258 5.4 4.789366
Plotting the metrics¶
def plot(history, variable1, variable2):
plt.plot(range(len(history[variable1])), history[variable1])
plt.plot(range(len(history[variable2])), history[variable2])
plt.legend([variable1, variable2])
plt.title(variable1)
plot(history.history, "loss", 'val_loss')
plot(history.history, "mae", 'val_mae')
deepC¶
model.save('fuel.h5')
!deepCC fuel.h5
[INFO] Reading [keras model] 'fuel.h5' [SUCCESS] Saved 'fuel_deepC/fuel.onnx' [INFO] Reading [onnx model] 'fuel_deepC/fuel.onnx' [INFO] Model info: ir_vesion : 4 doc : [WARNING] [ONNX]: terminal (input/output) dense_input's shape is less than 1. Changing it to 1. [WARNING] [ONNX]: terminal (input/output) dense_5's shape is less than 1. Changing it to 1. WARN (GRAPH): found operator node with the same name (dense_5) as io node. [INFO] Running DNNC graph sanity check ... [SUCCESS] Passed sanity check. [INFO] Writing C++ file 'fuel_deepC/fuel.cpp' [INFO] deepSea model files are ready in 'fuel_deepC/' [RUNNING COMMAND] g++ -std=c++11 -O3 -fno-rtti -fno-exceptions -I. -I/opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/include -isystem /opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/packages/eigen-eigen-323c052e1731 "fuel_deepC/fuel.cpp" -D_AITS_MAIN -o "fuel_deepC/fuel.exe" [RUNNING COMMAND] size "fuel_deepC/fuel.exe" text data bss dec hex filename 2864157 2736 760 2867653 2bc1c5 fuel_deepC/fuel.exe [SUCCESS] Saved model as executable "fuel_deepC/fuel.exe"