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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.

In [1]:
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

On Kaggle by Andreas Wagener

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)

In [2]:
df = pd.read_csv('https://cainvas-static.s3.amazonaws.com/media/user_data/cainvas-admin/measurements.csv')
df
Out[2]:
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

In [3]:
df.isna().sum()
Out[3]:
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.

In [4]:
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'.

In [5]:
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

In [6]:
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
Out[6]:
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

In [7]:
df.dtypes
Out[7]:
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.

In [8]:
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.

In [9]:
numeric_columns.extend(['speed','temp_outside'])

Change datatypes of the columns.

In [10]:
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

In [11]:
# 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.

In [12]:
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

In [13]:
input_columns = df.columns.tolist()
input_columns.remove('consume')

output_columns = ['consume']
In [14]:
# 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

In [15]:
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)
In [16]:
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
_________________________________________________________________
In [17]:
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
In [18]:
# 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

In [19]:
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)
In [20]:
plot(history.history, "loss", 'val_loss')
In [21]:
plot(history.history, "mae", 'val_mae')

deepC

In [22]:
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"