Loading the dataset

---

The dataset consists of drive cycle power profile calculated for an electric Ford F150 truck with a 35kWh battery pack scaled for a single Panasonic 18650PF cell at 25 degree Celcius.

import pandas as pd

data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Projects/SOC & SOE Estimation/Dataset/dataset_SOC.csv')
data

	Voltage (V)	Current (A)	Ah	Wh	Power (W)	Temp (C)	Time(s)	Chamber_Temp (C)
0	4.1819	-0.01062	0.00000	0.00000	-0.044412	25.619	0.00000	25
1	4.1806	-0.05063	0.00000	-0.00001	-0.211660	25.619	0.09500	25
2	4.1799	-0.06125	0.00000	-0.00001	-0.256020	25.418	0.19100	25
3	4.1793	-0.06533	0.00000	-0.00002	-0.273030	25.418	0.29700	25
4	4.1793	-0.06615	-0.00001	-0.00003	-0.276460	25.429	0.39599	25
...	...	...	...	...	...	...	...	...
116977	3.3534	0.00000	-2.54960	-8.63840	0.000000	27.480	11733.00000	25
116978	3.3534	0.00000	-2.54960	-8.63840	0.000000	27.480	11733.00000	25
116979	3.3534	0.00000	-2.54960	-8.63840	0.000000	27.480	11733.00000	25
116980	3.3527	0.00000	-2.54960	-8.63840	0.000000	27.267	11733.00000	25
116981	3.3527	0.00000	-2.54960	-8.63840	0.000000	27.267	11733.00000	25

116982 rows × 8 columns

Plots

import matplotlib.pyplot as plt
%matplotlib inline

plt.figure(figsize=(7,7))
plt.plot(data['Time(s)'], data['Voltage (V)'])
plt.title('Voltage Vs. Time')
plt.xlabel('Time (s)')
plt.ylabel('Voltage (V)')
plt.show()

plt.figure(figsize=(7,7))
plt.title('Energy Discharge Curve')
plt.xlabel('Time (s)')
plt.ylabel('Energy Remaining (Wh)')
plt.plot(data['Time(s)'], data['Wh']+9.9)
plt.show()

plt.figure(figsize=(7,7))
plt.title('Charge Discharge Curve')
plt.xlabel('Time (s)')
plt.ylabel('Charge Remaining (Ah)')
plt.plot(data['Time(s)'], data['Ah']+2.9)
plt.show()

Dropping unnecessary columns

---

For the estimation of SOC, we only require the volage, charging/ discharging current, and the temperature of the battery.

For the estimation of SOE, we will require volatage, chargin/ discharging current, temparature, and SOC.

data.drop(['Power (W)', 'Time(s)', 'Chamber_Temp (C)'], axis=1, inplace=True)
data

	Voltage (V)	Current (A)	Ah	Wh	Temp (C)
0	4.1819	-0.01062	0.00000	0.00000	25.619
1	4.1806	-0.05063	0.00000	-0.00001	25.619
2	4.1799	-0.06125	0.00000	-0.00001	25.418
3	4.1793	-0.06533	0.00000	-0.00002	25.418
4	4.1793	-0.06615	-0.00001	-0.00003	25.429
...	...	...	...	...	...
116977	3.3534	0.00000	-2.54960	-8.63840	27.480
116978	3.3534	0.00000	-2.54960	-8.63840	27.480
116979	3.3534	0.00000	-2.54960	-8.63840	27.480
116980	3.3527	0.00000	-2.54960	-8.63840	27.267
116981	3.3527	0.00000	-2.54960	-8.63840	27.267

116982 rows × 5 columns

Generating the target variables (SOC & SOE) from Amp-Hour & Watt-Hour discharging of the battery

---

The given values in "Ah" column shows the amount of amp-hour discharge from the battery with time. So, by adding the nominal amp-hour i.e. 2.9Ah, to all the values and dividing it again by 2.9Ah we will get out our target variable, SOC.

Similarly, the given values in "Wh" column shows the amount of watt-hour discharge from the battery with time. Panasonic 18650PF has a maximum energy capacity of 9.9Wh. So, by adding the maximum energy capacity to all the values and dividing it again by 9.9Wh, we will get our other target variable, SOE.

#Making sure that no column has missing values
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 116982 entries, 0 to 116981
Data columns (total 5 columns):
 #   Column       Non-Null Count   Dtype  
---  ------       --------------   -----  
 0   Voltage (V)  116982 non-null  float64
 1   Current (A)  116982 non-null  float64
 2   Ah           116982 non-null  float64
 3   Wh           116982 non-null  float64
 4   Temp (C)     116982 non-null  float64
dtypes: float64(5)
memory usage: 4.5 MB

data['SOC'] = (data['Ah'] + 2.9)/2.9
data['SOE'] = (data['Wh']+9.9)/9.9
data.drop(['Ah', 'Wh'], axis=1, inplace=True)
data

	Voltage (V)	Current (A)	Temp (C)	SOC	SOE
0	4.1819	-0.01062	25.619	1.000000	1.000000
1	4.1806	-0.05063	25.619	1.000000	0.999999
2	4.1799	-0.06125	25.418	1.000000	0.999999
3	4.1793	-0.06533	25.418	1.000000	0.999998
4	4.1793	-0.06615	25.429	0.999997	0.999997
...	...	...	...	...	...
116977	3.3534	0.00000	27.480	0.120828	0.127434
116978	3.3534	0.00000	27.480	0.120828	0.127434
116979	3.3534	0.00000	27.480	0.120828	0.127434
116980	3.3527	0.00000	27.267	0.120828	0.127434
116981	3.3527	0.00000	27.267	0.120828	0.127434

116982 rows × 5 columns

Shuffling and splitting the data it into training and test sets

---

from sklearn.model_selection import train_test_split 

# data = data.sample(frac=1)

Y_SOC = data['SOC']
Y_SOE = data['SOE']

X_SOC = data.drop(['SOC', 'SOE'], axis=1)
X_SOE = data.drop(['SOE'], axis=1)

X_train_SOC, X_test_SOC, Y_train_SOC, Y_test_SOC = train_test_split(X_SOC, Y_SOC, train_size=0.8, random_state=1)
X_train_SOE, X_test_SOE, Y_train_SOE, Y_test_SOE = train_test_split(X_SOE, Y_SOE, train_size=0.8, random_state=1)

X_train_SOC.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
X_test_SOC.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
Y_train_SOC.reset_index(level=None, drop=True, inplace=True)
Y_test_SOC.reset_index(level=None, drop=True, inplace=True)

X_train_SOE.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
X_test_SOE.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
Y_train_SOE.reset_index(level=None, drop=True, inplace=True)
Y_test_SOE.reset_index(level=None, drop=True, inplace=True)

X_train_SOC

	Voltage (V)	Current (A)	Temp (C)
0	3.3558	-5.76610	27.502
1	3.8197	-1.36620	27.312
2	4.0894	0.02613	27.290
3	3.5475	-0.71046	27.301
4	3.6802	2.51700	27.088
...	...	...	...
93580	3.7740	-0.07431	27.525
93581	3.4870	-0.08003	28.556
93582	4.0777	-0.85908	26.673
93583	3.7658	5.88980	26.662
93584	3.4853	0.64598	28.343

93585 rows × 3 columns

X_train_SOE

	Voltage (V)	Current (A)	Temp (C)	SOC
0	3.3558	-5.76610	27.502	0.394000
1	3.8197	-1.36620	27.312	0.759348
2	4.0894	0.02613	27.290	0.922900
3	3.5475	-0.71046	27.301	0.389414
4	3.6802	2.51700	27.088	0.435828
...	...	...	...	...
93580	3.7740	-0.07431	27.525	0.627759
93581	3.4870	-0.08003	28.556	0.255621
93582	4.0777	-0.85908	26.673	0.955103
93583	3.7658	5.88980	26.662	0.428759
93584	3.4853	0.64598	28.343	0.250379

93585 rows × 4 columns

Since there are no categorical variables or missing values, encoding and imputing the data is not required.

Selecting features using pearson correlation

---

import seaborn as sns

plt.figure()
sns.heatmap(X_train_SOC.corr(method='pearson'), annot=True)
plt.show()

plt.figure()
sns.heatmap(X_train_SOE.corr(method='pearson'), annot=True)
plt.show()

None of the features are heavily correlated to other features. Hence, all of them can be safely used for training the model.

Outlier Analysis

---

# Importing the necessary libraries
import numpy as np
from scipy.stats import norm

For Voltage (V)

plt.figure(figsize=(6,6))
plt.hist(X_train_SOC['Voltage (V)'], bins=30, rwidth=0.8)
plt.show()

rng = np.arange(X_train_SOC['Voltage (V)'].min(), X_train_SOC['Voltage (V)'].max(), 0.01)
plt.plot(rng, norm.pdf(rng, X_train_SOC['Voltage (V)'].mean(), X_train_SOC['Voltage (V)'].std()))
plt.show()

Removing all the data points with a z-score of greater than 3 or less than -3

X_train_SOC['voltage_z_score'] = (X_train_SOC['Voltage (V)'] - X_train_SOC['Voltage (V)'].mean())/X_train_SOC['Voltage (V)'].std()

to_be_included = ((X_train_SOC['voltage_z_score']>-3) & (X_train_SOC['voltage_z_score']<3))

X_train_SOC = X_train_SOC[to_be_included]
X_train_SOE = X_train_SOE[to_be_included]
Y_train_SOC = Y_train_SOC[to_be_included]
Y_train_SOE = Y_train_SOE[to_be_included]

X_train_SOC.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
Y_train_SOC.reset_index(level=None, drop=True, inplace=True)
X_train_SOE.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
Y_train_SOE.reset_index(level=None, drop=True, inplace=True)

X_train_SOC.drop(['voltage_z_score'], axis=1, inplace=True)

For Current (A)

plt.figure(figsize=(6,6))
plt.hist(X_train_SOC['Current (A)'], bins=30, rwidth=0.8)
plt.show()

rng = np.arange(X_train_SOC['Current (A)'].min(), X_train_SOC['Current (A)'].max(), 0.01)
plt.plot(rng, norm.pdf(rng, X_train_SOC['Current (A)'].mean(), X_train_SOC['Current (A)'].std()))
plt.show()

Removing all the data points with a z-score of greater than 3 or less than -3

X_train_SOC['current_z_score'] = (X_train_SOC['Current (A)'] - X_train_SOC['Current (A)'].mean())/X_train_SOC['Current (A)'].std()

to_be_included = ((X_train_SOC['current_z_score']>-3) & (X_train_SOC['current_z_score']<3))

X_train_SOC = X_train_SOC[to_be_included]
X_train_SOE = X_train_SOE[to_be_included]
Y_train_SOC = Y_train_SOC[to_be_included]
Y_train_SOE = Y_train_SOE[to_be_included]

X_train_SOC.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
Y_train_SOC.reset_index(level=None, drop=True, inplace=True)
X_train_SOE.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
Y_train_SOE.reset_index(level=None, drop=True, inplace=True)

X_train_SOC.drop(['current_z_score'], axis=1, inplace=True)

For Temp (C)

plt.figure(figsize=(6,6))
plt.hist(X_train_SOC['Temp (C)'], bins=30, rwidth=0.8)
plt.show()

rng = np.arange(X_train_SOC['Temp (C)'].min(), X_train_SOC['Temp (C)'].max(), 0.01)
plt.plot(rng, norm.pdf(rng, X_train_SOC['Temp (C)'].mean(), X_train_SOC['Temp (C)'].std()))
plt.show()

Removing all the data points with a z-score of greater than 3 or less than -3

X_train_SOC['temp_z_score'] = (X_train_SOC['Temp (C)'] - X_train_SOC['Temp (C)'].mean())/X_train_SOC['Temp (C)'].std()

to_be_included = ((X_train_SOC['temp_z_score']>-3) & (X_train_SOC['temp_z_score']<3))

X_train_SOC = X_train_SOC[to_be_included]
X_train_SOE = X_train_SOE[to_be_included]
Y_train_SOC = Y_train_SOC[to_be_included]
Y_train_SOE = Y_train_SOE[to_be_included]

X_train_SOC.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
Y_train_SOC.reset_index(level=None, drop=True, inplace=True)
X_train_SOE.reset_index(level=None, drop=True, inplace=True, col_level=0, col_fill='')
Y_train_SOE.reset_index(level=None, drop=True, inplace=True)

X_train_SOC.drop(['temp_z_score'], axis=1, inplace=True)

Tensorflow Model (SOC)

---

import tensorflow as tf
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from sklearn.metrics import mean_absolute_error

Normalizing the training and testing data

cols_to_be_normalized = ['Voltage (V)', 'Current (A)', 'Temp (C)']

for e in cols_to_be_normalized:
  mean = X_train_SOC[e].mean()
  std = X_train_SOC[e].std()
  X_train_SOC[e] = (X_train_SOC[e]-mean)/std
  X_train_SOE[e] = (X_train_SOE[e]-mean)/std
  
  X_test_SOC[e] = (X_test_SOC[e]-mean)/std
  X_test_SOE[e] = (X_test_SOE[e]-mean)/std

model_SOC = Sequential()
model_SOC.add(Dense(units=5, activation='relu', input_dim=len(X_train_SOC.columns)))
model_SOC.add(Dense(units=5, activation='relu'))
model_SOC.add(Dense(units=1, activation='linear'))

es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50)

opt = keras.optimizers.Adam(0.001)
model_SOC.compile(loss='mse', optimizer=opt, metrics='mse')
model_SOC.summary()

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_6 (Dense)              (None, 5)                 20        
_________________________________________________________________
dense_7 (Dense)              (None, 5)                 30        
_________________________________________________________________
dense_8 (Dense)              (None, 1)                 6         
=================================================================
Total params: 56
Trainable params: 56
Non-trainable params: 0
_________________________________________________________________

model_SOC.fit(X_train_SOC, Y_train_SOC, epochs=700, batch_size=512, validation_split=0.2, validation_data=None, verbose=1, callbacks=[es])

Epoch 1/700
142/142 [==============================] - 1s 3ms/step - loss: 0.1408 - mse: 0.1408 - val_loss: 0.0400 - val_mse: 0.0400
Epoch 2/700
142/142 [==============================] - 0s 2ms/step - loss: 0.0220 - mse: 0.0220 - val_loss: 0.0111 - val_mse: 0.0111
Epoch 3/700
142/142 [==============================] - 0s 2ms/step - loss: 0.0073 - mse: 0.0073 - val_loss: 0.0045 - val_mse: 0.0045
Epoch 4/700
142/142 [==============================] - 0s 2ms/step - loss: 0.0033 - mse: 0.0033 - val_loss: 0.0024 - val_mse: 0.0024
Epoch 5/700
142/142 [==============================] - 0s 2ms/step - loss: 0.0020 - mse: 0.0020 - val_loss: 0.0016 - val_mse: 0.0016
Epoch 6/700
142/142 [==============================] - 0s 2ms/step - loss: 0.0013 - mse: 0.0013 - val_loss: 0.0011 - val_mse: 0.0011
Epoch 7/700
142/142 [==============================] - 0s 2ms/step - loss: 0.0010 - mse: 0.0010 - val_loss: 9.0419e-04 - val_mse: 9.0419e-04
Epoch 8/700
142/142 [==============================] - 0s 2ms/step - loss: 8.5299e-04 - mse: 8.5299e-04 - val_loss: 8.0846e-04 - val_mse: 8.0846e-04
Epoch 9/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8768e-04 - mse: 7.8768e-04 - val_loss: 7.6579e-04 - val_mse: 7.6579e-04
Epoch 10/700
142/142 [==============================] - 0s 2ms/step - loss: 7.5577e-04 - mse: 7.5577e-04 - val_loss: 7.4190e-04 - val_mse: 7.4190e-04
Epoch 11/700
142/142 [==============================] - 0s 2ms/step - loss: 7.3333e-04 - mse: 7.3333e-04 - val_loss: 7.2310e-04 - val_mse: 7.2310e-04
Epoch 12/700
142/142 [==============================] - 0s 2ms/step - loss: 7.1736e-04 - mse: 7.1736e-04 - val_loss: 7.0729e-04 - val_mse: 7.0729e-04
Epoch 13/700
142/142 [==============================] - 0s 2ms/step - loss: 7.0285e-04 - mse: 7.0285e-04 - val_loss: 6.9338e-04 - val_mse: 6.9338e-04
Epoch 14/700
142/142 [==============================] - 0s 2ms/step - loss: 6.8667e-04 - mse: 6.8667e-04 - val_loss: 6.7902e-04 - val_mse: 6.7902e-04
Epoch 15/700
142/142 [==============================] - 0s 2ms/step - loss: 6.8090e-04 - mse: 6.8090e-04 - val_loss: 6.7951e-04 - val_mse: 6.7951e-04
Epoch 16/700
142/142 [==============================] - 0s 2ms/step - loss: 6.7437e-04 - mse: 6.7437e-04 - val_loss: 6.6716e-04 - val_mse: 6.6716e-04
Epoch 17/700
142/142 [==============================] - 0s 2ms/step - loss: 6.6967e-04 - mse: 6.6967e-04 - val_loss: 6.6151e-04 - val_mse: 6.6151e-04
Epoch 18/700
142/142 [==============================] - 0s 2ms/step - loss: 6.6580e-04 - mse: 6.6580e-04 - val_loss: 6.5682e-04 - val_mse: 6.5682e-04
Epoch 19/700
142/142 [==============================] - 0s 2ms/step - loss: 6.6150e-04 - mse: 6.6150e-04 - val_loss: 6.5541e-04 - val_mse: 6.5541e-04
Epoch 20/700
142/142 [==============================] - 0s 2ms/step - loss: 6.5924e-04 - mse: 6.5924e-04 - val_loss: 6.5001e-04 - val_mse: 6.5001e-04
Epoch 21/700
142/142 [==============================] - 0s 2ms/step - loss: 6.5621e-04 - mse: 6.5621e-04 - val_loss: 6.4932e-04 - val_mse: 6.4932e-04
Epoch 22/700
142/142 [==============================] - 0s 2ms/step - loss: 6.5393e-04 - mse: 6.5393e-04 - val_loss: 6.4399e-04 - val_mse: 6.4399e-04
Epoch 23/700
142/142 [==============================] - 0s 2ms/step - loss: 6.5055e-04 - mse: 6.5055e-04 - val_loss: 6.4215e-04 - val_mse: 6.4215e-04
Epoch 24/700
142/142 [==============================] - 0s 2ms/step - loss: 6.4706e-04 - mse: 6.4706e-04 - val_loss: 6.4020e-04 - val_mse: 6.4020e-04
Epoch 25/700
142/142 [==============================] - 0s 2ms/step - loss: 6.4599e-04 - mse: 6.4599e-04 - val_loss: 6.3980e-04 - val_mse: 6.3980e-04
Epoch 26/700
142/142 [==============================] - 0s 2ms/step - loss: 6.4424e-04 - mse: 6.4424e-04 - val_loss: 6.3289e-04 - val_mse: 6.3289e-04
Epoch 27/700
142/142 [==============================] - 0s 2ms/step - loss: 6.4049e-04 - mse: 6.4049e-04 - val_loss: 6.3040e-04 - val_mse: 6.3040e-04
Epoch 28/700
142/142 [==============================] - 0s 2ms/step - loss: 6.3858e-04 - mse: 6.3858e-04 - val_loss: 6.3013e-04 - val_mse: 6.3013e-04
Epoch 29/700
142/142 [==============================] - 0s 2ms/step - loss: 6.3526e-04 - mse: 6.3526e-04 - val_loss: 6.3363e-04 - val_mse: 6.3363e-04
Epoch 30/700
142/142 [==============================] - 0s 2ms/step - loss: 6.3291e-04 - mse: 6.3291e-04 - val_loss: 6.2255e-04 - val_mse: 6.2255e-04
Epoch 31/700
142/142 [==============================] - 0s 2ms/step - loss: 6.2980e-04 - mse: 6.2980e-04 - val_loss: 6.1878e-04 - val_mse: 6.1878e-04
Epoch 32/700
142/142 [==============================] - 0s 2ms/step - loss: 6.2564e-04 - mse: 6.2564e-04 - val_loss: 6.1478e-04 - val_mse: 6.1478e-04
Epoch 33/700
142/142 [==============================] - 0s 2ms/step - loss: 6.2198e-04 - mse: 6.2198e-04 - val_loss: 6.1536e-04 - val_mse: 6.1536e-04
Epoch 34/700
142/142 [==============================] - 0s 2ms/step - loss: 6.1897e-04 - mse: 6.1897e-04 - val_loss: 6.0593e-04 - val_mse: 6.0593e-04
Epoch 35/700
142/142 [==============================] - 0s 2ms/step - loss: 6.0961e-04 - mse: 6.0961e-04 - val_loss: 6.0049e-04 - val_mse: 6.0049e-04
Epoch 36/700
142/142 [==============================] - 0s 2ms/step - loss: 6.0416e-04 - mse: 6.0416e-04 - val_loss: 5.9199e-04 - val_mse: 5.9199e-04
Epoch 37/700
142/142 [==============================] - 0s 2ms/step - loss: 5.9685e-04 - mse: 5.9685e-04 - val_loss: 5.8336e-04 - val_mse: 5.8336e-04
Epoch 38/700
142/142 [==============================] - 0s 2ms/step - loss: 5.9072e-04 - mse: 5.9072e-04 - val_loss: 5.8458e-04 - val_mse: 5.8458e-04
Epoch 39/700
142/142 [==============================] - 0s 2ms/step - loss: 5.8867e-04 - mse: 5.8867e-04 - val_loss: 5.7766e-04 - val_mse: 5.7766e-04
Epoch 40/700
142/142 [==============================] - 0s 2ms/step - loss: 5.8395e-04 - mse: 5.8395e-04 - val_loss: 5.7029e-04 - val_mse: 5.7029e-04
Epoch 41/700
142/142 [==============================] - 0s 2ms/step - loss: 5.8056e-04 - mse: 5.8056e-04 - val_loss: 5.6597e-04 - val_mse: 5.6597e-04
Epoch 42/700
142/142 [==============================] - 0s 2ms/step - loss: 5.7748e-04 - mse: 5.7748e-04 - val_loss: 5.6561e-04 - val_mse: 5.6561e-04
Epoch 43/700
142/142 [==============================] - 0s 2ms/step - loss: 5.7378e-04 - mse: 5.7378e-04 - val_loss: 5.6676e-04 - val_mse: 5.6676e-04
Epoch 44/700
142/142 [==============================] - 0s 2ms/step - loss: 5.7049e-04 - mse: 5.7049e-04 - val_loss: 5.6343e-04 - val_mse: 5.6343e-04
Epoch 45/700
142/142 [==============================] - 0s 2ms/step - loss: 5.6857e-04 - mse: 5.6857e-04 - val_loss: 5.5889e-04 - val_mse: 5.5889e-04
Epoch 46/700
142/142 [==============================] - 0s 2ms/step - loss: 5.6741e-04 - mse: 5.6741e-04 - val_loss: 5.5987e-04 - val_mse: 5.5987e-04
Epoch 47/700
142/142 [==============================] - 0s 2ms/step - loss: 5.6793e-04 - mse: 5.6793e-04 - val_loss: 5.5968e-04 - val_mse: 5.5968e-04
Epoch 48/700
142/142 [==============================] - 0s 2ms/step - loss: 5.6331e-04 - mse: 5.6331e-04 - val_loss: 5.6126e-04 - val_mse: 5.6126e-04
Epoch 49/700
142/142 [==============================] - 0s 2ms/step - loss: 5.6433e-04 - mse: 5.6433e-04 - val_loss: 5.5260e-04 - val_mse: 5.5260e-04
Epoch 50/700
142/142 [==============================] - 0s 2ms/step - loss: 5.6020e-04 - mse: 5.6020e-04 - val_loss: 5.5022e-04 - val_mse: 5.5022e-04
Epoch 51/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5935e-04 - mse: 5.5935e-04 - val_loss: 5.5184e-04 - val_mse: 5.5184e-04
Epoch 52/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5733e-04 - mse: 5.5733e-04 - val_loss: 5.4542e-04 - val_mse: 5.4542e-04
Epoch 53/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5739e-04 - mse: 5.5739e-04 - val_loss: 5.5512e-04 - val_mse: 5.5512e-04
Epoch 54/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5404e-04 - mse: 5.5404e-04 - val_loss: 5.4842e-04 - val_mse: 5.4842e-04
Epoch 55/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5504e-04 - mse: 5.5504e-04 - val_loss: 5.4492e-04 - val_mse: 5.4492e-04
Epoch 56/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5398e-04 - mse: 5.5398e-04 - val_loss: 5.4163e-04 - val_mse: 5.4163e-04
Epoch 57/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5126e-04 - mse: 5.5126e-04 - val_loss: 5.5011e-04 - val_mse: 5.5011e-04
Epoch 58/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5070e-04 - mse: 5.5070e-04 - val_loss: 5.4042e-04 - val_mse: 5.4042e-04
Epoch 59/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5029e-04 - mse: 5.5029e-04 - val_loss: 5.4390e-04 - val_mse: 5.4390e-04
Epoch 60/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4988e-04 - mse: 5.4988e-04 - val_loss: 5.4226e-04 - val_mse: 5.4226e-04
Epoch 61/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5179e-04 - mse: 5.5179e-04 - val_loss: 5.4690e-04 - val_mse: 5.4690e-04
Epoch 62/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4858e-04 - mse: 5.4858e-04 - val_loss: 5.3948e-04 - val_mse: 5.3948e-04
Epoch 63/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4791e-04 - mse: 5.4791e-04 - val_loss: 5.4262e-04 - val_mse: 5.4262e-04
Epoch 64/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4694e-04 - mse: 5.4694e-04 - val_loss: 5.3686e-04 - val_mse: 5.3686e-04
Epoch 65/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4918e-04 - mse: 5.4918e-04 - val_loss: 5.4057e-04 - val_mse: 5.4057e-04
Epoch 66/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4724e-04 - mse: 5.4724e-04 - val_loss: 5.5264e-04 - val_mse: 5.5264e-04
Epoch 67/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4781e-04 - mse: 5.4781e-04 - val_loss: 5.4466e-04 - val_mse: 5.4466e-04
Epoch 68/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4943e-04 - mse: 5.4943e-04 - val_loss: 5.4194e-04 - val_mse: 5.4194e-04
Epoch 69/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4899e-04 - mse: 5.4899e-04 - val_loss: 5.4350e-04 - val_mse: 5.4350e-04
Epoch 70/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4622e-04 - mse: 5.4622e-04 - val_loss: 5.3874e-04 - val_mse: 5.3874e-04
Epoch 71/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4429e-04 - mse: 5.4429e-04 - val_loss: 5.4404e-04 - val_mse: 5.4404e-04
Epoch 72/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4573e-04 - mse: 5.4573e-04 - val_loss: 5.4542e-04 - val_mse: 5.4542e-04
Epoch 73/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4566e-04 - mse: 5.4566e-04 - val_loss: 5.3594e-04 - val_mse: 5.3594e-04
Epoch 74/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5011e-04 - mse: 5.5011e-04 - val_loss: 5.4389e-04 - val_mse: 5.4389e-04
Epoch 75/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4629e-04 - mse: 5.4629e-04 - val_loss: 5.3886e-04 - val_mse: 5.3886e-04
Epoch 76/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4661e-04 - mse: 5.4661e-04 - val_loss: 5.4052e-04 - val_mse: 5.4052e-04
Epoch 77/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4544e-04 - mse: 5.4544e-04 - val_loss: 5.4070e-04 - val_mse: 5.4070e-04
Epoch 78/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4690e-04 - mse: 5.4690e-04 - val_loss: 5.3474e-04 - val_mse: 5.3474e-04
Epoch 79/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4398e-04 - mse: 5.4398e-04 - val_loss: 5.3642e-04 - val_mse: 5.3642e-04
Epoch 80/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4468e-04 - mse: 5.4468e-04 - val_loss: 5.4781e-04 - val_mse: 5.4781e-04
Epoch 81/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4611e-04 - mse: 5.4611e-04 - val_loss: 5.3827e-04 - val_mse: 5.3827e-04
Epoch 82/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4763e-04 - mse: 5.4763e-04 - val_loss: 5.5026e-04 - val_mse: 5.5026e-04
Epoch 83/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4433e-04 - mse: 5.4433e-04 - val_loss: 5.3751e-04 - val_mse: 5.3751e-04
Epoch 84/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4570e-04 - mse: 5.4570e-04 - val_loss: 5.4161e-04 - val_mse: 5.4161e-04
Epoch 85/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4809e-04 - mse: 5.4809e-04 - val_loss: 5.5013e-04 - val_mse: 5.5013e-04
Epoch 86/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4555e-04 - mse: 5.4555e-04 - val_loss: 5.3773e-04 - val_mse: 5.3773e-04
Epoch 87/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4406e-04 - mse: 5.4406e-04 - val_loss: 5.3593e-04 - val_mse: 5.3593e-04
Epoch 88/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4479e-04 - mse: 5.4479e-04 - val_loss: 5.3826e-04 - val_mse: 5.3826e-04
Epoch 89/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4484e-04 - mse: 5.4484e-04 - val_loss: 5.3417e-04 - val_mse: 5.3417e-04
Epoch 90/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4542e-04 - mse: 5.4542e-04 - val_loss: 5.3638e-04 - val_mse: 5.3638e-04
Epoch 91/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4488e-04 - mse: 5.4488e-04 - val_loss: 5.4049e-04 - val_mse: 5.4049e-04
Epoch 92/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4619e-04 - mse: 5.4619e-04 - val_loss: 5.3700e-04 - val_mse: 5.3700e-04
Epoch 93/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4467e-04 - mse: 5.4467e-04 - val_loss: 5.3555e-04 - val_mse: 5.3555e-04
Epoch 94/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4427e-04 - mse: 5.4427e-04 - val_loss: 5.4004e-04 - val_mse: 5.4004e-04
Epoch 95/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4511e-04 - mse: 5.4511e-04 - val_loss: 5.3725e-04 - val_mse: 5.3725e-04
Epoch 96/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4706e-04 - mse: 5.4706e-04 - val_loss: 5.3387e-04 - val_mse: 5.3387e-04
Epoch 97/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4493e-04 - mse: 5.4493e-04 - val_loss: 5.3435e-04 - val_mse: 5.3435e-04
Epoch 98/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4413e-04 - mse: 5.4413e-04 - val_loss: 5.3749e-04 - val_mse: 5.3749e-04
Epoch 99/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4618e-04 - mse: 5.4618e-04 - val_loss: 5.3598e-04 - val_mse: 5.3598e-04
Epoch 100/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4365e-04 - mse: 5.4365e-04 - val_loss: 5.3713e-04 - val_mse: 5.3713e-04
Epoch 101/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4499e-04 - mse: 5.4499e-04 - val_loss: 5.5114e-04 - val_mse: 5.5114e-04
Epoch 102/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4784e-04 - mse: 5.4784e-04 - val_loss: 5.4231e-04 - val_mse: 5.4231e-04
Epoch 103/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4689e-04 - mse: 5.4689e-04 - val_loss: 5.3881e-04 - val_mse: 5.3881e-04
Epoch 104/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4539e-04 - mse: 5.4539e-04 - val_loss: 5.4484e-04 - val_mse: 5.4484e-04
Epoch 105/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4453e-04 - mse: 5.4453e-04 - val_loss: 5.3643e-04 - val_mse: 5.3643e-04
Epoch 106/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4530e-04 - mse: 5.4530e-04 - val_loss: 5.3770e-04 - val_mse: 5.3770e-04
Epoch 107/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4923e-04 - mse: 5.4923e-04 - val_loss: 5.3292e-04 - val_mse: 5.3292e-04
Epoch 108/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4573e-04 - mse: 5.4573e-04 - val_loss: 5.3479e-04 - val_mse: 5.3479e-04
Epoch 109/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4342e-04 - mse: 5.4342e-04 - val_loss: 5.3383e-04 - val_mse: 5.3383e-04
Epoch 110/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4392e-04 - mse: 5.4392e-04 - val_loss: 5.3932e-04 - val_mse: 5.3932e-04
Epoch 111/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4468e-04 - mse: 5.4468e-04 - val_loss: 5.3238e-04 - val_mse: 5.3238e-04
Epoch 112/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4450e-04 - mse: 5.4450e-04 - val_loss: 5.3488e-04 - val_mse: 5.3488e-04
Epoch 113/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4789e-04 - mse: 5.4789e-04 - val_loss: 5.3699e-04 - val_mse: 5.3699e-04
Epoch 114/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4421e-04 - mse: 5.4421e-04 - val_loss: 5.4024e-04 - val_mse: 5.4024e-04
Epoch 115/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4561e-04 - mse: 5.4561e-04 - val_loss: 5.4188e-04 - val_mse: 5.4188e-04
Epoch 116/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4455e-04 - mse: 5.4455e-04 - val_loss: 5.5406e-04 - val_mse: 5.5406e-04
Epoch 117/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4596e-04 - mse: 5.4596e-04 - val_loss: 5.3289e-04 - val_mse: 5.3289e-04
Epoch 118/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4470e-04 - mse: 5.4470e-04 - val_loss: 5.3853e-04 - val_mse: 5.3853e-04
Epoch 119/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4491e-04 - mse: 5.4491e-04 - val_loss: 5.3351e-04 - val_mse: 5.3351e-04
Epoch 120/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4435e-04 - mse: 5.4435e-04 - val_loss: 5.3289e-04 - val_mse: 5.3289e-04
Epoch 121/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4753e-04 - mse: 5.4753e-04 - val_loss: 5.3297e-04 - val_mse: 5.3297e-04
Epoch 122/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4381e-04 - mse: 5.4381e-04 - val_loss: 5.3739e-04 - val_mse: 5.3739e-04
Epoch 123/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4766e-04 - mse: 5.4766e-04 - val_loss: 5.3784e-04 - val_mse: 5.3784e-04
Epoch 124/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4605e-04 - mse: 5.4605e-04 - val_loss: 5.3425e-04 - val_mse: 5.3425e-04
Epoch 125/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4304e-04 - mse: 5.4304e-04 - val_loss: 5.4014e-04 - val_mse: 5.4014e-04
Epoch 126/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4409e-04 - mse: 5.4409e-04 - val_loss: 5.3254e-04 - val_mse: 5.3254e-04
Epoch 127/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4412e-04 - mse: 5.4412e-04 - val_loss: 5.4064e-04 - val_mse: 5.4064e-04
Epoch 128/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4609e-04 - mse: 5.4609e-04 - val_loss: 5.5627e-04 - val_mse: 5.5627e-04
Epoch 129/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4463e-04 - mse: 5.4463e-04 - val_loss: 5.3638e-04 - val_mse: 5.3638e-04
Epoch 130/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4382e-04 - mse: 5.4382e-04 - val_loss: 5.3355e-04 - val_mse: 5.3355e-04
Epoch 131/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4372e-04 - mse: 5.4372e-04 - val_loss: 5.3233e-04 - val_mse: 5.3233e-04
Epoch 132/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4313e-04 - mse: 5.4313e-04 - val_loss: 5.3826e-04 - val_mse: 5.3826e-04
Epoch 133/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4309e-04 - mse: 5.4309e-04 - val_loss: 5.3624e-04 - val_mse: 5.3624e-04
Epoch 134/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4573e-04 - mse: 5.4573e-04 - val_loss: 5.3812e-04 - val_mse: 5.3812e-04
Epoch 135/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4473e-04 - mse: 5.4473e-04 - val_loss: 5.3513e-04 - val_mse: 5.3513e-04
Epoch 136/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4614e-04 - mse: 5.4614e-04 - val_loss: 5.3920e-04 - val_mse: 5.3920e-04
Epoch 137/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4417e-04 - mse: 5.4417e-04 - val_loss: 5.5745e-04 - val_mse: 5.5745e-04
Epoch 138/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4483e-04 - mse: 5.4483e-04 - val_loss: 5.3346e-04 - val_mse: 5.3346e-04
Epoch 139/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4484e-04 - mse: 5.4484e-04 - val_loss: 5.3869e-04 - val_mse: 5.3869e-04
Epoch 140/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4406e-04 - mse: 5.4406e-04 - val_loss: 5.3642e-04 - val_mse: 5.3642e-04
Epoch 141/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4598e-04 - mse: 5.4598e-04 - val_loss: 5.4329e-04 - val_mse: 5.4329e-04
Epoch 142/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4443e-04 - mse: 5.4443e-04 - val_loss: 5.3356e-04 - val_mse: 5.3356e-04
Epoch 143/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4491e-04 - mse: 5.4491e-04 - val_loss: 5.3291e-04 - val_mse: 5.3291e-04
Epoch 144/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4482e-04 - mse: 5.4482e-04 - val_loss: 5.3612e-04 - val_mse: 5.3612e-04
Epoch 145/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4398e-04 - mse: 5.4398e-04 - val_loss: 5.3642e-04 - val_mse: 5.3642e-04
Epoch 146/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4408e-04 - mse: 5.4408e-04 - val_loss: 5.3582e-04 - val_mse: 5.3582e-04
Epoch 147/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4408e-04 - mse: 5.4408e-04 - val_loss: 5.3604e-04 - val_mse: 5.3604e-04
Epoch 148/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4324e-04 - mse: 5.4324e-04 - val_loss: 5.3617e-04 - val_mse: 5.3617e-04
Epoch 149/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4433e-04 - mse: 5.4433e-04 - val_loss: 5.3893e-04 - val_mse: 5.3893e-04
Epoch 150/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4398e-04 - mse: 5.4398e-04 - val_loss: 5.3692e-04 - val_mse: 5.3692e-04
Epoch 151/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4486e-04 - mse: 5.4486e-04 - val_loss: 5.3417e-04 - val_mse: 5.3417e-04
Epoch 152/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4300e-04 - mse: 5.4300e-04 - val_loss: 5.4568e-04 - val_mse: 5.4568e-04
Epoch 153/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4301e-04 - mse: 5.4301e-04 - val_loss: 5.3946e-04 - val_mse: 5.3946e-04
Epoch 154/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4579e-04 - mse: 5.4579e-04 - val_loss: 5.3478e-04 - val_mse: 5.3478e-04
Epoch 155/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4563e-04 - mse: 5.4563e-04 - val_loss: 5.3695e-04 - val_mse: 5.3695e-04
Epoch 156/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4380e-04 - mse: 5.4380e-04 - val_loss: 5.3637e-04 - val_mse: 5.3637e-04
Epoch 157/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4485e-04 - mse: 5.4485e-04 - val_loss: 5.4318e-04 - val_mse: 5.4318e-04
Epoch 158/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4556e-04 - mse: 5.4556e-04 - val_loss: 5.4564e-04 - val_mse: 5.4564e-04
Epoch 159/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4385e-04 - mse: 5.4385e-04 - val_loss: 5.4030e-04 - val_mse: 5.4030e-04
Epoch 160/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4671e-04 - mse: 5.4671e-04 - val_loss: 5.3846e-04 - val_mse: 5.3846e-04
Epoch 161/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4335e-04 - mse: 5.4335e-04 - val_loss: 5.3437e-04 - val_mse: 5.3437e-04
Epoch 162/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4458e-04 - mse: 5.4458e-04 - val_loss: 5.4125e-04 - val_mse: 5.4125e-04
Epoch 163/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4433e-04 - mse: 5.4433e-04 - val_loss: 5.3957e-04 - val_mse: 5.3957e-04
Epoch 164/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4345e-04 - mse: 5.4345e-04 - val_loss: 5.3835e-04 - val_mse: 5.3835e-04
Epoch 165/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4446e-04 - mse: 5.4446e-04 - val_loss: 5.5075e-04 - val_mse: 5.5075e-04
Epoch 166/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4375e-04 - mse: 5.4375e-04 - val_loss: 5.3977e-04 - val_mse: 5.3977e-04
Epoch 167/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4463e-04 - mse: 5.4463e-04 - val_loss: 5.4642e-04 - val_mse: 5.4642e-04
Epoch 168/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4612e-04 - mse: 5.4612e-04 - val_loss: 5.3830e-04 - val_mse: 5.3830e-04
Epoch 169/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4534e-04 - mse: 5.4534e-04 - val_loss: 5.3337e-04 - val_mse: 5.3337e-04
Epoch 170/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4414e-04 - mse: 5.4414e-04 - val_loss: 5.3807e-04 - val_mse: 5.3807e-04
Epoch 171/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4332e-04 - mse: 5.4332e-04 - val_loss: 5.4424e-04 - val_mse: 5.4424e-04
Epoch 172/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4423e-04 - mse: 5.4423e-04 - val_loss: 5.3580e-04 - val_mse: 5.3580e-04
Epoch 173/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4300e-04 - mse: 5.4300e-04 - val_loss: 5.3718e-04 - val_mse: 5.3718e-04
Epoch 174/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4571e-04 - mse: 5.4571e-04 - val_loss: 5.3890e-04 - val_mse: 5.3890e-04
Epoch 175/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4374e-04 - mse: 5.4374e-04 - val_loss: 5.3756e-04 - val_mse: 5.3756e-04
Epoch 176/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4482e-04 - mse: 5.4482e-04 - val_loss: 5.5066e-04 - val_mse: 5.5066e-04
Epoch 177/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4624e-04 - mse: 5.4624e-04 - val_loss: 5.4220e-04 - val_mse: 5.4220e-04
Epoch 178/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4595e-04 - mse: 5.4595e-04 - val_loss: 5.3553e-04 - val_mse: 5.3553e-04
Epoch 179/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4563e-04 - mse: 5.4563e-04 - val_loss: 5.3255e-04 - val_mse: 5.3255e-04
Epoch 180/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4441e-04 - mse: 5.4441e-04 - val_loss: 5.4427e-04 - val_mse: 5.4427e-04
Epoch 181/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4338e-04 - mse: 5.4338e-04 - val_loss: 5.3951e-04 - val_mse: 5.3951e-04
Epoch 00181: early stopping

<tensorflow.python.keras.callbacks.History at 0x7f1f02351710>

predictions = (model_SOC.predict(X_test_SOC))
print(mean_absolute_error(Y_test_SOC, predictions))

0.017012958412838373

compare = pd.concat([pd.DataFrame(predictions), pd.DataFrame(Y_test_SOC)], axis=1)
compare.columns = ['Predictions', 'Actual Output']
compare

	Predictions	Actual Output
0	0.790278	0.788331
1	0.915809	0.912569
2	0.345753	0.353759
3	0.249428	0.253276
4	0.373009	0.371483
...	...	...
23392	0.623608	0.625103
23393	0.794099	0.787421
23394	0.345132	0.342103
23395	0.198163	0.194517
23396	0.344576	0.337345

23397 rows × 2 columns

Tensorflow Model (SOE)

model_SOE = Sequential()
model_SOE.add(Dense(units=5, activation='relu', input_dim=len(X_train_SOE.columns)))
model_SOE.add(Dense(units=5, activation='relu'))
model_SOE.add(Dense(units=1, activation='linear'))

es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50)
model_SOE.compile(loss='mse', optimizer=opt, metrics='mse')
model_SOE.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_3 (Dense)              (None, 5)                 25        
_________________________________________________________________
dense_4 (Dense)              (None, 5)                 30        
_________________________________________________________________
dense_5 (Dense)              (None, 1)                 6         
=================================================================
Total params: 61
Trainable params: 61
Non-trainable params: 0
_________________________________________________________________

model_SOE.fit(X_train_SOE, Y_train_SOE, epochs=700, batch_size=512, validation_split=0.2, validation_data=None, verbose=1, callbacks=[es])

Epoch 1/700
142/142 [==============================] - 1s 3ms/step - loss: 0.0526 - mse: 0.0526 - val_loss: 0.0066 - val_mse: 0.0066
Epoch 2/700
142/142 [==============================] - 0s 2ms/step - loss: 0.0021 - mse: 0.0021 - val_loss: 5.3857e-04 - val_mse: 5.3857e-04
Epoch 3/700
142/142 [==============================] - 0s 2ms/step - loss: 2.9140e-04 - mse: 2.9140e-04 - val_loss: 1.7826e-04 - val_mse: 1.7826e-04
Epoch 4/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4224e-04 - mse: 1.4224e-04 - val_loss: 1.2226e-04 - val_mse: 1.2226e-04
Epoch 5/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1522e-04 - mse: 1.1522e-04 - val_loss: 1.0479e-04 - val_mse: 1.0479e-04
Epoch 6/700
142/142 [==============================] - 0s 2ms/step - loss: 9.9592e-05 - mse: 9.9592e-05 - val_loss: 8.9316e-05 - val_mse: 8.9316e-05
Epoch 7/700
142/142 [==============================] - 0s 2ms/step - loss: 8.3193e-05 - mse: 8.3193e-05 - val_loss: 7.3904e-05 - val_mse: 7.3904e-05
Epoch 8/700
142/142 [==============================] - 0s 2ms/step - loss: 6.8225e-05 - mse: 6.8225e-05 - val_loss: 5.9457e-05 - val_mse: 5.9457e-05
Epoch 9/700
142/142 [==============================] - 0s 2ms/step - loss: 5.5529e-05 - mse: 5.5529e-05 - val_loss: 4.8271e-05 - val_mse: 4.8271e-05
Epoch 10/700
142/142 [==============================] - 0s 2ms/step - loss: 4.5136e-05 - mse: 4.5136e-05 - val_loss: 4.0141e-05 - val_mse: 4.0141e-05
Epoch 11/700
142/142 [==============================] - 0s 2ms/step - loss: 3.7806e-05 - mse: 3.7806e-05 - val_loss: 3.4037e-05 - val_mse: 3.4037e-05
Epoch 12/700
142/142 [==============================] - 0s 2ms/step - loss: 3.2755e-05 - mse: 3.2755e-05 - val_loss: 3.0492e-05 - val_mse: 3.0492e-05
Epoch 13/700
142/142 [==============================] - 0s 2ms/step - loss: 2.9510e-05 - mse: 2.9510e-05 - val_loss: 2.8314e-05 - val_mse: 2.8314e-05
Epoch 14/700
142/142 [==============================] - 0s 2ms/step - loss: 2.7545e-05 - mse: 2.7545e-05 - val_loss: 2.6867e-05 - val_mse: 2.6867e-05
Epoch 15/700
142/142 [==============================] - 0s 2ms/step - loss: 2.6600e-05 - mse: 2.6600e-05 - val_loss: 2.5594e-05 - val_mse: 2.5594e-05
Epoch 16/700
142/142 [==============================] - 0s 2ms/step - loss: 2.6069e-05 - mse: 2.6069e-05 - val_loss: 2.7239e-05 - val_mse: 2.7239e-05
Epoch 17/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5456e-05 - mse: 2.5456e-05 - val_loss: 2.5045e-05 - val_mse: 2.5045e-05
Epoch 18/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5144e-05 - mse: 2.5144e-05 - val_loss: 2.5446e-05 - val_mse: 2.5446e-05
Epoch 19/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5115e-05 - mse: 2.5115e-05 - val_loss: 2.4581e-05 - val_mse: 2.4581e-05
Epoch 20/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4735e-05 - mse: 2.4735e-05 - val_loss: 2.4208e-05 - val_mse: 2.4208e-05
Epoch 21/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4446e-05 - mse: 2.4446e-05 - val_loss: 2.4709e-05 - val_mse: 2.4709e-05
Epoch 22/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4415e-05 - mse: 2.4415e-05 - val_loss: 2.4662e-05 - val_mse: 2.4662e-05
Epoch 23/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4095e-05 - mse: 2.4095e-05 - val_loss: 2.3363e-05 - val_mse: 2.3363e-05
Epoch 24/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4061e-05 - mse: 2.4061e-05 - val_loss: 2.4560e-05 - val_mse: 2.4560e-05
Epoch 25/700
142/142 [==============================] - 0s 2ms/step - loss: 2.3331e-05 - mse: 2.3331e-05 - val_loss: 2.3319e-05 - val_mse: 2.3319e-05
Epoch 26/700
142/142 [==============================] - 0s 2ms/step - loss: 2.2947e-05 - mse: 2.2947e-05 - val_loss: 2.3465e-05 - val_mse: 2.3465e-05
Epoch 27/700
142/142 [==============================] - 0s 2ms/step - loss: 2.2663e-05 - mse: 2.2663e-05 - val_loss: 2.2288e-05 - val_mse: 2.2288e-05
Epoch 28/700
142/142 [==============================] - 0s 2ms/step - loss: 2.2269e-05 - mse: 2.2269e-05 - val_loss: 2.2062e-05 - val_mse: 2.2062e-05
Epoch 29/700
142/142 [==============================] - 0s 2ms/step - loss: 2.2062e-05 - mse: 2.2062e-05 - val_loss: 2.1555e-05 - val_mse: 2.1555e-05
Epoch 30/700
142/142 [==============================] - 0s 2ms/step - loss: 2.1623e-05 - mse: 2.1623e-05 - val_loss: 2.1288e-05 - val_mse: 2.1288e-05
Epoch 31/700
142/142 [==============================] - 0s 2ms/step - loss: 2.1070e-05 - mse: 2.1070e-05 - val_loss: 2.0975e-05 - val_mse: 2.0975e-05
Epoch 32/700
142/142 [==============================] - 0s 2ms/step - loss: 2.0849e-05 - mse: 2.0849e-05 - val_loss: 2.0776e-05 - val_mse: 2.0776e-05
Epoch 33/700
142/142 [==============================] - 0s 2ms/step - loss: 2.0512e-05 - mse: 2.0512e-05 - val_loss: 2.0079e-05 - val_mse: 2.0079e-05
Epoch 34/700
142/142 [==============================] - 0s 2ms/step - loss: 2.0009e-05 - mse: 2.0009e-05 - val_loss: 1.9775e-05 - val_mse: 1.9775e-05
Epoch 35/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9613e-05 - mse: 1.9613e-05 - val_loss: 2.0653e-05 - val_mse: 2.0653e-05
Epoch 36/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9288e-05 - mse: 1.9288e-05 - val_loss: 1.8985e-05 - val_mse: 1.8985e-05
Epoch 37/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8640e-05 - mse: 1.8640e-05 - val_loss: 1.8776e-05 - val_mse: 1.8776e-05
Epoch 38/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8420e-05 - mse: 1.8420e-05 - val_loss: 1.8279e-05 - val_mse: 1.8279e-05
Epoch 39/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7976e-05 - mse: 1.7976e-05 - val_loss: 1.7663e-05 - val_mse: 1.7663e-05
Epoch 40/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7514e-05 - mse: 1.7514e-05 - val_loss: 1.7499e-05 - val_mse: 1.7499e-05
Epoch 41/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7211e-05 - mse: 1.7211e-05 - val_loss: 1.6851e-05 - val_mse: 1.6851e-05
Epoch 42/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6698e-05 - mse: 1.6698e-05 - val_loss: 1.7022e-05 - val_mse: 1.7022e-05
Epoch 43/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6414e-05 - mse: 1.6414e-05 - val_loss: 1.6256e-05 - val_mse: 1.6256e-05
Epoch 44/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6108e-05 - mse: 1.6108e-05 - val_loss: 1.5767e-05 - val_mse: 1.5767e-05
Epoch 45/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5775e-05 - mse: 1.5775e-05 - val_loss: 1.6060e-05 - val_mse: 1.6060e-05
Epoch 46/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5643e-05 - mse: 1.5643e-05 - val_loss: 1.5333e-05 - val_mse: 1.5333e-05
Epoch 47/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5057e-05 - mse: 1.5057e-05 - val_loss: 1.5073e-05 - val_mse: 1.5073e-05
Epoch 48/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4708e-05 - mse: 1.4708e-05 - val_loss: 1.4667e-05 - val_mse: 1.4667e-05
Epoch 49/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4553e-05 - mse: 1.4553e-05 - val_loss: 1.4488e-05 - val_mse: 1.4488e-05
Epoch 50/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4364e-05 - mse: 1.4364e-05 - val_loss: 1.3975e-05 - val_mse: 1.3975e-05
Epoch 51/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3986e-05 - mse: 1.3986e-05 - val_loss: 1.4223e-05 - val_mse: 1.4223e-05
Epoch 52/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3479e-05 - mse: 1.3479e-05 - val_loss: 1.3964e-05 - val_mse: 1.3964e-05
Epoch 53/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3292e-05 - mse: 1.3292e-05 - val_loss: 1.3270e-05 - val_mse: 1.3270e-05
Epoch 54/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3018e-05 - mse: 1.3018e-05 - val_loss: 1.3879e-05 - val_mse: 1.3879e-05
Epoch 55/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2641e-05 - mse: 1.2641e-05 - val_loss: 1.2411e-05 - val_mse: 1.2411e-05
Epoch 56/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2441e-05 - mse: 1.2441e-05 - val_loss: 1.2367e-05 - val_mse: 1.2367e-05
Epoch 57/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2409e-05 - mse: 1.2409e-05 - val_loss: 1.2927e-05 - val_mse: 1.2927e-05
Epoch 58/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1967e-05 - mse: 1.1967e-05 - val_loss: 1.2266e-05 - val_mse: 1.2266e-05
Epoch 59/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1680e-05 - mse: 1.1680e-05 - val_loss: 1.1234e-05 - val_mse: 1.1234e-05
Epoch 60/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1258e-05 - mse: 1.1258e-05 - val_loss: 1.1203e-05 - val_mse: 1.1203e-05
Epoch 61/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0683e-05 - mse: 1.0683e-05 - val_loss: 1.0489e-05 - val_mse: 1.0489e-05
Epoch 62/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0471e-05 - mse: 1.0471e-05 - val_loss: 1.0145e-05 - val_mse: 1.0145e-05
Epoch 63/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0108e-05 - mse: 1.0108e-05 - val_loss: 9.5963e-06 - val_mse: 9.5963e-06
Epoch 64/700
142/142 [==============================] - 0s 2ms/step - loss: 9.6301e-06 - mse: 9.6301e-06 - val_loss: 9.3115e-06 - val_mse: 9.3115e-06
Epoch 65/700
142/142 [==============================] - 0s 2ms/step - loss: 9.1588e-06 - mse: 9.1588e-06 - val_loss: 8.7716e-06 - val_mse: 8.7716e-06
Epoch 66/700
142/142 [==============================] - 0s 2ms/step - loss: 8.7787e-06 - mse: 8.7787e-06 - val_loss: 9.7685e-06 - val_mse: 9.7685e-06
Epoch 67/700
142/142 [==============================] - 0s 2ms/step - loss: 8.3238e-06 - mse: 8.3238e-06 - val_loss: 8.2768e-06 - val_mse: 8.2768e-06
Epoch 68/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8296e-06 - mse: 7.8296e-06 - val_loss: 7.5962e-06 - val_mse: 7.5962e-06
Epoch 69/700
142/142 [==============================] - 0s 2ms/step - loss: 7.4967e-06 - mse: 7.4967e-06 - val_loss: 7.9796e-06 - val_mse: 7.9796e-06
Epoch 70/700
142/142 [==============================] - 0s 2ms/step - loss: 6.8790e-06 - mse: 6.8790e-06 - val_loss: 7.2819e-06 - val_mse: 7.2819e-06
Epoch 71/700
142/142 [==============================] - 0s 2ms/step - loss: 6.5876e-06 - mse: 6.5876e-06 - val_loss: 6.2421e-06 - val_mse: 6.2421e-06
Epoch 72/700
142/142 [==============================] - 0s 2ms/step - loss: 6.2111e-06 - mse: 6.2111e-06 - val_loss: 5.9616e-06 - val_mse: 5.9616e-06
Epoch 73/700
142/142 [==============================] - 0s 2ms/step - loss: 5.9995e-06 - mse: 5.9995e-06 - val_loss: 6.5872e-06 - val_mse: 6.5872e-06
Epoch 74/700
142/142 [==============================] - 0s 2ms/step - loss: 5.7115e-06 - mse: 5.7115e-06 - val_loss: 5.8805e-06 - val_mse: 5.8805e-06
Epoch 75/700
142/142 [==============================] - 0s 2ms/step - loss: 5.3159e-06 - mse: 5.3159e-06 - val_loss: 5.0692e-06 - val_mse: 5.0692e-06
Epoch 76/700
142/142 [==============================] - 0s 2ms/step - loss: 5.4546e-06 - mse: 5.4546e-06 - val_loss: 4.7572e-06 - val_mse: 4.7572e-06
Epoch 77/700
142/142 [==============================] - 0s 2ms/step - loss: 4.9336e-06 - mse: 4.9336e-06 - val_loss: 4.7029e-06 - val_mse: 4.7029e-06
Epoch 78/700
142/142 [==============================] - 0s 2ms/step - loss: 4.6917e-06 - mse: 4.6917e-06 - val_loss: 4.7486e-06 - val_mse: 4.7486e-06
Epoch 79/700
142/142 [==============================] - 0s 2ms/step - loss: 4.7064e-06 - mse: 4.7064e-06 - val_loss: 4.6000e-06 - val_mse: 4.6000e-06
Epoch 80/700
142/142 [==============================] - 0s 2ms/step - loss: 4.6125e-06 - mse: 4.6125e-06 - val_loss: 4.2830e-06 - val_mse: 4.2830e-06
Epoch 81/700
142/142 [==============================] - 0s 2ms/step - loss: 4.4000e-06 - mse: 4.4000e-06 - val_loss: 4.2021e-06 - val_mse: 4.2021e-06
Epoch 82/700
142/142 [==============================] - 0s 2ms/step - loss: 4.3174e-06 - mse: 4.3174e-06 - val_loss: 4.3824e-06 - val_mse: 4.3824e-06
Epoch 83/700
142/142 [==============================] - 0s 2ms/step - loss: 4.3178e-06 - mse: 4.3178e-06 - val_loss: 4.0126e-06 - val_mse: 4.0126e-06
Epoch 84/700
142/142 [==============================] - 0s 2ms/step - loss: 4.1701e-06 - mse: 4.1701e-06 - val_loss: 4.1574e-06 - val_mse: 4.1574e-06
Epoch 85/700
142/142 [==============================] - 0s 2ms/step - loss: 4.2634e-06 - mse: 4.2634e-06 - val_loss: 4.1241e-06 - val_mse: 4.1241e-06
Epoch 86/700
142/142 [==============================] - 0s 2ms/step - loss: 4.2194e-06 - mse: 4.2194e-06 - val_loss: 4.0403e-06 - val_mse: 4.0403e-06
Epoch 87/700
142/142 [==============================] - 0s 2ms/step - loss: 4.1250e-06 - mse: 4.1250e-06 - val_loss: 4.3991e-06 - val_mse: 4.3991e-06
Epoch 88/700
142/142 [==============================] - 0s 2ms/step - loss: 4.2159e-06 - mse: 4.2159e-06 - val_loss: 3.8234e-06 - val_mse: 3.8234e-06
Epoch 89/700
142/142 [==============================] - 0s 2ms/step - loss: 4.0505e-06 - mse: 4.0505e-06 - val_loss: 3.8457e-06 - val_mse: 3.8457e-06
Epoch 90/700
142/142 [==============================] - 0s 2ms/step - loss: 4.2580e-06 - mse: 4.2580e-06 - val_loss: 4.0980e-06 - val_mse: 4.0980e-06
Epoch 91/700
142/142 [==============================] - 0s 2ms/step - loss: 3.9333e-06 - mse: 3.9333e-06 - val_loss: 3.7788e-06 - val_mse: 3.7788e-06
Epoch 92/700
142/142 [==============================] - 0s 2ms/step - loss: 4.2675e-06 - mse: 4.2675e-06 - val_loss: 3.7064e-06 - val_mse: 3.7064e-06
Epoch 93/700
142/142 [==============================] - 0s 2ms/step - loss: 3.8921e-06 - mse: 3.8921e-06 - val_loss: 3.8616e-06 - val_mse: 3.8616e-06
Epoch 94/700
142/142 [==============================] - 0s 2ms/step - loss: 3.9124e-06 - mse: 3.9124e-06 - val_loss: 4.2003e-06 - val_mse: 4.2003e-06
Epoch 95/700
142/142 [==============================] - 0s 2ms/step - loss: 4.0001e-06 - mse: 4.0001e-06 - val_loss: 4.4680e-06 - val_mse: 4.4680e-06
Epoch 96/700
142/142 [==============================] - 0s 2ms/step - loss: 3.9523e-06 - mse: 3.9523e-06 - val_loss: 4.3234e-06 - val_mse: 4.3234e-06
Epoch 97/700
142/142 [==============================] - 0s 2ms/step - loss: 3.8501e-06 - mse: 3.8501e-06 - val_loss: 3.6939e-06 - val_mse: 3.6939e-06
Epoch 98/700
142/142 [==============================] - 0s 2ms/step - loss: 3.9122e-06 - mse: 3.9122e-06 - val_loss: 4.0291e-06 - val_mse: 4.0291e-06
Epoch 99/700
142/142 [==============================] - 0s 2ms/step - loss: 3.9413e-06 - mse: 3.9413e-06 - val_loss: 3.5712e-06 - val_mse: 3.5712e-06
Epoch 100/700
142/142 [==============================] - 0s 2ms/step - loss: 3.8332e-06 - mse: 3.8332e-06 - val_loss: 3.6051e-06 - val_mse: 3.6051e-06
Epoch 101/700
142/142 [==============================] - 0s 2ms/step - loss: 3.8128e-06 - mse: 3.8128e-06 - val_loss: 3.6263e-06 - val_mse: 3.6263e-06
Epoch 102/700
142/142 [==============================] - 0s 2ms/step - loss: 3.8030e-06 - mse: 3.8030e-06 - val_loss: 3.6661e-06 - val_mse: 3.6661e-06
Epoch 103/700
142/142 [==============================] - 0s 2ms/step - loss: 3.8095e-06 - mse: 3.8095e-06 - val_loss: 5.2198e-06 - val_mse: 5.2198e-06
Epoch 104/700
142/142 [==============================] - 0s 2ms/step - loss: 3.7951e-06 - mse: 3.7951e-06 - val_loss: 3.6381e-06 - val_mse: 3.6381e-06
Epoch 105/700
142/142 [==============================] - 0s 2ms/step - loss: 3.7567e-06 - mse: 3.7567e-06 - val_loss: 3.9087e-06 - val_mse: 3.9087e-06
Epoch 106/700
142/142 [==============================] - 0s 2ms/step - loss: 3.8456e-06 - mse: 3.8456e-06 - val_loss: 4.7997e-06 - val_mse: 4.7997e-06
Epoch 107/700
142/142 [==============================] - 0s 2ms/step - loss: 3.7875e-06 - mse: 3.7875e-06 - val_loss: 3.5871e-06 - val_mse: 3.5871e-06
Epoch 108/700
142/142 [==============================] - 0s 2ms/step - loss: 3.6586e-06 - mse: 3.6586e-06 - val_loss: 3.4109e-06 - val_mse: 3.4109e-06
Epoch 109/700
142/142 [==============================] - 0s 2ms/step - loss: 3.6356e-06 - mse: 3.6356e-06 - val_loss: 3.3819e-06 - val_mse: 3.3819e-06
Epoch 110/700
142/142 [==============================] - 0s 2ms/step - loss: 3.5675e-06 - mse: 3.5675e-06 - val_loss: 3.3571e-06 - val_mse: 3.3571e-06
Epoch 111/700
142/142 [==============================] - 0s 2ms/step - loss: 3.4951e-06 - mse: 3.4951e-06 - val_loss: 3.3921e-06 - val_mse: 3.3921e-06
Epoch 112/700
142/142 [==============================] - 0s 2ms/step - loss: 3.5948e-06 - mse: 3.5948e-06 - val_loss: 3.9529e-06 - val_mse: 3.9529e-06
Epoch 113/700
142/142 [==============================] - 0s 2ms/step - loss: 3.4748e-06 - mse: 3.4748e-06 - val_loss: 3.6755e-06 - val_mse: 3.6755e-06
Epoch 114/700
142/142 [==============================] - 0s 2ms/step - loss: 3.4941e-06 - mse: 3.4941e-06 - val_loss: 3.5029e-06 - val_mse: 3.5029e-06
Epoch 115/700
142/142 [==============================] - 0s 2ms/step - loss: 3.4350e-06 - mse: 3.4350e-06 - val_loss: 3.9733e-06 - val_mse: 3.9733e-06
Epoch 116/700
142/142 [==============================] - 0s 2ms/step - loss: 3.4151e-06 - mse: 3.4151e-06 - val_loss: 3.9147e-06 - val_mse: 3.9147e-06
Epoch 117/700
142/142 [==============================] - 0s 2ms/step - loss: 3.4537e-06 - mse: 3.4537e-06 - val_loss: 3.2070e-06 - val_mse: 3.2070e-06
Epoch 118/700
142/142 [==============================] - 0s 2ms/step - loss: 3.2518e-06 - mse: 3.2518e-06 - val_loss: 3.2028e-06 - val_mse: 3.2028e-06
Epoch 119/700
142/142 [==============================] - 0s 2ms/step - loss: 3.1837e-06 - mse: 3.1837e-06 - val_loss: 3.0604e-06 - val_mse: 3.0604e-06
Epoch 120/700
142/142 [==============================] - 0s 2ms/step - loss: 3.1723e-06 - mse: 3.1723e-06 - val_loss: 3.0858e-06 - val_mse: 3.0858e-06
Epoch 121/700
142/142 [==============================] - 0s 2ms/step - loss: 3.2109e-06 - mse: 3.2109e-06 - val_loss: 3.8566e-06 - val_mse: 3.8566e-06
Epoch 122/700
142/142 [==============================] - 0s 2ms/step - loss: 3.1795e-06 - mse: 3.1795e-06 - val_loss: 3.7558e-06 - val_mse: 3.7558e-06
Epoch 123/700
142/142 [==============================] - 0s 2ms/step - loss: 3.1355e-06 - mse: 3.1355e-06 - val_loss: 3.0863e-06 - val_mse: 3.0863e-06
Epoch 124/700
142/142 [==============================] - 0s 2ms/step - loss: 2.9923e-06 - mse: 2.9923e-06 - val_loss: 2.8432e-06 - val_mse: 2.8432e-06
Epoch 125/700
142/142 [==============================] - 0s 2ms/step - loss: 2.9979e-06 - mse: 2.9979e-06 - val_loss: 3.2011e-06 - val_mse: 3.2011e-06
Epoch 126/700
142/142 [==============================] - 0s 2ms/step - loss: 3.0691e-06 - mse: 3.0691e-06 - val_loss: 2.9475e-06 - val_mse: 2.9475e-06
Epoch 127/700
142/142 [==============================] - 0s 2ms/step - loss: 2.8930e-06 - mse: 2.8930e-06 - val_loss: 3.0564e-06 - val_mse: 3.0564e-06
Epoch 128/700
142/142 [==============================] - 0s 2ms/step - loss: 2.9647e-06 - mse: 2.9647e-06 - val_loss: 3.2874e-06 - val_mse: 3.2874e-06
Epoch 129/700
142/142 [==============================] - 0s 2ms/step - loss: 2.9256e-06 - mse: 2.9256e-06 - val_loss: 2.8070e-06 - val_mse: 2.8070e-06
Epoch 130/700
142/142 [==============================] - 0s 2ms/step - loss: 2.9028e-06 - mse: 2.9028e-06 - val_loss: 3.4095e-06 - val_mse: 3.4095e-06
Epoch 131/700
142/142 [==============================] - 0s 2ms/step - loss: 2.9934e-06 - mse: 2.9934e-06 - val_loss: 2.6288e-06 - val_mse: 2.6288e-06
Epoch 132/700
142/142 [==============================] - 0s 2ms/step - loss: 2.7785e-06 - mse: 2.7785e-06 - val_loss: 2.5877e-06 - val_mse: 2.5877e-06
Epoch 133/700
142/142 [==============================] - 0s 2ms/step - loss: 2.8009e-06 - mse: 2.8009e-06 - val_loss: 2.5380e-06 - val_mse: 2.5380e-06
Epoch 134/700
142/142 [==============================] - 0s 2ms/step - loss: 2.6792e-06 - mse: 2.6792e-06 - val_loss: 3.4028e-06 - val_mse: 3.4028e-06
Epoch 135/700
142/142 [==============================] - 0s 2ms/step - loss: 2.6792e-06 - mse: 2.6792e-06 - val_loss: 2.5491e-06 - val_mse: 2.5491e-06
Epoch 136/700
142/142 [==============================] - 0s 2ms/step - loss: 2.6579e-06 - mse: 2.6579e-06 - val_loss: 2.5311e-06 - val_mse: 2.5311e-06
Epoch 137/700
142/142 [==============================] - 0s 2ms/step - loss: 2.7072e-06 - mse: 2.7072e-06 - val_loss: 2.5279e-06 - val_mse: 2.5279e-06
Epoch 138/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5650e-06 - mse: 2.5650e-06 - val_loss: 2.8701e-06 - val_mse: 2.8701e-06
Epoch 139/700
142/142 [==============================] - 0s 2ms/step - loss: 2.6272e-06 - mse: 2.6272e-06 - val_loss: 2.3327e-06 - val_mse: 2.3327e-06
Epoch 140/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5942e-06 - mse: 2.5942e-06 - val_loss: 2.4253e-06 - val_mse: 2.4253e-06
Epoch 141/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5971e-06 - mse: 2.5971e-06 - val_loss: 2.5316e-06 - val_mse: 2.5316e-06
Epoch 142/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4517e-06 - mse: 2.4517e-06 - val_loss: 2.2936e-06 - val_mse: 2.2936e-06
Epoch 143/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5778e-06 - mse: 2.5778e-06 - val_loss: 2.4225e-06 - val_mse: 2.4225e-06
Epoch 144/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4623e-06 - mse: 2.4623e-06 - val_loss: 2.2737e-06 - val_mse: 2.2737e-06
Epoch 145/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4166e-06 - mse: 2.4166e-06 - val_loss: 2.3751e-06 - val_mse: 2.3751e-06
Epoch 146/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5355e-06 - mse: 2.5355e-06 - val_loss: 3.1643e-06 - val_mse: 3.1643e-06
Epoch 147/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4142e-06 - mse: 2.4142e-06 - val_loss: 2.4713e-06 - val_mse: 2.4713e-06
Epoch 148/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5176e-06 - mse: 2.5176e-06 - val_loss: 2.2878e-06 - val_mse: 2.2878e-06
Epoch 149/700
142/142 [==============================] - 0s 2ms/step - loss: 2.5506e-06 - mse: 2.5506e-06 - val_loss: 2.9055e-06 - val_mse: 2.9055e-06
Epoch 150/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4419e-06 - mse: 2.4419e-06 - val_loss: 2.2676e-06 - val_mse: 2.2676e-06
Epoch 151/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4029e-06 - mse: 2.4029e-06 - val_loss: 2.3824e-06 - val_mse: 2.3824e-06
Epoch 152/700
142/142 [==============================] - 0s 2ms/step - loss: 2.3878e-06 - mse: 2.3878e-06 - val_loss: 2.2811e-06 - val_mse: 2.2811e-06
Epoch 153/700
142/142 [==============================] - 0s 2ms/step - loss: 2.3457e-06 - mse: 2.3457e-06 - val_loss: 2.2619e-06 - val_mse: 2.2619e-06
Epoch 154/700
142/142 [==============================] - 0s 2ms/step - loss: 2.2996e-06 - mse: 2.2996e-06 - val_loss: 2.3008e-06 - val_mse: 2.3008e-06
Epoch 155/700
142/142 [==============================] - 0s 2ms/step - loss: 2.4167e-06 - mse: 2.4167e-06 - val_loss: 2.4913e-06 - val_mse: 2.4913e-06
Epoch 156/700
142/142 [==============================] - 0s 2ms/step - loss: 2.2416e-06 - mse: 2.2416e-06 - val_loss: 2.0401e-06 - val_mse: 2.0401e-06
Epoch 157/700
142/142 [==============================] - 0s 2ms/step - loss: 2.1423e-06 - mse: 2.1423e-06 - val_loss: 1.9163e-06 - val_mse: 1.9163e-06
Epoch 158/700
142/142 [==============================] - 0s 2ms/step - loss: 2.0583e-06 - mse: 2.0583e-06 - val_loss: 2.2285e-06 - val_mse: 2.2285e-06
Epoch 159/700
142/142 [==============================] - 0s 2ms/step - loss: 2.1580e-06 - mse: 2.1580e-06 - val_loss: 2.1592e-06 - val_mse: 2.1592e-06
Epoch 160/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9882e-06 - mse: 1.9882e-06 - val_loss: 1.8847e-06 - val_mse: 1.8847e-06
Epoch 161/700
142/142 [==============================] - 0s 2ms/step - loss: 2.0554e-06 - mse: 2.0554e-06 - val_loss: 1.8690e-06 - val_mse: 1.8690e-06
Epoch 162/700
142/142 [==============================] - 0s 2ms/step - loss: 2.1573e-06 - mse: 2.1573e-06 - val_loss: 2.1519e-06 - val_mse: 2.1519e-06
Epoch 163/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9178e-06 - mse: 1.9178e-06 - val_loss: 1.8653e-06 - val_mse: 1.8653e-06
Epoch 164/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9433e-06 - mse: 1.9433e-06 - val_loss: 2.5234e-06 - val_mse: 2.5234e-06
Epoch 165/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9974e-06 - mse: 1.9974e-06 - val_loss: 1.9012e-06 - val_mse: 1.9012e-06
Epoch 166/700
142/142 [==============================] - 0s 2ms/step - loss: 2.0717e-06 - mse: 2.0717e-06 - val_loss: 1.9189e-06 - val_mse: 1.9189e-06
Epoch 167/700
142/142 [==============================] - 0s 2ms/step - loss: 2.0065e-06 - mse: 2.0065e-06 - val_loss: 1.8168e-06 - val_mse: 1.8168e-06
Epoch 168/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9869e-06 - mse: 1.9869e-06 - val_loss: 1.9986e-06 - val_mse: 1.9986e-06
Epoch 169/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9797e-06 - mse: 1.9797e-06 - val_loss: 1.7482e-06 - val_mse: 1.7482e-06
Epoch 170/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9344e-06 - mse: 1.9344e-06 - val_loss: 1.8689e-06 - val_mse: 1.8689e-06
Epoch 171/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9623e-06 - mse: 1.9623e-06 - val_loss: 1.9433e-06 - val_mse: 1.9433e-06
Epoch 172/700
142/142 [==============================] - 0s 2ms/step - loss: 2.0704e-06 - mse: 2.0704e-06 - val_loss: 1.7675e-06 - val_mse: 1.7675e-06
Epoch 173/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9393e-06 - mse: 1.9393e-06 - val_loss: 2.1516e-06 - val_mse: 2.1516e-06
Epoch 174/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9793e-06 - mse: 1.9793e-06 - val_loss: 1.7057e-06 - val_mse: 1.7057e-06
Epoch 175/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9518e-06 - mse: 1.9518e-06 - val_loss: 1.8150e-06 - val_mse: 1.8150e-06
Epoch 176/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9264e-06 - mse: 1.9264e-06 - val_loss: 2.0613e-06 - val_mse: 2.0613e-06
Epoch 177/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8382e-06 - mse: 1.8382e-06 - val_loss: 1.8560e-06 - val_mse: 1.8560e-06
Epoch 178/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8665e-06 - mse: 1.8665e-06 - val_loss: 1.6948e-06 - val_mse: 1.6948e-06
Epoch 179/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9202e-06 - mse: 1.9202e-06 - val_loss: 2.0363e-06 - val_mse: 2.0363e-06
Epoch 180/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8936e-06 - mse: 1.8936e-06 - val_loss: 1.8177e-06 - val_mse: 1.8177e-06
Epoch 181/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9515e-06 - mse: 1.9515e-06 - val_loss: 1.7732e-06 - val_mse: 1.7732e-06
Epoch 182/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9240e-06 - mse: 1.9240e-06 - val_loss: 1.8084e-06 - val_mse: 1.8084e-06
Epoch 183/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8502e-06 - mse: 1.8502e-06 - val_loss: 1.7841e-06 - val_mse: 1.7841e-06
Epoch 184/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8521e-06 - mse: 1.8521e-06 - val_loss: 1.6723e-06 - val_mse: 1.6723e-06
Epoch 185/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8712e-06 - mse: 1.8712e-06 - val_loss: 1.6767e-06 - val_mse: 1.6767e-06
Epoch 186/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8616e-06 - mse: 1.8616e-06 - val_loss: 1.7690e-06 - val_mse: 1.7690e-06
Epoch 187/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8666e-06 - mse: 1.8666e-06 - val_loss: 1.8384e-06 - val_mse: 1.8384e-06
Epoch 188/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8799e-06 - mse: 1.8799e-06 - val_loss: 2.3914e-06 - val_mse: 2.3914e-06
Epoch 189/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9375e-06 - mse: 1.9375e-06 - val_loss: 2.5198e-06 - val_mse: 2.5198e-06
Epoch 190/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9101e-06 - mse: 1.9101e-06 - val_loss: 1.6177e-06 - val_mse: 1.6177e-06
Epoch 191/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7985e-06 - mse: 1.7985e-06 - val_loss: 1.9597e-06 - val_mse: 1.9597e-06
Epoch 192/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7842e-06 - mse: 1.7842e-06 - val_loss: 1.7145e-06 - val_mse: 1.7145e-06
Epoch 193/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7713e-06 - mse: 1.7713e-06 - val_loss: 1.5993e-06 - val_mse: 1.5993e-06
Epoch 194/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8165e-06 - mse: 1.8165e-06 - val_loss: 1.7312e-06 - val_mse: 1.7312e-06
Epoch 195/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8538e-06 - mse: 1.8538e-06 - val_loss: 1.6675e-06 - val_mse: 1.6675e-06
Epoch 196/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7899e-06 - mse: 1.7899e-06 - val_loss: 1.6230e-06 - val_mse: 1.6230e-06
Epoch 197/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8146e-06 - mse: 1.8146e-06 - val_loss: 1.6054e-06 - val_mse: 1.6054e-06
Epoch 198/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7571e-06 - mse: 1.7571e-06 - val_loss: 1.6557e-06 - val_mse: 1.6557e-06
Epoch 199/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8720e-06 - mse: 1.8720e-06 - val_loss: 2.1931e-06 - val_mse: 2.1931e-06
Epoch 200/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8198e-06 - mse: 1.8198e-06 - val_loss: 1.6945e-06 - val_mse: 1.6945e-06
Epoch 201/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7781e-06 - mse: 1.7781e-06 - val_loss: 2.0501e-06 - val_mse: 2.0501e-06
Epoch 202/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8690e-06 - mse: 1.8690e-06 - val_loss: 1.7467e-06 - val_mse: 1.7467e-06
Epoch 203/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7713e-06 - mse: 1.7713e-06 - val_loss: 1.8343e-06 - val_mse: 1.8343e-06
Epoch 204/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8336e-06 - mse: 1.8336e-06 - val_loss: 2.2682e-06 - val_mse: 2.2682e-06
Epoch 205/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8136e-06 - mse: 1.8136e-06 - val_loss: 1.7124e-06 - val_mse: 1.7124e-06
Epoch 206/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7819e-06 - mse: 1.7819e-06 - val_loss: 1.5719e-06 - val_mse: 1.5719e-06
Epoch 207/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7781e-06 - mse: 1.7781e-06 - val_loss: 1.8519e-06 - val_mse: 1.8519e-06
Epoch 208/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8092e-06 - mse: 1.8092e-06 - val_loss: 1.5572e-06 - val_mse: 1.5572e-06
Epoch 209/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7379e-06 - mse: 1.7379e-06 - val_loss: 1.6350e-06 - val_mse: 1.6350e-06
Epoch 210/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7234e-06 - mse: 1.7234e-06 - val_loss: 1.5766e-06 - val_mse: 1.5766e-06
Epoch 211/700
142/142 [==============================] - 0s 3ms/step - loss: 1.7342e-06 - mse: 1.7342e-06 - val_loss: 1.6439e-06 - val_mse: 1.6439e-06
Epoch 212/700
142/142 [==============================] - 0s 3ms/step - loss: 1.7840e-06 - mse: 1.7840e-06 - val_loss: 1.8503e-06 - val_mse: 1.8503e-06
Epoch 213/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7472e-06 - mse: 1.7472e-06 - val_loss: 1.7141e-06 - val_mse: 1.7141e-06
Epoch 214/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8294e-06 - mse: 1.8294e-06 - val_loss: 1.5600e-06 - val_mse: 1.5600e-06
Epoch 215/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7577e-06 - mse: 1.7577e-06 - val_loss: 1.9313e-06 - val_mse: 1.9313e-06
Epoch 216/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7404e-06 - mse: 1.7404e-06 - val_loss: 2.2460e-06 - val_mse: 2.2460e-06
Epoch 217/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7825e-06 - mse: 1.7825e-06 - val_loss: 1.8295e-06 - val_mse: 1.8295e-06
Epoch 218/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7438e-06 - mse: 1.7438e-06 - val_loss: 1.5651e-06 - val_mse: 1.5651e-06
Epoch 219/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7781e-06 - mse: 1.7781e-06 - val_loss: 1.5920e-06 - val_mse: 1.5920e-06
Epoch 220/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6681e-06 - mse: 1.6681e-06 - val_loss: 1.6235e-06 - val_mse: 1.6235e-06
Epoch 221/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7035e-06 - mse: 1.7035e-06 - val_loss: 1.9393e-06 - val_mse: 1.9393e-06
Epoch 222/700
142/142 [==============================] - 0s 2ms/step - loss: 1.9324e-06 - mse: 1.9324e-06 - val_loss: 1.5876e-06 - val_mse: 1.5876e-06
Epoch 223/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7993e-06 - mse: 1.7993e-06 - val_loss: 1.6493e-06 - val_mse: 1.6493e-06
Epoch 224/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6789e-06 - mse: 1.6789e-06 - val_loss: 1.7913e-06 - val_mse: 1.7913e-06
Epoch 225/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7515e-06 - mse: 1.7515e-06 - val_loss: 1.7757e-06 - val_mse: 1.7757e-06
Epoch 226/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7096e-06 - mse: 1.7096e-06 - val_loss: 1.7952e-06 - val_mse: 1.7952e-06
Epoch 227/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7158e-06 - mse: 1.7158e-06 - val_loss: 1.6339e-06 - val_mse: 1.6339e-06
Epoch 228/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7398e-06 - mse: 1.7398e-06 - val_loss: 1.6639e-06 - val_mse: 1.6639e-06
Epoch 229/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7610e-06 - mse: 1.7610e-06 - val_loss: 2.7305e-06 - val_mse: 2.7305e-06
Epoch 230/700
142/142 [==============================] - 0s 2ms/step - loss: 1.8694e-06 - mse: 1.8694e-06 - val_loss: 1.5846e-06 - val_mse: 1.5846e-06
Epoch 231/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6318e-06 - mse: 1.6318e-06 - val_loss: 1.6638e-06 - val_mse: 1.6638e-06
Epoch 232/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6827e-06 - mse: 1.6827e-06 - val_loss: 2.0172e-06 - val_mse: 2.0172e-06
Epoch 233/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6748e-06 - mse: 1.6748e-06 - val_loss: 1.5042e-06 - val_mse: 1.5042e-06
Epoch 234/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6963e-06 - mse: 1.6963e-06 - val_loss: 1.4959e-06 - val_mse: 1.4959e-06
Epoch 235/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6453e-06 - mse: 1.6453e-06 - val_loss: 1.7960e-06 - val_mse: 1.7960e-06
Epoch 236/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7306e-06 - mse: 1.7306e-06 - val_loss: 1.5997e-06 - val_mse: 1.5997e-06
Epoch 237/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6961e-06 - mse: 1.6961e-06 - val_loss: 1.6318e-06 - val_mse: 1.6318e-06
Epoch 238/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7337e-06 - mse: 1.7337e-06 - val_loss: 2.1596e-06 - val_mse: 2.1596e-06
Epoch 239/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7378e-06 - mse: 1.7378e-06 - val_loss: 1.5988e-06 - val_mse: 1.5988e-06
Epoch 240/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6967e-06 - mse: 1.6967e-06 - val_loss: 1.5699e-06 - val_mse: 1.5699e-06
Epoch 241/700
142/142 [==============================] - 0s 2ms/step - loss: 1.7402e-06 - mse: 1.7402e-06 - val_loss: 1.7507e-06 - val_mse: 1.7507e-06
Epoch 242/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6738e-06 - mse: 1.6738e-06 - val_loss: 1.5954e-06 - val_mse: 1.5954e-06
Epoch 243/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6111e-06 - mse: 1.6111e-06 - val_loss: 1.8413e-06 - val_mse: 1.8413e-06
Epoch 244/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6955e-06 - mse: 1.6955e-06 - val_loss: 1.4800e-06 - val_mse: 1.4800e-06
Epoch 245/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5998e-06 - mse: 1.5998e-06 - val_loss: 1.5660e-06 - val_mse: 1.5660e-06
Epoch 246/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6697e-06 - mse: 1.6697e-06 - val_loss: 1.5123e-06 - val_mse: 1.5123e-06
Epoch 247/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6274e-06 - mse: 1.6274e-06 - val_loss: 1.5876e-06 - val_mse: 1.5876e-06
Epoch 248/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6403e-06 - mse: 1.6403e-06 - val_loss: 1.5390e-06 - val_mse: 1.5390e-06
Epoch 249/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6061e-06 - mse: 1.6061e-06 - val_loss: 1.6170e-06 - val_mse: 1.6170e-06
Epoch 250/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6596e-06 - mse: 1.6596e-06 - val_loss: 1.4746e-06 - val_mse: 1.4746e-06
Epoch 251/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5832e-06 - mse: 1.5832e-06 - val_loss: 1.6225e-06 - val_mse: 1.6225e-06
Epoch 252/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6855e-06 - mse: 1.6855e-06 - val_loss: 1.5883e-06 - val_mse: 1.5883e-06
Epoch 253/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6816e-06 - mse: 1.6816e-06 - val_loss: 1.5380e-06 - val_mse: 1.5380e-06
Epoch 254/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6046e-06 - mse: 1.6046e-06 - val_loss: 1.4227e-06 - val_mse: 1.4227e-06
Epoch 255/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6582e-06 - mse: 1.6582e-06 - val_loss: 1.7431e-06 - val_mse: 1.7431e-06
Epoch 256/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6359e-06 - mse: 1.6359e-06 - val_loss: 2.1848e-06 - val_mse: 2.1848e-06
Epoch 257/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6881e-06 - mse: 1.6881e-06 - val_loss: 1.4100e-06 - val_mse: 1.4100e-06
Epoch 258/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5395e-06 - mse: 1.5395e-06 - val_loss: 1.5482e-06 - val_mse: 1.5482e-06
Epoch 259/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5904e-06 - mse: 1.5904e-06 - val_loss: 1.5028e-06 - val_mse: 1.5028e-06
Epoch 260/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5497e-06 - mse: 1.5497e-06 - val_loss: 2.2848e-06 - val_mse: 2.2848e-06
Epoch 261/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5922e-06 - mse: 1.5922e-06 - val_loss: 1.4268e-06 - val_mse: 1.4268e-06
Epoch 262/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5178e-06 - mse: 1.5178e-06 - val_loss: 1.5017e-06 - val_mse: 1.5017e-06
Epoch 263/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6515e-06 - mse: 1.6515e-06 - val_loss: 1.4079e-06 - val_mse: 1.4079e-06
Epoch 264/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5721e-06 - mse: 1.5721e-06 - val_loss: 1.5650e-06 - val_mse: 1.5650e-06
Epoch 265/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5876e-06 - mse: 1.5876e-06 - val_loss: 1.5835e-06 - val_mse: 1.5835e-06
Epoch 266/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5988e-06 - mse: 1.5988e-06 - val_loss: 1.9810e-06 - val_mse: 1.9810e-06
Epoch 267/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5888e-06 - mse: 1.5888e-06 - val_loss: 1.4976e-06 - val_mse: 1.4976e-06
Epoch 268/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5544e-06 - mse: 1.5544e-06 - val_loss: 1.3969e-06 - val_mse: 1.3969e-06
Epoch 269/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5834e-06 - mse: 1.5834e-06 - val_loss: 1.4274e-06 - val_mse: 1.4274e-06
Epoch 270/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4822e-06 - mse: 1.4822e-06 - val_loss: 1.4216e-06 - val_mse: 1.4216e-06
Epoch 271/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5924e-06 - mse: 1.5924e-06 - val_loss: 1.4540e-06 - val_mse: 1.4540e-06
Epoch 272/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4653e-06 - mse: 1.4653e-06 - val_loss: 1.3228e-06 - val_mse: 1.3228e-06
Epoch 273/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4887e-06 - mse: 1.4887e-06 - val_loss: 1.3752e-06 - val_mse: 1.3752e-06
Epoch 274/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5132e-06 - mse: 1.5132e-06 - val_loss: 1.9071e-06 - val_mse: 1.9071e-06
Epoch 275/700
142/142 [==============================] - 0s 2ms/step - loss: 1.6019e-06 - mse: 1.6019e-06 - val_loss: 1.7927e-06 - val_mse: 1.7927e-06
Epoch 276/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5791e-06 - mse: 1.5791e-06 - val_loss: 1.3494e-06 - val_mse: 1.3494e-06
Epoch 277/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4894e-06 - mse: 1.4894e-06 - val_loss: 1.3480e-06 - val_mse: 1.3480e-06
Epoch 278/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5351e-06 - mse: 1.5351e-06 - val_loss: 1.3183e-06 - val_mse: 1.3183e-06
Epoch 279/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5001e-06 - mse: 1.5001e-06 - val_loss: 1.2960e-06 - val_mse: 1.2960e-06
Epoch 280/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4503e-06 - mse: 1.4503e-06 - val_loss: 1.4354e-06 - val_mse: 1.4354e-06
Epoch 281/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4357e-06 - mse: 1.4357e-06 - val_loss: 1.2998e-06 - val_mse: 1.2998e-06
Epoch 282/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4523e-06 - mse: 1.4523e-06 - val_loss: 1.2904e-06 - val_mse: 1.2904e-06
Epoch 283/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4965e-06 - mse: 1.4965e-06 - val_loss: 1.3368e-06 - val_mse: 1.3368e-06
Epoch 284/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4211e-06 - mse: 1.4211e-06 - val_loss: 1.9354e-06 - val_mse: 1.9354e-06
Epoch 285/700
142/142 [==============================] - 0s 2ms/step - loss: 1.5143e-06 - mse: 1.5143e-06 - val_loss: 1.3185e-06 - val_mse: 1.3185e-06
Epoch 286/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4138e-06 - mse: 1.4138e-06 - val_loss: 1.3314e-06 - val_mse: 1.3314e-06
Epoch 287/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3744e-06 - mse: 1.3744e-06 - val_loss: 1.4481e-06 - val_mse: 1.4481e-06
Epoch 288/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3585e-06 - mse: 1.3585e-06 - val_loss: 1.3310e-06 - val_mse: 1.3310e-06
Epoch 289/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3471e-06 - mse: 1.3471e-06 - val_loss: 1.2076e-06 - val_mse: 1.2076e-06
Epoch 290/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3697e-06 - mse: 1.3697e-06 - val_loss: 1.2694e-06 - val_mse: 1.2694e-06
Epoch 291/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3511e-06 - mse: 1.3511e-06 - val_loss: 1.3615e-06 - val_mse: 1.3615e-06
Epoch 292/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4013e-06 - mse: 1.4013e-06 - val_loss: 1.1871e-06 - val_mse: 1.1871e-06
Epoch 293/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2776e-06 - mse: 1.2776e-06 - val_loss: 1.1773e-06 - val_mse: 1.1773e-06
Epoch 294/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3219e-06 - mse: 1.3219e-06 - val_loss: 1.1459e-06 - val_mse: 1.1459e-06
Epoch 295/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3442e-06 - mse: 1.3442e-06 - val_loss: 1.3445e-06 - val_mse: 1.3445e-06
Epoch 296/700
142/142 [==============================] - 0s 2ms/step - loss: 1.3220e-06 - mse: 1.3220e-06 - val_loss: 1.2926e-06 - val_mse: 1.2926e-06
Epoch 297/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2998e-06 - mse: 1.2998e-06 - val_loss: 1.3618e-06 - val_mse: 1.3618e-06
Epoch 298/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2314e-06 - mse: 1.2314e-06 - val_loss: 1.1583e-06 - val_mse: 1.1583e-06
Epoch 299/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2576e-06 - mse: 1.2576e-06 - val_loss: 1.6748e-06 - val_mse: 1.6748e-06
Epoch 300/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2327e-06 - mse: 1.2327e-06 - val_loss: 1.1311e-06 - val_mse: 1.1311e-06
Epoch 301/700
142/142 [==============================] - 0s 2ms/step - loss: 1.4097e-06 - mse: 1.4097e-06 - val_loss: 1.1043e-06 - val_mse: 1.1043e-06
Epoch 302/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2062e-06 - mse: 1.2062e-06 - val_loss: 1.1684e-06 - val_mse: 1.1684e-06
Epoch 303/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1905e-06 - mse: 1.1905e-06 - val_loss: 1.0749e-06 - val_mse: 1.0749e-06
Epoch 304/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1415e-06 - mse: 1.1415e-06 - val_loss: 1.3599e-06 - val_mse: 1.3599e-06
Epoch 305/700
142/142 [==============================] - 0s 2ms/step - loss: 1.2144e-06 - mse: 1.2144e-06 - val_loss: 1.2149e-06 - val_mse: 1.2149e-06
Epoch 306/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1535e-06 - mse: 1.1535e-06 - val_loss: 1.2180e-06 - val_mse: 1.2180e-06
Epoch 307/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1327e-06 - mse: 1.1327e-06 - val_loss: 9.9884e-07 - val_mse: 9.9884e-07
Epoch 308/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1291e-06 - mse: 1.1291e-06 - val_loss: 9.9242e-07 - val_mse: 9.9242e-07
Epoch 309/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1364e-06 - mse: 1.1364e-06 - val_loss: 1.5889e-06 - val_mse: 1.5889e-06
Epoch 310/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1421e-06 - mse: 1.1421e-06 - val_loss: 9.9403e-07 - val_mse: 9.9403e-07
Epoch 311/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0659e-06 - mse: 1.0659e-06 - val_loss: 1.0559e-06 - val_mse: 1.0559e-06
Epoch 312/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0818e-06 - mse: 1.0818e-06 - val_loss: 9.5775e-07 - val_mse: 9.5775e-07
Epoch 313/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1300e-06 - mse: 1.1300e-06 - val_loss: 1.7114e-06 - val_mse: 1.7114e-06
Epoch 314/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1200e-06 - mse: 1.1200e-06 - val_loss: 9.4727e-07 - val_mse: 9.4727e-07
Epoch 315/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0443e-06 - mse: 1.0443e-06 - val_loss: 9.8706e-07 - val_mse: 9.8706e-07
Epoch 316/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1195e-06 - mse: 1.1195e-06 - val_loss: 9.5262e-07 - val_mse: 9.5262e-07
Epoch 317/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0479e-06 - mse: 1.0479e-06 - val_loss: 1.0873e-06 - val_mse: 1.0873e-06
Epoch 318/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0668e-06 - mse: 1.0668e-06 - val_loss: 9.1515e-07 - val_mse: 9.1515e-07
Epoch 319/700
142/142 [==============================] - 0s 2ms/step - loss: 9.8033e-07 - mse: 9.8033e-07 - val_loss: 9.3580e-07 - val_mse: 9.3580e-07
Epoch 320/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0340e-06 - mse: 1.0340e-06 - val_loss: 1.0126e-06 - val_mse: 1.0126e-06
Epoch 321/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0429e-06 - mse: 1.0429e-06 - val_loss: 1.3139e-06 - val_mse: 1.3139e-06
Epoch 322/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0346e-06 - mse: 1.0346e-06 - val_loss: 1.2621e-06 - val_mse: 1.2621e-06
Epoch 323/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0086e-06 - mse: 1.0086e-06 - val_loss: 8.8702e-07 - val_mse: 8.8702e-07
Epoch 324/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0323e-06 - mse: 1.0323e-06 - val_loss: 8.9923e-07 - val_mse: 8.9923e-07
Epoch 325/700
142/142 [==============================] - 0s 2ms/step - loss: 9.9674e-07 - mse: 9.9674e-07 - val_loss: 8.9350e-07 - val_mse: 8.9350e-07
Epoch 326/700
142/142 [==============================] - 0s 2ms/step - loss: 9.4936e-07 - mse: 9.4936e-07 - val_loss: 9.9111e-07 - val_mse: 9.9111e-07
Epoch 327/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0613e-06 - mse: 1.0613e-06 - val_loss: 9.6570e-07 - val_mse: 9.6570e-07
Epoch 328/700
142/142 [==============================] - 0s 2ms/step - loss: 9.2965e-07 - mse: 9.2965e-07 - val_loss: 1.0079e-06 - val_mse: 1.0079e-06
Epoch 329/700
142/142 [==============================] - 0s 2ms/step - loss: 1.0548e-06 - mse: 1.0548e-06 - val_loss: 8.6792e-07 - val_mse: 8.6792e-07
Epoch 330/700
142/142 [==============================] - 0s 2ms/step - loss: 9.9834e-07 - mse: 9.9834e-07 - val_loss: 9.2165e-07 - val_mse: 9.2165e-07
Epoch 331/700
142/142 [==============================] - 0s 2ms/step - loss: 9.5210e-07 - mse: 9.5210e-07 - val_loss: 8.6927e-07 - val_mse: 8.6927e-07
Epoch 332/700
142/142 [==============================] - 0s 2ms/step - loss: 9.1985e-07 - mse: 9.1985e-07 - val_loss: 1.0624e-06 - val_mse: 1.0624e-06
Epoch 333/700
142/142 [==============================] - 0s 2ms/step - loss: 8.8652e-07 - mse: 8.8652e-07 - val_loss: 7.9527e-07 - val_mse: 7.9527e-07
Epoch 334/700
142/142 [==============================] - 0s 2ms/step - loss: 9.0718e-07 - mse: 9.0718e-07 - val_loss: 1.0489e-06 - val_mse: 1.0489e-06
Epoch 335/700
142/142 [==============================] - 0s 2ms/step - loss: 8.9011e-07 - mse: 8.9011e-07 - val_loss: 1.0333e-06 - val_mse: 1.0333e-06
Epoch 336/700
142/142 [==============================] - 0s 2ms/step - loss: 8.7710e-07 - mse: 8.7710e-07 - val_loss: 1.4684e-06 - val_mse: 1.4684e-06
Epoch 337/700
142/142 [==============================] - 0s 2ms/step - loss: 8.2898e-07 - mse: 8.2898e-07 - val_loss: 8.5127e-07 - val_mse: 8.5127e-07
Epoch 338/700
142/142 [==============================] - 0s 2ms/step - loss: 8.7810e-07 - mse: 8.7810e-07 - val_loss: 7.7736e-07 - val_mse: 7.7736e-07
Epoch 339/700
142/142 [==============================] - 0s 2ms/step - loss: 1.1350e-06 - mse: 1.1350e-06 - val_loss: 7.9520e-07 - val_mse: 7.9520e-07
Epoch 340/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0444e-07 - mse: 8.0444e-07 - val_loss: 9.1199e-07 - val_mse: 9.1199e-07
Epoch 341/700
142/142 [==============================] - 0s 2ms/step - loss: 8.4070e-07 - mse: 8.4070e-07 - val_loss: 8.2165e-07 - val_mse: 8.2165e-07
Epoch 342/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0311e-07 - mse: 8.0311e-07 - val_loss: 7.7351e-07 - val_mse: 7.7351e-07
Epoch 343/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1178e-07 - mse: 8.1178e-07 - val_loss: 7.6994e-07 - val_mse: 7.6994e-07
Epoch 344/700
142/142 [==============================] - 0s 2ms/step - loss: 8.7785e-07 - mse: 8.7785e-07 - val_loss: 2.0417e-06 - val_mse: 2.0417e-06
Epoch 345/700
142/142 [==============================] - 0s 2ms/step - loss: 8.2921e-07 - mse: 8.2921e-07 - val_loss: 8.3213e-07 - val_mse: 8.3213e-07
Epoch 346/700
142/142 [==============================] - 0s 2ms/step - loss: 7.7014e-07 - mse: 7.7014e-07 - val_loss: 9.2369e-07 - val_mse: 9.2369e-07
Epoch 347/700
142/142 [==============================] - 0s 2ms/step - loss: 8.3109e-07 - mse: 8.3109e-07 - val_loss: 7.0241e-07 - val_mse: 7.0241e-07
Epoch 348/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1298e-07 - mse: 8.1298e-07 - val_loss: 7.3897e-07 - val_mse: 7.3897e-07
Epoch 349/700
142/142 [==============================] - 0s 2ms/step - loss: 8.8706e-07 - mse: 8.8706e-07 - val_loss: 9.1644e-07 - val_mse: 9.1644e-07
Epoch 350/700
142/142 [==============================] - 0s 2ms/step - loss: 8.2616e-07 - mse: 8.2616e-07 - val_loss: 7.7195e-07 - val_mse: 7.7195e-07
Epoch 351/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1528e-07 - mse: 8.1528e-07 - val_loss: 7.6620e-07 - val_mse: 7.6620e-07
Epoch 352/700
142/142 [==============================] - 0s 2ms/step - loss: 8.2316e-07 - mse: 8.2316e-07 - val_loss: 1.1676e-06 - val_mse: 1.1676e-06
Epoch 353/700
142/142 [==============================] - 0s 2ms/step - loss: 8.8990e-07 - mse: 8.8990e-07 - val_loss: 9.0672e-07 - val_mse: 9.0672e-07
Epoch 354/700
142/142 [==============================] - 0s 2ms/step - loss: 7.7826e-07 - mse: 7.7826e-07 - val_loss: 8.0776e-07 - val_mse: 8.0776e-07
Epoch 355/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0666e-07 - mse: 8.0666e-07 - val_loss: 1.0477e-06 - val_mse: 1.0477e-06
Epoch 356/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1057e-07 - mse: 8.1057e-07 - val_loss: 1.0240e-06 - val_mse: 1.0240e-06
Epoch 357/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1429e-07 - mse: 8.1429e-07 - val_loss: 1.5304e-06 - val_mse: 1.5304e-06
Epoch 358/700
142/142 [==============================] - 0s 2ms/step - loss: 8.9164e-07 - mse: 8.9164e-07 - val_loss: 7.8291e-07 - val_mse: 7.8291e-07
Epoch 359/700
142/142 [==============================] - 0s 2ms/step - loss: 8.7522e-07 - mse: 8.7522e-07 - val_loss: 7.9454e-07 - val_mse: 7.9454e-07
Epoch 360/700
142/142 [==============================] - 0s 2ms/step - loss: 7.7636e-07 - mse: 7.7636e-07 - val_loss: 8.1746e-07 - val_mse: 8.1746e-07
Epoch 361/700
142/142 [==============================] - 0s 2ms/step - loss: 8.9223e-07 - mse: 8.9223e-07 - val_loss: 1.0513e-06 - val_mse: 1.0513e-06
Epoch 362/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8900e-07 - mse: 7.8900e-07 - val_loss: 7.8554e-07 - val_mse: 7.8554e-07
Epoch 363/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8461e-07 - mse: 7.8461e-07 - val_loss: 1.0150e-06 - val_mse: 1.0150e-06
Epoch 364/700
142/142 [==============================] - 0s 2ms/step - loss: 8.4325e-07 - mse: 8.4325e-07 - val_loss: 9.9615e-07 - val_mse: 9.9615e-07
Epoch 365/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8726e-07 - mse: 7.8726e-07 - val_loss: 8.7058e-07 - val_mse: 8.7058e-07
Epoch 366/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1659e-07 - mse: 8.1659e-07 - val_loss: 9.0413e-07 - val_mse: 9.0413e-07
Epoch 367/700
142/142 [==============================] - 0s 2ms/step - loss: 8.5820e-07 - mse: 8.5820e-07 - val_loss: 7.3585e-07 - val_mse: 7.3585e-07
Epoch 368/700
142/142 [==============================] - 0s 2ms/step - loss: 8.9448e-07 - mse: 8.9448e-07 - val_loss: 7.0963e-07 - val_mse: 7.0963e-07
Epoch 369/700
142/142 [==============================] - 0s 2ms/step - loss: 8.5706e-07 - mse: 8.5706e-07 - val_loss: 8.5171e-07 - val_mse: 8.5171e-07
Epoch 370/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0615e-07 - mse: 8.0615e-07 - val_loss: 7.9439e-07 - val_mse: 7.9439e-07
Epoch 371/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8170e-07 - mse: 7.8170e-07 - val_loss: 8.1715e-07 - val_mse: 8.1715e-07
Epoch 372/700
142/142 [==============================] - 0s 2ms/step - loss: 8.4454e-07 - mse: 8.4454e-07 - val_loss: 8.6370e-07 - val_mse: 8.6370e-07
Epoch 373/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8911e-07 - mse: 7.8911e-07 - val_loss: 7.5578e-07 - val_mse: 7.5578e-07
Epoch 374/700
142/142 [==============================] - 0s 2ms/step - loss: 8.2841e-07 - mse: 8.2841e-07 - val_loss: 7.9511e-07 - val_mse: 7.9511e-07
Epoch 375/700
142/142 [==============================] - 0s 2ms/step - loss: 8.8004e-07 - mse: 8.8004e-07 - val_loss: 1.0129e-06 - val_mse: 1.0129e-06
Epoch 376/700
142/142 [==============================] - 0s 2ms/step - loss: 8.5530e-07 - mse: 8.5530e-07 - val_loss: 8.3400e-07 - val_mse: 8.3400e-07
Epoch 377/700
142/142 [==============================] - 0s 2ms/step - loss: 7.7209e-07 - mse: 7.7209e-07 - val_loss: 8.0430e-07 - val_mse: 8.0430e-07
Epoch 378/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1997e-07 - mse: 8.1997e-07 - val_loss: 8.9212e-07 - val_mse: 8.9212e-07
Epoch 379/700
142/142 [==============================] - 0s 2ms/step - loss: 7.7830e-07 - mse: 7.7830e-07 - val_loss: 8.5280e-07 - val_mse: 8.5280e-07
Epoch 380/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0102e-07 - mse: 8.0102e-07 - val_loss: 8.3789e-07 - val_mse: 8.3789e-07
Epoch 381/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0347e-07 - mse: 8.0347e-07 - val_loss: 9.4183e-07 - val_mse: 9.4183e-07
Epoch 382/700
142/142 [==============================] - 0s 2ms/step - loss: 9.0239e-07 - mse: 9.0239e-07 - val_loss: 7.5127e-07 - val_mse: 7.5127e-07
Epoch 383/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8123e-07 - mse: 7.8123e-07 - val_loss: 7.3560e-07 - val_mse: 7.3560e-07
Epoch 384/700
142/142 [==============================] - 0s 2ms/step - loss: 7.5776e-07 - mse: 7.5776e-07 - val_loss: 7.2054e-07 - val_mse: 7.2054e-07
Epoch 385/700
142/142 [==============================] - 0s 3ms/step - loss: 8.3697e-07 - mse: 8.3697e-07 - val_loss: 7.2343e-07 - val_mse: 7.2343e-07
Epoch 386/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9318e-07 - mse: 7.9318e-07 - val_loss: 8.3168e-07 - val_mse: 8.3168e-07
Epoch 387/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6731e-07 - mse: 7.6731e-07 - val_loss: 7.8642e-07 - val_mse: 7.8642e-07
Epoch 388/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1339e-07 - mse: 8.1339e-07 - val_loss: 9.3165e-07 - val_mse: 9.3165e-07
Epoch 389/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9526e-07 - mse: 7.9526e-07 - val_loss: 8.7384e-07 - val_mse: 8.7384e-07
Epoch 390/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0117e-07 - mse: 8.0117e-07 - val_loss: 9.9136e-07 - val_mse: 9.9136e-07
Epoch 391/700
142/142 [==============================] - 0s 2ms/step - loss: 9.8207e-07 - mse: 9.8207e-07 - val_loss: 9.2749e-07 - val_mse: 9.2749e-07
Epoch 392/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9797e-07 - mse: 7.9797e-07 - val_loss: 8.4860e-07 - val_mse: 8.4860e-07
Epoch 393/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0356e-07 - mse: 8.0356e-07 - val_loss: 7.2040e-07 - val_mse: 7.2040e-07
Epoch 394/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8661e-07 - mse: 7.8661e-07 - val_loss: 7.2646e-07 - val_mse: 7.2646e-07
Epoch 395/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9513e-07 - mse: 7.9513e-07 - val_loss: 1.3510e-06 - val_mse: 1.3510e-06
Epoch 396/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8165e-07 - mse: 7.8165e-07 - val_loss: 6.9141e-07 - val_mse: 6.9141e-07
Epoch 397/700
142/142 [==============================] - 0s 2ms/step - loss: 9.1431e-07 - mse: 9.1431e-07 - val_loss: 8.3900e-07 - val_mse: 8.3900e-07
Epoch 398/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9190e-07 - mse: 7.9190e-07 - val_loss: 6.8331e-07 - val_mse: 6.8331e-07
Epoch 399/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0279e-07 - mse: 8.0279e-07 - val_loss: 7.8488e-07 - val_mse: 7.8488e-07
Epoch 400/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9721e-07 - mse: 7.9721e-07 - val_loss: 9.1833e-07 - val_mse: 9.1833e-07
Epoch 401/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0846e-07 - mse: 8.0846e-07 - val_loss: 6.9048e-07 - val_mse: 6.9048e-07
Epoch 402/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1808e-07 - mse: 8.1808e-07 - val_loss: 7.1429e-07 - val_mse: 7.1429e-07
Epoch 403/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8865e-07 - mse: 7.8865e-07 - val_loss: 7.5931e-07 - val_mse: 7.5931e-07
Epoch 404/700
142/142 [==============================] - 0s 2ms/step - loss: 8.2620e-07 - mse: 8.2620e-07 - val_loss: 6.9002e-07 - val_mse: 6.9002e-07
Epoch 405/700
142/142 [==============================] - 0s 2ms/step - loss: 8.2065e-07 - mse: 8.2065e-07 - val_loss: 8.1378e-07 - val_mse: 8.1378e-07
Epoch 406/700
142/142 [==============================] - 0s 2ms/step - loss: 7.7963e-07 - mse: 7.7963e-07 - val_loss: 6.7222e-07 - val_mse: 6.7222e-07
Epoch 407/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8245e-07 - mse: 7.8245e-07 - val_loss: 8.9109e-07 - val_mse: 8.9109e-07
Epoch 408/700
142/142 [==============================] - 0s 3ms/step - loss: 8.9069e-07 - mse: 8.9069e-07 - val_loss: 7.8400e-07 - val_mse: 7.8400e-07
Epoch 409/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6608e-07 - mse: 7.6608e-07 - val_loss: 7.6462e-07 - val_mse: 7.6462e-07
Epoch 410/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6096e-07 - mse: 7.6096e-07 - val_loss: 7.2256e-07 - val_mse: 7.2256e-07
Epoch 411/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9382e-07 - mse: 7.9382e-07 - val_loss: 7.2781e-07 - val_mse: 7.2781e-07
Epoch 412/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8251e-07 - mse: 7.8251e-07 - val_loss: 7.9406e-07 - val_mse: 7.9406e-07
Epoch 413/700
142/142 [==============================] - 0s 2ms/step - loss: 9.2212e-07 - mse: 9.2212e-07 - val_loss: 7.2621e-07 - val_mse: 7.2621e-07
Epoch 414/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9333e-07 - mse: 7.9333e-07 - val_loss: 7.0332e-07 - val_mse: 7.0332e-07
Epoch 415/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8477e-07 - mse: 7.8477e-07 - val_loss: 7.2635e-07 - val_mse: 7.2635e-07
Epoch 416/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8288e-07 - mse: 7.8288e-07 - val_loss: 6.8243e-07 - val_mse: 6.8243e-07
Epoch 417/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8530e-07 - mse: 7.8530e-07 - val_loss: 7.1214e-07 - val_mse: 7.1214e-07
Epoch 418/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8924e-07 - mse: 7.8924e-07 - val_loss: 7.2192e-07 - val_mse: 7.2192e-07
Epoch 419/700
142/142 [==============================] - 0s 2ms/step - loss: 7.3717e-07 - mse: 7.3717e-07 - val_loss: 8.0350e-07 - val_mse: 8.0350e-07
Epoch 420/700
142/142 [==============================] - 0s 2ms/step - loss: 8.7653e-07 - mse: 8.7653e-07 - val_loss: 6.9436e-07 - val_mse: 6.9436e-07
Epoch 421/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0163e-07 - mse: 8.0163e-07 - val_loss: 7.1498e-07 - val_mse: 7.1498e-07
Epoch 422/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8169e-07 - mse: 7.8169e-07 - val_loss: 6.8436e-07 - val_mse: 6.8436e-07
Epoch 423/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6661e-07 - mse: 7.6661e-07 - val_loss: 8.3236e-07 - val_mse: 8.3236e-07
Epoch 424/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9550e-07 - mse: 7.9550e-07 - val_loss: 6.9059e-07 - val_mse: 6.9059e-07
Epoch 425/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1769e-07 - mse: 8.1769e-07 - val_loss: 9.2912e-07 - val_mse: 9.2912e-07
Epoch 426/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6582e-07 - mse: 7.6582e-07 - val_loss: 7.0654e-07 - val_mse: 7.0654e-07
Epoch 427/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6347e-07 - mse: 7.6347e-07 - val_loss: 6.7957e-07 - val_mse: 6.7957e-07
Epoch 428/700
142/142 [==============================] - 0s 2ms/step - loss: 8.3617e-07 - mse: 8.3617e-07 - val_loss: 8.3662e-07 - val_mse: 8.3662e-07
Epoch 429/700
142/142 [==============================] - 0s 2ms/step - loss: 7.7096e-07 - mse: 7.7096e-07 - val_loss: 1.2459e-06 - val_mse: 1.2459e-06
Epoch 430/700
142/142 [==============================] - 0s 2ms/step - loss: 8.3982e-07 - mse: 8.3982e-07 - val_loss: 9.1565e-07 - val_mse: 9.1565e-07
Epoch 431/700
142/142 [==============================] - 0s 2ms/step - loss: 7.3283e-07 - mse: 7.3283e-07 - val_loss: 7.3437e-07 - val_mse: 7.3437e-07
Epoch 432/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1090e-07 - mse: 8.1090e-07 - val_loss: 7.2025e-07 - val_mse: 7.2025e-07
Epoch 433/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6253e-07 - mse: 7.6253e-07 - val_loss: 1.0691e-06 - val_mse: 1.0691e-06
Epoch 434/700
142/142 [==============================] - 0s 2ms/step - loss: 8.6449e-07 - mse: 8.6449e-07 - val_loss: 1.2133e-06 - val_mse: 1.2133e-06
Epoch 435/700
142/142 [==============================] - 0s 2ms/step - loss: 8.1130e-07 - mse: 8.1130e-07 - val_loss: 8.5053e-07 - val_mse: 8.5053e-07
Epoch 436/700
142/142 [==============================] - 0s 2ms/step - loss: 7.7218e-07 - mse: 7.7218e-07 - val_loss: 7.5876e-07 - val_mse: 7.5876e-07
Epoch 437/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6400e-07 - mse: 7.6400e-07 - val_loss: 8.1241e-07 - val_mse: 8.1241e-07
Epoch 438/700
142/142 [==============================] - 0s 2ms/step - loss: 7.5659e-07 - mse: 7.5659e-07 - val_loss: 8.0431e-07 - val_mse: 8.0431e-07
Epoch 439/700
142/142 [==============================] - 0s 2ms/step - loss: 7.5098e-07 - mse: 7.5098e-07 - val_loss: 6.8856e-07 - val_mse: 6.8856e-07
Epoch 440/700
142/142 [==============================] - 0s 2ms/step - loss: 8.9272e-07 - mse: 8.9272e-07 - val_loss: 1.0547e-06 - val_mse: 1.0547e-06
Epoch 441/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6080e-07 - mse: 7.6080e-07 - val_loss: 7.4244e-07 - val_mse: 7.4244e-07
Epoch 442/700
142/142 [==============================] - 0s 2ms/step - loss: 7.6332e-07 - mse: 7.6332e-07 - val_loss: 7.0513e-07 - val_mse: 7.0513e-07
Epoch 443/700
142/142 [==============================] - 0s 2ms/step - loss: 8.0069e-07 - mse: 8.0069e-07 - val_loss: 9.7753e-07 - val_mse: 9.7753e-07
Epoch 444/700
142/142 [==============================] - 0s 2ms/step - loss: 7.7858e-07 - mse: 7.7858e-07 - val_loss: 7.1466e-07 - val_mse: 7.1466e-07
Epoch 445/700
142/142 [==============================] - 0s 2ms/step - loss: 7.5866e-07 - mse: 7.5866e-07 - val_loss: 7.4751e-07 - val_mse: 7.4751e-07
Epoch 446/700
142/142 [==============================] - 0s 2ms/step - loss: 8.3050e-07 - mse: 8.3050e-07 - val_loss: 9.9756e-07 - val_mse: 9.9756e-07
Epoch 447/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8022e-07 - mse: 7.8022e-07 - val_loss: 9.6815e-07 - val_mse: 9.6815e-07
Epoch 448/700
142/142 [==============================] - 0s 2ms/step - loss: 7.9787e-07 - mse: 7.9787e-07 - val_loss: 7.7960e-07 - val_mse: 7.7960e-07
Epoch 449/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8741e-07 - mse: 7.8741e-07 - val_loss: 7.4019e-07 - val_mse: 7.4019e-07
Epoch 450/700
142/142 [==============================] - 0s 2ms/step - loss: 7.3668e-07 - mse: 7.3668e-07 - val_loss: 8.1616e-07 - val_mse: 8.1616e-07
Epoch 451/700
142/142 [==============================] - 0s 2ms/step - loss: 7.8408e-07 - mse: 7.8408e-07 - val_loss: 7.2212e-07 - val_mse: 7.2212e-07
Epoch 452/700
142/142 [==============================] - 0s 2ms/step - loss: 8.8902e-07 - mse: 8.8902e-07 - val_loss: 1.8286e-06 - val_mse: 1.8286e-06
Epoch 453/700
142/142 [==============================] - 0s 2ms/step - loss: 8.5426e-07 - mse: 8.5426e-07 - val_loss: 7.7100e-07 - val_mse: 7.7100e-07
Epoch 454/700
142/142 [==============================] - 0s 3ms/step - loss: 7.7418e-07 - mse: 7.7418e-07 - val_loss: 7.5963e-07 - val_mse: 7.5963e-07
Epoch 455/700
142/142 [==============================] - 0s 3ms/step - loss: 7.7721e-07 - mse: 7.7721e-07 - val_loss: 8.6382e-07 - val_mse: 8.6382e-07
Epoch 456/700
142/142 [==============================] - 0s 2ms/step - loss: 7.4883e-07 - mse: 7.4883e-07 - val_loss: 8.3133e-07 - val_mse: 8.3133e-07
Epoch 00456: early stopping

<tensorflow.python.keras.callbacks.History at 0x7f1f00128150>

predictions = (model_SOE.predict(X_test_SOE))
print(mean_absolute_error(Y_test_SOE, predictions))

0.0007085477290306336

compare = pd.concat([pd.DataFrame(predictions), pd.DataFrame(Y_test_SOE)], axis=1)
compare.columns = ['Predictions', 'Actual Output']
compare

	Predictions	Actual Output
0	0.767613	0.768606
1	0.902220	0.902352
2	0.336005	0.335707
3	0.244512	0.242919
4	0.352368	0.352707
...	...	...
23392	0.600694	0.600253
23393	0.766633	0.767576
23394	0.324048	0.324848
23395	0.191132	0.189838
23396	0.319280	0.320394

23397 rows × 2 columns

Saving and exporting JSON version of the model for SOC

---

import tensorflowjs

model_SOC.save('/content/drive/MyDrive/Colab Notebooks/Projects/SOC & SOE Estimation/Model_SOC/model_soc.h5')
tensorflowjs.converters.save_keras_model(model_SOC, '/content/drive/MyDrive/Colab Notebooks/Projects/SOC & SOE Estimation/Model_SOC/JSON')

model_SOE.save('/content/drive/MyDrive/Colab Notebooks/Projects/SOC & SOE Estimation/Model_SOE/model_soe.h5')
tensorflowjs.converters.save_keras_model(model_SOE, '/content/drive/MyDrive/Colab Notebooks/Projects/SOC & SOE Estimation/Model_SOE/JSON')