# PRICING THE VOLATILITY SWAP USING CONSTANT BLACK & SCHOLES MODEL UNDER CONSTANT VOLATILITY


```
!pip install sobol-seq
!pip install tbb
```

    Collecting sobol-seq
      Downloading https://files.pythonhosted.org/packages/e4/df/6c4ad25c0b48545a537b631030f7de7e4abb939e6d2964ac2169d4379c85/sobol_seq-0.2.0-py3-none-any.whl
    Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sobol-seq) (1.4.1)
    Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sobol-seq) (1.19.5)
    Installing collected packages: sobol-seq
    Successfully installed sobol-seq-0.2.0
    Collecting tbb
    [?25l  Downloading https://files.pythonhosted.org/packages/d9/d2/405b466a345dddba90db0f0241fbaf0580665a82bad5a712aee31a0d7d94/tbb-2021.3.0-py2.py3-none-manylinux1_x86_64.whl (4.1MB)
    [K     |████████████████████████████████| 4.1MB 7.0MB/s 
    [?25hInstalling collected packages: tbb
    Successfully installed tbb-2021.3.0


IMPORT RELEVANT LIBRARIES


```
import math
import numpy as np
import pandas as pd
from scipy.stats import norm
from time import time
import sobol_seq
import scipy.stats as si
import sympy as sy
from sympy.stats import Normal, cdf
from sympy import init_printing
import random

#Visualizing Libraries
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

#Split the dataset
from sklearn.model_selection import train_test_split

#Normalize the dataset
from sklearn.preprocessing import MinMaxScaler

#Create the model
from keras import Input
from keras import layers
import tensorflow as tf
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping

#Evaluate the model
from sklearn.metrics import mean_squared_error,mean_absolute_error,explained_variance_score

#Multithreading
from numba import jit,njit, config, threading_layer
```


```
%tensorflow_version 2.x
import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
  raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))
```

    Found GPU at: /device:GPU:0


INPUT PARAMETERS FOR CALL & PUT OPTION, VARIANCE AND VOLATILITY SWAP


```
#Input Parameters:
# S: spot price
# K_array: array of strike prices
# T: time to maturity
# r: Risk free interest rate
# sigma_array: array of volatilities of underlying asset
# n: number of strike prices for approximation
S = 1020#float(input("Spot Price = "))
r = 0.04#float(input("Risk free interest rate in decimal = "))
T = 0.25#float(input("Time in months = "))/12
n = 9#int(input("How many strike prices do you want to enter? :"))
SD_avg_variance = 0.01
K_array = np.array([800,850,900,950,1000,1050,1100,1150,1200])
#vol_array = np.array([0.29,0.28,0.27,0.26,0.25,0.24,0.23,0.22,0.21])
SD_avg_variance = 0.01
principal = 100#million dollars
paid_vol = 0.23#paid volatility
```

CALCULATE F0:



```
F0 = S * np.exp(r*T)
print("F0=",F0)
```

    F0= 1030.2511704258513


CALCULATE S*



```
S_star = 0
for i in range(n):
    if(K_array[i]<F0):
        S_star = K_array[i]
    else:
        break

print(S_star)
```

    1000



```
def euro_vanilla_call(S, K_call, T, r, vol_call):
   
    d1 = (np.log(S / K_call) + (r + 0.5 * vol_call ** 2) * T) / (vol_call * np.sqrt(T))
    d2 = (np.log(S / K_call) + (r - 0.5 * vol_call ** 2) * T) / (vol_call * np.sqrt(T))

    call = (S * si.norm.cdf(d1, 0.0, 1.0) - K_call * np.exp(-r * T) * si.norm.cdf(d2, 0.0, 1.0))

    return call
```


```
def euro_vanilla_put(S,K_put,T,r,vol_put):
    
    d1 = (np.log(S / K_put) + (r + 0.5 * vol_put ** 2) * T) / (vol_put * np.sqrt(T))
    d2 = (np.log(S / K_put) + (r - 0.5 * vol_put ** 2) * T) / (vol_put * np.sqrt(T))

    put = ((K_put*np.exp(-r*T)* si.norm.cdf(-d2,0.0,1.0)) - (S * si.norm.cdf(-d1, 0.0, 1.0)))
    
    return put
```

CALCULATE THE APPROXIMATION FUNCTION


```
def Q(K_Q,vol_Q):
    if(K_Q < S_star):
        return euro_vanilla_put(S,K_Q,T,r,vol_Q)
    elif(K_Q > S_star):
        return euro_vanilla_call(S,K_Q,T,r,vol_Q)
    elif(K_Q == S_star):
        return (0.5*(euro_vanilla_call(S,K_Q,T,r,vol_Q) + euro_vanilla_put(S,K_Q,T,r,vol_Q)))
```


```
def approx(n,K,vol):
    approxed = 0
    for i in range(n):
        if(i == 0):
            delta_K = K[1] - K[0]
        elif(i >= 1 and i <= (n-2)):
            delta_K = 0.5*(K[i+1] - K[i-1])
        else:
            delta_K = K[i] - K[i-1]
        approxed += (delta_K/K[i]**2)*np.exp(r*T)*Q(K[i],vol[i])
    
    return approxed
```

VALUE THE VOLATILITY SWAP


```
def volatility_swap(vol_array):
    Expected_variance_price = ((2/T)*np.log(F0/S_star)) - ((2/T)*((F0/S_star)-1)) + ((2/T)*approx(n,K_array,vol_array))
    #print(Expected_variance_price)
    var_bar = SD_avg_variance**2
    Expected_vol_price = np.sqrt(Expected_variance_price) * (1 - ((1/8)*(var_bar/Expected_variance_price**2)))
    #print(Expected_vol_price)
    swap_price = principal*((Expected_vol_price - paid_vol)*np.exp(-r*T))
    #print(swap_price)
    #return round(swap_price,2)
    return swap_price
```


```
volatility_swap(np.array([0.29,0.28,0.27,0.26,0.25,0.24,0.23,0.22,0.21]))
```




    1.7761940425724816



# ANN FOR FAST PREDICTION OF SWAP PRICE FOR DIFFERENT VOLATILITES OF THE UNDERLYING

CREATE DATASET FOR TRAINING AND TESTING


```
round(random.uniform(50.50, 500.50), 2)
```




    307.49




```
df = pd.DataFrame()
df['Sigma1'] = np.random.uniform(0.2,0.6,1000000)
df['Sigma2'] = np.random.uniform(0.2,0.6,1000000)
df['Sigma3'] = np.random.uniform(0.2,0.6,1000000)
df['Sigma4'] = np.random.uniform(0.2,0.6,1000000)
df['Sigma5'] = np.random.uniform(0.2,0.6,1000000)
df['Sigma6'] = np.random.uniform(0.2,0.6,1000000)
df['Sigma7'] = np.random.uniform(0.2,0.6,1000000)
df['Sigma8'] = np.random.uniform(0.2,0.6,1000000)
df['Sigma9'] = np.random.uniform(0.2,0.6,1000000)

#df = round(df,2)

df
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Sigma1</th>
      <th>Sigma2</th>
      <th>Sigma3</th>
      <th>Sigma4</th>
      <th>Sigma5</th>
      <th>Sigma6</th>
      <th>Sigma7</th>
      <th>Sigma8</th>
      <th>Sigma9</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.566112</td>
      <td>0.465958</td>
      <td>0.206930</td>
      <td>0.407574</td>
      <td>0.500670</td>
      <td>0.564315</td>
      <td>0.332249</td>
      <td>0.324428</td>
      <td>0.564469</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.556269</td>
      <td>0.506850</td>
      <td>0.313259</td>
      <td>0.452980</td>
      <td>0.393666</td>
      <td>0.484757</td>
      <td>0.345936</td>
      <td>0.539861</td>
      <td>0.574729</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.577217</td>
      <td>0.205450</td>
      <td>0.522009</td>
      <td>0.355574</td>
      <td>0.430149</td>
      <td>0.565506</td>
      <td>0.352227</td>
      <td>0.503809</td>
      <td>0.395840</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.486204</td>
      <td>0.263333</td>
      <td>0.472796</td>
      <td>0.591504</td>
      <td>0.298740</td>
      <td>0.393468</td>
      <td>0.351547</td>
      <td>0.346432</td>
      <td>0.444187</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.558517</td>
      <td>0.232624</td>
      <td>0.393309</td>
      <td>0.254422</td>
      <td>0.409031</td>
      <td>0.527585</td>
      <td>0.408740</td>
      <td>0.315202</td>
      <td>0.378234</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>999995</th>
      <td>0.319219</td>
      <td>0.439904</td>
      <td>0.569632</td>
      <td>0.350193</td>
      <td>0.474548</td>
      <td>0.510800</td>
      <td>0.583514</td>
      <td>0.353221</td>
      <td>0.475620</td>
    </tr>
    <tr>
      <th>999996</th>
      <td>0.404504</td>
      <td>0.215001</td>
      <td>0.262104</td>
      <td>0.226461</td>
      <td>0.381425</td>
      <td>0.298641</td>
      <td>0.496023</td>
      <td>0.313943</td>
      <td>0.513095</td>
    </tr>
    <tr>
      <th>999997</th>
      <td>0.501005</td>
      <td>0.357932</td>
      <td>0.226633</td>
      <td>0.275796</td>
      <td>0.392505</td>
      <td>0.277055</td>
      <td>0.427020</td>
      <td>0.543148</td>
      <td>0.246774</td>
    </tr>
    <tr>
      <th>999998</th>
      <td>0.414424</td>
      <td>0.399121</td>
      <td>0.247482</td>
      <td>0.366483</td>
      <td>0.357625</td>
      <td>0.329986</td>
      <td>0.270809</td>
      <td>0.222088</td>
      <td>0.526260</td>
    </tr>
    <tr>
      <th>999999</th>
      <td>0.303598</td>
      <td>0.443491</td>
      <td>0.249565</td>
      <td>0.316673</td>
      <td>0.515157</td>
      <td>0.541437</td>
      <td>0.456295</td>
      <td>0.268424</td>
      <td>0.584462</td>
    </tr>
  </tbody>
</table>
<p>1000000 rows × 9 columns</p>
</div>




```
%%time
df['Swap Price'] = [volatility_swap(np.array(df.loc[x,:].values)) for x in range(1000000)]
```

    CPU times: user 39min 1s, sys: 2min 21s, total: 41min 23s
    Wall time: 38min 15s



```
df
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Sigma1</th>
      <th>Sigma2</th>
      <th>Sigma3</th>
      <th>Sigma4</th>
      <th>Sigma5</th>
      <th>Sigma6</th>
      <th>Sigma7</th>
      <th>Sigma8</th>
      <th>Sigma9</th>
      <th>Swap Price</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.315699</td>
      <td>0.296761</td>
      <td>0.492697</td>
      <td>0.380812</td>
      <td>0.458356</td>
      <td>0.595305</td>
      <td>0.550193</td>
      <td>0.505010</td>
      <td>0.338467</td>
      <td>18.239751</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.425359</td>
      <td>0.333196</td>
      <td>0.203899</td>
      <td>0.439249</td>
      <td>0.547067</td>
      <td>0.311228</td>
      <td>0.337775</td>
      <td>0.580026</td>
      <td>0.223251</td>
      <td>13.735260</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.301369</td>
      <td>0.481476</td>
      <td>0.598509</td>
      <td>0.536923</td>
      <td>0.423904</td>
      <td>0.449820</td>
      <td>0.466943</td>
      <td>0.518354</td>
      <td>0.564526</td>
      <td>21.128184</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.532343</td>
      <td>0.547209</td>
      <td>0.263670</td>
      <td>0.505458</td>
      <td>0.368612</td>
      <td>0.442494</td>
      <td>0.323394</td>
      <td>0.488251</td>
      <td>0.304966</td>
      <td>16.530578</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.450506</td>
      <td>0.453044</td>
      <td>0.506189</td>
      <td>0.500346</td>
      <td>0.284136</td>
      <td>0.487196</td>
      <td>0.506551</td>
      <td>0.515358</td>
      <td>0.220384</td>
      <td>18.031803</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>9995</th>
      <td>0.387541</td>
      <td>0.501126</td>
      <td>0.343789</td>
      <td>0.264203</td>
      <td>0.417322</td>
      <td>0.307275</td>
      <td>0.332555</td>
      <td>0.467238</td>
      <td>0.286493</td>
      <td>12.042590</td>
    </tr>
    <tr>
      <th>9996</th>
      <td>0.458114</td>
      <td>0.489728</td>
      <td>0.461926</td>
      <td>0.318790</td>
      <td>0.371646</td>
      <td>0.490841</td>
      <td>0.595863</td>
      <td>0.440407</td>
      <td>0.446100</td>
      <td>18.354496</td>
    </tr>
    <tr>
      <th>9997</th>
      <td>0.395812</td>
      <td>0.407319</td>
      <td>0.351811</td>
      <td>0.529293</td>
      <td>0.314632</td>
      <td>0.468473</td>
      <td>0.316840</td>
      <td>0.211521</td>
      <td>0.253720</td>
      <td>12.581864</td>
    </tr>
    <tr>
      <th>9998</th>
      <td>0.356289</td>
      <td>0.351813</td>
      <td>0.500286</td>
      <td>0.504051</td>
      <td>0.558816</td>
      <td>0.399909</td>
      <td>0.585904</td>
      <td>0.334587</td>
      <td>0.348231</td>
      <td>18.421847</td>
    </tr>
    <tr>
      <th>9999</th>
      <td>0.580080</td>
      <td>0.447754</td>
      <td>0.243524</td>
      <td>0.536369</td>
      <td>0.207917</td>
      <td>0.355799</td>
      <td>0.372215</td>
      <td>0.354239</td>
      <td>0.229864</td>
      <td>12.794040</td>
    </tr>
  </tbody>
</table>
<p>10000 rows × 10 columns</p>
</div>




```
X = df.iloc[:,:-1].values
y = df.iloc[:,-1].values
X
```




    array([[0.31569929, 0.29676074, 0.49269742, ..., 0.55019293, 0.50501011,
            0.33846684],
           [0.42535858, 0.33319566, 0.20389937, ..., 0.3377751 , 0.5800262 ,
            0.22325069],
           [0.30136929, 0.48147551, 0.59850869, ..., 0.46694342, 0.51835428,
            0.56452558],
           ...,
           [0.39581231, 0.40731934, 0.35181066, ..., 0.31684006, 0.21152115,
            0.2537203 ],
           [0.35628869, 0.35181299, 0.50028613, ..., 0.58590366, 0.33458668,
            0.34823096],
           [0.58007978, 0.44775359, 0.24352365, ..., 0.37221504, 0.35423883,
            0.22986405]])




```
X.shape
```




    (10000, 9)



SPLIT THE DATASET


```
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=42)
```


```
y_test.shape
```




    (3000,)



NORMALIZE THE DATASET


```
scaler = MinMaxScaler()
X_train= scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
```

CREATE THE MODEL


```
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)
```


```
model = Sequential()

model.add(Dense(9,activation='elu'))
model.add(Dropout(0.2))
model.add(Dense(9,activation='elu'))
model.add(Dropout(0.2))
model.add(Dense(9,activation='elu'))
model.add(Dropout(0.2))
model.add(Dense(9,activation='elu'))
model.add(Dropout(0.2))
model.add(Dense(1))

model.compile(optimizer='adam',loss='mse')
```


```
%%time
with tf.device('/device:GPU:0'):
  model.fit(x=X_train, y=y_train, epochs=1000,
            validation_data=(X_test, y_test),batch_size = 128, 
            verbose=1, callbacks=[early_stop])
```

    Epoch 1/1000
    55/55 [==============================] - 0s 7ms/step - loss: 197.3303 - val_loss: 151.7973
    Epoch 2/1000
    55/55 [==============================] - 0s 4ms/step - loss: 100.6612 - val_loss: 31.0812
    Epoch 3/1000
    55/55 [==============================] - 0s 4ms/step - loss: 35.9325 - val_loss: 6.1854
    Epoch 4/1000
    55/55 [==============================] - 0s 4ms/step - loss: 31.1147 - val_loss: 6.6468
    Epoch 5/1000
    55/55 [==============================] - 0s 4ms/step - loss: 28.6708 - val_loss: 6.4146
    Epoch 6/1000
    55/55 [==============================] - 0s 4ms/step - loss: 26.1647 - val_loss: 6.3388
    Epoch 7/1000
    55/55 [==============================] - 0s 4ms/step - loss: 24.3280 - val_loss: 5.9252
    Epoch 8/1000
    55/55 [==============================] - 0s 4ms/step - loss: 23.2901 - val_loss: 5.7565
    Epoch 9/1000
    55/55 [==============================] - 0s 4ms/step - loss: 22.3782 - val_loss: 5.7656
    Epoch 10/1000
    55/55 [==============================] - 0s 4ms/step - loss: 21.4751 - val_loss: 5.6868
    Epoch 11/1000
    55/55 [==============================] - 0s 4ms/step - loss: 20.2276 - val_loss: 5.7711
    Epoch 12/1000
    55/55 [==============================] - 0s 4ms/step - loss: 19.9931 - val_loss: 5.3436
    Epoch 13/1000
    55/55 [==============================] - 0s 4ms/step - loss: 18.7217 - val_loss: 5.3203
    Epoch 14/1000
    55/55 [==============================] - 0s 4ms/step - loss: 18.4174 - val_loss: 5.5354
    Epoch 15/1000
    55/55 [==============================] - 0s 4ms/step - loss: 18.1931 - val_loss: 5.2138
    Epoch 16/1000
    55/55 [==============================] - 0s 4ms/step - loss: 17.7401 - val_loss: 5.0591
    Epoch 17/1000
    55/55 [==============================] - 0s 4ms/step - loss: 16.7998 - val_loss: 4.7383
    Epoch 18/1000
    55/55 [==============================] - 0s 4ms/step - loss: 16.8031 - val_loss: 4.5641
    Epoch 19/1000
    55/55 [==============================] - 0s 4ms/step - loss: 16.1784 - val_loss: 4.3119
    Epoch 20/1000
    55/55 [==============================] - 0s 4ms/step - loss: 15.5653 - val_loss: 3.8762
    Epoch 21/1000
    55/55 [==============================] - 0s 4ms/step - loss: 15.1804 - val_loss: 3.5749
    Epoch 22/1000
    55/55 [==============================] - 0s 4ms/step - loss: 15.0928 - val_loss: 3.0728
    Epoch 23/1000
    55/55 [==============================] - 0s 4ms/step - loss: 13.8392 - val_loss: 2.5560
    Epoch 24/1000
    55/55 [==============================] - 0s 4ms/step - loss: 14.0409 - val_loss: 2.4099
    Epoch 25/1000
    55/55 [==============================] - 0s 4ms/step - loss: 13.2371 - val_loss: 2.0527
    Epoch 26/1000
    55/55 [==============================] - 0s 4ms/step - loss: 13.0500 - val_loss: 1.9741
    Epoch 27/1000
    55/55 [==============================] - 0s 4ms/step - loss: 12.6617 - val_loss: 1.5293
    Epoch 28/1000
    55/55 [==============================] - 0s 4ms/step - loss: 12.5551 - val_loss: 1.7103
    Epoch 29/1000
    55/55 [==============================] - 0s 4ms/step - loss: 12.3685 - val_loss: 1.3837
    Epoch 30/1000
    55/55 [==============================] - 0s 4ms/step - loss: 11.5502 - val_loss: 1.2422
    Epoch 31/1000
    55/55 [==============================] - 0s 4ms/step - loss: 11.4811 - val_loss: 1.3335
    Epoch 32/1000
    55/55 [==============================] - 0s 4ms/step - loss: 11.5345 - val_loss: 1.1141
    Epoch 33/1000
    55/55 [==============================] - 0s 4ms/step - loss: 11.4575 - val_loss: 1.1822
    Epoch 34/1000
    55/55 [==============================] - 0s 4ms/step - loss: 10.8486 - val_loss: 1.2683
    Epoch 35/1000
    55/55 [==============================] - 0s 4ms/step - loss: 10.9578 - val_loss: 1.1540
    Epoch 36/1000
    55/55 [==============================] - 0s 4ms/step - loss: 10.4943 - val_loss: 1.0300
    Epoch 37/1000
    55/55 [==============================] - 0s 4ms/step - loss: 10.5462 - val_loss: 0.9130
    Epoch 38/1000
    55/55 [==============================] - 0s 4ms/step - loss: 10.1971 - val_loss: 0.8783
    Epoch 39/1000
    55/55 [==============================] - 0s 4ms/step - loss: 9.9468 - val_loss: 1.1494
    Epoch 40/1000
    55/55 [==============================] - 0s 4ms/step - loss: 9.7794 - val_loss: 1.0657
    Epoch 41/1000
    55/55 [==============================] - 0s 4ms/step - loss: 9.8213 - val_loss: 0.9581
    Epoch 42/1000
    55/55 [==============================] - 0s 4ms/step - loss: 9.3182 - val_loss: 0.7463
    Epoch 43/1000
    55/55 [==============================] - 0s 4ms/step - loss: 9.3256 - val_loss: 0.6342
    Epoch 44/1000
    55/55 [==============================] - 0s 4ms/step - loss: 9.2404 - val_loss: 0.7308
    Epoch 45/1000
    55/55 [==============================] - 0s 4ms/step - loss: 9.0008 - val_loss: 0.6137
    Epoch 46/1000
    55/55 [==============================] - 0s 4ms/step - loss: 8.9232 - val_loss: 0.6393
    Epoch 47/1000
    55/55 [==============================] - 0s 4ms/step - loss: 8.5372 - val_loss: 0.7031
    Epoch 48/1000
    55/55 [==============================] - 0s 4ms/step - loss: 8.6893 - val_loss: 0.5833
    Epoch 49/1000
    55/55 [==============================] - 0s 4ms/step - loss: 8.7287 - val_loss: 0.6653
    Epoch 50/1000
    55/55 [==============================] - 0s 4ms/step - loss: 8.4215 - val_loss: 0.5417
    Epoch 51/1000
    55/55 [==============================] - 0s 4ms/step - loss: 8.1695 - val_loss: 0.9038
    Epoch 52/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.8222 - val_loss: 0.6503
    Epoch 53/1000
    55/55 [==============================] - 0s 4ms/step - loss: 8.1634 - val_loss: 0.4038
    Epoch 54/1000
    55/55 [==============================] - 0s 4ms/step - loss: 8.0199 - val_loss: 0.4679
    Epoch 55/1000
    55/55 [==============================] - 0s 4ms/step - loss: 8.2207 - val_loss: 0.7454
    Epoch 56/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.9551 - val_loss: 0.6381
    Epoch 57/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.7556 - val_loss: 0.5883
    Epoch 58/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.9602 - val_loss: 0.4042
    Epoch 59/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.6054 - val_loss: 0.5893
    Epoch 60/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.7794 - val_loss: 0.7432
    Epoch 61/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.4159 - val_loss: 0.7088
    Epoch 62/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.4920 - val_loss: 0.6456
    Epoch 63/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.4710 - val_loss: 0.4256
    Epoch 64/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.3103 - val_loss: 0.4721
    Epoch 65/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.4298 - val_loss: 0.4134
    Epoch 66/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.0596 - val_loss: 0.4365
    Epoch 67/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.0531 - val_loss: 0.5935
    Epoch 68/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.0907 - val_loss: 0.4731
    Epoch 69/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.9655 - val_loss: 0.6210
    Epoch 70/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.1114 - val_loss: 0.4156
    Epoch 71/1000
    55/55 [==============================] - 0s 4ms/step - loss: 7.2605 - val_loss: 0.3998
    Epoch 72/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.9630 - val_loss: 0.5292
    Epoch 73/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.9472 - val_loss: 0.3943
    Epoch 74/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.6696 - val_loss: 0.3760
    Epoch 75/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.6650 - val_loss: 0.6786
    Epoch 76/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.5979 - val_loss: 0.3851
    Epoch 77/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.6150 - val_loss: 0.4892
    Epoch 78/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.3827 - val_loss: 0.2756
    Epoch 79/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.5446 - val_loss: 0.5606
    Epoch 80/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.7146 - val_loss: 0.3850
    Epoch 81/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.5091 - val_loss: 0.3523
    Epoch 82/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.3260 - val_loss: 0.3588
    Epoch 83/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.5019 - val_loss: 0.4695
    Epoch 84/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.4666 - val_loss: 0.4681
    Epoch 85/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.4509 - val_loss: 0.3696
    Epoch 86/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.1192 - val_loss: 0.3799
    Epoch 87/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.2640 - val_loss: 0.4525
    Epoch 88/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.2150 - val_loss: 0.6877
    Epoch 89/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.9703 - val_loss: 0.4062
    Epoch 90/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.9750 - val_loss: 0.5189
    Epoch 91/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.0951 - val_loss: 0.3092
    Epoch 92/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.0977 - val_loss: 0.3467
    Epoch 93/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.6506 - val_loss: 0.5019
    Epoch 94/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.9291 - val_loss: 0.4270
    Epoch 95/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.7972 - val_loss: 0.4881
    Epoch 96/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.7589 - val_loss: 0.3968
    Epoch 97/1000
    55/55 [==============================] - 0s 4ms/step - loss: 6.0458 - val_loss: 0.2571
    Epoch 98/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.9674 - val_loss: 0.3695
    Epoch 99/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.8988 - val_loss: 0.4312
    Epoch 100/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.4820 - val_loss: 0.3314
    Epoch 101/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.7037 - val_loss: 0.3601
    Epoch 102/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.6615 - val_loss: 0.2495
    Epoch 103/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.4874 - val_loss: 0.2780
    Epoch 104/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.3977 - val_loss: 0.2619
    Epoch 105/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.4270 - val_loss: 0.3072
    Epoch 106/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.4961 - val_loss: 0.4269
    Epoch 107/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.3719 - val_loss: 0.3285
    Epoch 108/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.3593 - val_loss: 0.3247
    Epoch 109/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.2549 - val_loss: 0.3030
    Epoch 110/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.3040 - val_loss: 0.3568
    Epoch 111/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.3202 - val_loss: 0.2503
    Epoch 112/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.1512 - val_loss: 0.2449
    Epoch 113/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.2353 - val_loss: 0.3010
    Epoch 114/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.1001 - val_loss: 0.2788
    Epoch 115/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.2577 - val_loss: 0.2896
    Epoch 116/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.9788 - val_loss: 0.3777
    Epoch 117/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.1074 - val_loss: 0.3194
    Epoch 118/1000
    55/55 [==============================] - 0s 4ms/step - loss: 5.0950 - val_loss: 0.3150
    Epoch 119/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.7920 - val_loss: 0.3803
    Epoch 120/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.9400 - val_loss: 0.3039
    Epoch 121/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.9314 - val_loss: 0.2730
    Epoch 122/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.8499 - val_loss: 0.3894
    Epoch 123/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.7880 - val_loss: 0.2853
    Epoch 124/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.8416 - val_loss: 0.3963
    Epoch 125/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.6211 - val_loss: 0.2448
    Epoch 126/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.7178 - val_loss: 0.2926
    Epoch 127/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.7337 - val_loss: 0.2894
    Epoch 128/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.8330 - val_loss: 0.2770
    Epoch 129/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.7288 - val_loss: 0.3620
    Epoch 130/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.6064 - val_loss: 0.2466
    Epoch 131/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.5635 - val_loss: 0.3109
    Epoch 132/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.4738 - val_loss: 0.2298
    Epoch 133/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.6478 - val_loss: 0.3206
    Epoch 134/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.5766 - val_loss: 0.2271
    Epoch 135/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.4870 - val_loss: 0.3374
    Epoch 136/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.5062 - val_loss: 0.3708
    Epoch 137/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.5287 - val_loss: 0.2254
    Epoch 138/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.3932 - val_loss: 0.3588
    Epoch 139/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.4469 - val_loss: 0.3337
    Epoch 140/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.3593 - val_loss: 0.2315
    Epoch 141/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.2717 - val_loss: 0.2638
    Epoch 142/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.3175 - val_loss: 0.2777
    Epoch 143/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.1777 - val_loss: 0.3206
    Epoch 144/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.1818 - val_loss: 0.2681
    Epoch 145/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.3326 - val_loss: 0.3111
    Epoch 146/1000
    55/55 [==============================] - 0s 5ms/step - loss: 4.0496 - val_loss: 0.3844
    Epoch 147/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.1250 - val_loss: 0.2769
    Epoch 148/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.1550 - val_loss: 0.2285
    Epoch 149/1000
    55/55 [==============================] - 0s 5ms/step - loss: 3.9681 - val_loss: 0.4335
    Epoch 150/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.0806 - val_loss: 0.4388
    Epoch 151/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.0716 - val_loss: 0.3103
    Epoch 152/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.0515 - val_loss: 0.2851
    Epoch 153/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.0185 - val_loss: 0.4057
    Epoch 154/1000
    55/55 [==============================] - 0s 4ms/step - loss: 4.0041 - val_loss: 0.3024
    Epoch 155/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.9548 - val_loss: 0.2379
    Epoch 156/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.9035 - val_loss: 0.3543
    Epoch 157/1000
    55/55 [==============================] - 0s 5ms/step - loss: 3.8024 - val_loss: 0.1963
    Epoch 158/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.8982 - val_loss: 0.3483
    Epoch 159/1000
    55/55 [==============================] - 0s 5ms/step - loss: 3.8161 - val_loss: 0.2721
    Epoch 160/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.7627 - val_loss: 0.2143
    Epoch 161/1000
    55/55 [==============================] - 0s 5ms/step - loss: 3.7150 - val_loss: 0.3174
    Epoch 162/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.9008 - val_loss: 0.3383
    Epoch 163/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.6901 - val_loss: 0.2500
    Epoch 164/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.7985 - val_loss: 0.1990
    Epoch 165/1000
    55/55 [==============================] - 0s 5ms/step - loss: 3.8426 - val_loss: 0.4071
    Epoch 166/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.7023 - val_loss: 0.4326
    Epoch 167/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.6919 - val_loss: 0.3063
    Epoch 168/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.5880 - val_loss: 0.3338
    Epoch 169/1000
    55/55 [==============================] - 0s 5ms/step - loss: 3.6529 - val_loss: 0.3993
    Epoch 170/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.5338 - val_loss: 0.3305
    Epoch 171/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.5681 - val_loss: 0.3048
    Epoch 172/1000
    55/55 [==============================] - 0s 5ms/step - loss: 3.5881 - val_loss: 0.2775
    Epoch 173/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.4648 - val_loss: 0.2513
    Epoch 174/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.4769 - val_loss: 0.2457
    Epoch 175/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.5311 - val_loss: 0.2963
    Epoch 176/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.3668 - val_loss: 0.2545
    Epoch 177/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.4626 - val_loss: 0.2776
    Epoch 178/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.3921 - val_loss: 0.3274
    Epoch 179/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.2857 - val_loss: 0.2968
    Epoch 180/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.4026 - val_loss: 0.2289
    Epoch 181/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.3478 - val_loss: 0.1941
    Epoch 182/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.3629 - val_loss: 0.2583
    Epoch 183/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.2302 - val_loss: 0.2913
    Epoch 184/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.3640 - val_loss: 0.3140
    Epoch 185/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.3655 - val_loss: 0.3363
    Epoch 186/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1885 - val_loss: 0.2319
    Epoch 187/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1718 - val_loss: 0.3020
    Epoch 188/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1794 - val_loss: 0.2411
    Epoch 189/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1712 - val_loss: 0.2894
    Epoch 190/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1327 - val_loss: 0.2991
    Epoch 191/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1558 - val_loss: 0.2677
    Epoch 192/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.0792 - val_loss: 0.2783
    Epoch 193/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1333 - val_loss: 0.3554
    Epoch 194/1000
    55/55 [==============================] - 0s 4ms/step - loss: 2.9948 - val_loss: 0.2791
    Epoch 195/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1202 - val_loss: 0.3412
    Epoch 196/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1162 - val_loss: 0.2261
    Epoch 197/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.1060 - val_loss: 0.3200
    Epoch 198/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.0300 - val_loss: 0.3414
    Epoch 199/1000
    55/55 [==============================] - 0s 4ms/step - loss: 2.9808 - val_loss: 0.2095
    Epoch 200/1000
    55/55 [==============================] - 0s 4ms/step - loss: 2.9996 - val_loss: 0.2371
    Epoch 201/1000
    55/55 [==============================] - 0s 4ms/step - loss: 2.9229 - val_loss: 0.2489
    Epoch 202/1000
    55/55 [==============================] - 0s 4ms/step - loss: 2.9286 - val_loss: 0.2680
    Epoch 203/1000
    55/55 [==============================] - 0s 4ms/step - loss: 2.8367 - val_loss: 0.3976
    Epoch 204/1000
    55/55 [==============================] - 0s 4ms/step - loss: 3.0410 - val_loss: 0.1953
    Epoch 205/1000
    55/55 [==============================] - 0s 4ms/step - loss: 2.8672 - val_loss: 0.3142
    Epoch 206/1000
    55/55 [==============================] - 0s 4ms/step - loss: 2.8692 - val_loss: 0.2175
    Epoch 00206: early stopping
    CPU times: user 54.2 s, sys: 6.16 s, total: 1min
    Wall time: 48.9 s



```
model_loss = pd.DataFrame(model.history.history)
model_loss.plot()
```


```
y_test
```




    array([15.81033467, 18.71489648,  9.99736173, ..., 12.13598207,
           13.39542586, 19.23552297])




```
predictions = model.predict(X_test)
predictions
```




    array([[15.975969],
           [18.621649],
           [10.691629],
           ...,
           [12.463562],
           [13.073429],
           [18.821327]], dtype=float32)




```
np.sqrt(mean_squared_error(y_test,predictions))
```




    0.46633298390852657




```
explained_variance_score(y_test,predictions)
```




    0.9762129512287232




```
result_df = pd.DataFrame()
result_df['True values'] = y_test
result_df['Predictions'] = predictions
result_df
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>True values</th>
      <th>Predictions</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>15.810335</td>
      <td>15.975969</td>
    </tr>
    <tr>
      <th>1</th>
      <td>18.714896</td>
      <td>18.621649</td>
    </tr>
    <tr>
      <th>2</th>
      <td>9.997362</td>
      <td>10.691629</td>
    </tr>
    <tr>
      <th>3</th>
      <td>17.636960</td>
      <td>17.266794</td>
    </tr>
    <tr>
      <th>4</th>
      <td>15.433450</td>
      <td>15.575157</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2995</th>
      <td>14.035750</td>
      <td>13.697926</td>
    </tr>
    <tr>
      <th>2996</th>
      <td>12.230762</td>
      <td>12.391105</td>
    </tr>
    <tr>
      <th>2997</th>
      <td>12.135982</td>
      <td>12.463562</td>
    </tr>
    <tr>
      <th>2998</th>
      <td>13.395426</td>
      <td>13.073429</td>
    </tr>
    <tr>
      <th>2999</th>
      <td>19.235523</td>
      <td>18.821327</td>
    </tr>
  </tbody>
</table>
<p>3000 rows × 2 columns</p>
</div>




```
sns.scatterplot(x = result_df['True values'], y = result_df['Predictions'], hue = "Predictions", data = result_df)
```

# ANN USING TWIN NETWORK


```
input_tensor = Input(shape=(None,9))
l1 = layers.Dense(9,activation='softplus')(input_tensor)
tf.keras.layers.Dropout(rate=0.2)
l2 = layers.Dense(9, activation='softplus')(l1)
tf.keras.layers.Dropout(rate=0.2)
l3 = layers.Dense(9,activation='softplus')(l2)
tf.keras.layers.Dropout(rate=0.2)
l4 = layers.Dense(9,activation='softplus')(l3)
tf.keras.layers.Dropout(rate=0.2)
output = layers.Dense(1)(l4)
l5 = layers.Dense(9,activation='sigmoid')(output)
tf.keras.layers.Dropout(rate=0.2)
l6 = layers.Dense(9,activation='sigmoid')(l3)
tf.keras.layers.Dropout(rate=0.2)
l7 = layers.Dense(9,activation='sigmoid')(l3)
tf.keras.layers.Dropout(rate=0.2)
l8 = layers.Dense(9,activation='sigmoid')(l3)
tf.keras.layers.Dropout(rate=0.2)
output_tensor = layers.Dense(9)(l8)

model_TN = Model(input_tensor,[output,output_tensor])

model_TN.summary()

model_TN.compile(optimizer='adam',loss='mse')
```

    Model: "functional_1"
    __________________________________________________________________________________________________
    Layer (type)                    Output Shape         Param #     Connected to                     
    ==================================================================================================
    input_1 (InputLayer)            [(None, None, 9)]    0                                            
    __________________________________________________________________________________________________
    dense_5 (Dense)                 (None, None, 9)      90          input_1[0][0]                    
    __________________________________________________________________________________________________
    dense_6 (Dense)                 (None, None, 9)      90          dense_5[0][0]                    
    __________________________________________________________________________________________________
    dense_7 (Dense)                 (None, None, 9)      90          dense_6[0][0]                    
    __________________________________________________________________________________________________
    dense_8 (Dense)                 (None, None, 9)      90          dense_7[0][0]                    
    __________________________________________________________________________________________________
    dense_13 (Dense)                (None, None, 9)      90          dense_7[0][0]                    
    __________________________________________________________________________________________________
    dense_9 (Dense)                 (None, None, 1)      10          dense_8[0][0]                    
    __________________________________________________________________________________________________
    dense_14 (Dense)                (None, None, 9)      90          dense_13[0][0]                   
    ==================================================================================================
    Total params: 550
    Trainable params: 550
    Non-trainable params: 0
    __________________________________________________________________________________________________



```
%%time
model_TN.fit(x=X_train, y=y_train, epochs=2000,
          validation_data=(X_test, y_test),batch_size = 128, 
          verbose=1, callbacks=[early_stop])
```

    Epoch 1/2000
    WARNING:tensorflow:Model was constructed with shape (None, None, 9) for input Tensor("input_1:0", shape=(None, None, 9), dtype=float32), but it was called on an input with incompatible shape (None, 9).
    WARNING:tensorflow:Model was constructed with shape (None, None, 9) for input Tensor("input_1:0", shape=(None, None, 9), dtype=float32), but it was called on an input with incompatible shape (None, 9).
    45/55 [=======================>......] - ETA: 0s - loss: 415.5258 - dense_9_loss: 195.9660 - dense_14_loss: 219.5597WARNING:tensorflow:Model was constructed with shape (None, None, 9) for input Tensor("input_1:0", shape=(None, None, 9), dtype=float32), but it was called on an input with incompatible shape (None, 9).
    55/55 [==============================] - 1s 9ms/step - loss: 408.2701 - dense_9_loss: 189.8652 - dense_14_loss: 218.4049 - val_loss: 368.0501 - val_dense_9_loss: 154.9221 - val_dense_14_loss: 213.1280
    Epoch 2/2000
    55/55 [==============================] - 0s 6ms/step - loss: 302.7183 - dense_9_loss: 98.9719 - dense_14_loss: 203.7464 - val_loss: 226.7685 - val_dense_9_loss: 29.9944 - val_dense_14_loss: 196.7741
    Epoch 3/2000
    55/55 [==============================] - 0s 6ms/step - loss: 196.4344 - dense_9_loss: 8.7940 - dense_14_loss: 187.6405 - val_loss: 187.9365 - val_dense_9_loss: 5.9193 - val_dense_14_loss: 182.0173
    Epoch 4/2000
    55/55 [==============================] - 0s 6ms/step - loss: 178.5280 - dense_9_loss: 5.4275 - dense_14_loss: 173.1005 - val_loss: 171.7700 - val_dense_9_loss: 5.5311 - val_dense_14_loss: 166.2389
    Epoch 5/2000
    55/55 [==============================] - 0s 6ms/step - loss: 162.5228 - dense_9_loss: 5.0498 - dense_14_loss: 157.4730 - val_loss: 157.0544 - val_dense_9_loss: 5.1299 - val_dense_14_loss: 151.9245
    Epoch 6/2000
    55/55 [==============================] - 0s 6ms/step - loss: 149.1483 - dense_9_loss: 4.6609 - dense_14_loss: 144.4874 - val_loss: 144.6006 - val_dense_9_loss: 4.7174 - val_dense_14_loss: 139.8832
    Epoch 7/2000
    55/55 [==============================] - 0s 6ms/step - loss: 137.3835 - dense_9_loss: 4.2646 - dense_14_loss: 133.1189 - val_loss: 133.3577 - val_dense_9_loss: 4.3197 - val_dense_14_loss: 129.0379
    Epoch 8/2000
    55/55 [==============================] - 0s 8ms/step - loss: 126.6474 - dense_9_loss: 3.8671 - dense_14_loss: 122.7803 - val_loss: 123.0013 - val_dense_9_loss: 3.8908 - val_dense_14_loss: 119.1106
    Epoch 9/2000
    55/55 [==============================] - 0s 6ms/step - loss: 116.7466 - dense_9_loss: 3.4785 - dense_14_loss: 113.2681 - val_loss: 113.4386 - val_dense_9_loss: 3.4921 - val_dense_14_loss: 109.9466
    Epoch 10/2000
    55/55 [==============================] - 0s 6ms/step - loss: 107.5712 - dense_9_loss: 3.1017 - dense_14_loss: 104.4695 - val_loss: 104.5630 - val_dense_9_loss: 3.1253 - val_dense_14_loss: 101.4376
    Epoch 11/2000
    55/55 [==============================] - 0s 6ms/step - loss: 99.0440 - dense_9_loss: 2.7420 - dense_14_loss: 96.3020 - val_loss: 96.3012 - val_dense_9_loss: 2.7365 - val_dense_14_loss: 93.5647
    Epoch 12/2000
    55/55 [==============================] - 0s 6ms/step - loss: 91.1330 - dense_9_loss: 2.3997 - dense_14_loss: 88.7333 - val_loss: 88.6311 - val_dense_9_loss: 2.4103 - val_dense_14_loss: 86.2208
    Epoch 13/2000
    55/55 [==============================] - 0s 6ms/step - loss: 83.7800 - dense_9_loss: 2.0780 - dense_14_loss: 81.7020 - val_loss: 81.4814 - val_dense_9_loss: 2.0653 - val_dense_14_loss: 79.4160
    Epoch 14/2000
    55/55 [==============================] - 0s 6ms/step - loss: 76.9634 - dense_9_loss: 1.7825 - dense_14_loss: 75.1808 - val_loss: 74.8832 - val_dense_9_loss: 1.7676 - val_dense_14_loss: 73.1155
    Epoch 15/2000
    55/55 [==============================] - 0s 5ms/step - loss: 70.6495 - dense_9_loss: 1.5133 - dense_14_loss: 69.1361 - val_loss: 68.7702 - val_dense_9_loss: 1.4948 - val_dense_14_loss: 67.2754
    Epoch 16/2000
    55/55 [==============================] - 0s 5ms/step - loss: 64.8019 - dense_9_loss: 1.2633 - dense_14_loss: 63.5386 - val_loss: 63.1127 - val_dense_9_loss: 1.2474 - val_dense_14_loss: 61.8653
    Epoch 17/2000
    55/55 [==============================] - 0s 5ms/step - loss: 59.3984 - dense_9_loss: 1.0404 - dense_14_loss: 58.3580 - val_loss: 57.8980 - val_dense_9_loss: 1.0269 - val_dense_14_loss: 56.8711
    Epoch 18/2000
    55/55 [==============================] - 0s 5ms/step - loss: 54.4262 - dense_9_loss: 0.8475 - dense_14_loss: 53.5786 - val_loss: 53.0800 - val_dense_9_loss: 0.8416 - val_dense_14_loss: 52.2384
    Epoch 19/2000
    55/55 [==============================] - 0s 5ms/step - loss: 49.8558 - dense_9_loss: 0.6861 - dense_14_loss: 49.1697 - val_loss: 48.6737 - val_dense_9_loss: 0.6950 - val_dense_14_loss: 47.9787
    Epoch 20/2000
    55/55 [==============================] - 0s 5ms/step - loss: 45.6611 - dense_9_loss: 0.5513 - dense_14_loss: 45.1098 - val_loss: 44.6321 - val_dense_9_loss: 0.5555 - val_dense_14_loss: 44.0766
    Epoch 21/2000
    55/55 [==============================] - 0s 5ms/step - loss: 41.8259 - dense_9_loss: 0.4447 - dense_14_loss: 41.3812 - val_loss: 40.9432 - val_dense_9_loss: 0.4528 - val_dense_14_loss: 40.4904
    Epoch 22/2000
    55/55 [==============================] - 0s 5ms/step - loss: 38.3268 - dense_9_loss: 0.3604 - dense_14_loss: 37.9664 - val_loss: 37.5659 - val_dense_9_loss: 0.3766 - val_dense_14_loss: 37.1894
    Epoch 23/2000
    55/55 [==============================] - 0s 5ms/step - loss: 35.1338 - dense_9_loss: 0.2984 - dense_14_loss: 34.8354 - val_loss: 34.5074 - val_dense_9_loss: 0.3171 - val_dense_14_loss: 34.1902
    Epoch 24/2000
    55/55 [==============================] - 0s 5ms/step - loss: 32.2391 - dense_9_loss: 0.2538 - dense_14_loss: 31.9853 - val_loss: 31.7119 - val_dense_9_loss: 0.2770 - val_dense_14_loss: 31.4349
    Epoch 25/2000
    55/55 [==============================] - 0s 5ms/step - loss: 29.6074 - dense_9_loss: 0.2225 - dense_14_loss: 29.3849 - val_loss: 29.1884 - val_dense_9_loss: 0.2470 - val_dense_14_loss: 28.9415
    Epoch 26/2000
    55/55 [==============================] - 0s 5ms/step - loss: 27.2266 - dense_9_loss: 0.2003 - dense_14_loss: 27.0262 - val_loss: 26.8996 - val_dense_9_loss: 0.2242 - val_dense_14_loss: 26.6753
    Epoch 27/2000
    55/55 [==============================] - 0s 5ms/step - loss: 25.0733 - dense_9_loss: 0.1859 - dense_14_loss: 24.8874 - val_loss: 24.8383 - val_dense_9_loss: 0.2097 - val_dense_14_loss: 24.6287
    Epoch 28/2000
    55/55 [==============================] - 0s 5ms/step - loss: 23.1336 - dense_9_loss: 0.1765 - dense_14_loss: 22.9571 - val_loss: 22.9697 - val_dense_9_loss: 0.1997 - val_dense_14_loss: 22.7700
    Epoch 29/2000
    55/55 [==============================] - 0s 5ms/step - loss: 21.3858 - dense_9_loss: 0.1694 - dense_14_loss: 21.2163 - val_loss: 21.2966 - val_dense_9_loss: 0.1923 - val_dense_14_loss: 21.1043
    Epoch 30/2000
    55/55 [==============================] - 0s 5ms/step - loss: 19.8163 - dense_9_loss: 0.1645 - dense_14_loss: 19.6518 - val_loss: 19.7994 - val_dense_9_loss: 0.1880 - val_dense_14_loss: 19.6115
    Epoch 31/2000
    55/55 [==============================] - 0s 5ms/step - loss: 18.4149 - dense_9_loss: 0.1614 - dense_14_loss: 18.2536 - val_loss: 18.4505 - val_dense_9_loss: 0.1830 - val_dense_14_loss: 18.2675
    Epoch 32/2000
    55/55 [==============================] - 0s 5ms/step - loss: 17.1621 - dense_9_loss: 0.1584 - dense_14_loss: 17.0036 - val_loss: 17.2509 - val_dense_9_loss: 0.1798 - val_dense_14_loss: 17.0710
    Epoch 33/2000
    55/55 [==============================] - 0s 5ms/step - loss: 16.0476 - dense_9_loss: 0.1566 - dense_14_loss: 15.8910 - val_loss: 16.1834 - val_dense_9_loss: 0.1772 - val_dense_14_loss: 16.0062
    Epoch 34/2000
    55/55 [==============================] - 0s 5ms/step - loss: 15.0591 - dense_9_loss: 0.1547 - dense_14_loss: 14.9044 - val_loss: 15.2367 - val_dense_9_loss: 0.1748 - val_dense_14_loss: 15.0620
    Epoch 35/2000
    55/55 [==============================] - 0s 5ms/step - loss: 14.1831 - dense_9_loss: 0.1527 - dense_14_loss: 14.0304 - val_loss: 14.4055 - val_dense_9_loss: 0.1736 - val_dense_14_loss: 14.2319
    Epoch 36/2000
    55/55 [==============================] - 0s 5ms/step - loss: 13.4129 - dense_9_loss: 0.1513 - dense_14_loss: 13.2616 - val_loss: 13.6694 - val_dense_9_loss: 0.1714 - val_dense_14_loss: 13.4981
    Epoch 37/2000
    55/55 [==============================] - 0s 5ms/step - loss: 12.7357 - dense_9_loss: 0.1503 - dense_14_loss: 12.5854 - val_loss: 13.0312 - val_dense_9_loss: 0.1754 - val_dense_14_loss: 12.8558
    Epoch 38/2000
    55/55 [==============================] - 0s 5ms/step - loss: 12.1447 - dense_9_loss: 0.1494 - dense_14_loss: 11.9953 - val_loss: 12.4561 - val_dense_9_loss: 0.1672 - val_dense_14_loss: 12.2890
    Epoch 39/2000
    55/55 [==============================] - 0s 5ms/step - loss: 11.6264 - dense_9_loss: 0.1470 - dense_14_loss: 11.4795 - val_loss: 11.9597 - val_dense_9_loss: 0.1652 - val_dense_14_loss: 11.7944
    Epoch 40/2000
    55/55 [==============================] - 0s 5ms/step - loss: 11.1763 - dense_9_loss: 0.1454 - dense_14_loss: 11.0309 - val_loss: 11.5345 - val_dense_9_loss: 0.1641 - val_dense_14_loss: 11.3704
    Epoch 41/2000
    55/55 [==============================] - 0s 5ms/step - loss: 10.7883 - dense_9_loss: 0.1444 - dense_14_loss: 10.6440 - val_loss: 11.1658 - val_dense_9_loss: 0.1658 - val_dense_14_loss: 11.0001
    Epoch 42/2000
    55/55 [==============================] - 0s 5ms/step - loss: 10.4533 - dense_9_loss: 0.1441 - dense_14_loss: 10.3092 - val_loss: 10.8426 - val_dense_9_loss: 0.1605 - val_dense_14_loss: 10.6822
    Epoch 43/2000
    55/55 [==============================] - 0s 6ms/step - loss: 10.1633 - dense_9_loss: 0.1412 - dense_14_loss: 10.0221 - val_loss: 10.5655 - val_dense_9_loss: 0.1626 - val_dense_14_loss: 10.4029
    Epoch 44/2000
    55/55 [==============================] - 0s 5ms/step - loss: 9.9167 - dense_9_loss: 0.1418 - dense_14_loss: 9.7749 - val_loss: 10.3239 - val_dense_9_loss: 0.1570 - val_dense_14_loss: 10.1668
    Epoch 45/2000
    55/55 [==============================] - 0s 5ms/step - loss: 9.7023 - dense_9_loss: 0.1397 - dense_14_loss: 9.5626 - val_loss: 10.1202 - val_dense_9_loss: 0.1561 - val_dense_14_loss: 9.9641
    Epoch 46/2000
    55/55 [==============================] - 0s 6ms/step - loss: 9.5169 - dense_9_loss: 0.1375 - dense_14_loss: 9.3794 - val_loss: 9.9373 - val_dense_9_loss: 0.1543 - val_dense_14_loss: 9.7829
    Epoch 47/2000
    55/55 [==============================] - 0s 5ms/step - loss: 9.3484 - dense_9_loss: 0.1384 - dense_14_loss: 9.2101 - val_loss: 9.7610 - val_dense_9_loss: 0.1542 - val_dense_14_loss: 9.6068
    Epoch 48/2000
    55/55 [==============================] - 0s 5ms/step - loss: 9.1848 - dense_9_loss: 0.1366 - dense_14_loss: 9.0483 - val_loss: 9.5912 - val_dense_9_loss: 0.1524 - val_dense_14_loss: 9.4388
    Epoch 49/2000
    55/55 [==============================] - 0s 5ms/step - loss: 9.0131 - dense_9_loss: 0.1362 - dense_14_loss: 8.8768 - val_loss: 9.3827 - val_dense_9_loss: 0.1545 - val_dense_14_loss: 9.2282
    Epoch 50/2000
    55/55 [==============================] - 0s 5ms/step - loss: 8.7751 - dense_9_loss: 0.1370 - dense_14_loss: 8.6381 - val_loss: 9.1008 - val_dense_9_loss: 0.1520 - val_dense_14_loss: 8.9488
    Epoch 51/2000
    55/55 [==============================] - 0s 5ms/step - loss: 8.4962 - dense_9_loss: 0.1358 - dense_14_loss: 8.3604 - val_loss: 8.7903 - val_dense_9_loss: 0.1507 - val_dense_14_loss: 8.6396
    Epoch 52/2000
    55/55 [==============================] - 0s 5ms/step - loss: 8.1889 - dense_9_loss: 0.1357 - dense_14_loss: 8.0532 - val_loss: 8.4474 - val_dense_9_loss: 0.1505 - val_dense_14_loss: 8.2970
    Epoch 53/2000
    55/55 [==============================] - 0s 5ms/step - loss: 7.8176 - dense_9_loss: 0.1377 - dense_14_loss: 7.6799 - val_loss: 7.9775 - val_dense_9_loss: 0.1537 - val_dense_14_loss: 7.8238
    Epoch 54/2000
    55/55 [==============================] - 0s 5ms/step - loss: 7.3456 - dense_9_loss: 0.1414 - dense_14_loss: 7.2042 - val_loss: 7.4549 - val_dense_9_loss: 0.1537 - val_dense_14_loss: 7.3012
    Epoch 55/2000
    55/55 [==============================] - 0s 5ms/step - loss: 6.8196 - dense_9_loss: 0.1509 - dense_14_loss: 6.6688 - val_loss: 6.8378 - val_dense_9_loss: 0.1668 - val_dense_14_loss: 6.6710
    Epoch 56/2000
    55/55 [==============================] - 0s 5ms/step - loss: 6.1806 - dense_9_loss: 0.1632 - dense_14_loss: 6.0174 - val_loss: 6.0846 - val_dense_9_loss: 0.1760 - val_dense_14_loss: 5.9086
    Epoch 57/2000
    55/55 [==============================] - 0s 5ms/step - loss: 5.4324 - dense_9_loss: 0.1786 - dense_14_loss: 5.2537 - val_loss: 5.2295 - val_dense_9_loss: 0.1906 - val_dense_14_loss: 5.0389
    Epoch 58/2000
    55/55 [==============================] - 0s 5ms/step - loss: 4.5803 - dense_9_loss: 0.1848 - dense_14_loss: 4.3955 - val_loss: 4.3236 - val_dense_9_loss: 0.1816 - val_dense_14_loss: 4.1420
    Epoch 59/2000
    55/55 [==============================] - 0s 5ms/step - loss: 3.8384 - dense_9_loss: 0.1669 - dense_14_loss: 3.6715 - val_loss: 3.6674 - val_dense_9_loss: 0.1575 - val_dense_14_loss: 3.5100
    Epoch 60/2000
    55/55 [==============================] - 0s 5ms/step - loss: 3.3264 - dense_9_loss: 0.1442 - dense_14_loss: 3.1822 - val_loss: 3.2414 - val_dense_9_loss: 0.1424 - val_dense_14_loss: 3.0990
    Epoch 61/2000
    55/55 [==============================] - 0s 5ms/step - loss: 2.9870 - dense_9_loss: 0.1307 - dense_14_loss: 2.8563 - val_loss: 2.9512 - val_dense_9_loss: 0.1416 - val_dense_14_loss: 2.8095
    Epoch 62/2000
    55/55 [==============================] - 0s 5ms/step - loss: 2.7386 - dense_9_loss: 0.1275 - dense_14_loss: 2.6111 - val_loss: 2.7142 - val_dense_9_loss: 0.1385 - val_dense_14_loss: 2.5757
    Epoch 63/2000
    55/55 [==============================] - 0s 5ms/step - loss: 2.5277 - dense_9_loss: 0.1266 - dense_14_loss: 2.4011 - val_loss: 2.5058 - val_dense_9_loss: 0.1362 - val_dense_14_loss: 2.3696
    Epoch 64/2000
    55/55 [==============================] - 0s 5ms/step - loss: 2.3394 - dense_9_loss: 0.1241 - dense_14_loss: 2.2153 - val_loss: 2.3225 - val_dense_9_loss: 0.1346 - val_dense_14_loss: 2.1879
    Epoch 65/2000
    55/55 [==============================] - 0s 5ms/step - loss: 2.1710 - dense_9_loss: 0.1240 - dense_14_loss: 2.0470 - val_loss: 2.1554 - val_dense_9_loss: 0.1338 - val_dense_14_loss: 2.0216
    Epoch 66/2000
    55/55 [==============================] - 0s 5ms/step - loss: 2.0165 - dense_9_loss: 0.1211 - dense_14_loss: 1.8954 - val_loss: 2.0088 - val_dense_9_loss: 0.1309 - val_dense_14_loss: 1.8779
    Epoch 67/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.8819 - dense_9_loss: 0.1208 - dense_14_loss: 1.7611 - val_loss: 1.8672 - val_dense_9_loss: 0.1302 - val_dense_14_loss: 1.7371
    Epoch 68/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.7542 - dense_9_loss: 0.1209 - dense_14_loss: 1.6334 - val_loss: 1.7491 - val_dense_9_loss: 0.1301 - val_dense_14_loss: 1.6190
    Epoch 69/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.6334 - dense_9_loss: 0.1149 - dense_14_loss: 1.5185 - val_loss: 1.6327 - val_dense_9_loss: 0.1308 - val_dense_14_loss: 1.5020
    Epoch 70/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.5310 - dense_9_loss: 0.1149 - dense_14_loss: 1.4161 - val_loss: 1.5288 - val_dense_9_loss: 0.1288 - val_dense_14_loss: 1.4000
    Epoch 71/2000
    55/55 [==============================] - 0s 6ms/step - loss: 1.4347 - dense_9_loss: 0.1126 - dense_14_loss: 1.3222 - val_loss: 1.4295 - val_dense_9_loss: 0.1215 - val_dense_14_loss: 1.3080
    Epoch 72/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.3490 - dense_9_loss: 0.1099 - dense_14_loss: 1.2391 - val_loss: 1.3483 - val_dense_9_loss: 0.1239 - val_dense_14_loss: 1.2244
    Epoch 73/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.2720 - dense_9_loss: 0.1098 - dense_14_loss: 1.1622 - val_loss: 1.2694 - val_dense_9_loss: 0.1183 - val_dense_14_loss: 1.1512
    Epoch 74/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.2008 - dense_9_loss: 0.1078 - dense_14_loss: 1.0930 - val_loss: 1.2031 - val_dense_9_loss: 0.1208 - val_dense_14_loss: 1.0823
    Epoch 75/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.1367 - dense_9_loss: 0.1073 - dense_14_loss: 1.0294 - val_loss: 1.1350 - val_dense_9_loss: 0.1197 - val_dense_14_loss: 1.0153
    Epoch 76/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.0738 - dense_9_loss: 0.1036 - dense_14_loss: 0.9703 - val_loss: 1.0676 - val_dense_9_loss: 0.1122 - val_dense_14_loss: 0.9554
    Epoch 77/2000
    55/55 [==============================] - 0s 5ms/step - loss: 1.0197 - dense_9_loss: 0.1030 - dense_14_loss: 0.9167 - val_loss: 1.0277 - val_dense_9_loss: 0.1236 - val_dense_14_loss: 0.9041
    Epoch 78/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.9677 - dense_9_loss: 0.1015 - dense_14_loss: 0.8662 - val_loss: 0.9591 - val_dense_9_loss: 0.1087 - val_dense_14_loss: 0.8505
    Epoch 79/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.9161 - dense_9_loss: 0.0980 - dense_14_loss: 0.8180 - val_loss: 0.9105 - val_dense_9_loss: 0.1072 - val_dense_14_loss: 0.8033
    Epoch 80/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.8716 - dense_9_loss: 0.0967 - dense_14_loss: 0.7750 - val_loss: 0.8734 - val_dense_9_loss: 0.1055 - val_dense_14_loss: 0.7679
    Epoch 81/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.8351 - dense_9_loss: 0.0967 - dense_14_loss: 0.7384 - val_loss: 0.8277 - val_dense_9_loss: 0.1072 - val_dense_14_loss: 0.7206
    Epoch 82/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.7904 - dense_9_loss: 0.0938 - dense_14_loss: 0.6966 - val_loss: 0.7848 - val_dense_9_loss: 0.1036 - val_dense_14_loss: 0.6812
    Epoch 83/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.7535 - dense_9_loss: 0.0933 - dense_14_loss: 0.6602 - val_loss: 0.7469 - val_dense_9_loss: 0.1011 - val_dense_14_loss: 0.6458
    Epoch 84/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.7182 - dense_9_loss: 0.0915 - dense_14_loss: 0.6267 - val_loss: 0.7167 - val_dense_9_loss: 0.1008 - val_dense_14_loss: 0.6159
    Epoch 85/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.6845 - dense_9_loss: 0.0893 - dense_14_loss: 0.5952 - val_loss: 0.6823 - val_dense_9_loss: 0.0977 - val_dense_14_loss: 0.5846
    Epoch 86/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.6547 - dense_9_loss: 0.0886 - dense_14_loss: 0.5661 - val_loss: 0.6447 - val_dense_9_loss: 0.0948 - val_dense_14_loss: 0.5499
    Epoch 87/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.6250 - dense_9_loss: 0.0873 - dense_14_loss: 0.5377 - val_loss: 0.6198 - val_dense_9_loss: 0.0967 - val_dense_14_loss: 0.5232
    Epoch 88/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.5974 - dense_9_loss: 0.0857 - dense_14_loss: 0.5117 - val_loss: 0.5893 - val_dense_9_loss: 0.0924 - val_dense_14_loss: 0.4969
    Epoch 89/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.5741 - dense_9_loss: 0.0849 - dense_14_loss: 0.4892 - val_loss: 0.5663 - val_dense_9_loss: 0.0936 - val_dense_14_loss: 0.4727
    Epoch 90/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.5497 - dense_9_loss: 0.0820 - dense_14_loss: 0.4678 - val_loss: 0.5362 - val_dense_9_loss: 0.0873 - val_dense_14_loss: 0.4490
    Epoch 91/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.5251 - dense_9_loss: 0.0811 - dense_14_loss: 0.4440 - val_loss: 0.5133 - val_dense_9_loss: 0.0859 - val_dense_14_loss: 0.4274
    Epoch 92/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.5088 - dense_9_loss: 0.0827 - dense_14_loss: 0.4261 - val_loss: 0.5087 - val_dense_9_loss: 0.0890 - val_dense_14_loss: 0.4198
    Epoch 93/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.4840 - dense_9_loss: 0.0788 - dense_14_loss: 0.4053 - val_loss: 0.4753 - val_dense_9_loss: 0.0843 - val_dense_14_loss: 0.3910
    Epoch 94/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.4653 - dense_9_loss: 0.0770 - dense_14_loss: 0.3883 - val_loss: 0.4533 - val_dense_9_loss: 0.0809 - val_dense_14_loss: 0.3724
    Epoch 95/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.4466 - dense_9_loss: 0.0737 - dense_14_loss: 0.3728 - val_loss: 0.4330 - val_dense_9_loss: 0.0767 - val_dense_14_loss: 0.3563
    Epoch 96/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.4295 - dense_9_loss: 0.0717 - dense_14_loss: 0.3578 - val_loss: 0.4248 - val_dense_9_loss: 0.0778 - val_dense_14_loss: 0.3470
    Epoch 97/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.4135 - dense_9_loss: 0.0711 - dense_14_loss: 0.3424 - val_loss: 0.4019 - val_dense_9_loss: 0.0744 - val_dense_14_loss: 0.3275
    Epoch 98/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.3970 - dense_9_loss: 0.0682 - dense_14_loss: 0.3288 - val_loss: 0.3839 - val_dense_9_loss: 0.0709 - val_dense_14_loss: 0.3130
    Epoch 99/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.3835 - dense_9_loss: 0.0666 - dense_14_loss: 0.3169 - val_loss: 0.3728 - val_dense_9_loss: 0.0697 - val_dense_14_loss: 0.3031
    Epoch 100/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.3691 - dense_9_loss: 0.0645 - dense_14_loss: 0.3046 - val_loss: 0.3574 - val_dense_9_loss: 0.0666 - val_dense_14_loss: 0.2908
    Epoch 101/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.3548 - dense_9_loss: 0.0627 - dense_14_loss: 0.2921 - val_loss: 0.3403 - val_dense_9_loss: 0.0628 - val_dense_14_loss: 0.2775
    Epoch 102/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.3435 - dense_9_loss: 0.0611 - dense_14_loss: 0.2824 - val_loss: 0.3286 - val_dense_9_loss: 0.0605 - val_dense_14_loss: 0.2682
    Epoch 103/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.3325 - dense_9_loss: 0.0607 - dense_14_loss: 0.2718 - val_loss: 0.3162 - val_dense_9_loss: 0.0588 - val_dense_14_loss: 0.2573
    Epoch 104/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.3228 - dense_9_loss: 0.0591 - dense_14_loss: 0.2637 - val_loss: 0.3076 - val_dense_9_loss: 0.0582 - val_dense_14_loss: 0.2494
    Epoch 105/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.3114 - dense_9_loss: 0.0584 - dense_14_loss: 0.2531 - val_loss: 0.2965 - val_dense_9_loss: 0.0571 - val_dense_14_loss: 0.2394
    Epoch 106/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.3041 - dense_9_loss: 0.0591 - dense_14_loss: 0.2450 - val_loss: 0.2868 - val_dense_9_loss: 0.0566 - val_dense_14_loss: 0.2302
    Epoch 107/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.2945 - dense_9_loss: 0.0574 - dense_14_loss: 0.2371 - val_loss: 0.2846 - val_dense_9_loss: 0.0582 - val_dense_14_loss: 0.2264
    Epoch 108/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2870 - dense_9_loss: 0.0581 - dense_14_loss: 0.2290 - val_loss: 0.2723 - val_dense_9_loss: 0.0579 - val_dense_14_loss: 0.2143
    Epoch 109/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2803 - dense_9_loss: 0.0581 - dense_14_loss: 0.2222 - val_loss: 0.2642 - val_dense_9_loss: 0.0573 - val_dense_14_loss: 0.2069
    Epoch 110/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2721 - dense_9_loss: 0.0571 - dense_14_loss: 0.2150 - val_loss: 0.2559 - val_dense_9_loss: 0.0554 - val_dense_14_loss: 0.2005
    Epoch 111/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2653 - dense_9_loss: 0.0577 - dense_14_loss: 0.2076 - val_loss: 0.2504 - val_dense_9_loss: 0.0559 - val_dense_14_loss: 0.1945
    Epoch 112/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2592 - dense_9_loss: 0.0580 - dense_14_loss: 0.2012 - val_loss: 0.2418 - val_dense_9_loss: 0.0547 - val_dense_14_loss: 0.1871
    Epoch 113/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2511 - dense_9_loss: 0.0567 - dense_14_loss: 0.1943 - val_loss: 0.2379 - val_dense_9_loss: 0.0560 - val_dense_14_loss: 0.1820
    Epoch 114/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2453 - dense_9_loss: 0.0567 - dense_14_loss: 0.1885 - val_loss: 0.2402 - val_dense_9_loss: 0.0591 - val_dense_14_loss: 0.1811
    Epoch 115/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2416 - dense_9_loss: 0.0575 - dense_14_loss: 0.1840 - val_loss: 0.2260 - val_dense_9_loss: 0.0557 - val_dense_14_loss: 0.1703
    Epoch 116/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2371 - dense_9_loss: 0.0582 - dense_14_loss: 0.1789 - val_loss: 0.2277 - val_dense_9_loss: 0.0545 - val_dense_14_loss: 0.1733
    Epoch 117/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2311 - dense_9_loss: 0.0573 - dense_14_loss: 0.1738 - val_loss: 0.2164 - val_dense_9_loss: 0.0559 - val_dense_14_loss: 0.1605
    Epoch 118/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.2242 - dense_9_loss: 0.0566 - dense_14_loss: 0.1676 - val_loss: 0.2152 - val_dense_9_loss: 0.0569 - val_dense_14_loss: 0.1583
    Epoch 119/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2200 - dense_9_loss: 0.0569 - dense_14_loss: 0.1631 - val_loss: 0.2062 - val_dense_9_loss: 0.0548 - val_dense_14_loss: 0.1514
    Epoch 120/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2136 - dense_9_loss: 0.0562 - dense_14_loss: 0.1574 - val_loss: 0.2023 - val_dense_9_loss: 0.0552 - val_dense_14_loss: 0.1471
    Epoch 121/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2114 - dense_9_loss: 0.0575 - dense_14_loss: 0.1539 - val_loss: 0.1963 - val_dense_9_loss: 0.0536 - val_dense_14_loss: 0.1427
    Epoch 122/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2083 - dense_9_loss: 0.0568 - dense_14_loss: 0.1515 - val_loss: 0.1924 - val_dense_9_loss: 0.0537 - val_dense_14_loss: 0.1387
    Epoch 123/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.2027 - dense_9_loss: 0.0561 - dense_14_loss: 0.1466 - val_loss: 0.1874 - val_dense_9_loss: 0.0529 - val_dense_14_loss: 0.1345
    Epoch 124/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1985 - dense_9_loss: 0.0564 - dense_14_loss: 0.1420 - val_loss: 0.1879 - val_dense_9_loss: 0.0556 - val_dense_14_loss: 0.1323
    Epoch 125/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1940 - dense_9_loss: 0.0554 - dense_14_loss: 0.1386 - val_loss: 0.1884 - val_dense_9_loss: 0.0575 - val_dense_14_loss: 0.1310
    Epoch 126/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1926 - dense_9_loss: 0.0563 - dense_14_loss: 0.1363 - val_loss: 0.1785 - val_dense_9_loss: 0.0529 - val_dense_14_loss: 0.1257
    Epoch 127/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1882 - dense_9_loss: 0.0562 - dense_14_loss: 0.1320 - val_loss: 0.1807 - val_dense_9_loss: 0.0532 - val_dense_14_loss: 0.1275
    Epoch 128/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1831 - dense_9_loss: 0.0548 - dense_14_loss: 0.1284 - val_loss: 0.1758 - val_dense_9_loss: 0.0542 - val_dense_14_loss: 0.1216
    Epoch 129/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1823 - dense_9_loss: 0.0554 - dense_14_loss: 0.1269 - val_loss: 0.1702 - val_dense_9_loss: 0.0526 - val_dense_14_loss: 0.1176
    Epoch 130/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1787 - dense_9_loss: 0.0551 - dense_14_loss: 0.1237 - val_loss: 0.1651 - val_dense_9_loss: 0.0525 - val_dense_14_loss: 0.1126
    Epoch 131/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1752 - dense_9_loss: 0.0546 - dense_14_loss: 0.1206 - val_loss: 0.1658 - val_dense_9_loss: 0.0550 - val_dense_14_loss: 0.1107
    Epoch 132/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1745 - dense_9_loss: 0.0565 - dense_14_loss: 0.1180 - val_loss: 0.1605 - val_dense_9_loss: 0.0526 - val_dense_14_loss: 0.1079
    Epoch 133/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1693 - dense_9_loss: 0.0550 - dense_14_loss: 0.1143 - val_loss: 0.1629 - val_dense_9_loss: 0.0537 - val_dense_14_loss: 0.1092
    Epoch 134/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1685 - dense_9_loss: 0.0551 - dense_14_loss: 0.1134 - val_loss: 0.1580 - val_dense_9_loss: 0.0521 - val_dense_14_loss: 0.1059
    Epoch 135/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1697 - dense_9_loss: 0.0556 - dense_14_loss: 0.1141 - val_loss: 0.1543 - val_dense_9_loss: 0.0522 - val_dense_14_loss: 0.1021
    Epoch 136/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1658 - dense_9_loss: 0.0567 - dense_14_loss: 0.1091 - val_loss: 0.1604 - val_dense_9_loss: 0.0600 - val_dense_14_loss: 0.1004
    Epoch 137/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1636 - dense_9_loss: 0.0575 - dense_14_loss: 0.1061 - val_loss: 0.1513 - val_dense_9_loss: 0.0517 - val_dense_14_loss: 0.0996
    Epoch 138/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1607 - dense_9_loss: 0.0556 - dense_14_loss: 0.1051 - val_loss: 0.1487 - val_dense_9_loss: 0.0535 - val_dense_14_loss: 0.0953
    Epoch 139/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1564 - dense_9_loss: 0.0542 - dense_14_loss: 0.1022 - val_loss: 0.1451 - val_dense_9_loss: 0.0512 - val_dense_14_loss: 0.0939
    Epoch 140/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1556 - dense_9_loss: 0.0550 - dense_14_loss: 0.1005 - val_loss: 0.1460 - val_dense_9_loss: 0.0522 - val_dense_14_loss: 0.0938
    Epoch 141/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1521 - dense_9_loss: 0.0540 - dense_14_loss: 0.0981 - val_loss: 0.1413 - val_dense_9_loss: 0.0514 - val_dense_14_loss: 0.0899
    Epoch 142/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1538 - dense_9_loss: 0.0558 - dense_14_loss: 0.0980 - val_loss: 0.1405 - val_dense_9_loss: 0.0511 - val_dense_14_loss: 0.0894
    Epoch 143/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1487 - dense_9_loss: 0.0539 - dense_14_loss: 0.0948 - val_loss: 0.1497 - val_dense_9_loss: 0.0578 - val_dense_14_loss: 0.0919
    Epoch 144/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1496 - dense_9_loss: 0.0552 - dense_14_loss: 0.0944 - val_loss: 0.1420 - val_dense_9_loss: 0.0534 - val_dense_14_loss: 0.0886
    Epoch 145/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1461 - dense_9_loss: 0.0540 - dense_14_loss: 0.0922 - val_loss: 0.1352 - val_dense_9_loss: 0.0510 - val_dense_14_loss: 0.0842
    Epoch 146/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1446 - dense_9_loss: 0.0538 - dense_14_loss: 0.0908 - val_loss: 0.1366 - val_dense_9_loss: 0.0535 - val_dense_14_loss: 0.0832
    Epoch 147/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1509 - dense_9_loss: 0.0555 - dense_14_loss: 0.0954 - val_loss: 0.1374 - val_dense_9_loss: 0.0532 - val_dense_14_loss: 0.0842
    Epoch 148/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1457 - dense_9_loss: 0.0547 - dense_14_loss: 0.0910 - val_loss: 0.1314 - val_dense_9_loss: 0.0511 - val_dense_14_loss: 0.0802
    Epoch 149/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1408 - dense_9_loss: 0.0540 - dense_14_loss: 0.0868 - val_loss: 0.1321 - val_dense_9_loss: 0.0528 - val_dense_14_loss: 0.0793
    Epoch 150/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1384 - dense_9_loss: 0.0531 - dense_14_loss: 0.0852 - val_loss: 0.1306 - val_dense_9_loss: 0.0512 - val_dense_14_loss: 0.0793
    Epoch 151/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1368 - dense_9_loss: 0.0531 - dense_14_loss: 0.0838 - val_loss: 0.1278 - val_dense_9_loss: 0.0508 - val_dense_14_loss: 0.0770
    Epoch 152/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1367 - dense_9_loss: 0.0532 - dense_14_loss: 0.0835 - val_loss: 0.1324 - val_dense_9_loss: 0.0505 - val_dense_14_loss: 0.0819
    Epoch 153/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1368 - dense_9_loss: 0.0544 - dense_14_loss: 0.0824 - val_loss: 0.1282 - val_dense_9_loss: 0.0532 - val_dense_14_loss: 0.0750
    Epoch 154/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1354 - dense_9_loss: 0.0543 - dense_14_loss: 0.0811 - val_loss: 0.1314 - val_dense_9_loss: 0.0535 - val_dense_14_loss: 0.0779
    Epoch 155/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1346 - dense_9_loss: 0.0541 - dense_14_loss: 0.0805 - val_loss: 0.1269 - val_dense_9_loss: 0.0511 - val_dense_14_loss: 0.0758
    Epoch 156/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1363 - dense_9_loss: 0.0539 - dense_14_loss: 0.0824 - val_loss: 0.1260 - val_dense_9_loss: 0.0531 - val_dense_14_loss: 0.0729
    Epoch 157/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1359 - dense_9_loss: 0.0559 - dense_14_loss: 0.0800 - val_loss: 0.1352 - val_dense_9_loss: 0.0610 - val_dense_14_loss: 0.0742
    Epoch 158/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1343 - dense_9_loss: 0.0543 - dense_14_loss: 0.0800 - val_loss: 0.1225 - val_dense_9_loss: 0.0499 - val_dense_14_loss: 0.0726
    Epoch 159/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1296 - dense_9_loss: 0.0532 - dense_14_loss: 0.0763 - val_loss: 0.1202 - val_dense_9_loss: 0.0499 - val_dense_14_loss: 0.0702
    Epoch 160/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1298 - dense_9_loss: 0.0535 - dense_14_loss: 0.0763 - val_loss: 0.1208 - val_dense_9_loss: 0.0496 - val_dense_14_loss: 0.0711
    Epoch 161/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1279 - dense_9_loss: 0.0528 - dense_14_loss: 0.0751 - val_loss: 0.1285 - val_dense_9_loss: 0.0519 - val_dense_14_loss: 0.0766
    Epoch 162/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1273 - dense_9_loss: 0.0527 - dense_14_loss: 0.0746 - val_loss: 0.1204 - val_dense_9_loss: 0.0511 - val_dense_14_loss: 0.0693
    Epoch 163/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1265 - dense_9_loss: 0.0529 - dense_14_loss: 0.0735 - val_loss: 0.1185 - val_dense_9_loss: 0.0510 - val_dense_14_loss: 0.0675
    Epoch 164/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1255 - dense_9_loss: 0.0529 - dense_14_loss: 0.0726 - val_loss: 0.1200 - val_dense_9_loss: 0.0507 - val_dense_14_loss: 0.0692
    Epoch 165/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1255 - dense_9_loss: 0.0530 - dense_14_loss: 0.0725 - val_loss: 0.1284 - val_dense_9_loss: 0.0599 - val_dense_14_loss: 0.0685
    Epoch 166/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1289 - dense_9_loss: 0.0548 - dense_14_loss: 0.0741 - val_loss: 0.1212 - val_dense_9_loss: 0.0496 - val_dense_14_loss: 0.0716
    Epoch 167/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1262 - dense_9_loss: 0.0527 - dense_14_loss: 0.0734 - val_loss: 0.1215 - val_dense_9_loss: 0.0546 - val_dense_14_loss: 0.0669
    Epoch 168/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1255 - dense_9_loss: 0.0539 - dense_14_loss: 0.0716 - val_loss: 0.1181 - val_dense_9_loss: 0.0493 - val_dense_14_loss: 0.0688
    Epoch 169/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1253 - dense_9_loss: 0.0553 - dense_14_loss: 0.0700 - val_loss: 0.1163 - val_dense_9_loss: 0.0505 - val_dense_14_loss: 0.0658
    Epoch 170/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1221 - dense_9_loss: 0.0527 - dense_14_loss: 0.0694 - val_loss: 0.1252 - val_dense_9_loss: 0.0556 - val_dense_14_loss: 0.0695
    Epoch 171/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1246 - dense_9_loss: 0.0532 - dense_14_loss: 0.0715 - val_loss: 0.1132 - val_dense_9_loss: 0.0496 - val_dense_14_loss: 0.0637
    Epoch 172/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1201 - dense_9_loss: 0.0519 - dense_14_loss: 0.0682 - val_loss: 0.1160 - val_dense_9_loss: 0.0502 - val_dense_14_loss: 0.0658
    Epoch 173/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1231 - dense_9_loss: 0.0544 - dense_14_loss: 0.0687 - val_loss: 0.1130 - val_dense_9_loss: 0.0490 - val_dense_14_loss: 0.0640
    Epoch 174/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1203 - dense_9_loss: 0.0523 - dense_14_loss: 0.0680 - val_loss: 0.1121 - val_dense_9_loss: 0.0490 - val_dense_14_loss: 0.0630
    Epoch 175/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1210 - dense_9_loss: 0.0534 - dense_14_loss: 0.0676 - val_loss: 0.1126 - val_dense_9_loss: 0.0503 - val_dense_14_loss: 0.0624
    Epoch 176/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1206 - dense_9_loss: 0.0537 - dense_14_loss: 0.0669 - val_loss: 0.1111 - val_dense_9_loss: 0.0489 - val_dense_14_loss: 0.0621
    Epoch 177/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1220 - dense_9_loss: 0.0550 - dense_14_loss: 0.0670 - val_loss: 0.1100 - val_dense_9_loss: 0.0490 - val_dense_14_loss: 0.0610
    Epoch 178/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1182 - dense_9_loss: 0.0523 - dense_14_loss: 0.0658 - val_loss: 0.1120 - val_dense_9_loss: 0.0511 - val_dense_14_loss: 0.0609
    Epoch 179/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1185 - dense_9_loss: 0.0525 - dense_14_loss: 0.0660 - val_loss: 0.1104 - val_dense_9_loss: 0.0489 - val_dense_14_loss: 0.0615
    Epoch 180/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1159 - dense_9_loss: 0.0516 - dense_14_loss: 0.0643 - val_loss: 0.1098 - val_dense_9_loss: 0.0487 - val_dense_14_loss: 0.0610
    Epoch 181/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1166 - dense_9_loss: 0.0524 - dense_14_loss: 0.0642 - val_loss: 0.1118 - val_dense_9_loss: 0.0502 - val_dense_14_loss: 0.0617
    Epoch 182/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1182 - dense_9_loss: 0.0530 - dense_14_loss: 0.0653 - val_loss: 0.1231 - val_dense_9_loss: 0.0533 - val_dense_14_loss: 0.0698
    Epoch 183/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1165 - dense_9_loss: 0.0519 - dense_14_loss: 0.0646 - val_loss: 0.1087 - val_dense_9_loss: 0.0485 - val_dense_14_loss: 0.0601
    Epoch 184/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1155 - dense_9_loss: 0.0517 - dense_14_loss: 0.0638 - val_loss: 0.1096 - val_dense_9_loss: 0.0503 - val_dense_14_loss: 0.0593
    Epoch 185/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1158 - dense_9_loss: 0.0519 - dense_14_loss: 0.0639 - val_loss: 0.1086 - val_dense_9_loss: 0.0498 - val_dense_14_loss: 0.0588
    Epoch 186/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1150 - dense_9_loss: 0.0524 - dense_14_loss: 0.0626 - val_loss: 0.1074 - val_dense_9_loss: 0.0490 - val_dense_14_loss: 0.0584
    Epoch 187/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1155 - dense_9_loss: 0.0525 - dense_14_loss: 0.0630 - val_loss: 0.1074 - val_dense_9_loss: 0.0484 - val_dense_14_loss: 0.0590
    Epoch 188/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1145 - dense_9_loss: 0.0522 - dense_14_loss: 0.0623 - val_loss: 0.1100 - val_dense_9_loss: 0.0484 - val_dense_14_loss: 0.0616
    Epoch 189/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1150 - dense_9_loss: 0.0520 - dense_14_loss: 0.0630 - val_loss: 0.1171 - val_dense_9_loss: 0.0494 - val_dense_14_loss: 0.0677
    Epoch 190/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1141 - dense_9_loss: 0.0517 - dense_14_loss: 0.0624 - val_loss: 0.1063 - val_dense_9_loss: 0.0483 - val_dense_14_loss: 0.0580
    Epoch 191/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1127 - dense_9_loss: 0.0513 - dense_14_loss: 0.0614 - val_loss: 0.1124 - val_dense_9_loss: 0.0507 - val_dense_14_loss: 0.0618
    Epoch 192/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1138 - dense_9_loss: 0.0515 - dense_14_loss: 0.0623 - val_loss: 0.1098 - val_dense_9_loss: 0.0482 - val_dense_14_loss: 0.0617
    Epoch 193/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1140 - dense_9_loss: 0.0523 - dense_14_loss: 0.0617 - val_loss: 0.1087 - val_dense_9_loss: 0.0515 - val_dense_14_loss: 0.0572
    Epoch 194/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1122 - dense_9_loss: 0.0510 - dense_14_loss: 0.0612 - val_loss: 0.1054 - val_dense_9_loss: 0.0484 - val_dense_14_loss: 0.0570
    Epoch 195/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1146 - dense_9_loss: 0.0519 - dense_14_loss: 0.0626 - val_loss: 0.1067 - val_dense_9_loss: 0.0482 - val_dense_14_loss: 0.0585
    Epoch 196/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1107 - dense_9_loss: 0.0508 - dense_14_loss: 0.0599 - val_loss: 0.1047 - val_dense_9_loss: 0.0486 - val_dense_14_loss: 0.0561
    Epoch 197/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1124 - dense_9_loss: 0.0515 - dense_14_loss: 0.0609 - val_loss: 0.1053 - val_dense_9_loss: 0.0488 - val_dense_14_loss: 0.0564
    Epoch 198/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1105 - dense_9_loss: 0.0506 - dense_14_loss: 0.0599 - val_loss: 0.1083 - val_dense_9_loss: 0.0508 - val_dense_14_loss: 0.0576
    Epoch 199/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1112 - dense_9_loss: 0.0514 - dense_14_loss: 0.0599 - val_loss: 0.1074 - val_dense_9_loss: 0.0512 - val_dense_14_loss: 0.0562
    Epoch 200/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1099 - dense_9_loss: 0.0509 - dense_14_loss: 0.0590 - val_loss: 0.1035 - val_dense_9_loss: 0.0478 - val_dense_14_loss: 0.0556
    Epoch 201/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1095 - dense_9_loss: 0.0505 - dense_14_loss: 0.0591 - val_loss: 0.1067 - val_dense_9_loss: 0.0490 - val_dense_14_loss: 0.0577
    Epoch 202/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1106 - dense_9_loss: 0.0510 - dense_14_loss: 0.0596 - val_loss: 0.1049 - val_dense_9_loss: 0.0487 - val_dense_14_loss: 0.0561
    Epoch 203/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1097 - dense_9_loss: 0.0512 - dense_14_loss: 0.0586 - val_loss: 0.1049 - val_dense_9_loss: 0.0494 - val_dense_14_loss: 0.0554
    Epoch 204/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1147 - dense_9_loss: 0.0538 - dense_14_loss: 0.0609 - val_loss: 0.1153 - val_dense_9_loss: 0.0561 - val_dense_14_loss: 0.0591
    Epoch 205/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1103 - dense_9_loss: 0.0519 - dense_14_loss: 0.0584 - val_loss: 0.1033 - val_dense_9_loss: 0.0488 - val_dense_14_loss: 0.0545
    Epoch 206/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1085 - dense_9_loss: 0.0505 - dense_14_loss: 0.0580 - val_loss: 0.1025 - val_dense_9_loss: 0.0476 - val_dense_14_loss: 0.0549
    Epoch 207/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1107 - dense_9_loss: 0.0512 - dense_14_loss: 0.0595 - val_loss: 0.1072 - val_dense_9_loss: 0.0524 - val_dense_14_loss: 0.0548
    Epoch 208/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1118 - dense_9_loss: 0.0523 - dense_14_loss: 0.0595 - val_loss: 0.1032 - val_dense_9_loss: 0.0480 - val_dense_14_loss: 0.0552
    Epoch 209/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1082 - dense_9_loss: 0.0508 - dense_14_loss: 0.0574 - val_loss: 0.1042 - val_dense_9_loss: 0.0493 - val_dense_14_loss: 0.0549
    Epoch 210/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1099 - dense_9_loss: 0.0513 - dense_14_loss: 0.0586 - val_loss: 0.1023 - val_dense_9_loss: 0.0476 - val_dense_14_loss: 0.0548
    Epoch 211/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1085 - dense_9_loss: 0.0505 - dense_14_loss: 0.0579 - val_loss: 0.1044 - val_dense_9_loss: 0.0481 - val_dense_14_loss: 0.0563
    Epoch 212/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1074 - dense_9_loss: 0.0501 - dense_14_loss: 0.0573 - val_loss: 0.1016 - val_dense_9_loss: 0.0483 - val_dense_14_loss: 0.0533
    Epoch 213/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1089 - dense_9_loss: 0.0514 - dense_14_loss: 0.0574 - val_loss: 0.1021 - val_dense_9_loss: 0.0477 - val_dense_14_loss: 0.0544
    Epoch 214/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1082 - dense_9_loss: 0.0507 - dense_14_loss: 0.0575 - val_loss: 0.1009 - val_dense_9_loss: 0.0474 - val_dense_14_loss: 0.0535
    Epoch 215/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1069 - dense_9_loss: 0.0501 - dense_14_loss: 0.0569 - val_loss: 0.1032 - val_dense_9_loss: 0.0480 - val_dense_14_loss: 0.0552
    Epoch 216/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1074 - dense_9_loss: 0.0506 - dense_14_loss: 0.0568 - val_loss: 0.1024 - val_dense_9_loss: 0.0485 - val_dense_14_loss: 0.0540
    Epoch 217/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1083 - dense_9_loss: 0.0512 - dense_14_loss: 0.0572 - val_loss: 0.1011 - val_dense_9_loss: 0.0478 - val_dense_14_loss: 0.0533
    Epoch 218/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1077 - dense_9_loss: 0.0508 - dense_14_loss: 0.0569 - val_loss: 0.1123 - val_dense_9_loss: 0.0561 - val_dense_14_loss: 0.0562
    Epoch 219/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1073 - dense_9_loss: 0.0509 - dense_14_loss: 0.0565 - val_loss: 0.1004 - val_dense_9_loss: 0.0476 - val_dense_14_loss: 0.0528
    Epoch 220/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1055 - dense_9_loss: 0.0497 - dense_14_loss: 0.0559 - val_loss: 0.1000 - val_dense_9_loss: 0.0474 - val_dense_14_loss: 0.0526
    Epoch 221/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1054 - dense_9_loss: 0.0497 - dense_14_loss: 0.0557 - val_loss: 0.1138 - val_dense_9_loss: 0.0549 - val_dense_14_loss: 0.0589
    Epoch 222/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1104 - dense_9_loss: 0.0519 - dense_14_loss: 0.0584 - val_loss: 0.1051 - val_dense_9_loss: 0.0497 - val_dense_14_loss: 0.0553
    Epoch 223/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1076 - dense_9_loss: 0.0511 - dense_14_loss: 0.0565 - val_loss: 0.1019 - val_dense_9_loss: 0.0489 - val_dense_14_loss: 0.0530
    Epoch 224/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1102 - dense_9_loss: 0.0514 - dense_14_loss: 0.0589 - val_loss: 0.1039 - val_dense_9_loss: 0.0471 - val_dense_14_loss: 0.0568
    Epoch 225/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1058 - dense_9_loss: 0.0501 - dense_14_loss: 0.0557 - val_loss: 0.0992 - val_dense_9_loss: 0.0472 - val_dense_14_loss: 0.0520
    Epoch 226/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1054 - dense_9_loss: 0.0499 - dense_14_loss: 0.0555 - val_loss: 0.1057 - val_dense_9_loss: 0.0532 - val_dense_14_loss: 0.0525
    Epoch 227/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1060 - dense_9_loss: 0.0506 - dense_14_loss: 0.0555 - val_loss: 0.1034 - val_dense_9_loss: 0.0476 - val_dense_14_loss: 0.0558
    Epoch 228/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1075 - dense_9_loss: 0.0512 - dense_14_loss: 0.0563 - val_loss: 0.1054 - val_dense_9_loss: 0.0480 - val_dense_14_loss: 0.0574
    Epoch 229/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1064 - dense_9_loss: 0.0498 - dense_14_loss: 0.0567 - val_loss: 0.0999 - val_dense_9_loss: 0.0476 - val_dense_14_loss: 0.0523
    Epoch 230/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1075 - dense_9_loss: 0.0500 - dense_14_loss: 0.0575 - val_loss: 0.0996 - val_dense_9_loss: 0.0475 - val_dense_14_loss: 0.0521
    Epoch 231/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1057 - dense_9_loss: 0.0502 - dense_14_loss: 0.0555 - val_loss: 0.0980 - val_dense_9_loss: 0.0468 - val_dense_14_loss: 0.0512
    Epoch 232/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1064 - dense_9_loss: 0.0513 - dense_14_loss: 0.0551 - val_loss: 0.0987 - val_dense_9_loss: 0.0476 - val_dense_14_loss: 0.0511
    Epoch 233/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1062 - dense_9_loss: 0.0505 - dense_14_loss: 0.0556 - val_loss: 0.0980 - val_dense_9_loss: 0.0467 - val_dense_14_loss: 0.0514
    Epoch 234/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1053 - dense_9_loss: 0.0499 - dense_14_loss: 0.0554 - val_loss: 0.1013 - val_dense_9_loss: 0.0479 - val_dense_14_loss: 0.0534
    Epoch 235/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1042 - dense_9_loss: 0.0493 - dense_14_loss: 0.0550 - val_loss: 0.1024 - val_dense_9_loss: 0.0479 - val_dense_14_loss: 0.0545
    Epoch 236/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1048 - dense_9_loss: 0.0500 - dense_14_loss: 0.0549 - val_loss: 0.0997 - val_dense_9_loss: 0.0467 - val_dense_14_loss: 0.0530
    Epoch 237/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1042 - dense_9_loss: 0.0496 - dense_14_loss: 0.0546 - val_loss: 0.0977 - val_dense_9_loss: 0.0467 - val_dense_14_loss: 0.0510
    Epoch 238/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1029 - dense_9_loss: 0.0490 - dense_14_loss: 0.0539 - val_loss: 0.0982 - val_dense_9_loss: 0.0465 - val_dense_14_loss: 0.0517
    Epoch 239/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1092 - dense_9_loss: 0.0504 - dense_14_loss: 0.0588 - val_loss: 0.1002 - val_dense_9_loss: 0.0494 - val_dense_14_loss: 0.0508
    Epoch 240/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1048 - dense_9_loss: 0.0502 - dense_14_loss: 0.0546 - val_loss: 0.0967 - val_dense_9_loss: 0.0464 - val_dense_14_loss: 0.0504
    Epoch 241/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1043 - dense_9_loss: 0.0503 - dense_14_loss: 0.0540 - val_loss: 0.1023 - val_dense_9_loss: 0.0467 - val_dense_14_loss: 0.0556
    Epoch 242/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1055 - dense_9_loss: 0.0490 - dense_14_loss: 0.0565 - val_loss: 0.0989 - val_dense_9_loss: 0.0464 - val_dense_14_loss: 0.0525
    Epoch 243/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1024 - dense_9_loss: 0.0491 - dense_14_loss: 0.0534 - val_loss: 0.0986 - val_dense_9_loss: 0.0467 - val_dense_14_loss: 0.0519
    Epoch 244/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1027 - dense_9_loss: 0.0489 - dense_14_loss: 0.0538 - val_loss: 0.0965 - val_dense_9_loss: 0.0463 - val_dense_14_loss: 0.0502
    Epoch 245/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1024 - dense_9_loss: 0.0489 - dense_14_loss: 0.0535 - val_loss: 0.1029 - val_dense_9_loss: 0.0510 - val_dense_14_loss: 0.0519
    Epoch 246/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1017 - dense_9_loss: 0.0488 - dense_14_loss: 0.0530 - val_loss: 0.0961 - val_dense_9_loss: 0.0460 - val_dense_14_loss: 0.0501
    Epoch 247/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1023 - dense_9_loss: 0.0489 - dense_14_loss: 0.0534 - val_loss: 0.1034 - val_dense_9_loss: 0.0512 - val_dense_14_loss: 0.0522
    Epoch 248/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1026 - dense_9_loss: 0.0488 - dense_14_loss: 0.0537 - val_loss: 0.0965 - val_dense_9_loss: 0.0464 - val_dense_14_loss: 0.0501
    Epoch 249/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1025 - dense_9_loss: 0.0486 - dense_14_loss: 0.0539 - val_loss: 0.1018 - val_dense_9_loss: 0.0468 - val_dense_14_loss: 0.0550
    Epoch 250/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1018 - dense_9_loss: 0.0489 - dense_14_loss: 0.0529 - val_loss: 0.0963 - val_dense_9_loss: 0.0468 - val_dense_14_loss: 0.0495
    Epoch 251/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1061 - dense_9_loss: 0.0516 - dense_14_loss: 0.0546 - val_loss: 0.1042 - val_dense_9_loss: 0.0487 - val_dense_14_loss: 0.0555
    Epoch 252/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1057 - dense_9_loss: 0.0511 - dense_14_loss: 0.0547 - val_loss: 0.1063 - val_dense_9_loss: 0.0461 - val_dense_14_loss: 0.0602
    Epoch 253/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1012 - dense_9_loss: 0.0483 - dense_14_loss: 0.0529 - val_loss: 0.1021 - val_dense_9_loss: 0.0486 - val_dense_14_loss: 0.0535
    Epoch 254/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1054 - dense_9_loss: 0.0504 - dense_14_loss: 0.0549 - val_loss: 0.0967 - val_dense_9_loss: 0.0468 - val_dense_14_loss: 0.0499
    Epoch 255/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1048 - dense_9_loss: 0.0507 - dense_14_loss: 0.0541 - val_loss: 0.0960 - val_dense_9_loss: 0.0459 - val_dense_14_loss: 0.0501
    Epoch 256/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1001 - dense_9_loss: 0.0481 - dense_14_loss: 0.0520 - val_loss: 0.0990 - val_dense_9_loss: 0.0471 - val_dense_14_loss: 0.0518
    Epoch 257/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0996 - dense_9_loss: 0.0480 - dense_14_loss: 0.0516 - val_loss: 0.0944 - val_dense_9_loss: 0.0456 - val_dense_14_loss: 0.0488
    Epoch 258/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1008 - dense_9_loss: 0.0487 - dense_14_loss: 0.0521 - val_loss: 0.0947 - val_dense_9_loss: 0.0455 - val_dense_14_loss: 0.0492
    Epoch 259/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1022 - dense_9_loss: 0.0496 - dense_14_loss: 0.0526 - val_loss: 0.1103 - val_dense_9_loss: 0.0478 - val_dense_14_loss: 0.0625
    Epoch 260/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1010 - dense_9_loss: 0.0481 - dense_14_loss: 0.0528 - val_loss: 0.0943 - val_dense_9_loss: 0.0456 - val_dense_14_loss: 0.0487
    Epoch 261/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1036 - dense_9_loss: 0.0490 - dense_14_loss: 0.0547 - val_loss: 0.0978 - val_dense_9_loss: 0.0478 - val_dense_14_loss: 0.0499
    Epoch 262/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.1011 - dense_9_loss: 0.0484 - dense_14_loss: 0.0527 - val_loss: 0.1083 - val_dense_9_loss: 0.0488 - val_dense_14_loss: 0.0595
    Epoch 263/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1008 - dense_9_loss: 0.0481 - dense_14_loss: 0.0527 - val_loss: 0.0953 - val_dense_9_loss: 0.0458 - val_dense_14_loss: 0.0496
    Epoch 264/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1019 - dense_9_loss: 0.0490 - dense_14_loss: 0.0529 - val_loss: 0.1067 - val_dense_9_loss: 0.0459 - val_dense_14_loss: 0.0608
    Epoch 265/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1017 - dense_9_loss: 0.0484 - dense_14_loss: 0.0534 - val_loss: 0.0964 - val_dense_9_loss: 0.0455 - val_dense_14_loss: 0.0509
    Epoch 266/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0988 - dense_9_loss: 0.0477 - dense_14_loss: 0.0511 - val_loss: 0.0945 - val_dense_9_loss: 0.0459 - val_dense_14_loss: 0.0486
    Epoch 267/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1001 - dense_9_loss: 0.0483 - dense_14_loss: 0.0518 - val_loss: 0.0939 - val_dense_9_loss: 0.0461 - val_dense_14_loss: 0.0479
    Epoch 268/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1012 - dense_9_loss: 0.0489 - dense_14_loss: 0.0523 - val_loss: 0.0957 - val_dense_9_loss: 0.0459 - val_dense_14_loss: 0.0498
    Epoch 269/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0993 - dense_9_loss: 0.0477 - dense_14_loss: 0.0516 - val_loss: 0.0992 - val_dense_9_loss: 0.0481 - val_dense_14_loss: 0.0511
    Epoch 270/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0984 - dense_9_loss: 0.0473 - dense_14_loss: 0.0511 - val_loss: 0.0931 - val_dense_9_loss: 0.0448 - val_dense_14_loss: 0.0483
    Epoch 271/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0986 - dense_9_loss: 0.0478 - dense_14_loss: 0.0508 - val_loss: 0.0933 - val_dense_9_loss: 0.0453 - val_dense_14_loss: 0.0480
    Epoch 272/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0978 - dense_9_loss: 0.0470 - dense_14_loss: 0.0507 - val_loss: 0.0944 - val_dense_9_loss: 0.0452 - val_dense_14_loss: 0.0492
    Epoch 273/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1011 - dense_9_loss: 0.0478 - dense_14_loss: 0.0533 - val_loss: 0.0993 - val_dense_9_loss: 0.0448 - val_dense_14_loss: 0.0545
    Epoch 274/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0987 - dense_9_loss: 0.0471 - dense_14_loss: 0.0515 - val_loss: 0.0990 - val_dense_9_loss: 0.0449 - val_dense_14_loss: 0.0541
    Epoch 275/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1002 - dense_9_loss: 0.0475 - dense_14_loss: 0.0527 - val_loss: 0.0940 - val_dense_9_loss: 0.0460 - val_dense_14_loss: 0.0480
    Epoch 276/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0982 - dense_9_loss: 0.0473 - dense_14_loss: 0.0509 - val_loss: 0.0932 - val_dense_9_loss: 0.0449 - val_dense_14_loss: 0.0484
    Epoch 277/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0966 - dense_9_loss: 0.0465 - dense_14_loss: 0.0501 - val_loss: 0.0982 - val_dense_9_loss: 0.0474 - val_dense_14_loss: 0.0508
    Epoch 278/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0995 - dense_9_loss: 0.0474 - dense_14_loss: 0.0521 - val_loss: 0.0953 - val_dense_9_loss: 0.0479 - val_dense_14_loss: 0.0474
    Epoch 279/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.1055 - dense_9_loss: 0.0491 - dense_14_loss: 0.0564 - val_loss: 0.0937 - val_dense_9_loss: 0.0450 - val_dense_14_loss: 0.0487
    Epoch 280/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0975 - dense_9_loss: 0.0464 - dense_14_loss: 0.0511 - val_loss: 0.0914 - val_dense_9_loss: 0.0444 - val_dense_14_loss: 0.0470
    Epoch 281/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0973 - dense_9_loss: 0.0468 - dense_14_loss: 0.0505 - val_loss: 0.0941 - val_dense_9_loss: 0.0441 - val_dense_14_loss: 0.0500
    Epoch 282/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0993 - dense_9_loss: 0.0477 - dense_14_loss: 0.0516 - val_loss: 0.0972 - val_dense_9_loss: 0.0492 - val_dense_14_loss: 0.0480
    Epoch 283/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0982 - dense_9_loss: 0.0478 - dense_14_loss: 0.0504 - val_loss: 0.0936 - val_dense_9_loss: 0.0465 - val_dense_14_loss: 0.0471
    Epoch 284/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0963 - dense_9_loss: 0.0461 - dense_14_loss: 0.0503 - val_loss: 0.0923 - val_dense_9_loss: 0.0449 - val_dense_14_loss: 0.0474
    Epoch 285/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0969 - dense_9_loss: 0.0466 - dense_14_loss: 0.0503 - val_loss: 0.0918 - val_dense_9_loss: 0.0449 - val_dense_14_loss: 0.0469
    Epoch 286/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0957 - dense_9_loss: 0.0464 - dense_14_loss: 0.0493 - val_loss: 0.0912 - val_dense_9_loss: 0.0435 - val_dense_14_loss: 0.0477
    Epoch 287/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0971 - dense_9_loss: 0.0469 - dense_14_loss: 0.0502 - val_loss: 0.0910 - val_dense_9_loss: 0.0445 - val_dense_14_loss: 0.0465
    Epoch 288/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0969 - dense_9_loss: 0.0468 - dense_14_loss: 0.0500 - val_loss: 0.0952 - val_dense_9_loss: 0.0470 - val_dense_14_loss: 0.0482
    Epoch 289/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0961 - dense_9_loss: 0.0458 - dense_14_loss: 0.0503 - val_loss: 0.0951 - val_dense_9_loss: 0.0442 - val_dense_14_loss: 0.0509
    Epoch 290/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0947 - dense_9_loss: 0.0455 - dense_14_loss: 0.0492 - val_loss: 0.0907 - val_dense_9_loss: 0.0431 - val_dense_14_loss: 0.0476
    Epoch 291/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0963 - dense_9_loss: 0.0465 - dense_14_loss: 0.0498 - val_loss: 0.0915 - val_dense_9_loss: 0.0448 - val_dense_14_loss: 0.0467
    Epoch 292/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0962 - dense_9_loss: 0.0458 - dense_14_loss: 0.0504 - val_loss: 0.0945 - val_dense_9_loss: 0.0455 - val_dense_14_loss: 0.0490
    Epoch 293/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0976 - dense_9_loss: 0.0471 - dense_14_loss: 0.0505 - val_loss: 0.0896 - val_dense_9_loss: 0.0433 - val_dense_14_loss: 0.0463
    Epoch 294/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0951 - dense_9_loss: 0.0461 - dense_14_loss: 0.0490 - val_loss: 0.0930 - val_dense_9_loss: 0.0455 - val_dense_14_loss: 0.0475
    Epoch 295/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0974 - dense_9_loss: 0.0466 - dense_14_loss: 0.0508 - val_loss: 0.0915 - val_dense_9_loss: 0.0450 - val_dense_14_loss: 0.0466
    Epoch 296/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0981 - dense_9_loss: 0.0464 - dense_14_loss: 0.0517 - val_loss: 0.0964 - val_dense_9_loss: 0.0500 - val_dense_14_loss: 0.0464
    Epoch 297/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0952 - dense_9_loss: 0.0459 - dense_14_loss: 0.0494 - val_loss: 0.0966 - val_dense_9_loss: 0.0465 - val_dense_14_loss: 0.0501
    Epoch 298/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0942 - dense_9_loss: 0.0452 - dense_14_loss: 0.0490 - val_loss: 0.0901 - val_dense_9_loss: 0.0439 - val_dense_14_loss: 0.0462
    Epoch 299/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0933 - dense_9_loss: 0.0448 - dense_14_loss: 0.0485 - val_loss: 0.0917 - val_dense_9_loss: 0.0433 - val_dense_14_loss: 0.0484
    Epoch 300/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0942 - dense_9_loss: 0.0454 - dense_14_loss: 0.0488 - val_loss: 0.0907 - val_dense_9_loss: 0.0444 - val_dense_14_loss: 0.0463
    Epoch 301/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0934 - dense_9_loss: 0.0448 - dense_14_loss: 0.0485 - val_loss: 0.0936 - val_dense_9_loss: 0.0474 - val_dense_14_loss: 0.0461
    Epoch 302/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0931 - dense_9_loss: 0.0448 - dense_14_loss: 0.0483 - val_loss: 0.1057 - val_dense_9_loss: 0.0502 - val_dense_14_loss: 0.0555
    Epoch 303/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0936 - dense_9_loss: 0.0447 - dense_14_loss: 0.0489 - val_loss: 0.0892 - val_dense_9_loss: 0.0424 - val_dense_14_loss: 0.0467
    Epoch 304/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0922 - dense_9_loss: 0.0439 - dense_14_loss: 0.0482 - val_loss: 0.0874 - val_dense_9_loss: 0.0420 - val_dense_14_loss: 0.0454
    Epoch 305/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0928 - dense_9_loss: 0.0439 - dense_14_loss: 0.0489 - val_loss: 0.0877 - val_dense_9_loss: 0.0419 - val_dense_14_loss: 0.0457
    Epoch 306/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0949 - dense_9_loss: 0.0455 - dense_14_loss: 0.0495 - val_loss: 0.0887 - val_dense_9_loss: 0.0432 - val_dense_14_loss: 0.0455
    Epoch 307/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0922 - dense_9_loss: 0.0438 - dense_14_loss: 0.0484 - val_loss: 0.0972 - val_dense_9_loss: 0.0469 - val_dense_14_loss: 0.0503
    Epoch 308/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0937 - dense_9_loss: 0.0447 - dense_14_loss: 0.0490 - val_loss: 0.0877 - val_dense_9_loss: 0.0419 - val_dense_14_loss: 0.0458
    Epoch 309/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0930 - dense_9_loss: 0.0447 - dense_14_loss: 0.0484 - val_loss: 0.0877 - val_dense_9_loss: 0.0422 - val_dense_14_loss: 0.0454
    Epoch 310/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0921 - dense_9_loss: 0.0439 - dense_14_loss: 0.0482 - val_loss: 0.0877 - val_dense_9_loss: 0.0415 - val_dense_14_loss: 0.0462
    Epoch 311/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0930 - dense_9_loss: 0.0442 - dense_14_loss: 0.0488 - val_loss: 0.0919 - val_dense_9_loss: 0.0419 - val_dense_14_loss: 0.0500
    Epoch 312/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0941 - dense_9_loss: 0.0441 - dense_14_loss: 0.0500 - val_loss: 0.0865 - val_dense_9_loss: 0.0421 - val_dense_14_loss: 0.0443
    Epoch 313/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0900 - dense_9_loss: 0.0430 - dense_14_loss: 0.0469 - val_loss: 0.0908 - val_dense_9_loss: 0.0432 - val_dense_14_loss: 0.0476
    Epoch 314/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0904 - dense_9_loss: 0.0425 - dense_14_loss: 0.0479 - val_loss: 0.0851 - val_dense_9_loss: 0.0404 - val_dense_14_loss: 0.0447
    Epoch 315/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0890 - dense_9_loss: 0.0422 - dense_14_loss: 0.0468 - val_loss: 0.0843 - val_dense_9_loss: 0.0403 - val_dense_14_loss: 0.0440
    Epoch 316/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0891 - dense_9_loss: 0.0422 - dense_14_loss: 0.0469 - val_loss: 0.0854 - val_dense_9_loss: 0.0412 - val_dense_14_loss: 0.0441
    Epoch 317/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0888 - dense_9_loss: 0.0421 - dense_14_loss: 0.0467 - val_loss: 0.0851 - val_dense_9_loss: 0.0408 - val_dense_14_loss: 0.0443
    Epoch 318/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0899 - dense_9_loss: 0.0426 - dense_14_loss: 0.0473 - val_loss: 0.0867 - val_dense_9_loss: 0.0422 - val_dense_14_loss: 0.0445
    Epoch 319/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0898 - dense_9_loss: 0.0424 - dense_14_loss: 0.0475 - val_loss: 0.0864 - val_dense_9_loss: 0.0421 - val_dense_14_loss: 0.0443
    Epoch 320/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0904 - dense_9_loss: 0.0423 - dense_14_loss: 0.0481 - val_loss: 0.0868 - val_dense_9_loss: 0.0400 - val_dense_14_loss: 0.0468
    Epoch 321/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0871 - dense_9_loss: 0.0412 - dense_14_loss: 0.0460 - val_loss: 0.0860 - val_dense_9_loss: 0.0408 - val_dense_14_loss: 0.0452
    Epoch 322/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0942 - dense_9_loss: 0.0439 - dense_14_loss: 0.0503 - val_loss: 0.0910 - val_dense_9_loss: 0.0462 - val_dense_14_loss: 0.0448
    Epoch 323/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0914 - dense_9_loss: 0.0414 - dense_14_loss: 0.0500 - val_loss: 0.0840 - val_dense_9_loss: 0.0403 - val_dense_14_loss: 0.0437
    Epoch 324/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0884 - dense_9_loss: 0.0413 - dense_14_loss: 0.0470 - val_loss: 0.0830 - val_dense_9_loss: 0.0395 - val_dense_14_loss: 0.0435
    Epoch 325/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0862 - dense_9_loss: 0.0407 - dense_14_loss: 0.0455 - val_loss: 0.0820 - val_dense_9_loss: 0.0388 - val_dense_14_loss: 0.0431
    Epoch 326/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0888 - dense_9_loss: 0.0423 - dense_14_loss: 0.0465 - val_loss: 0.0827 - val_dense_9_loss: 0.0386 - val_dense_14_loss: 0.0442
    Epoch 327/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0884 - dense_9_loss: 0.0419 - dense_14_loss: 0.0465 - val_loss: 0.0983 - val_dense_9_loss: 0.0501 - val_dense_14_loss: 0.0482
    Epoch 328/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0896 - dense_9_loss: 0.0409 - dense_14_loss: 0.0487 - val_loss: 0.0854 - val_dense_9_loss: 0.0385 - val_dense_14_loss: 0.0469
    Epoch 329/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0900 - dense_9_loss: 0.0412 - dense_14_loss: 0.0489 - val_loss: 0.0930 - val_dense_9_loss: 0.0494 - val_dense_14_loss: 0.0435
    Epoch 330/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0858 - dense_9_loss: 0.0406 - dense_14_loss: 0.0452 - val_loss: 0.0818 - val_dense_9_loss: 0.0381 - val_dense_14_loss: 0.0437
    Epoch 331/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0848 - dense_9_loss: 0.0394 - dense_14_loss: 0.0454 - val_loss: 0.0904 - val_dense_9_loss: 0.0427 - val_dense_14_loss: 0.0477
    Epoch 332/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0893 - dense_9_loss: 0.0414 - dense_14_loss: 0.0479 - val_loss: 0.0845 - val_dense_9_loss: 0.0407 - val_dense_14_loss: 0.0438
    Epoch 333/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0849 - dense_9_loss: 0.0396 - dense_14_loss: 0.0453 - val_loss: 0.0865 - val_dense_9_loss: 0.0436 - val_dense_14_loss: 0.0429
    Epoch 334/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0880 - dense_9_loss: 0.0402 - dense_14_loss: 0.0477 - val_loss: 0.0835 - val_dense_9_loss: 0.0377 - val_dense_14_loss: 0.0457
    Epoch 335/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0844 - dense_9_loss: 0.0390 - dense_14_loss: 0.0453 - val_loss: 0.0805 - val_dense_9_loss: 0.0388 - val_dense_14_loss: 0.0417
    Epoch 336/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0829 - dense_9_loss: 0.0383 - dense_14_loss: 0.0446 - val_loss: 0.0779 - val_dense_9_loss: 0.0363 - val_dense_14_loss: 0.0416
    Epoch 337/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0819 - dense_9_loss: 0.0379 - dense_14_loss: 0.0440 - val_loss: 0.0791 - val_dense_9_loss: 0.0360 - val_dense_14_loss: 0.0431
    Epoch 338/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0816 - dense_9_loss: 0.0376 - dense_14_loss: 0.0440 - val_loss: 0.0789 - val_dense_9_loss: 0.0363 - val_dense_14_loss: 0.0426
    Epoch 339/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0833 - dense_9_loss: 0.0385 - dense_14_loss: 0.0448 - val_loss: 0.0904 - val_dense_9_loss: 0.0447 - val_dense_14_loss: 0.0456
    Epoch 340/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0865 - dense_9_loss: 0.0403 - dense_14_loss: 0.0462 - val_loss: 0.0860 - val_dense_9_loss: 0.0424 - val_dense_14_loss: 0.0436
    Epoch 341/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0806 - dense_9_loss: 0.0370 - dense_14_loss: 0.0436 - val_loss: 0.0771 - val_dense_9_loss: 0.0349 - val_dense_14_loss: 0.0421
    Epoch 342/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0806 - dense_9_loss: 0.0369 - dense_14_loss: 0.0437 - val_loss: 0.0810 - val_dense_9_loss: 0.0365 - val_dense_14_loss: 0.0445
    Epoch 343/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0799 - dense_9_loss: 0.0363 - dense_14_loss: 0.0437 - val_loss: 0.0883 - val_dense_9_loss: 0.0382 - val_dense_14_loss: 0.0501
    Epoch 344/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0804 - dense_9_loss: 0.0365 - dense_14_loss: 0.0439 - val_loss: 0.0756 - val_dense_9_loss: 0.0345 - val_dense_14_loss: 0.0411
    Epoch 345/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0807 - dense_9_loss: 0.0371 - dense_14_loss: 0.0437 - val_loss: 0.0751 - val_dense_9_loss: 0.0349 - val_dense_14_loss: 0.0402
    Epoch 346/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0788 - dense_9_loss: 0.0359 - dense_14_loss: 0.0429 - val_loss: 0.0752 - val_dense_9_loss: 0.0347 - val_dense_14_loss: 0.0405
    Epoch 347/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0779 - dense_9_loss: 0.0353 - dense_14_loss: 0.0426 - val_loss: 0.0752 - val_dense_9_loss: 0.0335 - val_dense_14_loss: 0.0416
    Epoch 348/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0796 - dense_9_loss: 0.0365 - dense_14_loss: 0.0431 - val_loss: 0.0735 - val_dense_9_loss: 0.0330 - val_dense_14_loss: 0.0405
    Epoch 349/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0759 - dense_9_loss: 0.0344 - dense_14_loss: 0.0414 - val_loss: 0.0727 - val_dense_9_loss: 0.0329 - val_dense_14_loss: 0.0398
    Epoch 350/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0783 - dense_9_loss: 0.0355 - dense_14_loss: 0.0428 - val_loss: 0.0721 - val_dense_9_loss: 0.0325 - val_dense_14_loss: 0.0396
    Epoch 351/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0787 - dense_9_loss: 0.0351 - dense_14_loss: 0.0436 - val_loss: 0.0971 - val_dense_9_loss: 0.0422 - val_dense_14_loss: 0.0549
    Epoch 352/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0778 - dense_9_loss: 0.0350 - dense_14_loss: 0.0428 - val_loss: 0.0710 - val_dense_9_loss: 0.0324 - val_dense_14_loss: 0.0386
    Epoch 353/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0769 - dense_9_loss: 0.0348 - dense_14_loss: 0.0421 - val_loss: 0.0720 - val_dense_9_loss: 0.0332 - val_dense_14_loss: 0.0388
    Epoch 354/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0760 - dense_9_loss: 0.0341 - dense_14_loss: 0.0419 - val_loss: 0.0700 - val_dense_9_loss: 0.0318 - val_dense_14_loss: 0.0382
    Epoch 355/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0727 - dense_9_loss: 0.0327 - dense_14_loss: 0.0400 - val_loss: 0.0697 - val_dense_9_loss: 0.0313 - val_dense_14_loss: 0.0384
    Epoch 356/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0736 - dense_9_loss: 0.0327 - dense_14_loss: 0.0409 - val_loss: 0.0696 - val_dense_9_loss: 0.0317 - val_dense_14_loss: 0.0379
    Epoch 357/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0728 - dense_9_loss: 0.0329 - dense_14_loss: 0.0399 - val_loss: 0.0685 - val_dense_9_loss: 0.0311 - val_dense_14_loss: 0.0374
    Epoch 358/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0717 - dense_9_loss: 0.0320 - dense_14_loss: 0.0397 - val_loss: 0.0676 - val_dense_9_loss: 0.0309 - val_dense_14_loss: 0.0367
    Epoch 359/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0700 - dense_9_loss: 0.0312 - dense_14_loss: 0.0387 - val_loss: 0.0682 - val_dense_9_loss: 0.0304 - val_dense_14_loss: 0.0378
    Epoch 360/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0744 - dense_9_loss: 0.0329 - dense_14_loss: 0.0415 - val_loss: 0.0683 - val_dense_9_loss: 0.0312 - val_dense_14_loss: 0.0370
    Epoch 361/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0706 - dense_9_loss: 0.0320 - dense_14_loss: 0.0386 - val_loss: 0.0750 - val_dense_9_loss: 0.0368 - val_dense_14_loss: 0.0382
    Epoch 362/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0712 - dense_9_loss: 0.0318 - dense_14_loss: 0.0395 - val_loss: 0.0680 - val_dense_9_loss: 0.0296 - val_dense_14_loss: 0.0385
    Epoch 363/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0687 - dense_9_loss: 0.0312 - dense_14_loss: 0.0376 - val_loss: 0.0657 - val_dense_9_loss: 0.0300 - val_dense_14_loss: 0.0357
    Epoch 364/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0697 - dense_9_loss: 0.0322 - dense_14_loss: 0.0375 - val_loss: 0.0663 - val_dense_9_loss: 0.0311 - val_dense_14_loss: 0.0352
    Epoch 365/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0683 - dense_9_loss: 0.0307 - dense_14_loss: 0.0377 - val_loss: 0.0633 - val_dense_9_loss: 0.0289 - val_dense_14_loss: 0.0344
    Epoch 366/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0670 - dense_9_loss: 0.0302 - dense_14_loss: 0.0369 - val_loss: 0.0651 - val_dense_9_loss: 0.0292 - val_dense_14_loss: 0.0359
    Epoch 367/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0652 - dense_9_loss: 0.0293 - dense_14_loss: 0.0359 - val_loss: 0.0657 - val_dense_9_loss: 0.0293 - val_dense_14_loss: 0.0364
    Epoch 368/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0663 - dense_9_loss: 0.0304 - dense_14_loss: 0.0359 - val_loss: 0.0646 - val_dense_9_loss: 0.0312 - val_dense_14_loss: 0.0334
    Epoch 369/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0643 - dense_9_loss: 0.0294 - dense_14_loss: 0.0348 - val_loss: 0.0614 - val_dense_9_loss: 0.0284 - val_dense_14_loss: 0.0330
    Epoch 370/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0654 - dense_9_loss: 0.0303 - dense_14_loss: 0.0351 - val_loss: 0.0638 - val_dense_9_loss: 0.0300 - val_dense_14_loss: 0.0338
    Epoch 371/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0629 - dense_9_loss: 0.0286 - dense_14_loss: 0.0344 - val_loss: 0.0607 - val_dense_9_loss: 0.0290 - val_dense_14_loss: 0.0317
    Epoch 372/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0648 - dense_9_loss: 0.0297 - dense_14_loss: 0.0350 - val_loss: 0.0656 - val_dense_9_loss: 0.0339 - val_dense_14_loss: 0.0317
    Epoch 373/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0640 - dense_9_loss: 0.0302 - dense_14_loss: 0.0338 - val_loss: 0.0614 - val_dense_9_loss: 0.0291 - val_dense_14_loss: 0.0323
    Epoch 374/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0607 - dense_9_loss: 0.0279 - dense_14_loss: 0.0328 - val_loss: 0.0582 - val_dense_9_loss: 0.0270 - val_dense_14_loss: 0.0312
    Epoch 375/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0618 - dense_9_loss: 0.0285 - dense_14_loss: 0.0332 - val_loss: 0.0570 - val_dense_9_loss: 0.0272 - val_dense_14_loss: 0.0299
    Epoch 376/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0588 - dense_9_loss: 0.0275 - dense_14_loss: 0.0313 - val_loss: 0.0566 - val_dense_9_loss: 0.0268 - val_dense_14_loss: 0.0298
    Epoch 377/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0578 - dense_9_loss: 0.0269 - dense_14_loss: 0.0309 - val_loss: 0.0565 - val_dense_9_loss: 0.0267 - val_dense_14_loss: 0.0298
    Epoch 378/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0585 - dense_9_loss: 0.0273 - dense_14_loss: 0.0312 - val_loss: 0.0562 - val_dense_9_loss: 0.0268 - val_dense_14_loss: 0.0294
    Epoch 379/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0569 - dense_9_loss: 0.0267 - dense_14_loss: 0.0302 - val_loss: 0.0544 - val_dense_9_loss: 0.0261 - val_dense_14_loss: 0.0283
    Epoch 380/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0567 - dense_9_loss: 0.0270 - dense_14_loss: 0.0296 - val_loss: 0.0534 - val_dense_9_loss: 0.0255 - val_dense_14_loss: 0.0279
    Epoch 381/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0561 - dense_9_loss: 0.0266 - dense_14_loss: 0.0295 - val_loss: 0.0556 - val_dense_9_loss: 0.0269 - val_dense_14_loss: 0.0288
    Epoch 382/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0580 - dense_9_loss: 0.0278 - dense_14_loss: 0.0302 - val_loss: 0.0586 - val_dense_9_loss: 0.0288 - val_dense_14_loss: 0.0298
    Epoch 383/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0540 - dense_9_loss: 0.0259 - dense_14_loss: 0.0281 - val_loss: 0.0512 - val_dense_9_loss: 0.0248 - val_dense_14_loss: 0.0264
    Epoch 384/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0534 - dense_9_loss: 0.0255 - dense_14_loss: 0.0279 - val_loss: 0.0508 - val_dense_9_loss: 0.0246 - val_dense_14_loss: 0.0262
    Epoch 385/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0526 - dense_9_loss: 0.0251 - dense_14_loss: 0.0275 - val_loss: 0.0497 - val_dense_9_loss: 0.0241 - val_dense_14_loss: 0.0255
    Epoch 386/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0512 - dense_9_loss: 0.0246 - dense_14_loss: 0.0266 - val_loss: 0.0509 - val_dense_9_loss: 0.0239 - val_dense_14_loss: 0.0270
    Epoch 387/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0516 - dense_9_loss: 0.0244 - dense_14_loss: 0.0272 - val_loss: 0.0483 - val_dense_9_loss: 0.0236 - val_dense_14_loss: 0.0247
    Epoch 388/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0495 - dense_9_loss: 0.0240 - dense_14_loss: 0.0255 - val_loss: 0.0493 - val_dense_9_loss: 0.0245 - val_dense_14_loss: 0.0248
    Epoch 389/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0504 - dense_9_loss: 0.0243 - dense_14_loss: 0.0261 - val_loss: 0.0466 - val_dense_9_loss: 0.0229 - val_dense_14_loss: 0.0236
    Epoch 390/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0489 - dense_9_loss: 0.0234 - dense_14_loss: 0.0255 - val_loss: 0.0468 - val_dense_9_loss: 0.0228 - val_dense_14_loss: 0.0240
    Epoch 391/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0478 - dense_9_loss: 0.0233 - dense_14_loss: 0.0245 - val_loss: 0.0459 - val_dense_9_loss: 0.0227 - val_dense_14_loss: 0.0232
    Epoch 392/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0484 - dense_9_loss: 0.0239 - dense_14_loss: 0.0245 - val_loss: 0.0473 - val_dense_9_loss: 0.0243 - val_dense_14_loss: 0.0230
    Epoch 393/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0465 - dense_9_loss: 0.0229 - dense_14_loss: 0.0235 - val_loss: 0.0440 - val_dense_9_loss: 0.0217 - val_dense_14_loss: 0.0223
    Epoch 394/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0451 - dense_9_loss: 0.0222 - dense_14_loss: 0.0229 - val_loss: 0.0458 - val_dense_9_loss: 0.0236 - val_dense_14_loss: 0.0222
    Epoch 395/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0450 - dense_9_loss: 0.0226 - dense_14_loss: 0.0224 - val_loss: 0.0423 - val_dense_9_loss: 0.0211 - val_dense_14_loss: 0.0212
    Epoch 396/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0462 - dense_9_loss: 0.0223 - dense_14_loss: 0.0239 - val_loss: 0.0524 - val_dense_9_loss: 0.0253 - val_dense_14_loss: 0.0271
    Epoch 397/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0427 - dense_9_loss: 0.0209 - dense_14_loss: 0.0217 - val_loss: 0.0416 - val_dense_9_loss: 0.0208 - val_dense_14_loss: 0.0208
    Epoch 398/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0421 - dense_9_loss: 0.0209 - dense_14_loss: 0.0212 - val_loss: 0.0411 - val_dense_9_loss: 0.0203 - val_dense_14_loss: 0.0208
    Epoch 399/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0413 - dense_9_loss: 0.0204 - dense_14_loss: 0.0208 - val_loss: 0.0425 - val_dense_9_loss: 0.0207 - val_dense_14_loss: 0.0219
    Epoch 400/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0410 - dense_9_loss: 0.0205 - dense_14_loss: 0.0205 - val_loss: 0.0430 - val_dense_9_loss: 0.0216 - val_dense_14_loss: 0.0214
    Epoch 401/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0402 - dense_9_loss: 0.0202 - dense_14_loss: 0.0200 - val_loss: 0.0414 - val_dense_9_loss: 0.0202 - val_dense_14_loss: 0.0212
    Epoch 402/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0402 - dense_9_loss: 0.0200 - dense_14_loss: 0.0202 - val_loss: 0.0409 - val_dense_9_loss: 0.0200 - val_dense_14_loss: 0.0208
    Epoch 403/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0406 - dense_9_loss: 0.0201 - dense_14_loss: 0.0204 - val_loss: 0.0370 - val_dense_9_loss: 0.0186 - val_dense_14_loss: 0.0183
    Epoch 404/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0381 - dense_9_loss: 0.0191 - dense_14_loss: 0.0190 - val_loss: 0.0382 - val_dense_9_loss: 0.0195 - val_dense_14_loss: 0.0187
    Epoch 405/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0385 - dense_9_loss: 0.0192 - dense_14_loss: 0.0193 - val_loss: 0.0381 - val_dense_9_loss: 0.0186 - val_dense_14_loss: 0.0194
    Epoch 406/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0368 - dense_9_loss: 0.0183 - dense_14_loss: 0.0185 - val_loss: 0.0374 - val_dense_9_loss: 0.0184 - val_dense_14_loss: 0.0190
    Epoch 407/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0371 - dense_9_loss: 0.0183 - dense_14_loss: 0.0188 - val_loss: 0.0348 - val_dense_9_loss: 0.0176 - val_dense_14_loss: 0.0172
    Epoch 408/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0365 - dense_9_loss: 0.0183 - dense_14_loss: 0.0182 - val_loss: 0.0399 - val_dense_9_loss: 0.0193 - val_dense_14_loss: 0.0205
    Epoch 409/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0371 - dense_9_loss: 0.0186 - dense_14_loss: 0.0185 - val_loss: 0.0353 - val_dense_9_loss: 0.0184 - val_dense_14_loss: 0.0169
    Epoch 410/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0356 - dense_9_loss: 0.0179 - dense_14_loss: 0.0177 - val_loss: 0.0339 - val_dense_9_loss: 0.0169 - val_dense_14_loss: 0.0170
    Epoch 411/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0355 - dense_9_loss: 0.0177 - dense_14_loss: 0.0178 - val_loss: 0.0331 - val_dense_9_loss: 0.0166 - val_dense_14_loss: 0.0165
    Epoch 412/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0341 - dense_9_loss: 0.0170 - dense_14_loss: 0.0171 - val_loss: 0.0369 - val_dense_9_loss: 0.0185 - val_dense_14_loss: 0.0185
    Epoch 413/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0340 - dense_9_loss: 0.0171 - dense_14_loss: 0.0170 - val_loss: 0.0321 - val_dense_9_loss: 0.0162 - val_dense_14_loss: 0.0158
    Epoch 414/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0347 - dense_9_loss: 0.0173 - dense_14_loss: 0.0174 - val_loss: 0.0321 - val_dense_9_loss: 0.0166 - val_dense_14_loss: 0.0156
    Epoch 415/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0323 - dense_9_loss: 0.0162 - dense_14_loss: 0.0161 - val_loss: 0.0316 - val_dense_9_loss: 0.0159 - val_dense_14_loss: 0.0157
    Epoch 416/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0323 - dense_9_loss: 0.0162 - dense_14_loss: 0.0161 - val_loss: 0.0323 - val_dense_9_loss: 0.0162 - val_dense_14_loss: 0.0161
    Epoch 417/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0320 - dense_9_loss: 0.0161 - dense_14_loss: 0.0160 - val_loss: 0.0310 - val_dense_9_loss: 0.0156 - val_dense_14_loss: 0.0154
    Epoch 418/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0323 - dense_9_loss: 0.0163 - dense_14_loss: 0.0160 - val_loss: 0.0350 - val_dense_9_loss: 0.0178 - val_dense_14_loss: 0.0172
    Epoch 419/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0316 - dense_9_loss: 0.0159 - dense_14_loss: 0.0157 - val_loss: 0.0307 - val_dense_9_loss: 0.0152 - val_dense_14_loss: 0.0155
    Epoch 420/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0325 - dense_9_loss: 0.0163 - dense_14_loss: 0.0162 - val_loss: 0.0304 - val_dense_9_loss: 0.0154 - val_dense_14_loss: 0.0149
    Epoch 421/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0301 - dense_9_loss: 0.0150 - dense_14_loss: 0.0151 - val_loss: 0.0326 - val_dense_9_loss: 0.0165 - val_dense_14_loss: 0.0161
    Epoch 422/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0303 - dense_9_loss: 0.0152 - dense_14_loss: 0.0152 - val_loss: 0.0293 - val_dense_9_loss: 0.0147 - val_dense_14_loss: 0.0147
    Epoch 423/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0297 - dense_9_loss: 0.0149 - dense_14_loss: 0.0149 - val_loss: 0.0303 - val_dense_9_loss: 0.0154 - val_dense_14_loss: 0.0148
    Epoch 424/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0313 - dense_9_loss: 0.0154 - dense_14_loss: 0.0159 - val_loss: 0.0286 - val_dense_9_loss: 0.0144 - val_dense_14_loss: 0.0142
    Epoch 425/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0299 - dense_9_loss: 0.0147 - dense_14_loss: 0.0152 - val_loss: 0.0293 - val_dense_9_loss: 0.0144 - val_dense_14_loss: 0.0149
    Epoch 426/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0301 - dense_9_loss: 0.0150 - dense_14_loss: 0.0151 - val_loss: 0.0338 - val_dense_9_loss: 0.0167 - val_dense_14_loss: 0.0171
    Epoch 427/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0301 - dense_9_loss: 0.0149 - dense_14_loss: 0.0152 - val_loss: 0.0285 - val_dense_9_loss: 0.0142 - val_dense_14_loss: 0.0143
    Epoch 428/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0306 - dense_9_loss: 0.0149 - dense_14_loss: 0.0156 - val_loss: 0.0281 - val_dense_9_loss: 0.0142 - val_dense_14_loss: 0.0139
    Epoch 429/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0300 - dense_9_loss: 0.0147 - dense_14_loss: 0.0152 - val_loss: 0.0285 - val_dense_9_loss: 0.0146 - val_dense_14_loss: 0.0139
    Epoch 430/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0284 - dense_9_loss: 0.0141 - dense_14_loss: 0.0144 - val_loss: 0.0298 - val_dense_9_loss: 0.0152 - val_dense_14_loss: 0.0146
    Epoch 431/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0300 - dense_9_loss: 0.0148 - dense_14_loss: 0.0152 - val_loss: 0.0282 - val_dense_9_loss: 0.0138 - val_dense_14_loss: 0.0144
    Epoch 432/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0291 - dense_9_loss: 0.0145 - dense_14_loss: 0.0146 - val_loss: 0.0281 - val_dense_9_loss: 0.0139 - val_dense_14_loss: 0.0143
    Epoch 433/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0283 - dense_9_loss: 0.0140 - dense_14_loss: 0.0142 - val_loss: 0.0294 - val_dense_9_loss: 0.0146 - val_dense_14_loss: 0.0147
    Epoch 434/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0290 - dense_9_loss: 0.0143 - dense_14_loss: 0.0147 - val_loss: 0.0354 - val_dense_9_loss: 0.0183 - val_dense_14_loss: 0.0171
    Epoch 435/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0289 - dense_9_loss: 0.0143 - dense_14_loss: 0.0146 - val_loss: 0.0272 - val_dense_9_loss: 0.0136 - val_dense_14_loss: 0.0136
    Epoch 436/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0281 - dense_9_loss: 0.0137 - dense_14_loss: 0.0144 - val_loss: 0.0279 - val_dense_9_loss: 0.0140 - val_dense_14_loss: 0.0139
    Epoch 437/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0287 - dense_9_loss: 0.0141 - dense_14_loss: 0.0146 - val_loss: 0.0270 - val_dense_9_loss: 0.0135 - val_dense_14_loss: 0.0135
    Epoch 438/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0293 - dense_9_loss: 0.0144 - dense_14_loss: 0.0149 - val_loss: 0.0276 - val_dense_9_loss: 0.0138 - val_dense_14_loss: 0.0137
    Epoch 439/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0275 - dense_9_loss: 0.0136 - dense_14_loss: 0.0139 - val_loss: 0.0269 - val_dense_9_loss: 0.0133 - val_dense_14_loss: 0.0135
    Epoch 440/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0280 - dense_9_loss: 0.0137 - dense_14_loss: 0.0143 - val_loss: 0.0294 - val_dense_9_loss: 0.0145 - val_dense_14_loss: 0.0149
    Epoch 441/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0280 - dense_9_loss: 0.0137 - dense_14_loss: 0.0143 - val_loss: 0.0278 - val_dense_9_loss: 0.0146 - val_dense_14_loss: 0.0132
    Epoch 442/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0287 - dense_9_loss: 0.0143 - dense_14_loss: 0.0144 - val_loss: 0.0272 - val_dense_9_loss: 0.0136 - val_dense_14_loss: 0.0136
    Epoch 443/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0276 - dense_9_loss: 0.0137 - dense_14_loss: 0.0139 - val_loss: 0.0272 - val_dense_9_loss: 0.0138 - val_dense_14_loss: 0.0134
    Epoch 444/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0291 - dense_9_loss: 0.0147 - dense_14_loss: 0.0144 - val_loss: 0.0273 - val_dense_9_loss: 0.0133 - val_dense_14_loss: 0.0140
    Epoch 445/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0295 - dense_9_loss: 0.0149 - dense_14_loss: 0.0146 - val_loss: 0.0277 - val_dense_9_loss: 0.0133 - val_dense_14_loss: 0.0144
    Epoch 446/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0278 - dense_9_loss: 0.0137 - dense_14_loss: 0.0141 - val_loss: 0.0271 - val_dense_9_loss: 0.0133 - val_dense_14_loss: 0.0138
    Epoch 447/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0273 - dense_9_loss: 0.0135 - dense_14_loss: 0.0138 - val_loss: 0.0270 - val_dense_9_loss: 0.0135 - val_dense_14_loss: 0.0135
    Epoch 448/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0274 - dense_9_loss: 0.0136 - dense_14_loss: 0.0138 - val_loss: 0.0263 - val_dense_9_loss: 0.0131 - val_dense_14_loss: 0.0132
    Epoch 449/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0285 - dense_9_loss: 0.0140 - dense_14_loss: 0.0145 - val_loss: 0.0274 - val_dense_9_loss: 0.0138 - val_dense_14_loss: 0.0136
    Epoch 450/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0280 - dense_9_loss: 0.0137 - dense_14_loss: 0.0143 - val_loss: 0.0262 - val_dense_9_loss: 0.0129 - val_dense_14_loss: 0.0133
    Epoch 451/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0272 - dense_9_loss: 0.0134 - dense_14_loss: 0.0138 - val_loss: 0.0266 - val_dense_9_loss: 0.0132 - val_dense_14_loss: 0.0134
    Epoch 452/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0263 - dense_9_loss: 0.0129 - dense_14_loss: 0.0134 - val_loss: 0.0297 - val_dense_9_loss: 0.0144 - val_dense_14_loss: 0.0154
    Epoch 453/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0271 - dense_9_loss: 0.0135 - dense_14_loss: 0.0136 - val_loss: 0.0274 - val_dense_9_loss: 0.0135 - val_dense_14_loss: 0.0139
    Epoch 454/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0264 - dense_9_loss: 0.0131 - dense_14_loss: 0.0133 - val_loss: 0.0258 - val_dense_9_loss: 0.0129 - val_dense_14_loss: 0.0129
    Epoch 455/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0266 - dense_9_loss: 0.0132 - dense_14_loss: 0.0134 - val_loss: 0.0280 - val_dense_9_loss: 0.0138 - val_dense_14_loss: 0.0142
    Epoch 456/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0265 - dense_9_loss: 0.0131 - dense_14_loss: 0.0134 - val_loss: 0.0272 - val_dense_9_loss: 0.0138 - val_dense_14_loss: 0.0134
    Epoch 457/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0285 - dense_9_loss: 0.0140 - dense_14_loss: 0.0145 - val_loss: 0.0257 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0129
    Epoch 458/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0268 - dense_9_loss: 0.0132 - dense_14_loss: 0.0136 - val_loss: 0.0285 - val_dense_9_loss: 0.0151 - val_dense_14_loss: 0.0134
    Epoch 459/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0271 - dense_9_loss: 0.0135 - dense_14_loss: 0.0135 - val_loss: 0.0255 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0128
    Epoch 460/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0283 - dense_9_loss: 0.0140 - dense_14_loss: 0.0143 - val_loss: 0.0263 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0134
    Epoch 461/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0267 - dense_9_loss: 0.0131 - dense_14_loss: 0.0136 - val_loss: 0.0262 - val_dense_9_loss: 0.0130 - val_dense_14_loss: 0.0132
    Epoch 462/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0285 - dense_9_loss: 0.0139 - dense_14_loss: 0.0146 - val_loss: 0.0260 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0131
    Epoch 463/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0271 - dense_9_loss: 0.0134 - dense_14_loss: 0.0137 - val_loss: 0.0266 - val_dense_9_loss: 0.0130 - val_dense_14_loss: 0.0136
    Epoch 464/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0275 - dense_9_loss: 0.0136 - dense_14_loss: 0.0140 - val_loss: 0.0258 - val_dense_9_loss: 0.0127 - val_dense_14_loss: 0.0131
    Epoch 465/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0273 - dense_9_loss: 0.0135 - dense_14_loss: 0.0137 - val_loss: 0.0254 - val_dense_9_loss: 0.0127 - val_dense_14_loss: 0.0128
    Epoch 466/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0261 - dense_9_loss: 0.0128 - dense_14_loss: 0.0133 - val_loss: 0.0259 - val_dense_9_loss: 0.0129 - val_dense_14_loss: 0.0130
    Epoch 467/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0266 - dense_9_loss: 0.0130 - dense_14_loss: 0.0136 - val_loss: 0.0254 - val_dense_9_loss: 0.0126 - val_dense_14_loss: 0.0128
    Epoch 468/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0259 - dense_9_loss: 0.0127 - dense_14_loss: 0.0132 - val_loss: 0.0261 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0133
    Epoch 469/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0259 - dense_9_loss: 0.0126 - dense_14_loss: 0.0133 - val_loss: 0.0263 - val_dense_9_loss: 0.0127 - val_dense_14_loss: 0.0136
    Epoch 470/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0262 - dense_9_loss: 0.0129 - dense_14_loss: 0.0133 - val_loss: 0.0256 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0128
    Epoch 471/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0258 - dense_9_loss: 0.0127 - dense_14_loss: 0.0131 - val_loss: 0.0254 - val_dense_9_loss: 0.0126 - val_dense_14_loss: 0.0128
    Epoch 472/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0276 - dense_9_loss: 0.0138 - dense_14_loss: 0.0139 - val_loss: 0.0259 - val_dense_9_loss: 0.0127 - val_dense_14_loss: 0.0132
    Epoch 473/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0267 - dense_9_loss: 0.0132 - dense_14_loss: 0.0135 - val_loss: 0.0258 - val_dense_9_loss: 0.0131 - val_dense_14_loss: 0.0126
    Epoch 474/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0269 - dense_9_loss: 0.0134 - dense_14_loss: 0.0134 - val_loss: 0.0250 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0125
    Epoch 475/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0260 - dense_9_loss: 0.0129 - dense_14_loss: 0.0131 - val_loss: 0.0255 - val_dense_9_loss: 0.0125 - val_dense_14_loss: 0.0130
    Epoch 476/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0268 - dense_9_loss: 0.0132 - dense_14_loss: 0.0136 - val_loss: 0.0263 - val_dense_9_loss: 0.0125 - val_dense_14_loss: 0.0137
    Epoch 477/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0268 - dense_9_loss: 0.0132 - dense_14_loss: 0.0136 - val_loss: 0.0303 - val_dense_9_loss: 0.0156 - val_dense_14_loss: 0.0147
    Epoch 478/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0261 - dense_9_loss: 0.0129 - dense_14_loss: 0.0132 - val_loss: 0.0252 - val_dense_9_loss: 0.0126 - val_dense_14_loss: 0.0127
    Epoch 479/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0260 - dense_9_loss: 0.0128 - dense_14_loss: 0.0131 - val_loss: 0.0258 - val_dense_9_loss: 0.0132 - val_dense_14_loss: 0.0126
    Epoch 480/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0270 - dense_9_loss: 0.0135 - dense_14_loss: 0.0135 - val_loss: 0.0252 - val_dense_9_loss: 0.0125 - val_dense_14_loss: 0.0127
    Epoch 481/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0264 - dense_9_loss: 0.0128 - dense_14_loss: 0.0136 - val_loss: 0.0251 - val_dense_9_loss: 0.0125 - val_dense_14_loss: 0.0125
    Epoch 482/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0260 - dense_9_loss: 0.0128 - dense_14_loss: 0.0133 - val_loss: 0.0248 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0124
    Epoch 483/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0259 - dense_9_loss: 0.0128 - dense_14_loss: 0.0131 - val_loss: 0.0254 - val_dense_9_loss: 0.0129 - val_dense_14_loss: 0.0125
    Epoch 484/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0278 - dense_9_loss: 0.0138 - dense_14_loss: 0.0139 - val_loss: 0.0323 - val_dense_9_loss: 0.0171 - val_dense_14_loss: 0.0152
    Epoch 485/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0266 - dense_9_loss: 0.0131 - dense_14_loss: 0.0135 - val_loss: 0.0250 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0127
    Epoch 486/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0273 - dense_9_loss: 0.0134 - dense_14_loss: 0.0139 - val_loss: 0.0252 - val_dense_9_loss: 0.0126 - val_dense_14_loss: 0.0126
    Epoch 487/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0258 - dense_9_loss: 0.0127 - dense_14_loss: 0.0131 - val_loss: 0.0264 - val_dense_9_loss: 0.0133 - val_dense_14_loss: 0.0131
    Epoch 488/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0264 - dense_9_loss: 0.0130 - dense_14_loss: 0.0134 - val_loss: 0.0253 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0128
    Epoch 489/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0265 - dense_9_loss: 0.0128 - dense_14_loss: 0.0137 - val_loss: 0.0265 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0138
    Epoch 490/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0263 - dense_9_loss: 0.0128 - dense_14_loss: 0.0134 - val_loss: 0.0259 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0135
    Epoch 491/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0273 - dense_9_loss: 0.0135 - dense_14_loss: 0.0138 - val_loss: 0.0247 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0124
    Epoch 492/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0255 - dense_9_loss: 0.0125 - dense_14_loss: 0.0129 - val_loss: 0.0251 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0126
    Epoch 493/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0268 - dense_9_loss: 0.0129 - dense_14_loss: 0.0140 - val_loss: 0.0263 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0140
    Epoch 494/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0257 - dense_9_loss: 0.0128 - dense_14_loss: 0.0129 - val_loss: 0.0247 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0125
    Epoch 495/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0259 - dense_9_loss: 0.0126 - dense_14_loss: 0.0133 - val_loss: 0.0253 - val_dense_9_loss: 0.0123 - val_dense_14_loss: 0.0130
    Epoch 496/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0251 - dense_9_loss: 0.0123 - dense_14_loss: 0.0128 - val_loss: 0.0260 - val_dense_9_loss: 0.0130 - val_dense_14_loss: 0.0130
    Epoch 497/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0262 - dense_9_loss: 0.0130 - dense_14_loss: 0.0132 - val_loss: 0.0262 - val_dense_9_loss: 0.0131 - val_dense_14_loss: 0.0131
    Epoch 498/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0276 - dense_9_loss: 0.0137 - dense_14_loss: 0.0139 - val_loss: 0.0245 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0123
    Epoch 499/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0261 - dense_9_loss: 0.0128 - dense_14_loss: 0.0133 - val_loss: 0.0248 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0126
    Epoch 500/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0256 - dense_9_loss: 0.0127 - dense_14_loss: 0.0129 - val_loss: 0.0247 - val_dense_9_loss: 0.0123 - val_dense_14_loss: 0.0125
    Epoch 501/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0251 - dense_9_loss: 0.0124 - dense_14_loss: 0.0127 - val_loss: 0.0247 - val_dense_9_loss: 0.0123 - val_dense_14_loss: 0.0124
    Epoch 502/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0251 - dense_9_loss: 0.0123 - dense_14_loss: 0.0128 - val_loss: 0.0292 - val_dense_9_loss: 0.0149 - val_dense_14_loss: 0.0143
    Epoch 503/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0256 - dense_9_loss: 0.0125 - dense_14_loss: 0.0130 - val_loss: 0.0245 - val_dense_9_loss: 0.0121 - val_dense_14_loss: 0.0124
    Epoch 504/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0254 - dense_9_loss: 0.0124 - dense_14_loss: 0.0129 - val_loss: 0.0255 - val_dense_9_loss: 0.0129 - val_dense_14_loss: 0.0126
    Epoch 505/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0249 - dense_9_loss: 0.0123 - dense_14_loss: 0.0126 - val_loss: 0.0259 - val_dense_9_loss: 0.0126 - val_dense_14_loss: 0.0133
    Epoch 506/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0259 - dense_9_loss: 0.0126 - dense_14_loss: 0.0133 - val_loss: 0.0293 - val_dense_9_loss: 0.0145 - val_dense_14_loss: 0.0148
    Epoch 507/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0264 - dense_9_loss: 0.0128 - dense_14_loss: 0.0136 - val_loss: 0.0269 - val_dense_9_loss: 0.0139 - val_dense_14_loss: 0.0129
    Epoch 508/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0264 - dense_9_loss: 0.0133 - dense_14_loss: 0.0131 - val_loss: 0.0253 - val_dense_9_loss: 0.0130 - val_dense_14_loss: 0.0124
    Epoch 509/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0257 - dense_9_loss: 0.0128 - dense_14_loss: 0.0129 - val_loss: 0.0254 - val_dense_9_loss: 0.0125 - val_dense_14_loss: 0.0130
    Epoch 510/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0266 - dense_9_loss: 0.0130 - dense_14_loss: 0.0136 - val_loss: 0.0243 - val_dense_9_loss: 0.0121 - val_dense_14_loss: 0.0123
    Epoch 511/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0249 - dense_9_loss: 0.0122 - dense_14_loss: 0.0127 - val_loss: 0.0245 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0122
    Epoch 512/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0253 - dense_9_loss: 0.0124 - dense_14_loss: 0.0129 - val_loss: 0.0257 - val_dense_9_loss: 0.0125 - val_dense_14_loss: 0.0132
    Epoch 513/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0254 - dense_9_loss: 0.0124 - dense_14_loss: 0.0129 - val_loss: 0.0242 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0121
    Epoch 514/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0250 - dense_9_loss: 0.0123 - dense_14_loss: 0.0127 - val_loss: 0.0254 - val_dense_9_loss: 0.0123 - val_dense_14_loss: 0.0131
    Epoch 515/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0254 - dense_9_loss: 0.0124 - dense_14_loss: 0.0130 - val_loss: 0.0273 - val_dense_9_loss: 0.0126 - val_dense_14_loss: 0.0147
    Epoch 516/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0253 - dense_9_loss: 0.0124 - dense_14_loss: 0.0129 - val_loss: 0.0242 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0122
    Epoch 517/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0260 - dense_9_loss: 0.0127 - dense_14_loss: 0.0133 - val_loss: 0.0251 - val_dense_9_loss: 0.0129 - val_dense_14_loss: 0.0123
    Epoch 518/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0254 - dense_9_loss: 0.0127 - dense_14_loss: 0.0127 - val_loss: 0.0251 - val_dense_9_loss: 0.0125 - val_dense_14_loss: 0.0125
    Epoch 519/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0256 - dense_9_loss: 0.0126 - dense_14_loss: 0.0130 - val_loss: 0.0270 - val_dense_9_loss: 0.0136 - val_dense_14_loss: 0.0134
    Epoch 520/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0251 - dense_9_loss: 0.0124 - dense_14_loss: 0.0127 - val_loss: 0.0245 - val_dense_9_loss: 0.0121 - val_dense_14_loss: 0.0124
    Epoch 521/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0248 - dense_9_loss: 0.0123 - dense_14_loss: 0.0126 - val_loss: 0.0253 - val_dense_9_loss: 0.0127 - val_dense_14_loss: 0.0126
    Epoch 522/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0261 - dense_9_loss: 0.0132 - dense_14_loss: 0.0129 - val_loss: 0.0264 - val_dense_9_loss: 0.0137 - val_dense_14_loss: 0.0127
    Epoch 523/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0263 - dense_9_loss: 0.0130 - dense_14_loss: 0.0133 - val_loss: 0.0267 - val_dense_9_loss: 0.0134 - val_dense_14_loss: 0.0133
    Epoch 524/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0246 - dense_9_loss: 0.0121 - dense_14_loss: 0.0125 - val_loss: 0.0245 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0124
    Epoch 525/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0248 - dense_9_loss: 0.0123 - dense_14_loss: 0.0125 - val_loss: 0.0241 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0121
    Epoch 526/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0259 - dense_9_loss: 0.0127 - dense_14_loss: 0.0132 - val_loss: 0.0283 - val_dense_9_loss: 0.0131 - val_dense_14_loss: 0.0151
    Epoch 527/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0265 - dense_9_loss: 0.0127 - dense_14_loss: 0.0137 - val_loss: 0.0246 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0124
    Epoch 528/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0246 - dense_9_loss: 0.0121 - dense_14_loss: 0.0126 - val_loss: 0.0240 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0120
    Epoch 529/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0252 - dense_9_loss: 0.0125 - dense_14_loss: 0.0127 - val_loss: 0.0252 - val_dense_9_loss: 0.0127 - val_dense_14_loss: 0.0125
    Epoch 530/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0250 - dense_9_loss: 0.0121 - dense_14_loss: 0.0129 - val_loss: 0.0244 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0122
    Epoch 531/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0254 - dense_9_loss: 0.0126 - dense_14_loss: 0.0128 - val_loss: 0.0245 - val_dense_9_loss: 0.0123 - val_dense_14_loss: 0.0123
    Epoch 532/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0260 - dense_9_loss: 0.0127 - dense_14_loss: 0.0133 - val_loss: 0.0271 - val_dense_9_loss: 0.0126 - val_dense_14_loss: 0.0144
    Epoch 533/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0257 - dense_9_loss: 0.0125 - dense_14_loss: 0.0132 - val_loss: 0.0247 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0126
    Epoch 534/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0259 - dense_9_loss: 0.0125 - dense_14_loss: 0.0134 - val_loss: 0.0308 - val_dense_9_loss: 0.0133 - val_dense_14_loss: 0.0175
    Epoch 535/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0260 - dense_9_loss: 0.0126 - dense_14_loss: 0.0134 - val_loss: 0.0244 - val_dense_9_loss: 0.0119 - val_dense_14_loss: 0.0125
    Epoch 536/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0261 - dense_9_loss: 0.0125 - dense_14_loss: 0.0135 - val_loss: 0.0248 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0128
    Epoch 537/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0242 - dense_9_loss: 0.0119 - dense_14_loss: 0.0123 - val_loss: 0.0248 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0124
    Epoch 538/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0252 - dense_9_loss: 0.0123 - dense_14_loss: 0.0129 - val_loss: 0.0241 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0122
    Epoch 539/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0254 - dense_9_loss: 0.0126 - dense_14_loss: 0.0128 - val_loss: 0.0242 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0120
    Epoch 540/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0246 - dense_9_loss: 0.0122 - dense_14_loss: 0.0124 - val_loss: 0.0240 - val_dense_9_loss: 0.0119 - val_dense_14_loss: 0.0120
    Epoch 541/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0250 - dense_9_loss: 0.0123 - dense_14_loss: 0.0127 - val_loss: 0.0260 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0138
    Epoch 542/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0248 - dense_9_loss: 0.0122 - dense_14_loss: 0.0126 - val_loss: 0.0266 - val_dense_9_loss: 0.0134 - val_dense_14_loss: 0.0131
    Epoch 543/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0253 - dense_9_loss: 0.0125 - dense_14_loss: 0.0129 - val_loss: 0.0237 - val_dense_9_loss: 0.0118 - val_dense_14_loss: 0.0120
    Epoch 544/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0249 - dense_9_loss: 0.0124 - dense_14_loss: 0.0125 - val_loss: 0.0256 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0134
    Epoch 545/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0245 - dense_9_loss: 0.0121 - dense_14_loss: 0.0124 - val_loss: 0.0238 - val_dense_9_loss: 0.0118 - val_dense_14_loss: 0.0120
    Epoch 546/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0245 - dense_9_loss: 0.0120 - dense_14_loss: 0.0124 - val_loss: 0.0273 - val_dense_9_loss: 0.0142 - val_dense_14_loss: 0.0131
    Epoch 547/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0267 - dense_9_loss: 0.0133 - dense_14_loss: 0.0135 - val_loss: 0.0257 - val_dense_9_loss: 0.0135 - val_dense_14_loss: 0.0122
    Epoch 548/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0256 - dense_9_loss: 0.0131 - dense_14_loss: 0.0126 - val_loss: 0.0267 - val_dense_9_loss: 0.0130 - val_dense_14_loss: 0.0136
    Epoch 549/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0241 - dense_9_loss: 0.0119 - dense_14_loss: 0.0123 - val_loss: 0.0242 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0121
    Epoch 550/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0245 - dense_9_loss: 0.0118 - dense_14_loss: 0.0126 - val_loss: 0.0249 - val_dense_9_loss: 0.0119 - val_dense_14_loss: 0.0130
    Epoch 551/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0267 - dense_9_loss: 0.0135 - dense_14_loss: 0.0131 - val_loss: 0.0275 - val_dense_9_loss: 0.0141 - val_dense_14_loss: 0.0134
    Epoch 552/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0249 - dense_9_loss: 0.0123 - dense_14_loss: 0.0126 - val_loss: 0.0281 - val_dense_9_loss: 0.0142 - val_dense_14_loss: 0.0139
    Epoch 553/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0241 - dense_9_loss: 0.0119 - dense_14_loss: 0.0122 - val_loss: 0.0263 - val_dense_9_loss: 0.0133 - val_dense_14_loss: 0.0130
    Epoch 554/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0242 - dense_9_loss: 0.0120 - dense_14_loss: 0.0122 - val_loss: 0.0260 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0136
    Epoch 555/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0244 - dense_9_loss: 0.0120 - dense_14_loss: 0.0124 - val_loss: 0.0236 - val_dense_9_loss: 0.0118 - val_dense_14_loss: 0.0119
    Epoch 556/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0250 - dense_9_loss: 0.0123 - dense_14_loss: 0.0127 - val_loss: 0.0352 - val_dense_9_loss: 0.0178 - val_dense_14_loss: 0.0174
    Epoch 557/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0252 - dense_9_loss: 0.0125 - dense_14_loss: 0.0127 - val_loss: 0.0244 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0123
    Epoch 558/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0244 - dense_9_loss: 0.0120 - dense_14_loss: 0.0124 - val_loss: 0.0241 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0119
    Epoch 559/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0265 - dense_9_loss: 0.0134 - dense_14_loss: 0.0131 - val_loss: 0.0236 - val_dense_9_loss: 0.0117 - val_dense_14_loss: 0.0119
    Epoch 560/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0261 - dense_9_loss: 0.0129 - dense_14_loss: 0.0132 - val_loss: 0.0294 - val_dense_9_loss: 0.0143 - val_dense_14_loss: 0.0151
    Epoch 561/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0255 - dense_9_loss: 0.0127 - dense_14_loss: 0.0128 - val_loss: 0.0236 - val_dense_9_loss: 0.0117 - val_dense_14_loss: 0.0119
    Epoch 562/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0240 - dense_9_loss: 0.0119 - dense_14_loss: 0.0121 - val_loss: 0.0236 - val_dense_9_loss: 0.0117 - val_dense_14_loss: 0.0118
    Epoch 563/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0247 - dense_9_loss: 0.0120 - dense_14_loss: 0.0127 - val_loss: 0.0237 - val_dense_9_loss: 0.0117 - val_dense_14_loss: 0.0120
    Epoch 564/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0240 - dense_9_loss: 0.0117 - dense_14_loss: 0.0122 - val_loss: 0.0249 - val_dense_9_loss: 0.0121 - val_dense_14_loss: 0.0128
    Epoch 565/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0239 - dense_9_loss: 0.0116 - dense_14_loss: 0.0123 - val_loss: 0.0236 - val_dense_9_loss: 0.0117 - val_dense_14_loss: 0.0119
    Epoch 566/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0247 - dense_9_loss: 0.0122 - dense_14_loss: 0.0125 - val_loss: 0.0241 - val_dense_9_loss: 0.0119 - val_dense_14_loss: 0.0122
    Epoch 567/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0259 - dense_9_loss: 0.0127 - dense_14_loss: 0.0132 - val_loss: 0.0238 - val_dense_9_loss: 0.0119 - val_dense_14_loss: 0.0119
    Epoch 568/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0238 - dense_9_loss: 0.0117 - dense_14_loss: 0.0121 - val_loss: 0.0285 - val_dense_9_loss: 0.0139 - val_dense_14_loss: 0.0146
    Epoch 569/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0241 - dense_9_loss: 0.0118 - dense_14_loss: 0.0122 - val_loss: 0.0233 - val_dense_9_loss: 0.0115 - val_dense_14_loss: 0.0118
    Epoch 570/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0239 - dense_9_loss: 0.0117 - dense_14_loss: 0.0122 - val_loss: 0.0244 - val_dense_9_loss: 0.0118 - val_dense_14_loss: 0.0126
    Epoch 571/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0239 - dense_9_loss: 0.0117 - dense_14_loss: 0.0121 - val_loss: 0.0268 - val_dense_9_loss: 0.0131 - val_dense_14_loss: 0.0136
    Epoch 572/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0241 - dense_9_loss: 0.0119 - dense_14_loss: 0.0123 - val_loss: 0.0256 - val_dense_9_loss: 0.0126 - val_dense_14_loss: 0.0130
    Epoch 573/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0272 - dense_9_loss: 0.0135 - dense_14_loss: 0.0137 - val_loss: 0.0321 - val_dense_9_loss: 0.0168 - val_dense_14_loss: 0.0153
    Epoch 574/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0243 - dense_9_loss: 0.0119 - dense_14_loss: 0.0124 - val_loss: 0.0231 - val_dense_9_loss: 0.0115 - val_dense_14_loss: 0.0116
    Epoch 575/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0249 - dense_9_loss: 0.0122 - dense_14_loss: 0.0127 - val_loss: 0.0242 - val_dense_9_loss: 0.0119 - val_dense_14_loss: 0.0123
    Epoch 576/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0244 - dense_9_loss: 0.0121 - dense_14_loss: 0.0124 - val_loss: 0.0291 - val_dense_9_loss: 0.0147 - val_dense_14_loss: 0.0144
    Epoch 577/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0252 - dense_9_loss: 0.0124 - dense_14_loss: 0.0128 - val_loss: 0.0269 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0142
    Epoch 578/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0241 - dense_9_loss: 0.0118 - dense_14_loss: 0.0123 - val_loss: 0.0248 - val_dense_9_loss: 0.0127 - val_dense_14_loss: 0.0122
    Epoch 579/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0236 - dense_9_loss: 0.0116 - dense_14_loss: 0.0120 - val_loss: 0.0241 - val_dense_9_loss: 0.0118 - val_dense_14_loss: 0.0122
    Epoch 580/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0247 - dense_9_loss: 0.0121 - dense_14_loss: 0.0125 - val_loss: 0.0263 - val_dense_9_loss: 0.0134 - val_dense_14_loss: 0.0129
    Epoch 581/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0240 - dense_9_loss: 0.0118 - dense_14_loss: 0.0122 - val_loss: 0.0251 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0127
    Epoch 582/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0241 - dense_9_loss: 0.0118 - dense_14_loss: 0.0123 - val_loss: 0.0239 - val_dense_9_loss: 0.0119 - val_dense_14_loss: 0.0121
    Epoch 583/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0240 - dense_9_loss: 0.0118 - dense_14_loss: 0.0122 - val_loss: 0.0246 - val_dense_9_loss: 0.0118 - val_dense_14_loss: 0.0128
    Epoch 584/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0252 - dense_9_loss: 0.0122 - dense_14_loss: 0.0129 - val_loss: 0.0281 - val_dense_9_loss: 0.0135 - val_dense_14_loss: 0.0147
    Epoch 585/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0241 - dense_9_loss: 0.0118 - dense_14_loss: 0.0123 - val_loss: 0.0239 - val_dense_9_loss: 0.0118 - val_dense_14_loss: 0.0121
    Epoch 586/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0241 - dense_9_loss: 0.0117 - dense_14_loss: 0.0124 - val_loss: 0.0235 - val_dense_9_loss: 0.0115 - val_dense_14_loss: 0.0120
    Epoch 587/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0238 - dense_9_loss: 0.0117 - dense_14_loss: 0.0121 - val_loss: 0.0258 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0130
    Epoch 588/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0248 - dense_9_loss: 0.0122 - dense_14_loss: 0.0126 - val_loss: 0.0256 - val_dense_9_loss: 0.0135 - val_dense_14_loss: 0.0121
    Epoch 589/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0245 - dense_9_loss: 0.0120 - dense_14_loss: 0.0125 - val_loss: 0.0235 - val_dense_9_loss: 0.0116 - val_dense_14_loss: 0.0120
    Epoch 590/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0251 - dense_9_loss: 0.0121 - dense_14_loss: 0.0130 - val_loss: 0.0235 - val_dense_9_loss: 0.0116 - val_dense_14_loss: 0.0119
    Epoch 591/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0269 - dense_9_loss: 0.0130 - dense_14_loss: 0.0139 - val_loss: 0.0239 - val_dense_9_loss: 0.0115 - val_dense_14_loss: 0.0124
    Epoch 592/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0245 - dense_9_loss: 0.0121 - dense_14_loss: 0.0125 - val_loss: 0.0234 - val_dense_9_loss: 0.0115 - val_dense_14_loss: 0.0119
    Epoch 593/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0243 - dense_9_loss: 0.0118 - dense_14_loss: 0.0125 - val_loss: 0.0269 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0147
    Epoch 594/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0234 - dense_9_loss: 0.0114 - dense_14_loss: 0.0119 - val_loss: 0.0232 - val_dense_9_loss: 0.0117 - val_dense_14_loss: 0.0115
    Epoch 595/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0241 - dense_9_loss: 0.0122 - dense_14_loss: 0.0119 - val_loss: 0.0228 - val_dense_9_loss: 0.0113 - val_dense_14_loss: 0.0114
    Epoch 596/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0237 - dense_9_loss: 0.0118 - dense_14_loss: 0.0119 - val_loss: 0.0242 - val_dense_9_loss: 0.0119 - val_dense_14_loss: 0.0123
    Epoch 597/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0235 - dense_9_loss: 0.0115 - dense_14_loss: 0.0120 - val_loss: 0.0234 - val_dense_9_loss: 0.0115 - val_dense_14_loss: 0.0119
    Epoch 598/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0234 - dense_9_loss: 0.0114 - dense_14_loss: 0.0120 - val_loss: 0.0239 - val_dense_9_loss: 0.0120 - val_dense_14_loss: 0.0119
    Epoch 599/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0249 - dense_9_loss: 0.0123 - dense_14_loss: 0.0126 - val_loss: 0.0229 - val_dense_9_loss: 0.0113 - val_dense_14_loss: 0.0116
    Epoch 600/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0233 - dense_9_loss: 0.0115 - dense_14_loss: 0.0118 - val_loss: 0.0234 - val_dense_9_loss: 0.0113 - val_dense_14_loss: 0.0121
    Epoch 601/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0246 - dense_9_loss: 0.0121 - dense_14_loss: 0.0125 - val_loss: 0.0254 - val_dense_9_loss: 0.0134 - val_dense_14_loss: 0.0120
    Epoch 602/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0255 - dense_9_loss: 0.0123 - dense_14_loss: 0.0133 - val_loss: 0.0244 - val_dense_9_loss: 0.0116 - val_dense_14_loss: 0.0129
    Epoch 603/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0241 - dense_9_loss: 0.0118 - dense_14_loss: 0.0123 - val_loss: 0.0242 - val_dense_9_loss: 0.0115 - val_dense_14_loss: 0.0127
    Epoch 604/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0238 - dense_9_loss: 0.0120 - dense_14_loss: 0.0118 - val_loss: 0.0241 - val_dense_9_loss: 0.0122 - val_dense_14_loss: 0.0120
    Epoch 605/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0238 - dense_9_loss: 0.0117 - dense_14_loss: 0.0121 - val_loss: 0.0346 - val_dense_9_loss: 0.0169 - val_dense_14_loss: 0.0178
    Epoch 606/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0249 - dense_9_loss: 0.0124 - dense_14_loss: 0.0125 - val_loss: 0.0243 - val_dense_9_loss: 0.0118 - val_dense_14_loss: 0.0125
    Epoch 607/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0232 - dense_9_loss: 0.0114 - dense_14_loss: 0.0118 - val_loss: 0.0232 - val_dense_9_loss: 0.0117 - val_dense_14_loss: 0.0115
    Epoch 608/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0242 - dense_9_loss: 0.0120 - dense_14_loss: 0.0123 - val_loss: 0.0260 - val_dense_9_loss: 0.0132 - val_dense_14_loss: 0.0128
    Epoch 609/2000
    55/55 [==============================] - 0s 6ms/step - loss: 0.0243 - dense_9_loss: 0.0121 - dense_14_loss: 0.0122 - val_loss: 0.0261 - val_dense_9_loss: 0.0127 - val_dense_14_loss: 0.0134
    Epoch 610/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0234 - dense_9_loss: 0.0116 - dense_14_loss: 0.0119 - val_loss: 0.0252 - val_dense_9_loss: 0.0126 - val_dense_14_loss: 0.0125
    Epoch 611/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0240 - dense_9_loss: 0.0119 - dense_14_loss: 0.0120 - val_loss: 0.0231 - val_dense_9_loss: 0.0113 - val_dense_14_loss: 0.0118
    Epoch 612/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0233 - dense_9_loss: 0.0113 - dense_14_loss: 0.0120 - val_loss: 0.0242 - val_dense_9_loss: 0.0121 - val_dense_14_loss: 0.0122
    Epoch 613/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0227 - dense_9_loss: 0.0112 - dense_14_loss: 0.0115 - val_loss: 0.0234 - val_dense_9_loss: 0.0118 - val_dense_14_loss: 0.0117
    Epoch 614/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0237 - dense_9_loss: 0.0116 - dense_14_loss: 0.0121 - val_loss: 0.0299 - val_dense_9_loss: 0.0151 - val_dense_14_loss: 0.0148
    Epoch 615/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0236 - dense_9_loss: 0.0116 - dense_14_loss: 0.0120 - val_loss: 0.0241 - val_dense_9_loss: 0.0117 - val_dense_14_loss: 0.0125
    Epoch 616/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0232 - dense_9_loss: 0.0114 - dense_14_loss: 0.0118 - val_loss: 0.0231 - val_dense_9_loss: 0.0112 - val_dense_14_loss: 0.0119
    Epoch 617/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0237 - dense_9_loss: 0.0117 - dense_14_loss: 0.0120 - val_loss: 0.0236 - val_dense_9_loss: 0.0117 - val_dense_14_loss: 0.0119
    Epoch 618/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0235 - dense_9_loss: 0.0116 - dense_14_loss: 0.0119 - val_loss: 0.0247 - val_dense_9_loss: 0.0124 - val_dense_14_loss: 0.0123
    Epoch 619/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0238 - dense_9_loss: 0.0115 - dense_14_loss: 0.0122 - val_loss: 0.0229 - val_dense_9_loss: 0.0113 - val_dense_14_loss: 0.0116
    Epoch 620/2000
    55/55 [==============================] - 0s 5ms/step - loss: 0.0230 - dense_9_loss: 0.0114 - dense_14_loss: 0.0117 - val_loss: 0.0257 - val_dense_9_loss: 0.0128 - val_dense_14_loss: 0.0129
    Epoch 00620: early stopping
    CPU times: user 3min 34s, sys: 27.6 s, total: 4min 2s
    Wall time: 3min 4s





    <tensorflow.python.keras.callbacks.History at 0x7f0ac1b35668>




```
model_loss = pd.DataFrame(model_TN.history.history)
model_loss.plot()
```


```
predictions = model_TN.predict(X_test)
predictions
```

    WARNING:tensorflow:Model was constructed with shape (None, None, 9) for input Tensor("input_1:0", shape=(None, None, 9), dtype=float32), but it was called on an input with incompatible shape (None, 9).





    [array([[15.85364 ],
            [18.667889],
            [10.154156],
            ...,
            [12.208869],
            [13.35763 ],
            [19.152866]], dtype=float32),
     array([[15.853533, 15.848686, 15.846228, ..., 15.847078, 15.853299,
             15.850801],
            [18.699324, 18.702284, 18.702812, ..., 18.701149, 18.700705,
             18.701807],
            [10.147136, 10.137468, 10.143947, ..., 10.132139, 10.142564,
             10.136511],
            ...,
            [12.193449, 12.199344, 12.198813, ..., 12.198003, 12.196786,
             12.198474],
            [13.383968, 13.391096, 13.386312, ..., 13.394348, 13.387536,
             13.394094],
            [19.156517, 19.157717, 19.156803, ..., 19.156223, 19.157715,
             19.15767 ]], dtype=float32)]




```
np.sqrt(mean_squared_error(y_test,predictions[0]))
```




    0.11305163164997559




```
explained_variance_score(y_test,predictions[0])
```




    0.9987473305979403




```
result_df = pd.DataFrame()
result_df['True values'] = y_test
result_df['Predictions'] = predictions[0]

result_df
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>True values</th>
      <th>Predictions</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>15.810335</td>
      <td>15.853640</td>
    </tr>
    <tr>
      <th>1</th>
      <td>18.714896</td>
      <td>18.667889</td>
    </tr>
    <tr>
      <th>2</th>
      <td>9.997362</td>
      <td>10.154156</td>
    </tr>
    <tr>
      <th>3</th>
      <td>17.636960</td>
      <td>17.593699</td>
    </tr>
    <tr>
      <th>4</th>
      <td>15.433450</td>
      <td>15.363018</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2995</th>
      <td>14.035750</td>
      <td>13.856400</td>
    </tr>
    <tr>
      <th>2996</th>
      <td>12.230762</td>
      <td>12.135015</td>
    </tr>
    <tr>
      <th>2997</th>
      <td>12.135982</td>
      <td>12.208869</td>
    </tr>
    <tr>
      <th>2998</th>
      <td>13.395426</td>
      <td>13.357630</td>
    </tr>
    <tr>
      <th>2999</th>
      <td>19.235523</td>
      <td>19.152866</td>
    </tr>
  </tbody>
</table>
<p>3000 rows × 2 columns</p>
</div>




```
sns.scatterplot(x = result_df['True values'], y = result_df['Predictions'], hue = "Predictions", data = result_df)
```

# DEVELOPING THE TERM STRUCTURE OF THE VOLATILITY SWAP


```
def bs_vega(S,K,T,r,v,q=0.0):
    d1 = (np.log(S/K)+(r+v*v/2.)*T)/(v*np.sqrt(T))
    return S * np.sqrt(T)*si.norm.pdf(d1)
```


```
def find_vol(target_value, S, K, T, r):
    MAX_ITERATIONS = 100
    PRECISION = 1.0e-5

    sigma = 0.5
    for i in range(0, MAX_ITERATIONS):
        if (K < S):
          price = euro_vanilla_call(S,K,T,r,sigma)
          vega = bs_vega(S, K, T, r, sigma)
          diff = target_value - price  # our root
          #print (i,sigma, diff)
          if (abs(diff) < PRECISION):
              return sigma, i
          sigma = sigma + diff/vega # f(x) / f'(x)
        elif (K > S):
          price = euro_vanilla_put(S,K,T,r,sigma)
          vega = bs_vega(S, K, T, r, sigma)
          diff = target_value - price  # our root
          #print (i,sigma, diff)
          if (abs(diff) < PRECISION):
              return sigma, i
          sigma = sigma + diff/vega # f(x) / f'(x)

    iteration = i
    # value wasn't found, return best guess so far
    return sigma, iteration
```


```
def local_vol(S0,S,volvol, rho, sigma):
    x = np.log(S/S0)
    volvec = volvol * volvol * x * x;
    volvec += 2.0 * volvol * rho * sigma * x;
    volvec += sigma * sigma;
    volvec = np.sqrt(volvec);
    return volvec;
```


```
#region Monte Carlo

np.random.seed(20000)

# Parameters
S0 = 1000.; T = 1.0; r = 0.0;
sigma = 0.20;
volvol = 0.50;
rho = -0.750;
rhoBar = np.sqrt(1.0-rho*rho);

M = 20; dt = T / M; I = 15000
# Simulating I paths with M time steps

S = np.zeros((M + 1, I))
Sbs = np.zeros((M + 1, I))
Slv = np.zeros((M + 1, I))
vol = np.zeros((M + 1, I))
vollv= np.zeros((M + 1, I))
Slv[0] = S0
Sbs[0] = S0
S[0] = S0
vol[0] = sigma
vollv[0] = sigma

z = sobol_seq.i4_sobol_generate(2*M,I);

for t in range(1, M + 1):
 z1 = z[:,t-1];
 z2 = z[:,M+t-1];
 x1 = norm.ppf(z1);
 x2 = norm.ppf(z2);

 vol[t] = vol[t - 1] * np.exp((- 0.5 * volvol ** 2) * dt + volvol * math.sqrt(dt) * (rho*x1+rhoBar*x2))
 S[t] = S[t - 1] * np.exp((r - 0.5 * vol[t-1] ** 2) * dt + vol[t-1] * math.sqrt(dt) * x1)
 Sbs[t] = Sbs[t - 1] * np.exp((r - 0.5 * sigma ** 2) * dt + sigma * math.sqrt(dt) * x1)
 Slv[t] = Slv[t - 1] * np.exp((r - 0.5 * vollv[t-1] ** 2) * dt + vollv[t-1] * math.sqrt(dt) * x1)
 vollv[t] =  local_vol(S0, Slv[t], volvol, rho, sigma)

```


```
Slv[-1].shape
```




    (15000,)




```

# Calculating the Monte Carlo estimator
Kmin =  math.exp(-2.0*sigma*math.sqrt(T))
sigma_array = np.array([0.29,0.28,0.27,0.26,0.25,0.24,0.23,0.22,0.21])
C0 = [];
iv = [];
calc = [];
ivBS = [];
ivLV = [];

total_bs = 0
total_lv = 0
total_sv = 0

for i in range(len(K_array)):
      #calc.append(K_array[i])
      clv = math.exp(-r * T) * np.sum( np.maximum(Slv[-1] - K_array[i], 0) - np.maximum(Sbs[-1] - K_array[i], 0) ) / I + euro_vanilla_call(S0,K_array[i],T,r,sigma_array[i])
      cbs = math.exp(-r * T) * np.sum( np.maximum(Sbs[-1] - K_array[i], 0) ) / I
      csv = math.exp(-r * T) * np.sum( np.maximum(S[-1] - K_array[i], 0) - np.maximum(Sbs[-1] - K_array[i], 0) ) / I + euro_vanilla_call(S0,K_array[i],T,r,sigma_array[i])
      plv = math.exp(-r * T) * np.sum( np.maximum(K_array[i] - Slv[-1], 0) - np.maximum(K_array[i] - Sbs[-1], 0) ) / I + euro_vanilla_put(S0,K_array[i],T,r,sigma_array[i])
      pbs = math.exp(-r * T) * np.sum( K_array[i] - np.maximum(Sbs[-1], 0) ) / I
      psv = math.exp(-r * T) * np.sum( K_array[i] - np.maximum(S[-1], 0) - np.maximum(K_array[i] - Sbs[-1], 0) ) / I + euro_vanilla_put(S0,K_array[i],T,r,sigma_array[i])
      if i==0:
        delta_K = K_array[i+1] - K_array[i]
        if K_array[i] < S_star:
          total_bs += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( K_array[i] - np.maximum(Sbs[-1], 0) ) / I)
          total_lv += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( np.maximum(K_array[i] - Slv[-1], 0) - np.maximum(K_array[i] - Sbs[-1], 0) ) / I + euro_vanilla_put(S0,K_array[i],T,r,sigma_array[i]))
          total_sv += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( K_array[i] - np.maximum(S[-1], 0) - np.maximum(K_array[i] - Sbs[-1], 0) ) / I + euro_vanilla_put(S0,K_array[i],T,r,sigma_array[i])) 
          break
        elif K_array[i] > S_star:
          total_bs += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( np.maximum(Sbs[-1] - K_array[i], 0) ) / I)
          total_lv += ((delta_K/K_array**2)*math.exp(-r * T) * np.sum( np.maximum(Slv[-1] - K_array[i], 0) - np.maximum(Sbs[-1] - K_array[i], 0) ) / I + euro_vanilla_call(S0,K_array[i],T,r,sigma_array[i]))
          total_sv += ((delta_K/K_array**2)*math.exp(-r * T) * np.sum( np.maximum(S[-1] - K_array[i], 0) - np.maximum(Sbs[-1] - K_array[i], 0) ) / I + euro_vanilla_call(S0,K_array[i],T,r,sigma_array[i]))
          break
        break

      elif ((i>=1) and (i<=n-2)):
        delta_K = 0.5*(K_array[i+1] - K_array[i-1])
        if K_array[i] < S_star:
          total_bs += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( K_array[i] - np.maximum(Sbs[-1], 0) ) / I)
          total_lv += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( np.maximum(K_array[i] - Slv[-1], 0) - np.maximum(K_array[i] - Sbs[-1], 0) ) / I + euro_vanilla_put(S0,K_array[i],T,r,sigma_array[i]))
          total_sv += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( K_array[i] - np.maximum(S[-1], 0) - np.maximum(K_array[i] - Sbs[-1], 0) ) / I + euro_vanilla_put(S0,K_array[i],T,r,sigma_array[i])) 
          break
        elif K_array[i] > S_star:
          total_bs += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( np.maximum(Sbs[-1] - K_array[i], 0) ) / I)
          total_lv += ((delta_K/K_array**2)*math.exp(-r * T) * np.sum( np.maximum(Slv[-1] - K_array[i], 0) - np.maximum(Sbs[-1] - K_array[i], 0) ) / I + euro_vanilla_call(S0,K_array[i],T,r,sigma_array[i]))
          total_sv += ((delta_K/K_array**2)*math.exp(-r * T) * np.sum( np.maximum(S[-1] - K_array[i], 0) - np.maximum(Sbs[-1] - K_array[i], 0) ) / I + euro_vanilla_call(S0,K_array[i],T,r,sigma_array[i]))
          break
        break

      else:
        delta_K = K_array[i] - K_array[i-1]
        if K_array[i] < S_star:
          total_bs += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( K_array[i] - np.maximum(Sbs[-1], 0) ) / I)
          total_lv += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( np.maximum(K_array[i] - Slv[-1], 0) - np.maximum(K_array[i] - Sbs[-1], 0) ) / I + euro_vanilla_put(S0,K_array[i],T,r,sigma_array[i]))
          total_sv += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( K_array[i] - np.maximum(S[-1], 0) - np.maximum(K_array[i] - Sbs[-1], 0) ) / I + euro_vanilla_put(S0,K_array[i],T,r,sigma_array[i])) 
          break
        elif K_array[i] > S_star:
          total_bs += ((delta_K/K_array**2)*np.exp(r*T)*math.exp(-r * T) * np.sum( np.maximum(Sbs[-1] - K_array[i], 0) ) / I)
          total_lv += ((delta_K/K_array**2)*math.exp(-r * T) * np.sum( np.maximum(Slv[-1] - K_array[i], 0) - np.maximum(Sbs[-1] - K_array[i], 0) ) / I + euro_vanilla_call(S0,K_array[i],T,r,sigma_array[i]))
          total_sv += ((delta_K/K_array**2)*math.exp(-r * T) * np.sum( np.maximum(S[-1] - K_array[i], 0) - np.maximum(Sbs[-1] - K_array[i], 0) ) / I + euro_vanilla_call(S0,K_array[i],T,r,sigma_array[i]))
          break
        break

```


```
j_bs
```




    array([-0.03213439, -0.0285675 , -0.02557841, -0.02304874, -0.02088896,
           -0.0190303 , -0.01741929, -0.01601381, -0.01478033])




```
j_bs = ((2/T)*np.log(F0/S_star)) - ((2/T)*((F0/S_star)-1)) + ((2/T)*total_bs)
j_lv = ((2/T)*np.log(F0/S_star)) - ((2/T)*((F0/S_star)-1)) + ((2/T)*total_bs)
j_sv = ((2/T)*np.log(F0/S_star)) - ((2/T)*((F0/S_star)-1)) + ((2/T)*total_bs)
var_bar = SD_avg_variance**2
vol_price_bs = np.sqrt(j_bs) * (1 - ((1/8)*(var_bar/j_bs**2)))
swap_price_bs = principal*((vol_price_bs - paid_vol)*np.exp(-r*T))
vol_price_lv = np.sqrt(j_lv) * (1 - ((1/8)*(var_bar/j_lv**2)))
swap_price_lv = principal*((vol_price_lv - paid_vol)*np.exp(-r*T))
vol_price_sv = np.sqrt(j_sv) * (1 - ((1/8)*(var_bar/j_sv**2)))
swap_price_sv = principal*((vol_price_sv - paid_vol)*np.exp(-r*T))
      
```


```
iv.append(find_vol(swap_price_sv, S0,S_star,T,r))
ivBS.append(find_vol(swap_price_bs, S0,S_star,T,r))
ivLV.append(find_vol(swap_price_lv, S0,S_star,T,r))
```


```
swap_price_bs
```




    array([ -28.1004235 ,  -31.4120858 ,  -35.49041231,  -40.60217361,
            -47.12579719,  -55.61643689,  -66.91941151,  -82.37891265,
           -104.24755153])



036e7299128da5dac13451b260c1b3e7847de125
ipynb
Jupyter Notebook
Volatility_Swap_Pricing.ipynb
j0rl3yJ/Volatility-Swap-Pricing
d5698d8594a15665aaba112cf3a60506b2c114ab
MIT
Volatility_Swap_Pricing.ipynb
j0rl3yJ/Volatility-Swap-Pricing
d5698d8594a15665aaba112cf3a60506b2c114ab
MIT
Volatility_Swap_Pricing.ipynb
j0rl3yJ/Volatility-Swap-Pricing
d5698d8594a15665aaba112cf3a60506b2c114ab
MIT
Qwen/Qwen-72B
1. YES
2. YES