# import required packages from Keras
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow import random
Using TensorFlow backend.
# import required packages for plotting
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
import matplotlib.patches as mpatches
# import the function for plotting decision boundary
from utils import plot_decision_boundary
# define a seed for random number generator so the result will be reproducible
seed = 1
np.random.seed(seed)
random.set_seed(seed)
# load the dataset, print the shapes of input and output and the number of examples
feats = pd.read_csv('../data/outlier_feats.csv')
target = pd.read_csv('../data/outlier_target.csv')
print("X size = ", feats.shape)
print("Y size = ", target.shape)
print("Number of examples = ", feats.shape[0])
X size = (3359, 2) Y size = (3359, 1) Number of examples = 3359
Plot the features and target
# changing the size of the plots
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
class_1=plt.scatter(feats.loc[target['Class']==0,'feature1'], feats.loc[target['Class']==0,'feature2'], c="red", s=40, edgecolor='k')
class_2=plt.scatter(feats.loc[target['Class']==1,'feature1'], feats.loc[target['Class']==1,'feature2'], c="blue", s=40, edgecolor='k')
plt.legend((class_1, class_2),('Fail','Pass'))
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
Text(0, 0.5, 'Feature 2')
# Logistic Regression model
np.random.seed(seed)
random.set_seed(seed)
model_1 = Sequential()
model_1.add(Dense(1, activation='sigmoid', input_dim=2))
model_1.compile(optimizer='sgd', loss='binary_crossentropy')
# train the model for 100 epoches and batch size 5
model_1.fit(feats, target, batch_size=5, epochs=100, verbose=1, validation_split=0.2, shuffle=False)
Train on 2687 samples, validate on 672 samples Epoch 1/100 2687/2687 [==============================] - 1s 214us/step - loss: 0.5405 - val_loss: 0.4194 Epoch 2/100 2687/2687 [==============================] - 0s 173us/step - loss: 0.3729 - val_loss: 0.3672 Epoch 3/100 2687/2687 [==============================] - 0s 176us/step - loss: 0.3459 - val_loss: 0.3574 Epoch 4/100 2687/2687 [==============================] - 1s 220us/step - loss: 0.3396 - val_loss: 0.3551 Epoch 5/100 2687/2687 [==============================] - 1s 249us/step - loss: 0.3378 - val_loss: 0.3545 Epoch 6/100 2687/2687 [==============================] - 1s 222us/step - loss: 0.3371 - val_loss: 0.3543 Epoch 7/100 2687/2687 [==============================] - 1s 274us/step - loss: 0.3369 - val_loss: 0.3543 Epoch 8/100 2687/2687 [==============================] - 1s 200us/step - loss: 0.3368 - val_loss: 0.3542 Epoch 9/100 2687/2687 [==============================] - 0s 182us/step - loss: 0.3367 - val_loss: 0.3542 Epoch 10/100 2687/2687 [==============================] - 1s 186us/step - loss: 0.3367 - val_loss: 0.3542 Epoch 11/100 2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3542 Epoch 12/100 2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 13/100 2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 14/100 2687/2687 [==============================] - 0s 166us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 15/100 2687/2687 [==============================] - 0s 171us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 16/100 2687/2687 [==============================] - 0s 174us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 17/100 2687/2687 [==============================] - 0s 174us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 18/100 2687/2687 [==============================] - 0s 167us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 19/100 2687/2687 [==============================] - 0s 169us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 20/100 2687/2687 [==============================] - 0s 172us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 21/100 2687/2687 [==============================] - 1s 212us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 22/100 2687/2687 [==============================] - 1s 221us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 23/100 2687/2687 [==============================] - 0s 167us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 24/100 2687/2687 [==============================] - 0s 176us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 25/100 2687/2687 [==============================] - 0s 168us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 26/100 2687/2687 [==============================] - 0s 175us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 27/100 2687/2687 [==============================] - 1s 199us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 28/100 2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 29/100 2687/2687 [==============================] - 0s 185us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 30/100 2687/2687 [==============================] - 0s 186us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 31/100 2687/2687 [==============================] - 0s 170us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 32/100 2687/2687 [==============================] - 0s 165us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 33/100 2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 34/100 2687/2687 [==============================] - 0s 170us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 35/100 2687/2687 [==============================] - 0s 165us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 36/100 2687/2687 [==============================] - 0s 169us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 37/100 2687/2687 [==============================] - 0s 170us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 38/100 2687/2687 [==============================] - 0s 165us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 39/100 2687/2687 [==============================] - 0s 172us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 40/100 2687/2687 [==============================] - 0s 166us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 41/100 2687/2687 [==============================] - 0s 169us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 42/100 2687/2687 [==============================] - 0s 183us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 43/100 2687/2687 [==============================] - 0s 179us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 44/100 2687/2687 [==============================] - 0s 186us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 45/100 2687/2687 [==============================] - 0s 181us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 46/100 2687/2687 [==============================] - 0s 169us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 47/100 2687/2687 [==============================] - 0s 176us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 48/100 2687/2687 [==============================] - 0s 177us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 49/100 2687/2687 [==============================] - 1s 188us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 50/100 2687/2687 [==============================] - 0s 176us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 51/100 2687/2687 [==============================] - 0s 179us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 52/100 2687/2687 [==============================] - 0s 177us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 53/100 2687/2687 [==============================] - 0s 168us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 54/100 2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 55/100 2687/2687 [==============================] - 1s 194us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 56/100 2687/2687 [==============================] - 0s 183us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 57/100 2687/2687 [==============================] - 0s 168us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 58/100 2687/2687 [==============================] - 0s 170us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 59/100 2687/2687 [==============================] - 0s 164us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 60/100 2687/2687 [==============================] - 0s 166us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 61/100 2687/2687 [==============================] - 0s 172us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 62/100 2687/2687 [==============================] - 0s 175us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 63/100 2687/2687 [==============================] - 0s 181us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 64/100 2687/2687 [==============================] - 0s 167us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 65/100 2687/2687 [==============================] - 1s 192us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 66/100 2687/2687 [==============================] - 1s 200us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 67/100 2687/2687 [==============================] - 0s 185us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 68/100 2687/2687 [==============================] - 0s 180us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 69/100 2687/2687 [==============================] - 0s 180us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 70/100 2687/2687 [==============================] - 0s 174us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 71/100 2687/2687 [==============================] - 0s 177us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 72/100 2687/2687 [==============================] - 0s 175us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 73/100 2687/2687 [==============================] - 1s 241us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 74/100 2687/2687 [==============================] - 1s 237us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 75/100 2687/2687 [==============================] - 1s 212us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 76/100 2687/2687 [==============================] - 1s 205us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 77/100 2687/2687 [==============================] - 1s 199us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 78/100 2687/2687 [==============================] - 1s 204us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 79/100 2687/2687 [==============================] - 1s 227us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 80/100 2687/2687 [==============================] - 1s 206us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 81/100 2687/2687 [==============================] - 1s 221us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 82/100 2687/2687 [==============================] - 1s 221us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 83/100 2687/2687 [==============================] - 1s 216us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 84/100 2687/2687 [==============================] - 1s 207us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 85/100 2687/2687 [==============================] - 1s 201us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 86/100 2687/2687 [==============================] - 1s 206us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 87/100 2687/2687 [==============================] - 1s 217us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 88/100 2687/2687 [==============================] - 1s 212us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 89/100 2687/2687 [==============================] - 1s 202us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 90/100 2687/2687 [==============================] - 1s 196us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 91/100 2687/2687 [==============================] - 1s 188us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 92/100 2687/2687 [==============================] - 1s 202us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 93/100 2687/2687 [==============================] - 1s 205us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 94/100 2687/2687 [==============================] - 1s 240us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 95/100 2687/2687 [==============================] - 1s 207us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 96/100 2687/2687 [==============================] - 1s 199us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 97/100 2687/2687 [==============================] - 1s 197us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 98/100 2687/2687 [==============================] - 1s 215us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 99/100 2687/2687 [==============================] - 1s 199us/step - loss: 0.3367 - val_loss: 0.3541 Epoch 100/100 2687/2687 [==============================] - 0s 180us/step - loss: 0.3367 - val_loss: 0.3541
<keras.callbacks.callbacks.History at 0x142dc5240>
Plot the decision boundary
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
plot_decision_boundary(lambda x: model_1.predict(x), feats, target)
plt.title("Logistic Regression")
Text(0.5, 1.0, 'Logistic Regression')
Evaluate the loss and accuracy on the test dataset
# Neural network with hidden layer size = 3
np.random.seed(seed)
random.set_seed(seed)
model_2 = Sequential()
model_2.add(Dense(3, activation='relu', input_dim=2))
model_2.add(Dense(1, activation='sigmoid'))
model_2.compile(optimizer='sgd', loss='binary_crossentropy')
# train the model for 200 epoches and batch size 5
model_2.fit(feats, target, batch_size=5, epochs=200, verbose=1, validation_split=0.2, shuffle=False)
Train on 2687 samples, validate on 672 samples Epoch 1/200 2687/2687 [==============================] - 1s 216us/step - loss: 0.4889 - val_loss: 0.3847 Epoch 2/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.3135 - val_loss: 0.2946 Epoch 3/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.2509 - val_loss: 0.2408 Epoch 4/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.2078 - val_loss: 0.2044 Epoch 5/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.1776 - val_loss: 0.1790 Epoch 6/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.1562 - val_loss: 0.1609 Epoch 7/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.1402 - val_loss: 0.1472 Epoch 8/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.1279 - val_loss: 0.1362 Epoch 9/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.1185 - val_loss: 0.1276 Epoch 10/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.1108 - val_loss: 0.1209 Epoch 11/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.1046 - val_loss: 0.1153 Epoch 12/200 2687/2687 [==============================] - 1s 235us/step - loss: 0.0994 - val_loss: 0.1106 Epoch 13/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0949 - val_loss: 0.1066 Epoch 14/200 2687/2687 [==============================] - 1s 186us/step - loss: 0.0909 - val_loss: 0.1030 Epoch 15/200 2687/2687 [==============================] - 0s 182us/step - loss: 0.0873 - val_loss: 0.1000 Epoch 16/200 2687/2687 [==============================] - 0s 182us/step - loss: 0.0840 - val_loss: 0.0973 Epoch 17/200 2687/2687 [==============================] - 1s 211us/step - loss: 0.0811 - val_loss: 0.0948 Epoch 18/200 2687/2687 [==============================] - 1s 204us/step - loss: 0.0784 - val_loss: 0.0925 Epoch 19/200 2687/2687 [==============================] - 1s 216us/step - loss: 0.0759 - val_loss: 0.0902 Epoch 20/200 2687/2687 [==============================] - 1s 211us/step - loss: 0.0736 - val_loss: 0.0881 Epoch 21/200 2687/2687 [==============================] - 1s 240us/step - loss: 0.0714 - val_loss: 0.0861 Epoch 22/200 2687/2687 [==============================] - 1s 188us/step - loss: 0.0694 - val_loss: 0.0840 Epoch 23/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0673 - val_loss: 0.0821 Epoch 24/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0654 - val_loss: 0.0804 Epoch 25/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0635 - val_loss: 0.0787 Epoch 26/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0618 - val_loss: 0.0770 Epoch 27/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0601 - val_loss: 0.0753 Epoch 28/200 2687/2687 [==============================] - 1s 188us/step - loss: 0.0585 - val_loss: 0.0737 Epoch 29/200 2687/2687 [==============================] - 1s 218us/step - loss: 0.0571 - val_loss: 0.0720 Epoch 30/200 2687/2687 [==============================] - 1s 204us/step - loss: 0.0555 - val_loss: 0.0705 Epoch 31/200 2687/2687 [==============================] - 1s 209us/step - loss: 0.0541 - val_loss: 0.0687 Epoch 32/200 2687/2687 [==============================] - 1s 197us/step - loss: 0.0526 - val_loss: 0.0670 Epoch 33/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0511 - val_loss: 0.0654 Epoch 34/200 2687/2687 [==============================] - 1s 198us/step - loss: 0.0498 - val_loss: 0.0639 Epoch 35/200 2687/2687 [==============================] - 1s 223us/step - loss: 0.0485 - val_loss: 0.0626 Epoch 36/200 2687/2687 [==============================] - 1s 218us/step - loss: 0.0473 - val_loss: 0.0612 Epoch 37/200 2687/2687 [==============================] - 1s 215us/step - loss: 0.0461 - val_loss: 0.0599 Epoch 38/200 2687/2687 [==============================] - 1s 216us/step - loss: 0.0449 - val_loss: 0.0585 Epoch 39/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.0438 - val_loss: 0.0573 Epoch 40/200 2687/2687 [==============================] - 1s 207us/step - loss: 0.0427 - val_loss: 0.0561 Epoch 41/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.0417 - val_loss: 0.0550 Epoch 42/200 2687/2687 [==============================] - 1s 209us/step - loss: 0.0408 - val_loss: 0.0540 Epoch 43/200 2687/2687 [==============================] - 1s 240us/step - loss: 0.0399 - val_loss: 0.0531 Epoch 44/200 2687/2687 [==============================] - 1s 203us/step - loss: 0.0390 - val_loss: 0.0522 Epoch 45/200 2687/2687 [==============================] - 1s 204us/step - loss: 0.0382 - val_loss: 0.0513 Epoch 46/200 2687/2687 [==============================] - 1s 206us/step - loss: 0.0374 - val_loss: 0.0504 Epoch 47/200 2687/2687 [==============================] - 1s 201us/step - loss: 0.0366 - val_loss: 0.0495 Epoch 48/200 2687/2687 [==============================] - 1s 230us/step - loss: 0.0359 - val_loss: 0.0487 Epoch 49/200 2687/2687 [==============================] - 1s 213us/step - loss: 0.0352 - val_loss: 0.0479 Epoch 50/200 2687/2687 [==============================] - 1s 275us/step - loss: 0.0346 - val_loss: 0.0472 Epoch 51/200 2687/2687 [==============================] - 1s 311us/step - loss: 0.0339 - val_loss: 0.0466 Epoch 52/200 2687/2687 [==============================] - 1s 267us/step - loss: 0.0333 - val_loss: 0.0460 Epoch 53/200 2687/2687 [==============================] - 1s 201us/step - loss: 0.0328 - val_loss: 0.0453 Epoch 54/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.0322 - val_loss: 0.0447 Epoch 55/200 2687/2687 [==============================] - 1s 243us/step - loss: 0.0317 - val_loss: 0.0442 Epoch 56/200 2687/2687 [==============================] - 1s 219us/step - loss: 0.0312 - val_loss: 0.0437 Epoch 57/200 2687/2687 [==============================] - 1s 209us/step - loss: 0.0307 - val_loss: 0.0432 Epoch 58/200 2687/2687 [==============================] - 1s 206us/step - loss: 0.0302 - val_loss: 0.0427 Epoch 59/200 2687/2687 [==============================] - 1s 202us/step - loss: 0.0297 - val_loss: 0.0423 Epoch 60/200 2687/2687 [==============================] - 1s 209us/step - loss: 0.0293 - val_loss: 0.0418 Epoch 61/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0289 - val_loss: 0.0414 Epoch 62/200 2687/2687 [==============================] - 0s 179us/step - loss: 0.0285 - val_loss: 0.0410 Epoch 63/200 2687/2687 [==============================] - 1s 210us/step - loss: 0.0281 - val_loss: 0.0406 Epoch 64/200 2687/2687 [==============================] - 1s 212us/step - loss: 0.0277 - val_loss: 0.0401 Epoch 65/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.0272 - val_loss: 0.0396 Epoch 66/200 2687/2687 [==============================] - 1s 197us/step - loss: 0.0268 - val_loss: 0.0392 Epoch 67/200 2687/2687 [==============================] - 1s 201us/step - loss: 0.0263 - val_loss: 0.0387 Epoch 68/200 2687/2687 [==============================] - 1s 199us/step - loss: 0.0259 - val_loss: 0.0383 Epoch 69/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0255 - val_loss: 0.0380 Epoch 70/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0252 - val_loss: 0.0376 Epoch 71/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0248 - val_loss: 0.0373 Epoch 72/200 2687/2687 [==============================] - 1s 221us/step - loss: 0.0245 - val_loss: 0.0369 Epoch 73/200 2687/2687 [==============================] - 1s 221us/step - loss: 0.0242 - val_loss: 0.0366 Epoch 74/200 2687/2687 [==============================] - 1s 209us/step - loss: 0.0239 - val_loss: 0.0362 Epoch 75/200 2687/2687 [==============================] - 1s 209us/step - loss: 0.0236 - val_loss: 0.0359 Epoch 76/200 2687/2687 [==============================] - 1s 203us/step - loss: 0.0233 - val_loss: 0.0356 Epoch 77/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0230 - val_loss: 0.0354 Epoch 78/200 2687/2687 [==============================] - 1s 203us/step - loss: 0.0228 - val_loss: 0.0351 Epoch 79/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0225 - val_loss: 0.0349 Epoch 80/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0223 - val_loss: 0.0346 Epoch 81/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0221 - val_loss: 0.0344 Epoch 82/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0218 - val_loss: 0.0342 Epoch 83/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0216 - val_loss: 0.0340 Epoch 84/200 2687/2687 [==============================] - 1s 203us/step - loss: 0.0214 - val_loss: 0.0338 Epoch 85/200 2687/2687 [==============================] - 1s 213us/step - loss: 0.0212 - val_loss: 0.0336 Epoch 86/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0210 - val_loss: 0.0335 Epoch 87/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0208 - val_loss: 0.0333 Epoch 88/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0207 - val_loss: 0.0331 Epoch 89/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0205 - val_loss: 0.0329 Epoch 90/200 2687/2687 [==============================] - 1s 198us/step - loss: 0.0203 - val_loss: 0.0328 Epoch 91/200 2687/2687 [==============================] - 1s 199us/step - loss: 0.0201 - val_loss: 0.0326 Epoch 92/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.0200 - val_loss: 0.0325 Epoch 93/200 2687/2687 [==============================] - 1s 207us/step - loss: 0.0198 - val_loss: 0.0323 Epoch 94/200 2687/2687 [==============================] - 1s 197us/step - loss: 0.0196 - val_loss: 0.0322 Epoch 95/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0195 - val_loss: 0.0321 Epoch 96/200 2687/2687 [==============================] - 1s 206us/step - loss: 0.0193 - val_loss: 0.0320 Epoch 97/200 2687/2687 [==============================] - 1s 204us/step - loss: 0.0192 - val_loss: 0.0319 Epoch 98/200 2687/2687 [==============================] - 1s 212us/step - loss: 0.0191 - val_loss: 0.0318 Epoch 99/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0189 - val_loss: 0.0317 Epoch 100/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0188 - val_loss: 0.0316 Epoch 101/200 2687/2687 [==============================] - 1s 188us/step - loss: 0.0187 - val_loss: 0.0315 Epoch 102/200 2687/2687 [==============================] - 1s 237us/step - loss: 0.0185 - val_loss: 0.0314 Epoch 103/200 2687/2687 [==============================] - 1s 236us/step - loss: 0.0184 - val_loss: 0.0313 Epoch 104/200 2687/2687 [==============================] - 1s 206us/step - loss: 0.0183 - val_loss: 0.0312 Epoch 105/200 2687/2687 [==============================] - 1s 203us/step - loss: 0.0182 - val_loss: 0.0312 Epoch 106/200 2687/2687 [==============================] - 1s 212us/step - loss: 0.0181 - val_loss: 0.0311 Epoch 107/200 2687/2687 [==============================] - 1s 213us/step - loss: 0.0179 - val_loss: 0.0310 Epoch 108/200 2687/2687 [==============================] - 1s 213us/step - loss: 0.0178 - val_loss: 0.0309 Epoch 109/200 2687/2687 [==============================] - 1s 203us/step - loss: 0.0177 - val_loss: 0.0309 Epoch 110/200 2687/2687 [==============================] - 1s 209us/step - loss: 0.0176 - val_loss: 0.0308 Epoch 111/200 2687/2687 [==============================] - 1s 198us/step - loss: 0.0175 - val_loss: 0.0307 Epoch 112/200 2687/2687 [==============================] - 1s 218us/step - loss: 0.0174 - val_loss: 0.0307 Epoch 113/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0173 - val_loss: 0.0306 Epoch 114/200 2687/2687 [==============================] - 1s 274us/step - loss: 0.0172 - val_loss: 0.0306 Epoch 115/200 2687/2687 [==============================] - 1s 247us/step - loss: 0.0171 - val_loss: 0.0305 Epoch 116/200 2687/2687 [==============================] - 1s 214us/step - loss: 0.0170 - val_loss: 0.0305 Epoch 117/200 2687/2687 [==============================] - 1s 228us/step - loss: 0.0169 - val_loss: 0.0304 Epoch 118/200 2687/2687 [==============================] - 1s 248us/step - loss: 0.0169 - val_loss: 0.0304 Epoch 119/200 2687/2687 [==============================] - 1s 199us/step - loss: 0.0168 - val_loss: 0.0303 Epoch 120/200 2687/2687 [==============================] - 1s 214us/step - loss: 0.0167 - val_loss: 0.0303 Epoch 121/200 2687/2687 [==============================] - 1s 210us/step - loss: 0.0166 - val_loss: 0.0302 Epoch 122/200 2687/2687 [==============================] - 1s 212us/step - loss: 0.0165 - val_loss: 0.0302 Epoch 123/200 2687/2687 [==============================] - 1s 217us/step - loss: 0.0164 - val_loss: 0.0301 Epoch 124/200 2687/2687 [==============================] - 1s 233us/step - loss: 0.0164 - val_loss: 0.0301 Epoch 125/200 2687/2687 [==============================] - 1s 257us/step - loss: 0.0163 - val_loss: 0.0301 Epoch 126/200 2687/2687 [==============================] - 0s 180us/step - loss: 0.0162 - val_loss: 0.0300 Epoch 127/200 2687/2687 [==============================] - 0s 178us/step - loss: 0.0161 - val_loss: 0.0300 Epoch 128/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0161 - val_loss: 0.0299 Epoch 129/200 2687/2687 [==============================] - 0s 174us/step - loss: 0.0160 - val_loss: 0.0299 Epoch 130/200 2687/2687 [==============================] - 0s 175us/step - loss: 0.0159 - val_loss: 0.0299 Epoch 131/200 2687/2687 [==============================] - 0s 172us/step - loss: 0.0158 - val_loss: 0.0298 Epoch 132/200 2687/2687 [==============================] - 0s 174us/step - loss: 0.0158 - val_loss: 0.0298 Epoch 133/200 2687/2687 [==============================] - 0s 173us/step - loss: 0.0157 - val_loss: 0.0298 Epoch 134/200 2687/2687 [==============================] - 0s 174us/step - loss: 0.0156 - val_loss: 0.0297 Epoch 135/200 2687/2687 [==============================] - 0s 173us/step - loss: 0.0156 - val_loss: 0.0297 Epoch 136/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0155 - val_loss: 0.0297 Epoch 137/200 2687/2687 [==============================] - 0s 175us/step - loss: 0.0154 - val_loss: 0.0297 Epoch 138/200 2687/2687 [==============================] - 0s 173us/step - loss: 0.0154 - val_loss: 0.0296 Epoch 139/200 2687/2687 [==============================] - 0s 174us/step - loss: 0.0153 - val_loss: 0.0296 Epoch 140/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0153 - val_loss: 0.0296 Epoch 141/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0152 - val_loss: 0.0296 Epoch 142/200 2687/2687 [==============================] - 0s 186us/step - loss: 0.0151 - val_loss: 0.0295 Epoch 143/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0151 - val_loss: 0.0295 Epoch 144/200 2687/2687 [==============================] - 0s 186us/step - loss: 0.0150 - val_loss: 0.0295 Epoch 145/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0150 - val_loss: 0.0295 Epoch 146/200 2687/2687 [==============================] - 1s 212us/step - loss: 0.0149 - val_loss: 0.0295 Epoch 147/200 2687/2687 [==============================] - 1s 188us/step - loss: 0.0149 - val_loss: 0.0294 Epoch 148/200 2687/2687 [==============================] - 1s 201us/step - loss: 0.0148 - val_loss: 0.0294 Epoch 149/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0148 - val_loss: 0.0294 Epoch 150/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0147 - val_loss: 0.0294 Epoch 151/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0147 - val_loss: 0.0294 Epoch 152/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0146 - val_loss: 0.0294 Epoch 153/200 2687/2687 [==============================] - 1s 245us/step - loss: 0.0146 - val_loss: 0.0293 Epoch 154/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.0145 - val_loss: 0.0293 Epoch 155/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0145 - val_loss: 0.0293 Epoch 156/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0144 - val_loss: 0.0293 Epoch 157/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0144 - val_loss: 0.0293 Epoch 158/200 2687/2687 [==============================] - 1s 197us/step - loss: 0.0143 - val_loss: 0.0293 Epoch 159/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0143 - val_loss: 0.0293 Epoch 160/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.0142 - val_loss: 0.0292 Epoch 161/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0142 - val_loss: 0.0292 Epoch 162/200 2687/2687 [==============================] - 1s 201us/step - loss: 0.0142 - val_loss: 0.0292 Epoch 163/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0141 - val_loss: 0.0292 Epoch 164/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0141 - val_loss: 0.0292 Epoch 165/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0140 - val_loss: 0.0292 Epoch 166/200 2687/2687 [==============================] - 1s 198us/step - loss: 0.0140 - val_loss: 0.0292 Epoch 167/200 2687/2687 [==============================] - 1s 199us/step - loss: 0.0139 - val_loss: 0.0292 Epoch 168/200 2687/2687 [==============================] - 1s 215us/step - loss: 0.0139 - val_loss: 0.0292 Epoch 169/200 2687/2687 [==============================] - 1s 222us/step - loss: 0.0139 - val_loss: 0.0292 Epoch 170/200 2687/2687 [==============================] - 1s 245us/step - loss: 0.0138 - val_loss: 0.0291 Epoch 171/200 2687/2687 [==============================] - 1s 222us/step - loss: 0.0138 - val_loss: 0.0291 Epoch 172/200 2687/2687 [==============================] - 1s 210us/step - loss: 0.0138 - val_loss: 0.0291 Epoch 173/200 2687/2687 [==============================] - 1s 207us/step - loss: 0.0137 - val_loss: 0.0291 Epoch 174/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0137 - val_loss: 0.0291 Epoch 175/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0136 - val_loss: 0.0291 Epoch 176/200 2687/2687 [==============================] - 1s 208us/step - loss: 0.0136 - val_loss: 0.0291 Epoch 177/200 2687/2687 [==============================] - 1s 186us/step - loss: 0.0136 - val_loss: 0.0291 Epoch 178/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0135 - val_loss: 0.0291 Epoch 179/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.0135 - val_loss: 0.0291 Epoch 180/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0135 - val_loss: 0.0291 Epoch 181/200 2687/2687 [==============================] - 1s 188us/step - loss: 0.0134 - val_loss: 0.0291 Epoch 182/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0134 - val_loss: 0.0291 Epoch 183/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0134 - val_loss: 0.0291 Epoch 184/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0133 - val_loss: 0.0291 Epoch 185/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0133 - val_loss: 0.0291 Epoch 186/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0133 - val_loss: 0.0291 Epoch 187/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0132 - val_loss: 0.0291 Epoch 188/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0132 - val_loss: 0.0291 Epoch 189/200 2687/2687 [==============================] - 0s 178us/step - loss: 0.0132 - val_loss: 0.0291 Epoch 190/200 2687/2687 [==============================] - 0s 176us/step - loss: 0.0131 - val_loss: 0.0291 Epoch 191/200 2687/2687 [==============================] - 1s 186us/step - loss: 0.0131 - val_loss: 0.0291 Epoch 192/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0131 - val_loss: 0.0291 Epoch 193/200 2687/2687 [==============================] - 0s 182us/step - loss: 0.0131 - val_loss: 0.0291 Epoch 194/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0130 - val_loss: 0.0291 Epoch 195/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0130 - val_loss: 0.0290 Epoch 196/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0130 - val_loss: 0.0289 Epoch 197/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0129 - val_loss: 0.0289 Epoch 198/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0129 - val_loss: 0.0288 Epoch 199/200 2687/2687 [==============================] - 0s 181us/step - loss: 0.0129 - val_loss: 0.0287 Epoch 200/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0128 - val_loss: 0.0286
<keras.callbacks.callbacks.History at 0x140c266a0>
Plot the decision boundary
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
plot_decision_boundary(lambda x: model_2.predict(x), feats, target)
plt.title("Decision Boundary for Neural Network with hidden layer size 3")
Text(0.5, 1.0, 'Decision Boundary for Neural Network with hidden layer size 3')
Create a neural network with hidden layer of size 6
np.random.seed(seed)
random.set_seed(seed)
model_3 = Sequential()
model_3.add(Dense(6, activation='relu', input_dim=2))
model_3.add(Dense(1, activation='sigmoid'))
model_3.compile(optimizer='sgd', loss='binary_crossentropy')
# train the model for 400 epoches
model_3.fit(feats, target, batch_size=5, epochs=400, verbose=1, validation_split=0.2, shuffle=False)
Train on 2687 samples, validate on 672 samples Epoch 1/400 2687/2687 [==============================] - 1s 214us/step - loss: 0.4972 - val_loss: 0.3903 Epoch 2/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.3003 - val_loss: 0.2724 Epoch 3/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.2293 - val_loss: 0.2229 Epoch 4/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.1907 - val_loss: 0.1910 Epoch 5/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.1625 - val_loss: 0.1667 Epoch 6/400 2687/2687 [==============================] - 1s 191us/step - loss: 0.1395 - val_loss: 0.1462 Epoch 7/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.1199 - val_loss: 0.1275 Epoch 8/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.1031 - val_loss: 0.1110 Epoch 9/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0890 - val_loss: 0.0973 Epoch 10/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0774 - val_loss: 0.0862 Epoch 11/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0679 - val_loss: 0.0772 Epoch 12/400 2687/2687 [==============================] - 0s 177us/step - loss: 0.0605 - val_loss: 0.0704 Epoch 13/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0547 - val_loss: 0.0649 Epoch 14/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0501 - val_loss: 0.0606 Epoch 15/400 2687/2687 [==============================] - 0s 175us/step - loss: 0.0463 - val_loss: 0.0571 Epoch 16/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0432 - val_loss: 0.0541 Epoch 17/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0406 - val_loss: 0.0517 Epoch 18/400 2687/2687 [==============================] - 0s 176us/step - loss: 0.0383 - val_loss: 0.0496 Epoch 19/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0364 - val_loss: 0.0477 Epoch 20/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0347 - val_loss: 0.0461 Epoch 21/400 2687/2687 [==============================] - 0s 175us/step - loss: 0.0332 - val_loss: 0.0446 Epoch 22/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0318 - val_loss: 0.0433 Epoch 23/400 2687/2687 [==============================] - 0s 171us/step - loss: 0.0306 - val_loss: 0.0422 Epoch 24/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0295 - val_loss: 0.0411 Epoch 25/400 2687/2687 [==============================] - 0s 173us/step - loss: 0.0285 - val_loss: 0.0401 Epoch 26/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0276 - val_loss: 0.0393 Epoch 27/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0268 - val_loss: 0.0385 Epoch 28/400 2687/2687 [==============================] - 0s 177us/step - loss: 0.0260 - val_loss: 0.0377 Epoch 29/400 2687/2687 [==============================] - 0s 177us/step - loss: 0.0253 - val_loss: 0.0370 Epoch 30/400 2687/2687 [==============================] - 0s 172us/step - loss: 0.0246 - val_loss: 0.0364 Epoch 31/400 2687/2687 [==============================] - 0s 177us/step - loss: 0.0240 - val_loss: 0.0358 Epoch 32/400 2687/2687 [==============================] - 0s 176us/step - loss: 0.0235 - val_loss: 0.0353 Epoch 33/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0229 - val_loss: 0.0348 Epoch 34/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0224 - val_loss: 0.0343 Epoch 35/400 2687/2687 [==============================] - 0s 175us/step - loss: 0.0220 - val_loss: 0.0338 Epoch 36/400 2687/2687 [==============================] - 0s 170us/step - loss: 0.0215 - val_loss: 0.0334 Epoch 37/400 2687/2687 [==============================] - 0s 175us/step - loss: 0.0211 - val_loss: 0.0330 Epoch 38/400 2687/2687 [==============================] - 0s 172us/step - loss: 0.0207 - val_loss: 0.0326 Epoch 39/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0204 - val_loss: 0.0323 Epoch 40/400 2687/2687 [==============================] - 0s 176us/step - loss: 0.0200 - val_loss: 0.0319 Epoch 41/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0197 - val_loss: 0.0316 Epoch 42/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0194 - val_loss: 0.0313 Epoch 43/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0191 - val_loss: 0.0310 Epoch 44/400 2687/2687 [==============================] - 0s 174us/step - loss: 0.0188 - val_loss: 0.0308 Epoch 45/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0185 - val_loss: 0.0305 Epoch 46/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0183 - val_loss: 0.0303 Epoch 47/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0180 - val_loss: 0.0301 Epoch 48/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0178 - val_loss: 0.0299 Epoch 49/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0176 - val_loss: 0.0297 Epoch 50/400 2687/2687 [==============================] - 0s 173us/step - loss: 0.0173 - val_loss: 0.0295 Epoch 51/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0171 - val_loss: 0.0293 Epoch 52/400 2687/2687 [==============================] - 0s 177us/step - loss: 0.0169 - val_loss: 0.0291 Epoch 53/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0167 - val_loss: 0.0289 Epoch 54/400 2687/2687 [==============================] - 0s 173us/step - loss: 0.0166 - val_loss: 0.0288 Epoch 55/400 2687/2687 [==============================] - 0s 175us/step - loss: 0.0164 - val_loss: 0.0286 Epoch 56/400 2687/2687 [==============================] - 0s 177us/step - loss: 0.0162 - val_loss: 0.0284 Epoch 57/400 2687/2687 [==============================] - 1s 227us/step - loss: 0.0160 - val_loss: 0.0283 Epoch 58/400 2687/2687 [==============================] - 1s 248us/step - loss: 0.0159 - val_loss: 0.0282 Epoch 59/400 2687/2687 [==============================] - 1s 222us/step - loss: 0.0157 - val_loss: 0.0280 Epoch 60/400 2687/2687 [==============================] - 1s 196us/step - loss: 0.0156 - val_loss: 0.0279 Epoch 61/400 2687/2687 [==============================] - 1s 214us/step - loss: 0.0154 - val_loss: 0.0278 Epoch 62/400 2687/2687 [==============================] - 1s 215us/step - loss: 0.0153 - val_loss: 0.0276 Epoch 63/400 2687/2687 [==============================] - 1s 204us/step - loss: 0.0152 - val_loss: 0.0275 Epoch 64/400 2687/2687 [==============================] - 1s 205us/step - loss: 0.0150 - val_loss: 0.0274 Epoch 65/400 2687/2687 [==============================] - 1s 205us/step - loss: 0.0149 - val_loss: 0.0273 Epoch 66/400 2687/2687 [==============================] - 1s 222us/step - loss: 0.0148 - val_loss: 0.0272 Epoch 67/400 2687/2687 [==============================] - 1s 199us/step - loss: 0.0147 - val_loss: 0.0271 Epoch 68/400 2687/2687 [==============================] - 1s 207us/step - loss: 0.0146 - val_loss: 0.0270 Epoch 69/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.0145 - val_loss: 0.0269 Epoch 70/400 2687/2687 [==============================] - 1s 199us/step - loss: 0.0143 - val_loss: 0.0269 Epoch 71/400 2687/2687 [==============================] - 1s 208us/step - loss: 0.0142 - val_loss: 0.0268 Epoch 72/400 2687/2687 [==============================] - 1s 265us/step - loss: 0.0141 - val_loss: 0.0267 Epoch 73/400 2687/2687 [==============================] - 1s 227us/step - loss: 0.0140 - val_loss: 0.0266 Epoch 74/400 2687/2687 [==============================] - 1s 219us/step - loss: 0.0139 - val_loss: 0.0265 Epoch 75/400 2687/2687 [==============================] - 1s 206us/step - loss: 0.0139 - val_loss: 0.0265 Epoch 76/400 2687/2687 [==============================] - 1s 206us/step - loss: 0.0138 - val_loss: 0.0264 Epoch 77/400 2687/2687 [==============================] - 1s 206us/step - loss: 0.0137 - val_loss: 0.0263 Epoch 78/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0136 - val_loss: 0.0262 Epoch 79/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0135 - val_loss: 0.0262 Epoch 80/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0134 - val_loss: 0.0261 Epoch 81/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0133 - val_loss: 0.0260 Epoch 82/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0133 - val_loss: 0.0260 Epoch 83/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0132 - val_loss: 0.0259 Epoch 84/400 2687/2687 [==============================] - 1s 192us/step - loss: 0.0131 - val_loss: 0.0258 Epoch 85/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0130 - val_loss: 0.0258 Epoch 86/400 2687/2687 [==============================] - 1s 196us/step - loss: 0.0130 - val_loss: 0.0257 Epoch 87/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0129 - val_loss: 0.0257 Epoch 88/400 2687/2687 [==============================] - 1s 192us/step - loss: 0.0128 - val_loss: 0.0256 Epoch 89/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0128 - val_loss: 0.0256 Epoch 90/400 2687/2687 [==============================] - 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0s 182us/step - loss: 0.0088 - val_loss: 0.0238 Epoch 241/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0088 - val_loss: 0.0238 Epoch 242/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0088 - val_loss: 0.0238 Epoch 243/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0088 - val_loss: 0.0238 Epoch 244/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0088 - val_loss: 0.0238 Epoch 245/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0088 - val_loss: 0.0238 Epoch 246/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0087 - val_loss: 0.0238 Epoch 247/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0087 - val_loss: 0.0238 Epoch 248/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0087 - val_loss: 0.0238 Epoch 249/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0087 - val_loss: 0.0238 Epoch 250/400 2687/2687 [==============================] - 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0s 183us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 281/400 2687/2687 [==============================] - 1s 196us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 282/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 283/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 284/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 285/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 286/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 287/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 288/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 289/400 2687/2687 [==============================] - 1s 191us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 290/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0084 - val_loss: 0.0239 Epoch 291/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0083 - val_loss: 0.0239 Epoch 292/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0083 - val_loss: 0.0239 Epoch 293/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0083 - val_loss: 0.0239 Epoch 294/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0083 - val_loss: 0.0239 Epoch 295/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0083 - val_loss: 0.0239 Epoch 296/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0083 - val_loss: 0.0239 Epoch 297/400 2687/2687 [==============================] - 1s 186us/step - loss: 0.0083 - val_loss: 0.0239 Epoch 298/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0083 - val_loss: 0.0240 Epoch 299/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0083 - val_loss: 0.0240 Epoch 300/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0083 - val_loss: 0.0240 Epoch 301/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0083 - val_loss: 0.0240 Epoch 302/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0083 - val_loss: 0.0240 Epoch 303/400 2687/2687 [==============================] - 1s 191us/step - loss: 0.0083 - val_loss: 0.0240 Epoch 304/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 305/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 306/400 2687/2687 [==============================] - 1s 207us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 307/400 2687/2687 [==============================] - 1s 202us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 308/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 309/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 310/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 311/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 312/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 313/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 314/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 315/400 2687/2687 [==============================] - 1s 197us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 316/400 2687/2687 [==============================] - 1s 202us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 317/400 2687/2687 [==============================] - 1s 235us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 318/400 2687/2687 [==============================] - 1s 240us/step - loss: 0.0082 - val_loss: 0.0240 Epoch 319/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0081 - val_loss: 0.0240 Epoch 320/400 2687/2687 [==============================] - 1s 186us/step - loss: 0.0081 - val_loss: 0.0240 Epoch 321/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0081 - val_loss: 0.0240 Epoch 322/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.0081 - val_loss: 0.0240 Epoch 323/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0081 - val_loss: 0.0240 Epoch 324/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0081 - val_loss: 0.0240 Epoch 325/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 326/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 327/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 328/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 329/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 330/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 331/400 2687/2687 [==============================] - 1s 206us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 332/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 333/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 334/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0081 - val_loss: 0.0241 Epoch 335/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 336/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 337/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 338/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 339/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 340/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 341/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 342/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 343/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 344/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 345/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 346/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 347/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 348/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0080 - val_loss: 0.0241 Epoch 349/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0080 - val_loss: 0.0242 Epoch 350/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0080 - val_loss: 0.0242 Epoch 351/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0080 - val_loss: 0.0242 Epoch 352/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 353/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 354/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 355/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 356/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 357/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 358/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 359/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 360/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 361/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 362/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 363/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 364/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 365/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 366/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 367/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 368/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0079 - val_loss: 0.0242 Epoch 369/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 370/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 371/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 372/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 373/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 374/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 375/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 376/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 377/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 378/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 379/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 380/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 381/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 382/400 2687/2687 [==============================] - 1s 186us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 383/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 384/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 385/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0242 Epoch 386/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0243 Epoch 387/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0243 Epoch 388/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 389/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 390/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 391/400 2687/2687 [==============================] - 1s 205us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 392/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 393/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 394/400 2687/2687 [==============================] - 1s 203us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 395/400 2687/2687 [==============================] - 1s 199us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 396/400 2687/2687 [==============================] - 1s 207us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 397/400 2687/2687 [==============================] - 1s 289us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 398/400 2687/2687 [==============================] - 1s 244us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 399/400 2687/2687 [==============================] - 1s 261us/step - loss: 0.0077 - val_loss: 0.0243 Epoch 400/400 2687/2687 [==============================] - 1s 313us/step - loss: 0.0077 - val_loss: 0.0243
<keras.callbacks.callbacks.History at 0x144a08128>
Plot the decision boundary
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
plot_decision_boundary(lambda x: model_3.predict(x), feats, target)
plt.title("Decision Boundary for Neural Network with hidden layer size 6")
Text(0.5, 1.0, 'Decision Boundary for Neural Network with hidden layer size 6')
Create a neural network with hidden layer of size 3 and tanh activation function
# Neural network with hidden layer size = 3 with tanh activation function
np.random.seed(seed)
random.set_seed(seed)
model_4 = Sequential()
model_4.add(Dense(3, activation='tanh', input_dim=2))
model_4.add(Dense(1, activation='sigmoid'))
model_4.compile(optimizer='sgd', loss='binary_crossentropy')
# train the model for 200 epoches and batch size 5
model_4.fit(feats, target, batch_size=5, epochs=200, verbose=1, validation_split=0.2, shuffle=False)
Train on 2687 samples, validate on 672 samples Epoch 1/200 2687/2687 [==============================] - 1s 292us/step - loss: 0.4163 - val_loss: 0.3552 Epoch 2/200 2687/2687 [==============================] - 1s 218us/step - loss: 0.3127 - val_loss: 0.3385 Epoch 3/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.3005 - val_loss: 0.3292 Epoch 4/200 2687/2687 [==============================] - 1s 204us/step - loss: 0.2925 - val_loss: 0.3203 Epoch 5/200 2687/2687 [==============================] - 1s 208us/step - loss: 0.2853 - val_loss: 0.3109 Epoch 6/200 2687/2687 [==============================] - 1s 241us/step - loss: 0.2782 - val_loss: 0.3007 Epoch 7/200 2687/2687 [==============================] - 1s 266us/step - loss: 0.2706 - val_loss: 0.2898 Epoch 8/200 2687/2687 [==============================] - 1s 205us/step - loss: 0.2623 - val_loss: 0.2784 Epoch 9/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.2533 - val_loss: 0.2666 Epoch 10/200 2687/2687 [==============================] - 1s 216us/step - loss: 0.2436 - val_loss: 0.2548 Epoch 11/200 2687/2687 [==============================] - 1s 238us/step - loss: 0.2337 - val_loss: 0.2437 Epoch 12/200 2687/2687 [==============================] - 1s 384us/step - loss: 0.2246 - val_loss: 0.2337 Epoch 13/200 2687/2687 [==============================] - 1s 363us/step - loss: 0.2165 - val_loss: 0.2248 Epoch 14/200 2687/2687 [==============================] - 1s 392us/step - loss: 0.2094 - val_loss: 0.2168 Epoch 15/200 2687/2687 [==============================] - 1s 249us/step - loss: 0.2029 - val_loss: 0.2095 Epoch 16/200 2687/2687 [==============================] - 1s 236us/step - loss: 0.1969 - val_loss: 0.2028 Epoch 17/200 2687/2687 [==============================] - 1s 211us/step - loss: 0.1913 - val_loss: 0.1966 Epoch 18/200 2687/2687 [==============================] - 1s 245us/step - loss: 0.1859 - val_loss: 0.1907 Epoch 19/200 2687/2687 [==============================] - 1s 266us/step - loss: 0.1807 - val_loss: 0.1849 Epoch 20/200 2687/2687 [==============================] - 1s 216us/step - loss: 0.1755 - val_loss: 0.1793 Epoch 21/200 2687/2687 [==============================] - 1s 210us/step - loss: 0.1700 - val_loss: 0.1737 Epoch 22/200 2687/2687 [==============================] - 1s 230us/step - loss: 0.1644 - val_loss: 0.1683 Epoch 23/200 2687/2687 [==============================] - 1s 212us/step - loss: 0.1585 - val_loss: 0.1632 Epoch 24/200 2687/2687 [==============================] - 1s 214us/step - loss: 0.1523 - val_loss: 0.1586 Epoch 25/200 2687/2687 [==============================] - 1s 241us/step - loss: 0.1462 - val_loss: 0.1543 Epoch 26/200 2687/2687 [==============================] - 1s 246us/step - loss: 0.1403 - val_loss: 0.1502 Epoch 27/200 2687/2687 [==============================] - 1s 235us/step - loss: 0.1348 - val_loss: 0.1462 Epoch 28/200 2687/2687 [==============================] - 1s 303us/step - loss: 0.1297 - val_loss: 0.1421 Epoch 29/200 2687/2687 [==============================] - 1s 238us/step - loss: 0.1250 - val_loss: 0.1381 Epoch 30/200 2687/2687 [==============================] - 1s 244us/step - loss: 0.1206 - val_loss: 0.1342 Epoch 31/200 2687/2687 [==============================] - 1s 248us/step - loss: 0.1166 - val_loss: 0.1303 Epoch 32/200 2687/2687 [==============================] - 1s 232us/step - loss: 0.1128 - val_loss: 0.1267 Epoch 33/200 2687/2687 [==============================] - 1s 236us/step - loss: 0.1092 - val_loss: 0.1231 Epoch 34/200 2687/2687 [==============================] - 1s 250us/step - loss: 0.1058 - val_loss: 0.1196 Epoch 35/200 2687/2687 [==============================] - 1s 203us/step - loss: 0.1026 - val_loss: 0.1163 Epoch 36/200 2687/2687 [==============================] - 1s 223us/step - loss: 0.0995 - val_loss: 0.1131 Epoch 37/200 2687/2687 [==============================] - 1s 296us/step - loss: 0.0966 - val_loss: 0.1101 Epoch 38/200 2687/2687 [==============================] - 1s 269us/step - loss: 0.0939 - val_loss: 0.1072 Epoch 39/200 2687/2687 [==============================] - 1s 253us/step - loss: 0.0913 - val_loss: 0.1044 Epoch 40/200 2687/2687 [==============================] - 1s 223us/step - loss: 0.0888 - val_loss: 0.1018 Epoch 41/200 2687/2687 [==============================] - 1s 222us/step - loss: 0.0865 - val_loss: 0.0994 Epoch 42/200 2687/2687 [==============================] - 1s 224us/step - loss: 0.0843 - val_loss: 0.0971 Epoch 43/200 2687/2687 [==============================] - 1s 261us/step - loss: 0.0823 - val_loss: 0.0949 Epoch 44/200 2687/2687 [==============================] - 1s 270us/step - loss: 0.0803 - val_loss: 0.0928 Epoch 45/200 2687/2687 [==============================] - 1s 208us/step - loss: 0.0785 - val_loss: 0.0909 Epoch 46/200 2687/2687 [==============================] - 1s 246us/step - loss: 0.0767 - val_loss: 0.0891 Epoch 47/200 2687/2687 [==============================] - 1s 213us/step - loss: 0.0751 - val_loss: 0.0873 Epoch 48/200 2687/2687 [==============================] - 1s 202us/step - loss: 0.0735 - val_loss: 0.0857 Epoch 49/200 2687/2687 [==============================] - 1s 207us/step - loss: 0.0720 - val_loss: 0.0841 Epoch 50/200 2687/2687 [==============================] - 1s 255us/step - loss: 0.0705 - val_loss: 0.0827 Epoch 51/200 2687/2687 [==============================] - 1s 264us/step - loss: 0.0692 - val_loss: 0.0813 Epoch 52/200 2687/2687 [==============================] - 1s 294us/step - loss: 0.0678 - val_loss: 0.0799 Epoch 53/200 2687/2687 [==============================] - 1s 301us/step - loss: 0.0666 - val_loss: 0.0787 Epoch 54/200 2687/2687 [==============================] - 1s 201us/step - loss: 0.0654 - val_loss: 0.0775 Epoch 55/200 2687/2687 [==============================] - 1s 225us/step - loss: 0.0642 - val_loss: 0.0763 Epoch 56/200 2687/2687 [==============================] - 1s 228us/step - loss: 0.0631 - val_loss: 0.0752 Epoch 57/200 2687/2687 [==============================] - 1s 234us/step - loss: 0.0621 - val_loss: 0.0742 Epoch 58/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.0611 - val_loss: 0.0732 Epoch 59/200 2687/2687 [==============================] - 1s 202us/step - loss: 0.0601 - val_loss: 0.0722 Epoch 60/200 2687/2687 [==============================] - 1s 224us/step - loss: 0.0592 - val_loss: 0.0713 Epoch 61/200 2687/2687 [==============================] - 1s 242us/step - loss: 0.0583 - val_loss: 0.0704 Epoch 62/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0574 - val_loss: 0.0696 Epoch 63/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.0566 - val_loss: 0.0688 Epoch 64/200 2687/2687 [==============================] - 1s 216us/step - loss: 0.0558 - val_loss: 0.0680 Epoch 65/200 2687/2687 [==============================] - 1s 202us/step - loss: 0.0550 - val_loss: 0.0673 Epoch 66/200 2687/2687 [==============================] - 1s 237us/step - loss: 0.0542 - val_loss: 0.0665 Epoch 67/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0535 - val_loss: 0.0659 Epoch 68/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0528 - val_loss: 0.0652 Epoch 69/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0521 - val_loss: 0.0646 Epoch 70/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.0515 - val_loss: 0.0639 Epoch 71/200 2687/2687 [==============================] - 1s 186us/step - loss: 0.0509 - val_loss: 0.0634 Epoch 72/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0502 - val_loss: 0.0628 Epoch 73/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0497 - val_loss: 0.0622 Epoch 74/200 2687/2687 [==============================] - 1s 209us/step - loss: 0.0491 - val_loss: 0.0617 Epoch 75/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0485 - val_loss: 0.0612 Epoch 76/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.0480 - val_loss: 0.0607 Epoch 77/200 2687/2687 [==============================] - 1s 188us/step - loss: 0.0475 - val_loss: 0.0602 Epoch 78/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0470 - val_loss: 0.0598 Epoch 79/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0465 - val_loss: 0.0593 Epoch 80/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0460 - val_loss: 0.0589 Epoch 81/200 2687/2687 [==============================] - 1s 202us/step - loss: 0.0456 - val_loss: 0.0585 Epoch 82/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0451 - val_loss: 0.0581 Epoch 83/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0447 - val_loss: 0.0577 Epoch 84/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.0443 - val_loss: 0.0573 Epoch 85/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0439 - val_loss: 0.0569 Epoch 86/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0435 - val_loss: 0.0566 Epoch 87/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0431 - val_loss: 0.0562 Epoch 88/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0427 - val_loss: 0.0559 Epoch 89/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0423 - val_loss: 0.0556 Epoch 90/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0420 - val_loss: 0.0552 Epoch 91/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0416 - val_loss: 0.0549 Epoch 92/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0413 - val_loss: 0.0546 Epoch 93/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0410 - val_loss: 0.0543 Epoch 94/200 2687/2687 [==============================] - 1s 197us/step - loss: 0.0406 - val_loss: 0.0541 Epoch 95/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0403 - val_loss: 0.0538 Epoch 96/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0400 - val_loss: 0.0535 Epoch 97/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0397 - val_loss: 0.0533 Epoch 98/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0394 - val_loss: 0.0530 Epoch 99/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0392 - val_loss: 0.0528 Epoch 100/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0389 - val_loss: 0.0525 Epoch 101/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0386 - val_loss: 0.0523 Epoch 102/200 2687/2687 [==============================] - 0s 186us/step - loss: 0.0384 - val_loss: 0.0521 Epoch 103/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0381 - val_loss: 0.0518 Epoch 104/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0379 - val_loss: 0.0516 Epoch 105/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0376 - val_loss: 0.0514 Epoch 106/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0374 - val_loss: 0.0512 Epoch 107/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0371 - val_loss: 0.0510 Epoch 108/200 2687/2687 [==============================] - 1s 198us/step - loss: 0.0369 - val_loss: 0.0508 Epoch 109/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0367 - val_loss: 0.0506 Epoch 110/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0365 - val_loss: 0.0504 Epoch 111/200 2687/2687 [==============================] - 1s 194us/step - loss: 0.0363 - val_loss: 0.0502 Epoch 112/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0360 - val_loss: 0.0501 Epoch 113/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0358 - val_loss: 0.0499 Epoch 114/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0356 - val_loss: 0.0497 Epoch 115/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0355 - val_loss: 0.0495 Epoch 116/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0353 - val_loss: 0.0494 Epoch 117/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0351 - val_loss: 0.0492 Epoch 118/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0349 - val_loss: 0.0491 Epoch 119/200 2687/2687 [==============================] - 1s 186us/step - loss: 0.0347 - val_loss: 0.0489 Epoch 120/200 2687/2687 [==============================] - 0s 181us/step - loss: 0.0345 - val_loss: 0.0488 Epoch 121/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0344 - val_loss: 0.0486 Epoch 122/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0342 - val_loss: 0.0485 Epoch 123/200 2687/2687 [==============================] - 1s 186us/step - loss: 0.0340 - val_loss: 0.0483 Epoch 124/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0339 - val_loss: 0.0482 Epoch 125/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0337 - val_loss: 0.0481 Epoch 126/200 2687/2687 [==============================] - 0s 182us/step - loss: 0.0336 - val_loss: 0.0479 Epoch 127/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0334 - val_loss: 0.0478 Epoch 128/200 2687/2687 [==============================] - 0s 180us/step - loss: 0.0333 - val_loss: 0.0477 Epoch 129/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0331 - val_loss: 0.0475 Epoch 130/200 2687/2687 [==============================] - 0s 183us/step - loss: 0.0330 - val_loss: 0.0474 Epoch 131/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0328 - val_loss: 0.0473 Epoch 132/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0327 - val_loss: 0.0472 Epoch 133/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.0326 - val_loss: 0.0471 Epoch 134/200 2687/2687 [==============================] - 0s 183us/step - loss: 0.0324 - val_loss: 0.0470 Epoch 135/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0323 - val_loss: 0.0468 Epoch 136/200 2687/2687 [==============================] - 0s 182us/step - loss: 0.0322 - val_loss: 0.0467 Epoch 137/200 2687/2687 [==============================] - 1s 188us/step - loss: 0.0320 - val_loss: 0.0466 Epoch 138/200 2687/2687 [==============================] - 1s 206us/step - loss: 0.0319 - val_loss: 0.0465 Epoch 139/200 2687/2687 [==============================] - 1s 221us/step - loss: 0.0318 - val_loss: 0.0464 Epoch 140/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0317 - val_loss: 0.0463 Epoch 141/200 2687/2687 [==============================] - 1s 188us/step - loss: 0.0316 - val_loss: 0.0462 Epoch 142/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0314 - val_loss: 0.0461 Epoch 143/200 2687/2687 [==============================] - 1s 190us/step - loss: 0.0313 - val_loss: 0.0460 Epoch 144/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0312 - val_loss: 0.0459 Epoch 145/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0311 - val_loss: 0.0458 Epoch 146/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0310 - val_loss: 0.0458 Epoch 147/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0309 - val_loss: 0.0457 Epoch 148/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0308 - val_loss: 0.0456 Epoch 149/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0307 - val_loss: 0.0455 Epoch 150/200 2687/2687 [==============================] - 0s 181us/step - loss: 0.0306 - val_loss: 0.0454 Epoch 151/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0305 - val_loss: 0.0453 Epoch 152/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0304 - val_loss: 0.0452 Epoch 153/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0303 - val_loss: 0.0452 Epoch 154/200 2687/2687 [==============================] - 1s 192us/step - loss: 0.0302 - val_loss: 0.0451 Epoch 155/200 2687/2687 [==============================] - 1s 186us/step - loss: 0.0301 - val_loss: 0.0450 Epoch 156/200 2687/2687 [==============================] - 0s 182us/step - loss: 0.0300 - val_loss: 0.0449 Epoch 157/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0299 - val_loss: 0.0448 Epoch 158/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0299 - val_loss: 0.0448 Epoch 159/200 2687/2687 [==============================] - 1s 187us/step - loss: 0.0298 - val_loss: 0.0447 Epoch 160/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0297 - val_loss: 0.0446 Epoch 161/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0296 - val_loss: 0.0446 Epoch 162/200 2687/2687 [==============================] - 1s 189us/step - loss: 0.0295 - val_loss: 0.0445 Epoch 163/200 2687/2687 [==============================] - 1s 191us/step - loss: 0.0294 - val_loss: 0.0444 Epoch 164/200 2687/2687 [==============================] - 0s 185us/step - loss: 0.0293 - val_loss: 0.0444 Epoch 165/200 2687/2687 [==============================] - 0s 186us/step - loss: 0.0293 - val_loss: 0.0443 Epoch 166/200 2687/2687 [==============================] - 0s 186us/step - loss: 0.0292 - val_loss: 0.0442 Epoch 167/200 2687/2687 [==============================] - 1s 209us/step - loss: 0.0291 - val_loss: 0.0442 Epoch 168/200 2687/2687 [==============================] - 1s 196us/step - loss: 0.0290 - val_loss: 0.0441 Epoch 169/200 2687/2687 [==============================] - 0s 184us/step - loss: 0.0290 - val_loss: 0.0440 Epoch 170/200 2687/2687 [==============================] - 1s 219us/step - loss: 0.0289 - val_loss: 0.0440 Epoch 171/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0288 - val_loss: 0.0439 Epoch 172/200 2687/2687 [==============================] - 1s 213us/step - loss: 0.0287 - val_loss: 0.0438 Epoch 173/200 2687/2687 [==============================] - 1s 193us/step - loss: 0.0287 - val_loss: 0.0438 Epoch 174/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0286 - val_loss: 0.0437 Epoch 175/200 2687/2687 [==============================] - 1s 207us/step - loss: 0.0285 - val_loss: 0.0437 Epoch 176/200 2687/2687 [==============================] - 1s 207us/step - loss: 0.0285 - val_loss: 0.0436 Epoch 177/200 2687/2687 [==============================] - 1s 218us/step - loss: 0.0284 - val_loss: 0.0436 Epoch 178/200 2687/2687 [==============================] - 1s 215us/step - loss: 0.0283 - val_loss: 0.0435 Epoch 179/200 2687/2687 [==============================] - 1s 244us/step - loss: 0.0283 - val_loss: 0.0435 Epoch 180/200 2687/2687 [==============================] - 1s 210us/step - loss: 0.0282 - val_loss: 0.0434 Epoch 181/200 2687/2687 [==============================] - 1s 205us/step - loss: 0.0281 - val_loss: 0.0433 Epoch 182/200 2687/2687 [==============================] - 1s 212us/step - loss: 0.0281 - val_loss: 0.0433 Epoch 183/200 2687/2687 [==============================] - 1s 257us/step - loss: 0.0280 - val_loss: 0.0432 Epoch 184/200 2687/2687 [==============================] - 1s 203us/step - loss: 0.0280 - val_loss: 0.0432 Epoch 185/200 2687/2687 [==============================] - 1s 207us/step - loss: 0.0279 - val_loss: 0.0431 Epoch 186/200 2687/2687 [==============================] - 1s 212us/step - loss: 0.0278 - val_loss: 0.0431 Epoch 187/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.0278 - val_loss: 0.0430 Epoch 188/200 2687/2687 [==============================] - 1s 207us/step - loss: 0.0277 - val_loss: 0.0430 Epoch 189/200 2687/2687 [==============================] - 1s 207us/step - loss: 0.0277 - val_loss: 0.0430 Epoch 190/200 2687/2687 [==============================] - 1s 202us/step - loss: 0.0276 - val_loss: 0.0429 Epoch 191/200 2687/2687 [==============================] - 1s 270us/step - loss: 0.0276 - val_loss: 0.0429 Epoch 192/200 2687/2687 [==============================] - 1s 283us/step - loss: 0.0275 - val_loss: 0.0428 Epoch 193/200 2687/2687 [==============================] - 1s 252us/step - loss: 0.0274 - val_loss: 0.0428 Epoch 194/200 2687/2687 [==============================] - 1s 202us/step - loss: 0.0274 - val_loss: 0.0427 Epoch 195/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.0273 - val_loss: 0.0427 Epoch 196/200 2687/2687 [==============================] - 1s 210us/step - loss: 0.0273 - val_loss: 0.0426 Epoch 197/200 2687/2687 [==============================] - 1s 200us/step - loss: 0.0272 - val_loss: 0.0426 Epoch 198/200 2687/2687 [==============================] - 1s 214us/step - loss: 0.0272 - val_loss: 0.0426 Epoch 199/200 2687/2687 [==============================] - 1s 195us/step - loss: 0.0271 - val_loss: 0.0425 Epoch 200/200 2687/2687 [==============================] - 1s 201us/step - loss: 0.0271 - val_loss: 0.0425
<keras.callbacks.callbacks.History at 0x144ee5470>
Plotting the decision boundary on the training dataset
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
plot_decision_boundary(lambda x: model_4.predict(x), feats, target)
plt.title("Decision Boundary for Neural Network with hidden layer size 3")
Text(0.5, 1.0, 'Decision Boundary for Neural Network with hidden layer size 3')
Neural network with hidden layer size = 6 with tanh activation function
np.random.seed(seed)
random.set_seed(seed)
model_5 = Sequential()
model_5.add(Dense(6, activation='tanh', input_dim=2))
model_5.add(Dense(1, activation='sigmoid'))
model_5.compile(optimizer='sgd', loss='binary_crossentropy')
# train the model for 400 epoches
model_5.fit(feats, target, batch_size=5, epochs=400, verbose=1, validation_split=0.2, shuffle=False)
Train on 2687 samples, validate on 672 samples Epoch 1/400 2687/2687 [==============================] - 1s 234us/step - loss: 0.3822 - val_loss: 0.3031 Epoch 2/400 2687/2687 [==============================] - 1s 211us/step - loss: 0.2594 - val_loss: 0.2736 Epoch 3/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.2388 - val_loss: 0.2546 Epoch 4/400 2687/2687 [==============================] - 1s 202us/step - loss: 0.2239 - val_loss: 0.2387 Epoch 5/400 2687/2687 [==============================] - 1s 229us/step - loss: 0.2112 - val_loss: 0.2250 Epoch 6/400 2687/2687 [==============================] - 1s 214us/step - loss: 0.1998 - val_loss: 0.2130 Epoch 7/400 2687/2687 [==============================] - 1s 204us/step - loss: 0.1895 - val_loss: 0.2021 Epoch 8/400 2687/2687 [==============================] - 1s 203us/step - loss: 0.1800 - val_loss: 0.1922 Epoch 9/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.1712 - val_loss: 0.1831 Epoch 10/400 2687/2687 [==============================] - 1s 201us/step - loss: 0.1631 - val_loss: 0.1747 Epoch 11/400 2687/2687 [==============================] - 1s 212us/step - loss: 0.1555 - val_loss: 0.1670 Epoch 12/400 2687/2687 [==============================] - 1s 213us/step - loss: 0.1485 - val_loss: 0.1598 Epoch 13/400 2687/2687 [==============================] - 1s 242us/step - loss: 0.1420 - val_loss: 0.1531 Epoch 14/400 2687/2687 [==============================] - 1s 241us/step - loss: 0.1358 - val_loss: 0.1469 Epoch 15/400 2687/2687 [==============================] - 1s 204us/step - loss: 0.1301 - val_loss: 0.1411 Epoch 16/400 2687/2687 [==============================] - 1s 204us/step - loss: 0.1247 - val_loss: 0.1356 Epoch 17/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.1197 - val_loss: 0.1305 Epoch 18/400 2687/2687 [==============================] - 1s 203us/step - loss: 0.1149 - val_loss: 0.1257 Epoch 19/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.1104 - val_loss: 0.1211 Epoch 20/400 2687/2687 [==============================] - 1s 202us/step - loss: 0.1061 - val_loss: 0.1168 Epoch 21/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.1020 - val_loss: 0.1128 Epoch 22/400 2687/2687 [==============================] - 1s 201us/step - loss: 0.0982 - val_loss: 0.1089 Epoch 23/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0946 - val_loss: 0.1053 Epoch 24/400 2687/2687 [==============================] - 1s 196us/step - loss: 0.0913 - val_loss: 0.1019 Epoch 25/400 2687/2687 [==============================] - 1s 196us/step - loss: 0.0882 - val_loss: 0.0986 Epoch 26/400 2687/2687 [==============================] - 1s 199us/step - loss: 0.0853 - val_loss: 0.0956 Epoch 27/400 2687/2687 [==============================] - 1s 196us/step - loss: 0.0825 - val_loss: 0.0927 Epoch 28/400 2687/2687 [==============================] - 1s 192us/step - loss: 0.0799 - val_loss: 0.0900 Epoch 29/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0775 - val_loss: 0.0875 Epoch 30/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0753 - val_loss: 0.0851 Epoch 31/400 2687/2687 [==============================] - 1s 199us/step - loss: 0.0731 - val_loss: 0.0828 Epoch 32/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0711 - val_loss: 0.0807 Epoch 33/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0692 - val_loss: 0.0787 Epoch 34/400 2687/2687 [==============================] - 1s 197us/step - loss: 0.0675 - val_loss: 0.0769 Epoch 35/400 2687/2687 [==============================] - 1s 201us/step - loss: 0.0658 - val_loss: 0.0751 Epoch 36/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0642 - val_loss: 0.0734 Epoch 37/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0627 - val_loss: 0.0718 Epoch 38/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0613 - val_loss: 0.0704 Epoch 39/400 2687/2687 [==============================] - 1s 198us/step - loss: 0.0599 - val_loss: 0.0689 Epoch 40/400 2687/2687 [==============================] - 1s 205us/step - loss: 0.0587 - val_loss: 0.0676 Epoch 41/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.0574 - val_loss: 0.0663 Epoch 42/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0563 - val_loss: 0.0651 Epoch 43/400 2687/2687 [==============================] - 1s 199us/step - loss: 0.0552 - val_loss: 0.0639 Epoch 44/400 2687/2687 [==============================] - 1s 198us/step - loss: 0.0541 - val_loss: 0.0628 Epoch 45/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0531 - val_loss: 0.0618 Epoch 46/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0522 - val_loss: 0.0608 Epoch 47/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0513 - val_loss: 0.0598 Epoch 48/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0504 - val_loss: 0.0589 Epoch 49/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0496 - val_loss: 0.0581 Epoch 50/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0488 - val_loss: 0.0572 Epoch 51/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0480 - val_loss: 0.0564 Epoch 52/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0473 - val_loss: 0.0557 Epoch 53/400 2687/2687 [==============================] - 1s 217us/step - loss: 0.0466 - val_loss: 0.0550 Epoch 54/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0459 - val_loss: 0.0543 Epoch 55/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0452 - val_loss: 0.0536 Epoch 56/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0446 - val_loss: 0.0529 Epoch 57/400 2687/2687 [==============================] - 1s 186us/step - loss: 0.0440 - val_loss: 0.0523 Epoch 58/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0434 - val_loss: 0.0517 Epoch 59/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0428 - val_loss: 0.0512 Epoch 60/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0423 - val_loss: 0.0506 Epoch 61/400 2687/2687 [==============================] - 1s 186us/step - loss: 0.0418 - val_loss: 0.0501 Epoch 62/400 2687/2687 [==============================] - 1s 196us/step - loss: 0.0413 - val_loss: 0.0496 Epoch 63/400 2687/2687 [==============================] - 1s 198us/step - loss: 0.0408 - val_loss: 0.0491 Epoch 64/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0403 - val_loss: 0.0487 Epoch 65/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0398 - val_loss: 0.0482 Epoch 66/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0394 - val_loss: 0.0478 Epoch 67/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0390 - val_loss: 0.0474 Epoch 68/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0386 - val_loss: 0.0470 Epoch 69/400 2687/2687 [==============================] - 1s 198us/step - loss: 0.0382 - val_loss: 0.0466 Epoch 70/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0378 - val_loss: 0.0462 Epoch 71/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0374 - val_loss: 0.0458 Epoch 72/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0370 - val_loss: 0.0455 Epoch 73/400 2687/2687 [==============================] - 1s 191us/step - loss: 0.0367 - val_loss: 0.0451 Epoch 74/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0363 - val_loss: 0.0448 Epoch 75/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0360 - val_loss: 0.0445 Epoch 76/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0357 - val_loss: 0.0442 Epoch 77/400 2687/2687 [==============================] - 1s 231us/step - loss: 0.0353 - val_loss: 0.0439 Epoch 78/400 2687/2687 [==============================] - 1s 203us/step - loss: 0.0350 - val_loss: 0.0436 Epoch 79/400 2687/2687 [==============================] - 1s 242us/step - loss: 0.0347 - val_loss: 0.0433 Epoch 80/400 2687/2687 [==============================] - 1s 228us/step - loss: 0.0344 - val_loss: 0.0430 Epoch 81/400 2687/2687 [==============================] - 1s 208us/step - loss: 0.0342 - val_loss: 0.0428 Epoch 82/400 2687/2687 [==============================] - 1s 218us/step - loss: 0.0339 - val_loss: 0.0425 Epoch 83/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0336 - val_loss: 0.0423 Epoch 84/400 2687/2687 [==============================] - 1s 197us/step - loss: 0.0334 - val_loss: 0.0420 Epoch 85/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0331 - val_loss: 0.0418 Epoch 86/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0329 - val_loss: 0.0416 Epoch 87/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0326 - val_loss: 0.0413 Epoch 88/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0324 - val_loss: 0.0411 Epoch 89/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0321 - val_loss: 0.0409 Epoch 90/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0319 - val_loss: 0.0407 Epoch 91/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0317 - val_loss: 0.0405 Epoch 92/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0315 - val_loss: 0.0403 Epoch 93/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0313 - val_loss: 0.0401 Epoch 94/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0311 - val_loss: 0.0399 Epoch 95/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0309 - val_loss: 0.0397 Epoch 96/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0307 - val_loss: 0.0396 Epoch 97/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0305 - val_loss: 0.0394 Epoch 98/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0303 - val_loss: 0.0392 Epoch 99/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0301 - val_loss: 0.0391 Epoch 100/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0299 - val_loss: 0.0389 Epoch 101/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0298 - val_loss: 0.0387 Epoch 102/400 2687/2687 [==============================] - 1s 197us/step - loss: 0.0296 - val_loss: 0.0386 Epoch 103/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0294 - val_loss: 0.0384 Epoch 104/400 2687/2687 [==============================] - 1s 192us/step - loss: 0.0293 - val_loss: 0.0383 Epoch 105/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0291 - val_loss: 0.0381 Epoch 106/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0289 - val_loss: 0.0380 Epoch 107/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0288 - val_loss: 0.0379 Epoch 108/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.0286 - val_loss: 0.0377 Epoch 109/400 2687/2687 [==============================] - 1s 213us/step - loss: 0.0285 - val_loss: 0.0376 Epoch 110/400 2687/2687 [==============================] - 1s 239us/step - loss: 0.0284 - val_loss: 0.0375 Epoch 111/400 2687/2687 [==============================] - 1s 250us/step - loss: 0.0282 - val_loss: 0.0373 Epoch 112/400 2687/2687 [==============================] - 1s 206us/step - loss: 0.0281 - val_loss: 0.0372 Epoch 113/400 2687/2687 [==============================] - 1s 192us/step - loss: 0.0279 - val_loss: 0.0371 Epoch 114/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0278 - val_loss: 0.0369 Epoch 115/400 2687/2687 [==============================] - 1s 216us/step - loss: 0.0277 - val_loss: 0.0368 Epoch 116/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0275 - val_loss: 0.0367 Epoch 117/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0274 - val_loss: 0.0366 Epoch 118/400 2687/2687 [==============================] - 1s 192us/step - loss: 0.0273 - val_loss: 0.0365 Epoch 119/400 2687/2687 [==============================] - 1s 216us/step - loss: 0.0272 - val_loss: 0.0364 Epoch 120/400 2687/2687 [==============================] - 1s 202us/step - loss: 0.0270 - val_loss: 0.0363 Epoch 121/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0269 - val_loss: 0.0362 Epoch 122/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0268 - val_loss: 0.0361 Epoch 123/400 2687/2687 [==============================] - 1s 237us/step - loss: 0.0267 - val_loss: 0.0360 Epoch 124/400 2687/2687 [==============================] - 1s 194us/step - loss: 0.0266 - val_loss: 0.0358 Epoch 125/400 2687/2687 [==============================] - 1s 192us/step - loss: 0.0265 - val_loss: 0.0357 Epoch 126/400 2687/2687 [==============================] - 1s 197us/step - loss: 0.0264 - val_loss: 0.0357 Epoch 127/400 2687/2687 [==============================] - 1s 199us/step - loss: 0.0263 - val_loss: 0.0356 Epoch 128/400 2687/2687 [==============================] - 1s 221us/step - loss: 0.0261 - val_loss: 0.0355 Epoch 129/400 2687/2687 [==============================] - 1s 202us/step - loss: 0.0260 - val_loss: 0.0354 Epoch 130/400 2687/2687 [==============================] - 1s 212us/step - loss: 0.0259 - val_loss: 0.0353 Epoch 131/400 2687/2687 [==============================] - 1s 217us/step - loss: 0.0258 - val_loss: 0.0352 Epoch 132/400 2687/2687 [==============================] - 1s 232us/step - loss: 0.0257 - val_loss: 0.0351 Epoch 133/400 2687/2687 [==============================] - 1s 226us/step - loss: 0.0256 - val_loss: 0.0350 Epoch 134/400 2687/2687 [==============================] - 1s 214us/step - loss: 0.0255 - val_loss: 0.0349 Epoch 135/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0255 - val_loss: 0.0348 Epoch 136/400 2687/2687 [==============================] - 1s 208us/step - loss: 0.0254 - val_loss: 0.0347 Epoch 137/400 2687/2687 [==============================] - 1s 240us/step - loss: 0.0253 - val_loss: 0.0347 Epoch 138/400 2687/2687 [==============================] - 1s 219us/step - loss: 0.0252 - val_loss: 0.0346 Epoch 139/400 2687/2687 [==============================] - 0s 177us/step - loss: 0.0251 - val_loss: 0.0345 Epoch 140/400 2687/2687 [==============================] - 1s 211us/step - loss: 0.0250 - val_loss: 0.0344 Epoch 141/400 2687/2687 [==============================] - 1s 209us/step - loss: 0.0249 - val_loss: 0.0343 Epoch 142/400 2687/2687 [==============================] - 1s 211us/step - loss: 0.0248 - val_loss: 0.0343 Epoch 143/400 2687/2687 [==============================] - 1s 222us/step - loss: 0.0247 - val_loss: 0.0342 Epoch 144/400 2687/2687 [==============================] - 1s 204us/step - loss: 0.0247 - val_loss: 0.0341 Epoch 145/400 2687/2687 [==============================] - 1s 220us/step - loss: 0.0246 - val_loss: 0.0340 Epoch 146/400 2687/2687 [==============================] - 1s 213us/step - loss: 0.0245 - val_loss: 0.0340 Epoch 147/400 2687/2687 [==============================] - 1s 205us/step - loss: 0.0244 - val_loss: 0.0339 Epoch 148/400 2687/2687 [==============================] - 1s 244us/step - loss: 0.0243 - val_loss: 0.0338 Epoch 149/400 2687/2687 [==============================] - 1s 258us/step - loss: 0.0243 - val_loss: 0.0338 Epoch 150/400 2687/2687 [==============================] - 1s 279us/step - loss: 0.0242 - val_loss: 0.0337 Epoch 151/400 2687/2687 [==============================] - 1s 385us/step - loss: 0.0241 - val_loss: 0.0336 Epoch 152/400 2687/2687 [==============================] - 1s 313us/step - loss: 0.0240 - val_loss: 0.0335 Epoch 153/400 2687/2687 [==============================] - 1s 296us/step - loss: 0.0240 - val_loss: 0.0335 Epoch 154/400 2687/2687 [==============================] - 1s 282us/step - loss: 0.0239 - val_loss: 0.0334 Epoch 155/400 2687/2687 [==============================] - 1s 336us/step - loss: 0.0238 - val_loss: 0.0333 Epoch 156/400 2687/2687 [==============================] - 1s 229us/step - loss: 0.0237 - val_loss: 0.0333 Epoch 157/400 2687/2687 [==============================] - 1s 207us/step - loss: 0.0237 - val_loss: 0.0332 Epoch 158/400 2687/2687 [==============================] - 1s 239us/step - loss: 0.0236 - val_loss: 0.0332 Epoch 159/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0235 - val_loss: 0.0331 Epoch 160/400 2687/2687 [==============================] - 1s 207us/step - loss: 0.0235 - val_loss: 0.0330 Epoch 161/400 2687/2687 [==============================] - 1s 220us/step - loss: 0.0234 - val_loss: 0.0330 Epoch 162/400 2687/2687 [==============================] - 1s 234us/step - loss: 0.0233 - val_loss: 0.0329 Epoch 163/400 2687/2687 [==============================] - 1s 234us/step - loss: 0.0233 - val_loss: 0.0328 Epoch 164/400 2687/2687 [==============================] - 1s 245us/step - loss: 0.0232 - val_loss: 0.0328 Epoch 165/400 2687/2687 [==============================] - 1s 240us/step - loss: 0.0231 - val_loss: 0.0327 Epoch 166/400 2687/2687 [==============================] - 1s 222us/step - loss: 0.0231 - val_loss: 0.0327 Epoch 167/400 2687/2687 [==============================] - 1s 218us/step - loss: 0.0230 - val_loss: 0.0326 Epoch 168/400 2687/2687 [==============================] - 1s 217us/step - loss: 0.0230 - val_loss: 0.0326 Epoch 169/400 2687/2687 [==============================] - 1s 212us/step - loss: 0.0229 - val_loss: 0.0325 Epoch 170/400 2687/2687 [==============================] - 1s 215us/step - loss: 0.0228 - val_loss: 0.0324 Epoch 171/400 2687/2687 [==============================] - 1s 217us/step - loss: 0.0228 - val_loss: 0.0324 Epoch 172/400 2687/2687 [==============================] - 1s 213us/step - loss: 0.0227 - val_loss: 0.0323 Epoch 173/400 2687/2687 [==============================] - 1s 212us/step - loss: 0.0227 - val_loss: 0.0323 Epoch 174/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.0226 - val_loss: 0.0322 Epoch 175/400 2687/2687 [==============================] - 1s 209us/step - loss: 0.0225 - val_loss: 0.0322 Epoch 176/400 2687/2687 [==============================] - 1s 267us/step - loss: 0.0225 - val_loss: 0.0321 Epoch 177/400 2687/2687 [==============================] - 1s 215us/step - loss: 0.0224 - val_loss: 0.0321 Epoch 178/400 2687/2687 [==============================] - 1s 222us/step - loss: 0.0224 - val_loss: 0.0320 Epoch 179/400 2687/2687 [==============================] - 1s 207us/step - loss: 0.0223 - val_loss: 0.0320 Epoch 180/400 2687/2687 [==============================] - 1s 206us/step - loss: 0.0223 - val_loss: 0.0319 Epoch 181/400 2687/2687 [==============================] - 1s 227us/step - loss: 0.0222 - val_loss: 0.0319 Epoch 182/400 2687/2687 [==============================] - 1s 217us/step - loss: 0.0222 - val_loss: 0.0318 Epoch 183/400 2687/2687 [==============================] - 1s 209us/step - loss: 0.0221 - val_loss: 0.0318 Epoch 184/400 2687/2687 [==============================] - 1s 208us/step - loss: 0.0221 - val_loss: 0.0317 Epoch 185/400 2687/2687 [==============================] - 1s 240us/step - loss: 0.0220 - val_loss: 0.0317 Epoch 186/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0219 - val_loss: 0.0316 Epoch 187/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0219 - val_loss: 0.0316 Epoch 188/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0218 - val_loss: 0.0315 Epoch 189/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0218 - val_loss: 0.0315 Epoch 190/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0217 - val_loss: 0.0314 Epoch 191/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0217 - val_loss: 0.0314 Epoch 192/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0217 - val_loss: 0.0314 Epoch 193/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0216 - val_loss: 0.0313 Epoch 194/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0216 - val_loss: 0.0313 Epoch 195/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0215 - val_loss: 0.0312 Epoch 196/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0215 - val_loss: 0.0312 Epoch 197/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0214 - val_loss: 0.0311 Epoch 198/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0214 - val_loss: 0.0311 Epoch 199/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0213 - val_loss: 0.0311 Epoch 200/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0213 - val_loss: 0.0310 Epoch 201/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0212 - val_loss: 0.0310 Epoch 202/400 2687/2687 [==============================] - 1s 239us/step - loss: 0.0212 - val_loss: 0.0309 Epoch 203/400 2687/2687 [==============================] - 1s 232us/step - loss: 0.0211 - val_loss: 0.0309 Epoch 204/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0211 - val_loss: 0.0308 Epoch 205/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0211 - val_loss: 0.0308 Epoch 206/400 2687/2687 [==============================] - 1s 213us/step - loss: 0.0210 - val_loss: 0.0308 Epoch 207/400 2687/2687 [==============================] - 1s 241us/step - loss: 0.0210 - val_loss: 0.0307 Epoch 208/400 2687/2687 [==============================] - 1s 198us/step - loss: 0.0209 - val_loss: 0.0307 Epoch 209/400 2687/2687 [==============================] - 1s 227us/step - loss: 0.0209 - val_loss: 0.0306 Epoch 210/400 2687/2687 [==============================] - 1s 186us/step - loss: 0.0209 - val_loss: 0.0306 Epoch 211/400 2687/2687 [==============================] - 1s 204us/step - loss: 0.0208 - val_loss: 0.0306 Epoch 212/400 2687/2687 [==============================] - 1s 205us/step - loss: 0.0208 - val_loss: 0.0305 Epoch 213/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0207 - val_loss: 0.0305 Epoch 214/400 2687/2687 [==============================] - 1s 377us/step - loss: 0.0207 - val_loss: 0.0305 Epoch 215/400 2687/2687 [==============================] - 1s 304us/step - loss: 0.0207 - val_loss: 0.0304 Epoch 216/400 2687/2687 [==============================] - 1s 303us/step - loss: 0.0206 - val_loss: 0.0304 Epoch 217/400 2687/2687 [==============================] - 1s 366us/step - loss: 0.0206 - val_loss: 0.0303 Epoch 218/400 2687/2687 [==============================] - 1s 274us/step - loss: 0.0205 - val_loss: 0.0303 Epoch 219/400 2687/2687 [==============================] - 1s 252us/step - loss: 0.0205 - val_loss: 0.0303 Epoch 220/400 2687/2687 [==============================] - 1s 210us/step - loss: 0.0205 - val_loss: 0.0302 Epoch 221/400 2687/2687 [==============================] - 1s 203us/step - loss: 0.0204 - val_loss: 0.0302 Epoch 222/400 2687/2687 [==============================] - 1s 241us/step - loss: 0.0204 - val_loss: 0.0302 Epoch 223/400 2687/2687 [==============================] - 1s 186us/step - loss: 0.0203 - val_loss: 0.0301 Epoch 224/400 2687/2687 [==============================] - 1s 191us/step - loss: 0.0203 - val_loss: 0.0301 Epoch 225/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0203 - val_loss: 0.0301 Epoch 226/400 2687/2687 [==============================] - 1s 198us/step - loss: 0.0202 - val_loss: 0.0300 Epoch 227/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0202 - val_loss: 0.0300 Epoch 228/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0202 - val_loss: 0.0300 Epoch 229/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0201 - val_loss: 0.0299 Epoch 230/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0201 - val_loss: 0.0299 Epoch 231/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0201 - val_loss: 0.0299 Epoch 232/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0200 - val_loss: 0.0298 Epoch 233/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0200 - val_loss: 0.0298 Epoch 234/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0200 - val_loss: 0.0298 Epoch 235/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0199 - val_loss: 0.0297 Epoch 236/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0199 - val_loss: 0.0297 Epoch 237/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0199 - val_loss: 0.0297 Epoch 238/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0198 - val_loss: 0.0296 Epoch 239/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0198 - val_loss: 0.0296 Epoch 240/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0198 - val_loss: 0.0296 Epoch 241/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0197 - val_loss: 0.0295 Epoch 242/400 2687/2687 [==============================] - 1s 199us/step - loss: 0.0197 - val_loss: 0.0295 Epoch 243/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0197 - val_loss: 0.0295 Epoch 244/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0196 - val_loss: 0.0295 Epoch 245/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0196 - val_loss: 0.0294 Epoch 246/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0196 - val_loss: 0.0294 Epoch 247/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0195 - val_loss: 0.0294 Epoch 248/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0195 - val_loss: 0.0293 Epoch 249/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0195 - val_loss: 0.0293 Epoch 250/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0194 - val_loss: 0.0293 Epoch 251/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0194 - val_loss: 0.0293 Epoch 252/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0194 - val_loss: 0.0292 Epoch 253/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0193 - val_loss: 0.0292 Epoch 254/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0193 - val_loss: 0.0292 Epoch 255/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0193 - val_loss: 0.0291 Epoch 256/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0193 - val_loss: 0.0291 Epoch 257/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0192 - val_loss: 0.0291 Epoch 258/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0192 - val_loss: 0.0291 Epoch 259/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0192 - val_loss: 0.0290 Epoch 260/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0191 - val_loss: 0.0290 Epoch 261/400 2687/2687 [==============================] - 1s 191us/step - loss: 0.0191 - val_loss: 0.0290 Epoch 262/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0191 - val_loss: 0.0290 Epoch 263/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0191 - val_loss: 0.0289 Epoch 264/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0190 - val_loss: 0.0289 Epoch 265/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0190 - val_loss: 0.0289 Epoch 266/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0190 - val_loss: 0.0288 Epoch 267/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0189 - val_loss: 0.0288 Epoch 268/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0189 - val_loss: 0.0288 Epoch 269/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0189 - val_loss: 0.0288 Epoch 270/400 2687/2687 [==============================] - 1s 202us/step - loss: 0.0189 - val_loss: 0.0287 Epoch 271/400 2687/2687 [==============================] - 1s 191us/step - loss: 0.0188 - val_loss: 0.0287 Epoch 272/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0188 - val_loss: 0.0287 Epoch 273/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0188 - val_loss: 0.0287 Epoch 274/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0187 - val_loss: 0.0286 Epoch 275/400 2687/2687 [==============================] - 1s 197us/step - loss: 0.0187 - val_loss: 0.0286 Epoch 276/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0187 - val_loss: 0.0286 Epoch 277/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0187 - val_loss: 0.0286 Epoch 278/400 2687/2687 [==============================] - 0s 177us/step - loss: 0.0186 - val_loss: 0.0286 Epoch 279/400 2687/2687 [==============================] - 1s 204us/step - loss: 0.0186 - val_loss: 0.0285 Epoch 280/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0186 - val_loss: 0.0285 Epoch 281/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.0186 - val_loss: 0.0285 Epoch 282/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0185 - val_loss: 0.0285 Epoch 283/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0185 - val_loss: 0.0284 Epoch 284/400 2687/2687 [==============================] - 0s 176us/step - loss: 0.0185 - val_loss: 0.0284 Epoch 285/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0185 - val_loss: 0.0284 Epoch 286/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0184 - val_loss: 0.0284 Epoch 287/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0184 - val_loss: 0.0283 Epoch 288/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0184 - val_loss: 0.0283 Epoch 289/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0184 - val_loss: 0.0283 Epoch 290/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0183 - val_loss: 0.0283 Epoch 291/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0183 - val_loss: 0.0283 Epoch 292/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0183 - val_loss: 0.0282 Epoch 293/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0183 - val_loss: 0.0282 Epoch 294/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0182 - val_loss: 0.0282 Epoch 295/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0182 - val_loss: 0.0282 Epoch 296/400 2687/2687 [==============================] - 0s 176us/step - loss: 0.0182 - val_loss: 0.0281 Epoch 297/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0182 - val_loss: 0.0281 Epoch 298/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0182 - val_loss: 0.0281 Epoch 299/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0181 - val_loss: 0.0281 Epoch 300/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0181 - val_loss: 0.0281 Epoch 301/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0181 - val_loss: 0.0280 Epoch 302/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0181 - val_loss: 0.0280 Epoch 303/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0180 - val_loss: 0.0280 Epoch 304/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0180 - val_loss: 0.0280 Epoch 305/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0180 - val_loss: 0.0280 Epoch 306/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0180 - val_loss: 0.0279 Epoch 307/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0179 - val_loss: 0.0279 Epoch 308/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0179 - val_loss: 0.0279 Epoch 309/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0179 - val_loss: 0.0279 Epoch 310/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0179 - val_loss: 0.0279 Epoch 311/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0179 - val_loss: 0.0278 Epoch 312/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0178 - val_loss: 0.0278 Epoch 313/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0178 - val_loss: 0.0278 Epoch 314/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0178 - val_loss: 0.0278 Epoch 315/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0178 - val_loss: 0.0278 Epoch 316/400 2687/2687 [==============================] - 0s 186us/step - loss: 0.0178 - val_loss: 0.0277 Epoch 317/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0177 - val_loss: 0.0277 Epoch 318/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0177 - val_loss: 0.0277 Epoch 319/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0177 - val_loss: 0.0277 Epoch 320/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0177 - val_loss: 0.0277 Epoch 321/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0176 - val_loss: 0.0276 Epoch 322/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0176 - val_loss: 0.0276 Epoch 323/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0176 - val_loss: 0.0276 Epoch 324/400 2687/2687 [==============================] - 1s 196us/step - loss: 0.0176 - val_loss: 0.0276 Epoch 325/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0176 - val_loss: 0.0276 Epoch 326/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0175 - val_loss: 0.0275 Epoch 327/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0175 - val_loss: 0.0275 Epoch 328/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0175 - val_loss: 0.0275 Epoch 329/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0175 - val_loss: 0.0275 Epoch 330/400 2687/2687 [==============================] - 1s 187us/step - loss: 0.0175 - val_loss: 0.0275 Epoch 331/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.0174 - val_loss: 0.0275 Epoch 332/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0174 - val_loss: 0.0274 Epoch 333/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0174 - val_loss: 0.0274 Epoch 334/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0174 - val_loss: 0.0274 Epoch 335/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0174 - val_loss: 0.0274 Epoch 336/400 2687/2687 [==============================] - 1s 188us/step - loss: 0.0173 - val_loss: 0.0274 Epoch 337/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0173 - val_loss: 0.0273 Epoch 338/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0173 - val_loss: 0.0273 Epoch 339/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0173 - val_loss: 0.0273 Epoch 340/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0173 - val_loss: 0.0273 Epoch 341/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0173 - val_loss: 0.0273 Epoch 342/400 2687/2687 [==============================] - 0s 183us/step - loss: 0.0172 - val_loss: 0.0273 Epoch 343/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0172 - val_loss: 0.0272 Epoch 344/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0172 - val_loss: 0.0272 Epoch 345/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0172 - val_loss: 0.0272 Epoch 346/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0172 - val_loss: 0.0272 Epoch 347/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0171 - val_loss: 0.0272 Epoch 348/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0171 - val_loss: 0.0272 Epoch 349/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0171 - val_loss: 0.0271 Epoch 350/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0171 - val_loss: 0.0271 Epoch 351/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0171 - val_loss: 0.0271 Epoch 352/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0170 - val_loss: 0.0271 Epoch 353/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0170 - val_loss: 0.0271 Epoch 354/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0170 - val_loss: 0.0271 Epoch 355/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0170 - val_loss: 0.0270 Epoch 356/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0170 - val_loss: 0.0270 Epoch 357/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0170 - val_loss: 0.0270 Epoch 358/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0169 - val_loss: 0.0270 Epoch 359/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0169 - val_loss: 0.0270 Epoch 360/400 2687/2687 [==============================] - 0s 176us/step - loss: 0.0169 - val_loss: 0.0270 Epoch 361/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0169 - val_loss: 0.0270 Epoch 362/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0169 - val_loss: 0.0269 Epoch 363/400 2687/2687 [==============================] - 0s 182us/step - loss: 0.0169 - val_loss: 0.0269 Epoch 364/400 2687/2687 [==============================] - 0s 180us/step - loss: 0.0168 - val_loss: 0.0269 Epoch 365/400 2687/2687 [==============================] - 0s 184us/step - loss: 0.0168 - val_loss: 0.0269 Epoch 366/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0168 - val_loss: 0.0269 Epoch 367/400 2687/2687 [==============================] - 0s 185us/step - loss: 0.0168 - val_loss: 0.0269 Epoch 368/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0168 - val_loss: 0.0268 Epoch 369/400 2687/2687 [==============================] - 0s 178us/step - loss: 0.0168 - val_loss: 0.0268 Epoch 370/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0167 - val_loss: 0.0268 Epoch 371/400 2687/2687 [==============================] - 0s 181us/step - loss: 0.0167 - val_loss: 0.0268 Epoch 372/400 2687/2687 [==============================] - 1s 195us/step - loss: 0.0167 - val_loss: 0.0268 Epoch 373/400 2687/2687 [==============================] - 1s 222us/step - loss: 0.0167 - val_loss: 0.0268 Epoch 374/400 2687/2687 [==============================] - 1s 207us/step - loss: 0.0167 - val_loss: 0.0268 Epoch 375/400 2687/2687 [==============================] - 1s 196us/step - loss: 0.0167 - val_loss: 0.0267 Epoch 376/400 2687/2687 [==============================] - 1s 226us/step - loss: 0.0166 - val_loss: 0.0267 Epoch 377/400 2687/2687 [==============================] - 1s 225us/step - loss: 0.0166 - val_loss: 0.0267 Epoch 378/400 2687/2687 [==============================] - 1s 217us/step - loss: 0.0166 - val_loss: 0.0267 Epoch 379/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.0166 - val_loss: 0.0267 Epoch 380/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0166 - val_loss: 0.0267 Epoch 381/400 2687/2687 [==============================] - 1s 200us/step - loss: 0.0166 - val_loss: 0.0267 Epoch 382/400 2687/2687 [==============================] - 1s 205us/step - loss: 0.0165 - val_loss: 0.0266 Epoch 383/400 2687/2687 [==============================] - 1s 198us/step - loss: 0.0165 - val_loss: 0.0266 Epoch 384/400 2687/2687 [==============================] - 1s 190us/step - loss: 0.0165 - val_loss: 0.0266 Epoch 385/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0165 - val_loss: 0.0266 Epoch 386/400 2687/2687 [==============================] - 1s 189us/step - loss: 0.0165 - val_loss: 0.0266 Epoch 387/400 2687/2687 [==============================] - 1s 197us/step - loss: 0.0165 - val_loss: 0.0266 Epoch 388/400 2687/2687 [==============================] - 1s 197us/step - loss: 0.0164 - val_loss: 0.0266 Epoch 389/400 2687/2687 [==============================] - 1s 208us/step - loss: 0.0164 - val_loss: 0.0265 Epoch 390/400 2687/2687 [==============================] - 1s 225us/step - loss: 0.0164 - val_loss: 0.0265 Epoch 391/400 2687/2687 [==============================] - 1s 263us/step - loss: 0.0164 - val_loss: 0.0265 Epoch 392/400 2687/2687 [==============================] - 1s 261us/step - loss: 0.0164 - val_loss: 0.0265 Epoch 393/400 2687/2687 [==============================] - 1s 192us/step - loss: 0.0164 - val_loss: 0.0265 Epoch 394/400 2687/2687 [==============================] - 1s 232us/step - loss: 0.0164 - val_loss: 0.0265 Epoch 395/400 2687/2687 [==============================] - 1s 202us/step - loss: 0.0163 - val_loss: 0.0265 Epoch 396/400 2687/2687 [==============================] - 1s 193us/step - loss: 0.0163 - val_loss: 0.0265 Epoch 397/400 2687/2687 [==============================] - 0s 179us/step - loss: 0.0163 - val_loss: 0.0264 Epoch 398/400 2687/2687 [==============================] - 1s 186us/step - loss: 0.0163 - val_loss: 0.0264 Epoch 399/400 2687/2687 [==============================] - 1s 210us/step - loss: 0.0163 - val_loss: 0.0264 Epoch 400/400 2687/2687 [==============================] - 1s 212us/step - loss: 0.0163 - val_loss: 0.0264
<keras.callbacks.callbacks.History at 0x1453b5198>
Plot the decision boundary
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
plot_decision_boundary(lambda x: model_5.predict(x), feats, target)
plt.title("Decision Boundary for Neural Network with hidden layer size 6")
Text(0.5, 1.0, 'Decision Boundary for Neural Network with hidden layer size 6')