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Neural Networks with TensorFlow and Keras





In [1]:



    


import warnings
warnings.filterwarnings('ignore')



    











In [2]:



    


%matplotlib inline
%pylab inline



    


















    






Populating the interactive namespace from numpy and matplotlib

















In [3]:



    


import pandas as pd
print(pd.__version__)



    


















    






0.22.0

















In [4]:



    


import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)



    


















    






1.7.0

First Step: Load Data and disassemble for our purposes

We need a few more data point samples for this approach





In [5]:



    


df = pd.read_csv('./insurance-customers-1500.csv', sep=';')



    











In [6]:



    


y=df['group']



    











In [7]:



    


df.drop('group', axis='columns', inplace=True)



    











In [8]:



    


X = df.as_matrix()



    











In [9]:



    


df.describe()



    


















    
Out[9]:











  
    

      
      max speed
      age
      thousand km per year
    

  
  
    

      count
      1500.000000
      1500.000000
      1500.000000
    

    

      mean
      171.386000
      44.969333
      30.511333
    

    

      std
      19.269126
      16.935040
      15.112317
    

    

      min
      118.000000
      18.000000
      5.000000
    

    

      25%
      158.000000
      32.000000
      18.000000
    

    

      50%
      170.000000
      42.000000
      29.000000
    

    

      75%
      187.000000
      55.000000
      42.000000
    

    

      max
      216.000000
      90.000000
      84.000000

Second Step: Deep Learning as Alchemy





In [10]:



    


# ignore this, it is just technical code
# should come from a lib, consider it to appear magically 
# http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

cmap_print = ListedColormap(['#AA8888', '#004000', '#FFFFDD'])
cmap_bold = ListedColormap(['#AA4444', '#006000', '#AAAA00'])
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#FFFFDD'])
font_size=25

def meshGrid(x_data, y_data):
    h = 1  # step size in the mesh
    x_min, x_max = x_data.min() - 1, x_data.max() + 1
    y_min, y_max = y_data.min() - 1, y_data.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    return (xx,yy)
    
def plotPrediction(clf, x_data, y_data, x_label, y_label, colors, title="", mesh=True, fixed=None, fname=None, print=False):
    xx,yy = meshGrid(x_data, y_data)
    plt.figure(figsize=(20,10))

    if clf and mesh:
        grid_X = np.array(np.c_[yy.ravel(), xx.ravel()])
        if fixed:
            fill_values = np.full((len(grid_X), 1), fixed)
            grid_X = np.append(grid_X, fill_values, axis=1)
        Z = clf.predict(grid_X)
        Z = np.argmax(Z, axis=1)
        Z = Z.reshape(xx.shape)
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    if print:
        plt.scatter(x_data, y_data, c=colors, cmap=cmap_print, s=200, marker='o', edgecolors='k')
    else:
        plt.scatter(x_data, y_data, c=colors, cmap=cmap_bold, s=80, marker='o', edgecolors='k')
    plt.xlabel(x_label, fontsize=font_size)
    plt.ylabel(y_label, fontsize=font_size)
    plt.title(title, fontsize=font_size)
    if fname:
        plt.savefig(fname)



    











In [11]:



    


from sklearn.model_selection import train_test_split



    











In [12]:



    


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)



    











In [13]:



    


X_train.shape, y_train.shape, X_test.shape, y_test.shape



    


















    
Out[13]:








((900, 3), (900,), (600, 3), (600,))

















In [14]:



    


# tiny little pieces of feature engeneering
num_categories = 3

y_train_categorical = tf.keras.utils.to_categorical(y_train, num_categories)
y_test_categorical = tf.keras.utils.to_categorical(y_test, num_categories)



    











In [15]:



    


inputs = tf.keras.Input(name='input', shape=(3, ))
x = tf.keras.layers.Dense(100, name='hidden1', activation='relu')(inputs)
x = tf.keras.layers.Dense(100, name='hidden2', activation='relu')(x)
predictions = tf.keras.layers.Dense(3, name='softmax', activation='softmax')(x)
model = tf.keras.models.Model(inputs=inputs, outputs=predictions)

# loss function: http://cs231n.github.io/linear-classify/#softmax
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.summary()



    


















    






_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input (InputLayer)           (None, 3)                 0         
_________________________________________________________________
hidden1 (Dense)              (None, 100)               400       
_________________________________________________________________
hidden2 (Dense)              (None, 100)               10100     
_________________________________________________________________
softmax (Dense)              (None, 3)                 303       
=================================================================
Total params: 10,803
Trainable params: 10,803
Non-trainable params: 0
_________________________________________________________________

















In [16]:



    


%time model.fit(X_train, y_train_categorical, epochs=500, batch_size=100)



    


















    






Epoch 1/500
900/900 [==============================] - 2s 2ms/step - loss: 3.7265 - acc: 0.3722
Epoch 2/500
900/900 [==============================] - 0s 103us/step - loss: 1.8053 - acc: 0.3733
Epoch 3/500
900/900 [==============================] - 0s 87us/step - loss: 1.1904 - acc: 0.4833
Epoch 4/500
900/900 [==============================] - 0s 93us/step - loss: 0.9094 - acc: 0.5800
Epoch 5/500
900/900 [==============================] - 0s 88us/step - loss: 0.8769 - acc: 0.5722
Epoch 6/500
900/900 [==============================] - 0s 88us/step - loss: 0.8562 - acc: 0.5889
Epoch 7/500
900/900 [==============================] - 0s 77us/step - loss: 0.8215 - acc: 0.6400
Epoch 8/500
900/900 [==============================] - 0s 81us/step - loss: 0.8634 - acc: 0.6233
Epoch 9/500
900/900 [==============================] - 0s 105us/step - loss: 0.8800 - acc: 0.5822
Epoch 10/500
900/900 [==============================] - 0s 92us/step - loss: 0.8384 - acc: 0.6378
Epoch 11/500
900/900 [==============================] - 0s 79us/step - loss: 0.7961 - acc: 0.6656
Epoch 12/500
900/900 [==============================] - 0s 80us/step - loss: 0.8828 - acc: 0.5933
Epoch 13/500
900/900 [==============================] - 0s 62us/step - loss: 0.8550 - acc: 0.6133
Epoch 14/500
900/900 [==============================] - 0s 81us/step - loss: 0.8020 - acc: 0.6478
Epoch 15/500
900/900 [==============================] - 0s 101us/step - loss: 0.8028 - acc: 0.6611
Epoch 16/500
900/900 [==============================] - 0s 88us/step - loss: 0.7923 - acc: 0.6644
Epoch 17/500
900/900 [==============================] - 0s 91us/step - loss: 0.8004 - acc: 0.6544
Epoch 18/500
900/900 [==============================] - 0s 90us/step - loss: 0.7975 - acc: 0.6511
Epoch 19/500
900/900 [==============================] - 0s 90us/step - loss: 0.8069 - acc: 0.6500
Epoch 20/500
900/900 [==============================] - 0s 84us/step - loss: 0.7863 - acc: 0.6678
Epoch 21/500
900/900 [==============================] - 0s 91us/step - loss: 0.8627 - acc: 0.6133
Epoch 22/500
900/900 [==============================] - 0s 97us/step - loss: 0.8256 - acc: 0.6300
Epoch 23/500
900/900 [==============================] - 0s 107us/step - loss: 0.8154 - acc: 0.6544
Epoch 24/500
900/900 [==============================] - 0s 83us/step - loss: 0.8055 - acc: 0.6689
Epoch 25/500
900/900 [==============================] - 0s 84us/step - loss: 0.7841 - acc: 0.6567
Epoch 26/500
900/900 [==============================] - 0s 82us/step - loss: 0.7889 - acc: 0.6733
Epoch 27/500
900/900 [==============================] - 0s 66us/step - loss: 0.7855 - acc: 0.6756
Epoch 28/500
900/900 [==============================] - 0s 66us/step - loss: 0.7903 - acc: 0.6633
Epoch 29/500
900/900 [==============================] - 0s 76us/step - loss: 0.8087 - acc: 0.6300
Epoch 30/500
900/900 [==============================] - 0s 88us/step - loss: 0.7877 - acc: 0.6678
Epoch 31/500
900/900 [==============================] - 0s 79us/step - loss: 0.8353 - acc: 0.6289
Epoch 32/500
900/900 [==============================] - 0s 76us/step - loss: 0.8043 - acc: 0.6433
Epoch 33/500
900/900 [==============================] - 0s 92us/step - loss: 0.7736 - acc: 0.6656
Epoch 34/500
900/900 [==============================] - 0s 89us/step - loss: 0.7777 - acc: 0.6678
Epoch 35/500
900/900 [==============================] - 0s 94us/step - loss: 0.8171 - acc: 0.6433
Epoch 36/500
900/900 [==============================] - 0s 82us/step - loss: 0.8903 - acc: 0.6056
Epoch 37/500
900/900 [==============================] - 0s 84us/step - loss: 0.8889 - acc: 0.6189
Epoch 38/500
900/900 [==============================] - 0s 91us/step - loss: 0.8516 - acc: 0.6533
Epoch 39/500
900/900 [==============================] - 0s 66us/step - loss: 0.8674 - acc: 0.6489
Epoch 40/500
900/900 [==============================] - 0s 70us/step - loss: 0.8030 - acc: 0.6656
Epoch 41/500
900/900 [==============================] - 0s 92us/step - loss: 0.7689 - acc: 0.6867
Epoch 42/500
900/900 [==============================] - 0s 62us/step - loss: 0.7653 - acc: 0.6722
Epoch 43/500
900/900 [==============================] - 0s 77us/step - loss: 0.7572 - acc: 0.6700
Epoch 44/500
900/900 [==============================] - 0s 64us/step - loss: 0.7668 - acc: 0.6733
Epoch 45/500
900/900 [==============================] - 0s 63us/step - loss: 0.7821 - acc: 0.6678
Epoch 46/500
900/900 [==============================] - 0s 78us/step - loss: 0.8292 - acc: 0.6333
Epoch 47/500
900/900 [==============================] - 0s 88us/step - loss: 0.8588 - acc: 0.6256
Epoch 48/500
900/900 [==============================] - 0s 63us/step - loss: 0.8111 - acc: 0.6600
Epoch 49/500
900/900 [==============================] - 0s 66us/step - loss: 0.8355 - acc: 0.6378
Epoch 50/500
900/900 [==============================] - 0s 68us/step - loss: 0.8108 - acc: 0.6433
Epoch 51/500
900/900 [==============================] - 0s 71us/step - loss: 0.7697 - acc: 0.6767
Epoch 52/500
900/900 [==============================] - 0s 83us/step - loss: 0.7766 - acc: 0.6744
Epoch 53/500
900/900 [==============================] - 0s 62us/step - loss: 0.7557 - acc: 0.6833
Epoch 54/500
900/900 [==============================] - 0s 67us/step - loss: 0.7645 - acc: 0.6789
Epoch 55/500
900/900 [==============================] - 0s 69us/step - loss: 0.8460 - acc: 0.6144
Epoch 56/500
900/900 [==============================] - 0s 84us/step - loss: 0.9281 - acc: 0.6044
Epoch 57/500
900/900 [==============================] - 0s 63us/step - loss: 0.9122 - acc: 0.6067
Epoch 58/500
900/900 [==============================] - 0s 91us/step - loss: 0.8876 - acc: 0.6233
Epoch 59/500
900/900 [==============================] - 0s 74us/step - loss: 0.7985 - acc: 0.6611
Epoch 60/500
900/900 [==============================] - 0s 62us/step - loss: 0.7719 - acc: 0.6700
Epoch 61/500
900/900 [==============================] - 0s 82us/step - loss: 0.7661 - acc: 0.6900
Epoch 62/500
900/900 [==============================] - 0s 83us/step - loss: 0.7642 - acc: 0.6733
Epoch 63/500
900/900 [==============================] - 0s 77us/step - loss: 0.8100 - acc: 0.6611
Epoch 64/500
900/900 [==============================] - 0s 51us/step - loss: 0.8458 - acc: 0.6178
Epoch 65/500
900/900 [==============================] - 0s 78us/step - loss: 0.8670 - acc: 0.6178
Epoch 66/500
900/900 [==============================] - 0s 80us/step - loss: 0.8694 - acc: 0.6267
Epoch 67/500
900/900 [==============================] - 0s 67us/step - loss: 0.9008 - acc: 0.6133
Epoch 68/500
900/900 [==============================] - 0s 88us/step - loss: 0.8481 - acc: 0.6356
Epoch 69/500
900/900 [==============================] - 0s 73us/step - loss: 0.8399 - acc: 0.6267
Epoch 70/500
900/900 [==============================] - 0s 68us/step - loss: 0.8252 - acc: 0.6511
Epoch 71/500
900/900 [==============================] - 0s 72us/step - loss: 0.8538 - acc: 0.6111
Epoch 72/500
900/900 [==============================] - 0s 59us/step - loss: 0.7911 - acc: 0.6533
Epoch 73/500
900/900 [==============================] - 0s 76us/step - loss: 0.8005 - acc: 0.6589
Epoch 74/500
900/900 [==============================] - 0s 63us/step - loss: 0.7637 - acc: 0.6789
Epoch 75/500
900/900 [==============================] - 0s 98us/step - loss: 0.7606 - acc: 0.6678
Epoch 76/500
900/900 [==============================] - 0s 63us/step - loss: 0.8158 - acc: 0.6456
Epoch 77/500
900/900 [==============================] - 0s 71us/step - loss: 0.8246 - acc: 0.6333
Epoch 78/500
900/900 [==============================] - 0s 72us/step - loss: 0.8106 - acc: 0.6500
Epoch 79/500
900/900 [==============================] - 0s 106us/step - loss: 0.7820 - acc: 0.6733
Epoch 80/500
900/900 [==============================] - 0s 96us/step - loss: 0.7967 - acc: 0.6633
Epoch 81/500
900/900 [==============================] - 0s 73us/step - loss: 0.8351 - acc: 0.6511
Epoch 82/500
900/900 [==============================] - 0s 48us/step - loss: 0.8075 - acc: 0.6600
Epoch 83/500
900/900 [==============================] - 0s 82us/step - loss: 0.7715 - acc: 0.6744
Epoch 84/500
900/900 [==============================] - 0s 84us/step - loss: 0.8005 - acc: 0.6622
Epoch 85/500
900/900 [==============================] - 0s 50us/step - loss: 0.7874 - acc: 0.6667
Epoch 86/500
900/900 [==============================] - 0s 99us/step - loss: 0.7530 - acc: 0.6811
Epoch 87/500
900/900 [==============================] - 0s 89us/step - loss: 0.7834 - acc: 0.6633
Epoch 88/500
900/900 [==============================] - 0s 94us/step - loss: 0.7812 - acc: 0.6689
Epoch 89/500
900/900 [==============================] - 0s 83us/step - loss: 0.7794 - acc: 0.6711
Epoch 90/500
900/900 [==============================] - 0s 89us/step - loss: 0.7671 - acc: 0.6644
Epoch 91/500
900/900 [==============================] - 0s 93us/step - loss: 0.8302 - acc: 0.6511
Epoch 92/500
900/900 [==============================] - 0s 92us/step - loss: 0.7916 - acc: 0.6567
Epoch 93/500
900/900 [==============================] - 0s 98us/step - loss: 0.7701 - acc: 0.6667
Epoch 94/500
900/900 [==============================] - 0s 89us/step - loss: 0.7645 - acc: 0.6722
Epoch 95/500
900/900 [==============================] - 0s 85us/step - loss: 0.8211 - acc: 0.6200
Epoch 96/500
900/900 [==============================] - 0s 88us/step - loss: 0.8072 - acc: 0.6622
Epoch 97/500
900/900 [==============================] - 0s 76us/step - loss: 0.7933 - acc: 0.6611
Epoch 98/500
900/900 [==============================] - 0s 79us/step - loss: 0.7785 - acc: 0.6711
Epoch 99/500
900/900 [==============================] - 0s 72us/step - loss: 0.7538 - acc: 0.6856
Epoch 100/500
900/900 [==============================] - 0s 76us/step - loss: 0.7736 - acc: 0.6578
Epoch 101/500
900/900 [==============================] - 0s 78us/step - loss: 0.7642 - acc: 0.6811
Epoch 102/500
900/900 [==============================] - 0s 88us/step - loss: 0.7764 - acc: 0.6689
Epoch 103/500
900/900 [==============================] - 0s 93us/step - loss: 0.7561 - acc: 0.6822
Epoch 104/500
900/900 [==============================] - 0s 86us/step - loss: 0.7448 - acc: 0.6933
Epoch 105/500
900/900 [==============================] - 0s 94us/step - loss: 0.7610 - acc: 0.6978
Epoch 106/500
900/900 [==============================] - 0s 80us/step - loss: 0.7714 - acc: 0.6844
Epoch 107/500
900/900 [==============================] - 0s 55us/step - loss: 0.7693 - acc: 0.6667
Epoch 108/500
900/900 [==============================] - 0s 99us/step - loss: 0.7834 - acc: 0.6656
Epoch 109/500
900/900 [==============================] - 0s 83us/step - loss: 0.7852 - acc: 0.6633
Epoch 110/500
900/900 [==============================] - 0s 90us/step - loss: 0.7562 - acc: 0.6889
Epoch 111/500
900/900 [==============================] - 0s 97us/step - loss: 0.7606 - acc: 0.6733
Epoch 112/500
900/900 [==============================] - 0s 87us/step - loss: 0.7783 - acc: 0.6600
Epoch 113/500
900/900 [==============================] - 0s 73us/step - loss: 0.7934 - acc: 0.6522
Epoch 114/500
900/900 [==============================] - 0s 79us/step - loss: 0.7856 - acc: 0.6589
Epoch 115/500
900/900 [==============================] - 0s 87us/step - loss: 0.8407 - acc: 0.6389
Epoch 116/500
900/900 [==============================] - 0s 71us/step - loss: 0.8234 - acc: 0.6444
Epoch 117/500
900/900 [==============================] - 0s 74us/step - loss: 0.7922 - acc: 0.6456
Epoch 118/500
900/900 [==============================] - 0s 72us/step - loss: 0.7577 - acc: 0.6822
Epoch 119/500
900/900 [==============================] - 0s 86us/step - loss: 0.7572 - acc: 0.6933
Epoch 120/500
900/900 [==============================] - 0s 75us/step - loss: 0.7844 - acc: 0.6578
Epoch 121/500
900/900 [==============================] - 0s 77us/step - loss: 0.7532 - acc: 0.6789
Epoch 122/500
900/900 [==============================] - 0s 79us/step - loss: 0.7479 - acc: 0.6889
Epoch 123/500
900/900 [==============================] - 0s 78us/step - loss: 0.7549 - acc: 0.6811
Epoch 124/500
900/900 [==============================] - 0s 89us/step - loss: 0.7513 - acc: 0.6833
Epoch 125/500
900/900 [==============================] - 0s 78us/step - loss: 0.7443 - acc: 0.6833
Epoch 126/500
900/900 [==============================] - 0s 76us/step - loss: 0.7612 - acc: 0.6733
Epoch 127/500
900/900 [==============================] - 0s 80us/step - loss: 0.7762 - acc: 0.6844
Epoch 128/500
900/900 [==============================] - 0s 80us/step - loss: 0.7633 - acc: 0.6811
Epoch 129/500
900/900 [==============================] - 0s 58us/step - loss: 0.7750 - acc: 0.6644
Epoch 130/500
900/900 [==============================] - 0s 64us/step - loss: 0.8151 - acc: 0.6622
Epoch 131/500
900/900 [==============================] - 0s 89us/step - loss: 0.7697 - acc: 0.6744
Epoch 132/500
900/900 [==============================] - 0s 79us/step - loss: 0.7672 - acc: 0.6800
Epoch 133/500
900/900 [==============================] - 0s 92us/step - loss: 0.7803 - acc: 0.6800
Epoch 134/500
900/900 [==============================] - 0s 74us/step - loss: 0.7936 - acc: 0.6644
Epoch 135/500
900/900 [==============================] - 0s 79us/step - loss: 0.7625 - acc: 0.6867
Epoch 136/500
900/900 [==============================] - 0s 69us/step - loss: 0.7399 - acc: 0.6944
Epoch 137/500
900/900 [==============================] - 0s 81us/step - loss: 0.7853 - acc: 0.6556
Epoch 138/500
900/900 [==============================] - 0s 79us/step - loss: 0.7777 - acc: 0.6644
Epoch 139/500
900/900 [==============================] - 0s 72us/step - loss: 0.7871 - acc: 0.6722
Epoch 140/500
900/900 [==============================] - 0s 72us/step - loss: 0.8379 - acc: 0.6378
Epoch 141/500
900/900 [==============================] - 0s 62us/step - loss: 0.8084 - acc: 0.6467
Epoch 142/500
900/900 [==============================] - 0s 72us/step - loss: 0.7881 - acc: 0.6678
Epoch 143/500
900/900 [==============================] - 0s 79us/step - loss: 0.7494 - acc: 0.6833
Epoch 144/500
900/900 [==============================] - 0s 78us/step - loss: 0.7491 - acc: 0.6833
Epoch 145/500
900/900 [==============================] - 0s 76us/step - loss: 0.8193 - acc: 0.6367
Epoch 146/500
900/900 [==============================] - 0s 77us/step - loss: 0.7938 - acc: 0.6767
Epoch 147/500
900/900 [==============================] - 0s 66us/step - loss: 0.8468 - acc: 0.6267
Epoch 148/500
900/900 [==============================] - 0s 92us/step - loss: 0.8664 - acc: 0.6433
Epoch 149/500
900/900 [==============================] - 0s 83us/step - loss: 0.7656 - acc: 0.6811
Epoch 150/500
900/900 [==============================] - 0s 82us/step - loss: 0.7635 - acc: 0.6756
Epoch 151/500
900/900 [==============================] - 0s 75us/step - loss: 0.7635 - acc: 0.6744
Epoch 152/500
900/900 [==============================] - 0s 70us/step - loss: 0.8083 - acc: 0.6611
Epoch 153/500
900/900 [==============================] - 0s 77us/step - loss: 0.8180 - acc: 0.6433
Epoch 154/500
900/900 [==============================] - 0s 61us/step - loss: 0.8417 - acc: 0.6389
Epoch 155/500
900/900 [==============================] - 0s 89us/step - loss: 0.8279 - acc: 0.6533
Epoch 156/500
900/900 [==============================] - 0s 73us/step - loss: 0.8008 - acc: 0.6633
Epoch 157/500
900/900 [==============================] - 0s 81us/step - loss: 0.8055 - acc: 0.6656
Epoch 158/500
900/900 [==============================] - 0s 80us/step - loss: 0.7776 - acc: 0.6589
Epoch 159/500
900/900 [==============================] - 0s 60us/step - loss: 0.7884 - acc: 0.6700
Epoch 160/500
900/900 [==============================] - 0s 86us/step - loss: 0.7848 - acc: 0.6500
Epoch 161/500
900/900 [==============================] - 0s 73us/step - loss: 0.8263 - acc: 0.6378
Epoch 162/500
900/900 [==============================] - 0s 62us/step - loss: 0.7898 - acc: 0.6711
Epoch 163/500
900/900 [==============================] - 0s 91us/step - loss: 0.7710 - acc: 0.6789
Epoch 164/500
900/900 [==============================] - 0s 77us/step - loss: 0.7525 - acc: 0.6856
Epoch 165/500
900/900 [==============================] - 0s 61us/step - loss: 0.7785 - acc: 0.6833
Epoch 166/500
900/900 [==============================] - 0s 73us/step - loss: 0.7806 - acc: 0.6589
Epoch 167/500
900/900 [==============================] - 0s 90us/step - loss: 0.8285 - acc: 0.6489
Epoch 168/500
900/900 [==============================] - 0s 72us/step - loss: 0.8091 - acc: 0.6656
Epoch 169/500
900/900 [==============================] - 0s 84us/step - loss: 0.7617 - acc: 0.6789
Epoch 170/500
900/900 [==============================] - 0s 86us/step - loss: 0.7469 - acc: 0.6856
Epoch 171/500
900/900 [==============================] - 0s 91us/step - loss: 0.8004 - acc: 0.6578
Epoch 172/500
900/900 [==============================] - 0s 91us/step - loss: 0.8079 - acc: 0.6567
Epoch 173/500
900/900 [==============================] - 0s 88us/step - loss: 0.7818 - acc: 0.6633
Epoch 174/500
900/900 [==============================] - 0s 89us/step - loss: 0.7809 - acc: 0.6567
Epoch 175/500
900/900 [==============================] - 0s 92us/step - loss: 0.7984 - acc: 0.6578
Epoch 176/500
900/900 [==============================] - 0s 88us/step - loss: 0.7492 - acc: 0.6889
Epoch 177/500
900/900 [==============================] - 0s 92us/step - loss: 0.7547 - acc: 0.6867
Epoch 178/500
900/900 [==============================] - 0s 85us/step - loss: 0.7617 - acc: 0.6811
Epoch 179/500
900/900 [==============================] - 0s 88us/step - loss: 0.7442 - acc: 0.6833
Epoch 180/500
900/900 [==============================] - 0s 94us/step - loss: 0.7527 - acc: 0.6844
Epoch 181/500
900/900 [==============================] - 0s 91us/step - loss: 0.7963 - acc: 0.6822
Epoch 182/500
900/900 [==============================] - 0s 92us/step - loss: 0.7870 - acc: 0.6844
Epoch 183/500
900/900 [==============================] - 0s 108us/step - loss: 0.7681 - acc: 0.6733
Epoch 184/500
900/900 [==============================] - 0s 96us/step - loss: 0.7591 - acc: 0.6700
Epoch 185/500
900/900 [==============================] - 0s 88us/step - loss: 0.7476 - acc: 0.6922
Epoch 186/500
900/900 [==============================] - 0s 88us/step - loss: 0.7709 - acc: 0.6778
Epoch 187/500
900/900 [==============================] - 0s 94us/step - loss: 0.7588 - acc: 0.6911
Epoch 188/500
900/900 [==============================] - 0s 90us/step - loss: 0.7663 - acc: 0.6878
Epoch 189/500
900/900 [==============================] - 0s 75us/step - loss: 0.7635 - acc: 0.6822
Epoch 190/500
900/900 [==============================] - 0s 76us/step - loss: 0.7365 - acc: 0.6956
Epoch 191/500
900/900 [==============================] - 0s 78us/step - loss: 0.7325 - acc: 0.6878
Epoch 192/500
900/900 [==============================] - 0s 74us/step - loss: 0.7388 - acc: 0.6978
Epoch 193/500
900/900 [==============================] - 0s 76us/step - loss: 0.7356 - acc: 0.6878
Epoch 194/500
900/900 [==============================] - 0s 78us/step - loss: 0.7476 - acc: 0.6911
Epoch 195/500
900/900 [==============================] - 0s 81us/step - loss: 0.7816 - acc: 0.6822
Epoch 196/500
900/900 [==============================] - 0s 77us/step - loss: 0.7853 - acc: 0.6667
Epoch 197/500
900/900 [==============================] - 0s 74us/step - loss: 0.7735 - acc: 0.6822
Epoch 198/500
900/900 [==============================] - 0s 71us/step - loss: 0.8062 - acc: 0.6467
Epoch 199/500
900/900 [==============================] - 0s 72us/step - loss: 0.7742 - acc: 0.6722
Epoch 200/500
900/900 [==============================] - 0s 73us/step - loss: 0.7532 - acc: 0.6844
Epoch 201/500
900/900 [==============================] - 0s 64us/step - loss: 0.7547 - acc: 0.6722
Epoch 202/500
900/900 [==============================] - 0s 84us/step - loss: 0.7646 - acc: 0.6700
Epoch 203/500
900/900 [==============================] - 0s 77us/step - loss: 0.7482 - acc: 0.6933
Epoch 204/500
900/900 [==============================] - 0s 73us/step - loss: 0.7591 - acc: 0.6811
Epoch 205/500
900/900 [==============================] - 0s 75us/step - loss: 0.7846 - acc: 0.6689
Epoch 206/500
900/900 [==============================] - 0s 92us/step - loss: 0.8368 - acc: 0.6567
Epoch 207/500
900/900 [==============================] - 0s 81us/step - loss: 0.8187 - acc: 0.6611
Epoch 208/500
900/900 [==============================] - 0s 81us/step - loss: 0.7869 - acc: 0.6556
Epoch 209/500
900/900 [==============================] - 0s 92us/step - loss: 0.7788 - acc: 0.6667
Epoch 210/500
900/900 [==============================] - 0s 91us/step - loss: 0.7602 - acc: 0.6722
Epoch 211/500
900/900 [==============================] - 0s 75us/step - loss: 0.7814 - acc: 0.6678
Epoch 212/500
900/900 [==============================] - 0s 77us/step - loss: 0.7605 - acc: 0.6722
Epoch 213/500
900/900 [==============================] - 0s 72us/step - loss: 0.7557 - acc: 0.6833
Epoch 214/500
900/900 [==============================] - 0s 82us/step - loss: 0.7524 - acc: 0.6756
Epoch 215/500
900/900 [==============================] - 0s 84us/step - loss: 0.7652 - acc: 0.6722
Epoch 216/500
900/900 [==============================] - 0s 86us/step - loss: 0.7864 - acc: 0.6689
Epoch 217/500
900/900 [==============================] - 0s 75us/step - loss: 0.7650 - acc: 0.6800
Epoch 218/500
900/900 [==============================] - 0s 78us/step - loss: 0.7791 - acc: 0.6789
Epoch 219/500
900/900 [==============================] - 0s 71us/step - loss: 0.7869 - acc: 0.6678
Epoch 220/500
900/900 [==============================] - 0s 78us/step - loss: 0.7414 - acc: 0.6844
Epoch 221/500
900/900 [==============================] - 0s 73us/step - loss: 0.7618 - acc: 0.6822
Epoch 222/500
900/900 [==============================] - 0s 82us/step - loss: 0.7699 - acc: 0.6789
Epoch 223/500
900/900 [==============================] - 0s 81us/step - loss: 0.7419 - acc: 0.6822
Epoch 224/500
900/900 [==============================] - 0s 74us/step - loss: 0.7455 - acc: 0.6811
Epoch 225/500
900/900 [==============================] - 0s 74us/step - loss: 0.7380 - acc: 0.6889
Epoch 226/500
900/900 [==============================] - 0s 70us/step - loss: 0.7404 - acc: 0.6789
Epoch 227/500
900/900 [==============================] - 0s 49us/step - loss: 0.7383 - acc: 0.6833
Epoch 228/500
900/900 [==============================] - 0s 84us/step - loss: 0.7557 - acc: 0.6711
Epoch 229/500
900/900 [==============================] - 0s 78us/step - loss: 0.7434 - acc: 0.6822
Epoch 230/500
900/900 [==============================] - 0s 71us/step - loss: 0.7548 - acc: 0.6744
Epoch 231/500
900/900 [==============================] - 0s 79us/step - loss: 0.8257 - acc: 0.6511
Epoch 232/500
900/900 [==============================] - 0s 71us/step - loss: 0.8473 - acc: 0.6167
Epoch 233/500
900/900 [==============================] - 0s 66us/step - loss: 0.7563 - acc: 0.6800
Epoch 234/500
900/900 [==============================] - 0s 80us/step - loss: 0.7825 - acc: 0.6778
Epoch 235/500
900/900 [==============================] - 0s 68us/step - loss: 0.7640 - acc: 0.6589
Epoch 236/500
900/900 [==============================] - 0s 80us/step - loss: 0.7558 - acc: 0.6900
Epoch 237/500
900/900 [==============================] - 0s 67us/step - loss: 0.7647 - acc: 0.6756
Epoch 238/500
900/900 [==============================] - 0s 86us/step - loss: 0.7959 - acc: 0.6622
Epoch 239/500
900/900 [==============================] - 0s 68us/step - loss: 0.7505 - acc: 0.6844
Epoch 240/500
900/900 [==============================] - 0s 84us/step - loss: 0.7543 - acc: 0.6733
Epoch 241/500
900/900 [==============================] - 0s 82us/step - loss: 0.7779 - acc: 0.6611
Epoch 242/500
900/900 [==============================] - 0s 77us/step - loss: 0.8150 - acc: 0.6467
Epoch 243/500
900/900 [==============================] - 0s 60us/step - loss: 0.8094 - acc: 0.6511
Epoch 244/500
900/900 [==============================] - 0s 84us/step - loss: 0.7491 - acc: 0.6800
Epoch 245/500
900/900 [==============================] - 0s 79us/step - loss: 0.7434 - acc: 0.6833
Epoch 246/500
900/900 [==============================] - 0s 76us/step - loss: 0.7649 - acc: 0.6833
Epoch 247/500
900/900 [==============================] - 0s 60us/step - loss: 0.7831 - acc: 0.6667
Epoch 248/500
900/900 [==============================] - 0s 91us/step - loss: 0.7647 - acc: 0.6722
Epoch 249/500
900/900 [==============================] - 0s 74us/step - loss: 0.8011 - acc: 0.6611
Epoch 250/500
900/900 [==============================] - 0s 76us/step - loss: 0.7513 - acc: 0.6822
Epoch 251/500
900/900 [==============================] - 0s 88us/step - loss: 0.7593 - acc: 0.6789
Epoch 252/500
900/900 [==============================] - 0s 78us/step - loss: 0.7509 - acc: 0.6767
Epoch 253/500
900/900 [==============================] - 0s 85us/step - loss: 0.7438 - acc: 0.6933
Epoch 254/500
900/900 [==============================] - 0s 90us/step - loss: 0.7288 - acc: 0.6956
Epoch 255/500
900/900 [==============================] - 0s 82us/step - loss: 0.7443 - acc: 0.6844
Epoch 256/500
900/900 [==============================] - 0s 76us/step - loss: 0.7520 - acc: 0.6811
Epoch 257/500
900/900 [==============================] - 0s 78us/step - loss: 0.7628 - acc: 0.6778
Epoch 258/500
900/900 [==============================] - 0s 81us/step - loss: 0.7430 - acc: 0.6811
Epoch 259/500
900/900 [==============================] - 0s 91us/step - loss: 0.7373 - acc: 0.6844
Epoch 260/500
900/900 [==============================] - 0s 86us/step - loss: 0.7708 - acc: 0.6778
Epoch 261/500
900/900 [==============================] - 0s 72us/step - loss: 0.7412 - acc: 0.6700
Epoch 262/500
900/900 [==============================] - 0s 76us/step - loss: 0.7978 - acc: 0.6600
Epoch 263/500
900/900 [==============================] - 0s 63us/step - loss: 0.8787 - acc: 0.6111
Epoch 264/500
900/900 [==============================] - 0s 78us/step - loss: 0.8064 - acc: 0.6578
Epoch 265/500
900/900 [==============================] - 0s 73us/step - loss: 0.7606 - acc: 0.6756
Epoch 266/500
900/900 [==============================] - 0s 73us/step - loss: 0.7686 - acc: 0.6800
Epoch 267/500
900/900 [==============================] - 0s 76us/step - loss: 0.7660 - acc: 0.6756
Epoch 268/500
900/900 [==============================] - 0s 78us/step - loss: 0.7427 - acc: 0.6822
Epoch 269/500
900/900 [==============================] - 0s 103us/step - loss: 0.7791 - acc: 0.6422
Epoch 270/500
900/900 [==============================] - 0s 97us/step - loss: 0.7817 - acc: 0.6600
Epoch 271/500
900/900 [==============================] - 0s 103us/step - loss: 0.7495 - acc: 0.6833
Epoch 272/500
900/900 [==============================] - 0s 94us/step - loss: 0.7544 - acc: 0.6867
Epoch 273/500
900/900 [==============================] - 0s 90us/step - loss: 0.7518 - acc: 0.6867
Epoch 274/500
900/900 [==============================] - 0s 75us/step - loss: 0.7894 - acc: 0.6644
Epoch 275/500
900/900 [==============================] - 0s 71us/step - loss: 0.7563 - acc: 0.6922
Epoch 276/500
900/900 [==============================] - 0s 73us/step - loss: 0.7789 - acc: 0.6767
Epoch 277/500
900/900 [==============================] - 0s 62us/step - loss: 0.7406 - acc: 0.6833
Epoch 278/500
900/900 [==============================] - 0s 79us/step - loss: 0.7673 - acc: 0.6844
Epoch 279/500
900/900 [==============================] - 0s 92us/step - loss: 0.7319 - acc: 0.6967
Epoch 280/500
900/900 [==============================] - 0s 89us/step - loss: 0.7541 - acc: 0.6822
Epoch 281/500
900/900 [==============================] - 0s 91us/step - loss: 0.7823 - acc: 0.6633
Epoch 282/500
900/900 [==============================] - 0s 87us/step - loss: 0.7449 - acc: 0.6733
Epoch 283/500
900/900 [==============================] - 0s 89us/step - loss: 0.7444 - acc: 0.6711
Epoch 284/500
900/900 [==============================] - 0s 77us/step - loss: 0.7565 - acc: 0.6778
Epoch 285/500
900/900 [==============================] - 0s 67us/step - loss: 0.7409 - acc: 0.6744
Epoch 286/500
900/900 [==============================] - 0s 81us/step - loss: 0.7998 - acc: 0.6567
Epoch 287/500
900/900 [==============================] - 0s 78us/step - loss: 0.7618 - acc: 0.6900
Epoch 288/500
900/900 [==============================] - 0s 77us/step - loss: 0.7719 - acc: 0.6856
Epoch 289/500
900/900 [==============================] - 0s 78us/step - loss: 0.7413 - acc: 0.6944
Epoch 290/500
900/900 [==============================] - 0s 77us/step - loss: 0.7637 - acc: 0.6678
Epoch 291/500
900/900 [==============================] - 0s 84us/step - loss: 0.7482 - acc: 0.6856
Epoch 292/500
900/900 [==============================] - 0s 69us/step - loss: 0.7383 - acc: 0.6800
Epoch 293/500
900/900 [==============================] - 0s 73us/step - loss: 0.7337 - acc: 0.6856
Epoch 294/500
900/900 [==============================] - 0s 84us/step - loss: 0.7694 - acc: 0.6844
Epoch 295/500
900/900 [==============================] - 0s 83us/step - loss: 0.7678 - acc: 0.6878
Epoch 296/500
900/900 [==============================] - 0s 76us/step - loss: 0.7569 - acc: 0.6700
Epoch 297/500
900/900 [==============================] - 0s 77us/step - loss: 0.7449 - acc: 0.6967
Epoch 298/500
900/900 [==============================] - 0s 60us/step - loss: 0.7518 - acc: 0.7111
Epoch 299/500
900/900 [==============================] - 0s 81us/step - loss: 0.8252 - acc: 0.6444
Epoch 300/500
900/900 [==============================] - 0s 78us/step - loss: 0.7533 - acc: 0.6811
Epoch 301/500
900/900 [==============================] - 0s 81us/step - loss: 0.7428 - acc: 0.6789
Epoch 302/500
900/900 [==============================] - 0s 74us/step - loss: 0.7604 - acc: 0.6756
Epoch 303/500
900/900 [==============================] - 0s 78us/step - loss: 0.7286 - acc: 0.7067
Epoch 304/500
900/900 [==============================] - 0s 76us/step - loss: 0.7270 - acc: 0.6867
Epoch 305/500
900/900 [==============================] - 0s 81us/step - loss: 0.7498 - acc: 0.6867
Epoch 306/500
900/900 [==============================] - 0s 77us/step - loss: 0.7693 - acc: 0.6767
Epoch 307/500
900/900 [==============================] - 0s 80us/step - loss: 0.7773 - acc: 0.6844
Epoch 308/500
900/900 [==============================] - 0s 74us/step - loss: 0.7409 - acc: 0.7011
Epoch 309/500
900/900 [==============================] - 0s 76us/step - loss: 0.7493 - acc: 0.6900
Epoch 310/500
900/900 [==============================] - 0s 77us/step - loss: 0.7397 - acc: 0.6822
Epoch 311/500
900/900 [==============================] - 0s 76us/step - loss: 0.7258 - acc: 0.6978
Epoch 312/500
900/900 [==============================] - 0s 83us/step - loss: 0.7373 - acc: 0.6911
Epoch 313/500
900/900 [==============================] - 0s 86us/step - loss: 0.7581 - acc: 0.6767
Epoch 314/500
900/900 [==============================] - 0s 82us/step - loss: 0.8111 - acc: 0.6511
Epoch 315/500
900/900 [==============================] - 0s 77us/step - loss: 0.7485 - acc: 0.6778
Epoch 316/500
900/900 [==============================] - 0s 57us/step - loss: 0.7405 - acc: 0.6933
Epoch 317/500
900/900 [==============================] - 0s 87us/step - loss: 0.7388 - acc: 0.6878
Epoch 318/500
900/900 [==============================] - 0s 64us/step - loss: 0.7430 - acc: 0.6822
Epoch 319/500
900/900 [==============================] - 0s 88us/step - loss: 0.7714 - acc: 0.6689
Epoch 320/500
900/900 [==============================] - 0s 76us/step - loss: 0.7436 - acc: 0.6822
Epoch 321/500
900/900 [==============================] - 0s 72us/step - loss: 0.7645 - acc: 0.6800
Epoch 322/500
900/900 [==============================] - 0s 47us/step - loss: 0.7467 - acc: 0.6889
Epoch 323/500
900/900 [==============================] - 0s 74us/step - loss: 0.7448 - acc: 0.6789
Epoch 324/500
900/900 [==============================] - 0s 58us/step - loss: 0.7329 - acc: 0.6978
Epoch 325/500
900/900 [==============================] - 0s 63us/step - loss: 0.7690 - acc: 0.6722
Epoch 326/500
900/900 [==============================] - 0s 76us/step - loss: 0.7659 - acc: 0.6811
Epoch 327/500
900/900 [==============================] - 0s 66us/step - loss: 0.7411 - acc: 0.6889
Epoch 328/500
900/900 [==============================] - 0s 78us/step - loss: 0.7571 - acc: 0.6933
Epoch 329/500
900/900 [==============================] - 0s 91us/step - loss: 0.7592 - acc: 0.6967
Epoch 330/500
900/900 [==============================] - 0s 78us/step - loss: 0.7425 - acc: 0.6922
Epoch 331/500
900/900 [==============================] - 0s 73us/step - loss: 0.7648 - acc: 0.6767
Epoch 332/500
900/900 [==============================] - 0s 76us/step - loss: 0.8067 - acc: 0.6367
Epoch 333/500
900/900 [==============================] - 0s 82us/step - loss: 0.7769 - acc: 0.6733
Epoch 334/500
900/900 [==============================] - 0s 86us/step - loss: 0.7568 - acc: 0.6667
Epoch 335/500
900/900 [==============================] - 0s 78us/step - loss: 0.7289 - acc: 0.7000
Epoch 336/500
900/900 [==============================] - 0s 82us/step - loss: 0.7284 - acc: 0.7022
Epoch 337/500
900/900 [==============================] - 0s 83us/step - loss: 0.7244 - acc: 0.6922
Epoch 338/500
900/900 [==============================] - 0s 74us/step - loss: 0.7387 - acc: 0.6878
Epoch 339/500
900/900 [==============================] - 0s 67us/step - loss: 0.7352 - acc: 0.6878
Epoch 340/500
900/900 [==============================] - 0s 78us/step - loss: 0.7470 - acc: 0.6744
Epoch 341/500
900/900 [==============================] - 0s 87us/step - loss: 0.7336 - acc: 0.6956
Epoch 342/500
900/900 [==============================] - 0s 99us/step - loss: 0.7446 - acc: 0.6822
Epoch 343/500
900/900 [==============================] - 0s 88us/step - loss: 0.7475 - acc: 0.6811
Epoch 344/500
900/900 [==============================] - 0s 87us/step - loss: 0.7917 - acc: 0.6711
Epoch 345/500
900/900 [==============================] - 0s 89us/step - loss: 0.7692 - acc: 0.6633
Epoch 346/500
900/900 [==============================] - 0s 81us/step - loss: 0.7896 - acc: 0.6722
Epoch 347/500
900/900 [==============================] - 0s 88us/step - loss: 0.7488 - acc: 0.6722
Epoch 348/500
900/900 [==============================] - 0s 81us/step - loss: 0.7502 - acc: 0.6744
Epoch 349/500
900/900 [==============================] - 0s 86us/step - loss: 0.7577 - acc: 0.6778
Epoch 350/500
900/900 [==============================] - 0s 83us/step - loss: 0.7289 - acc: 0.6900
Epoch 351/500
900/900 [==============================] - 0s 86us/step - loss: 0.7434 - acc: 0.6833
Epoch 352/500
900/900 [==============================] - 0s 84us/step - loss: 0.7433 - acc: 0.6656
Epoch 353/500
900/900 [==============================] - 0s 89us/step - loss: 0.7741 - acc: 0.6544
Epoch 354/500
900/900 [==============================] - 0s 87us/step - loss: 0.8050 - acc: 0.6622
Epoch 355/500
900/900 [==============================] - 0s 74us/step - loss: 0.8307 - acc: 0.6578
Epoch 356/500
900/900 [==============================] - 0s 85us/step - loss: 0.7965 - acc: 0.6322
Epoch 357/500
900/900 [==============================] - 0s 86us/step - loss: 0.7700 - acc: 0.6456
Epoch 358/500
900/900 [==============================] - 0s 70us/step - loss: 0.7513 - acc: 0.6711
Epoch 359/500
900/900 [==============================] - 0s 71us/step - loss: 0.7513 - acc: 0.6844
Epoch 360/500
900/900 [==============================] - 0s 71us/step - loss: 0.7344 - acc: 0.6978
Epoch 361/500
900/900 [==============================] - 0s 77us/step - loss: 0.7241 - acc: 0.6956
Epoch 362/500
900/900 [==============================] - 0s 71us/step - loss: 0.7451 - acc: 0.6844
Epoch 363/500
900/900 [==============================] - 0s 77us/step - loss: 0.7441 - acc: 0.6778
Epoch 364/500
900/900 [==============================] - 0s 91us/step - loss: 0.7307 - acc: 0.6789
Epoch 365/500
900/900 [==============================] - 0s 54us/step - loss: 0.7591 - acc: 0.6822
Epoch 366/500
900/900 [==============================] - 0s 72us/step - loss: 0.7454 - acc: 0.6900
Epoch 367/500
900/900 [==============================] - 0s 88us/step - loss: 0.7857 - acc: 0.6600
Epoch 368/500
900/900 [==============================] - 0s 88us/step - loss: 0.7650 - acc: 0.6833
Epoch 369/500
900/900 [==============================] - 0s 80us/step - loss: 0.8165 - acc: 0.6589
Epoch 370/500
900/900 [==============================] - 0s 76us/step - loss: 0.7809 - acc: 0.6500
Epoch 371/500
900/900 [==============================] - 0s 78us/step - loss: 0.7330 - acc: 0.6956
Epoch 372/500
900/900 [==============================] - 0s 74us/step - loss: 0.7487 - acc: 0.6833
Epoch 373/500
900/900 [==============================] - 0s 75us/step - loss: 0.7364 - acc: 0.6844
Epoch 374/500
900/900 [==============================] - 0s 72us/step - loss: 0.7302 - acc: 0.6933
Epoch 375/500
900/900 [==============================] - 0s 71us/step - loss: 0.7411 - acc: 0.6900
Epoch 376/500
900/900 [==============================] - 0s 73us/step - loss: 0.7664 - acc: 0.6878
Epoch 377/500
900/900 [==============================] - 0s 72us/step - loss: 0.7590 - acc: 0.6811
Epoch 378/500
900/900 [==============================] - 0s 74us/step - loss: 0.7643 - acc: 0.7044
Epoch 379/500
900/900 [==============================] - 0s 77us/step - loss: 0.7645 - acc: 0.6967
Epoch 380/500
900/900 [==============================] - 0s 73us/step - loss: 0.7776 - acc: 0.6811
Epoch 381/500
900/900 [==============================] - 0s 72us/step - loss: 0.7827 - acc: 0.6811
Epoch 382/500
900/900 [==============================] - 0s 70us/step - loss: 0.8150 - acc: 0.6611
Epoch 383/500
900/900 [==============================] - 0s 72us/step - loss: 0.7804 - acc: 0.6689
Epoch 384/500
900/900 [==============================] - 0s 72us/step - loss: 0.7530 - acc: 0.6844
Epoch 385/500
900/900 [==============================] - 0s 66us/step - loss: 0.7517 - acc: 0.6967
Epoch 386/500
900/900 [==============================] - 0s 80us/step - loss: 0.7701 - acc: 0.6911
Epoch 387/500
900/900 [==============================] - 0s 72us/step - loss: 0.7655 - acc: 0.6867
Epoch 388/500
900/900 [==============================] - 0s 90us/step - loss: 0.7579 - acc: 0.6856
Epoch 389/500
900/900 [==============================] - 0s 72us/step - loss: 0.7389 - acc: 0.6889
Epoch 390/500
900/900 [==============================] - 0s 82us/step - loss: 0.7407 - acc: 0.6944
Epoch 391/500
900/900 [==============================] - 0s 78us/step - loss: 0.7233 - acc: 0.6911
Epoch 392/500
900/900 [==============================] - 0s 76us/step - loss: 0.7238 - acc: 0.6956
Epoch 393/500
900/900 [==============================] - 0s 77us/step - loss: 0.7329 - acc: 0.6856
Epoch 394/500
900/900 [==============================] - 0s 78us/step - loss: 0.7311 - acc: 0.6867
Epoch 395/500
900/900 [==============================] - 0s 76us/step - loss: 0.7417 - acc: 0.6800
Epoch 396/500
900/900 [==============================] - 0s 70us/step - loss: 0.7709 - acc: 0.6822
Epoch 397/500
900/900 [==============================] - 0s 58us/step - loss: 0.7229 - acc: 0.6889
Epoch 398/500
900/900 [==============================] - 0s 93us/step - loss: 0.7388 - acc: 0.6744
Epoch 399/500
900/900 [==============================] - 0s 96us/step - loss: 0.7333 - acc: 0.6911
Epoch 400/500
900/900 [==============================] - 0s 91us/step - loss: 0.7402 - acc: 0.6811
Epoch 401/500
900/900 [==============================] - 0s 73us/step - loss: 0.8315 - acc: 0.6489
Epoch 402/500
900/900 [==============================] - 0s 60us/step - loss: 0.7787 - acc: 0.6644
Epoch 403/500
900/900 [==============================] - 0s 87us/step - loss: 0.7535 - acc: 0.6844
Epoch 404/500
900/900 [==============================] - 0s 77us/step - loss: 0.7394 - acc: 0.6844
Epoch 405/500
900/900 [==============================] - 0s 74us/step - loss: 0.7701 - acc: 0.6733
Epoch 406/500
900/900 [==============================] - 0s 67us/step - loss: 0.7677 - acc: 0.6900
Epoch 407/500
900/900 [==============================] - 0s 93us/step - loss: 0.7765 - acc: 0.6644
Epoch 408/500
900/900 [==============================] - 0s 73us/step - loss: 0.7623 - acc: 0.6689
Epoch 409/500
900/900 [==============================] - 0s 61us/step - loss: 0.7604 - acc: 0.6733
Epoch 410/500
900/900 [==============================] - 0s 71us/step - loss: 0.7823 - acc: 0.6656
Epoch 411/500
900/900 [==============================] - 0s 88us/step - loss: 0.7298 - acc: 0.6911
Epoch 412/500
900/900 [==============================] - 0s 78us/step - loss: 0.7305 - acc: 0.6856
Epoch 413/500
900/900 [==============================] - 0s 78us/step - loss: 0.7220 - acc: 0.6967
Epoch 414/500
900/900 [==============================] - 0s 81us/step - loss: 0.7385 - acc: 0.6933
Epoch 415/500
900/900 [==============================] - 0s 61us/step - loss: 0.8074 - acc: 0.6667
Epoch 416/500
900/900 [==============================] - 0s 87us/step - loss: 0.8000 - acc: 0.6656
Epoch 417/500
900/900 [==============================] - 0s 62us/step - loss: 0.7878 - acc: 0.6722
Epoch 418/500
900/900 [==============================] - 0s 86us/step - loss: 0.7739 - acc: 0.6500
Epoch 419/500
900/900 [==============================] - 0s 88us/step - loss: 0.7769 - acc: 0.6767
Epoch 420/500
900/900 [==============================] - 0s 67us/step - loss: 0.7521 - acc: 0.6878
Epoch 421/500
900/900 [==============================] - 0s 72us/step - loss: 0.7219 - acc: 0.7000
Epoch 422/500
900/900 [==============================] - 0s 73us/step - loss: 0.7246 - acc: 0.6967
Epoch 423/500
900/900 [==============================] - 0s 84us/step - loss: 0.7316 - acc: 0.6989
Epoch 424/500
900/900 [==============================] - 0s 73us/step - loss: 0.7480 - acc: 0.6889
Epoch 425/500
900/900 [==============================] - 0s 91us/step - loss: 0.7410 - acc: 0.6867
Epoch 426/500
900/900 [==============================] - 0s 89us/step - loss: 0.7309 - acc: 0.6878
Epoch 427/500
900/900 [==============================] - 0s 97us/step - loss: 0.7385 - acc: 0.6811
Epoch 428/500
900/900 [==============================] - 0s 89us/step - loss: 0.7316 - acc: 0.7033
Epoch 429/500
900/900 [==============================] - 0s 87us/step - loss: 0.7282 - acc: 0.6956
Epoch 430/500
900/900 [==============================] - 0s 89us/step - loss: 0.7279 - acc: 0.6989
Epoch 431/500
900/900 [==============================] - 0s 55us/step - loss: 0.7472 - acc: 0.6856
Epoch 432/500
900/900 [==============================] - 0s 86us/step - loss: 0.7497 - acc: 0.6856
Epoch 433/500
900/900 [==============================] - 0s 78us/step - loss: 0.7336 - acc: 0.6856
Epoch 434/500
900/900 [==============================] - 0s 81us/step - loss: 0.7468 - acc: 0.6989
Epoch 435/500
900/900 [==============================] - 0s 88us/step - loss: 0.7181 - acc: 0.7033
Epoch 436/500
900/900 [==============================] - 0s 88us/step - loss: 0.7164 - acc: 0.6978
Epoch 437/500
900/900 [==============================] - 0s 98us/step - loss: 0.7643 - acc: 0.6700
Epoch 438/500
900/900 [==============================] - 0s 75us/step - loss: 0.7568 - acc: 0.6822
Epoch 439/500
900/900 [==============================] - 0s 74us/step - loss: 0.7439 - acc: 0.6833
Epoch 440/500
900/900 [==============================] - 0s 83us/step - loss: 0.7306 - acc: 0.6933
Epoch 441/500
900/900 [==============================] - 0s 56us/step - loss: 0.7462 - acc: 0.6733
Epoch 442/500
900/900 [==============================] - 0s 73us/step - loss: 0.7600 - acc: 0.6522
Epoch 443/500
900/900 [==============================] - 0s 73us/step - loss: 0.7415 - acc: 0.6911
Epoch 444/500
900/900 [==============================] - 0s 76us/step - loss: 0.7266 - acc: 0.6867
Epoch 445/500
900/900 [==============================] - 0s 72us/step - loss: 0.7422 - acc: 0.6811: 0s - loss: 0.7401 - acc: 0.682
Epoch 446/500
900/900 [==============================] - 0s 71us/step - loss: 0.7305 - acc: 0.6822
Epoch 447/500
900/900 [==============================] - 0s 74us/step - loss: 0.7249 - acc: 0.6978
Epoch 448/500
900/900 [==============================] - 0s 81us/step - loss: 0.7379 - acc: 0.6867
Epoch 449/500
900/900 [==============================] - 0s 77us/step - loss: 0.7142 - acc: 0.6978
Epoch 450/500
900/900 [==============================] - 0s 75us/step - loss: 0.7202 - acc: 0.6956
Epoch 451/500
900/900 [==============================] - 0s 60us/step - loss: 0.7324 - acc: 0.7022
Epoch 452/500
900/900 [==============================] - 0s 81us/step - loss: 0.7199 - acc: 0.7011
Epoch 453/500
900/900 [==============================] - 0s 92us/step - loss: 0.7116 - acc: 0.7000
Epoch 454/500
900/900 [==============================] - 0s 79us/step - loss: 0.7283 - acc: 0.6878
Epoch 455/500
900/900 [==============================] - 0s 97us/step - loss: 0.7454 - acc: 0.6778
Epoch 456/500
900/900 [==============================] - 0s 91us/step - loss: 0.7261 - acc: 0.7144
Epoch 457/500
900/900 [==============================] - 0s 74us/step - loss: 0.7317 - acc: 0.6967
Epoch 458/500
900/900 [==============================] - 0s 77us/step - loss: 0.7145 - acc: 0.6989
Epoch 459/500
900/900 [==============================] - 0s 78us/step - loss: 0.7330 - acc: 0.6989
Epoch 460/500
900/900 [==============================] - 0s 75us/step - loss: 0.7456 - acc: 0.6922
Epoch 461/500
900/900 [==============================] - 0s 73us/step - loss: 0.7216 - acc: 0.7111
Epoch 462/500
900/900 [==============================] - 0s 76us/step - loss: 0.7206 - acc: 0.6956
Epoch 463/500
900/900 [==============================] - 0s 81us/step - loss: 0.7384 - acc: 0.6889
Epoch 464/500
900/900 [==============================] - 0s 75us/step - loss: 0.7472 - acc: 0.6944
Epoch 465/500
900/900 [==============================] - 0s 60us/step - loss: 0.7321 - acc: 0.6933
Epoch 466/500
900/900 [==============================] - 0s 73us/step - loss: 0.7464 - acc: 0.6733
Epoch 467/500
900/900 [==============================] - 0s 73us/step - loss: 0.7288 - acc: 0.6922
Epoch 468/500
900/900 [==============================] - 0s 83us/step - loss: 0.7224 - acc: 0.6978
Epoch 469/500
900/900 [==============================] - 0s 86us/step - loss: 0.7344 - acc: 0.6767
Epoch 470/500
900/900 [==============================] - 0s 81us/step - loss: 0.7183 - acc: 0.6889
Epoch 471/500
900/900 [==============================] - 0s 74us/step - loss: 0.7676 - acc: 0.6533
Epoch 472/500
900/900 [==============================] - 0s 76us/step - loss: 0.7703 - acc: 0.6756
Epoch 473/500
900/900 [==============================] - 0s 74us/step - loss: 0.8138 - acc: 0.6389
Epoch 474/500
900/900 [==============================] - 0s 79us/step - loss: 0.7933 - acc: 0.6611
Epoch 475/500
900/900 [==============================] - 0s 77us/step - loss: 0.8034 - acc: 0.6467
Epoch 476/500
900/900 [==============================] - 0s 70us/step - loss: 0.7676 - acc: 0.6633
Epoch 477/500
900/900 [==============================] - 0s 80us/step - loss: 0.7538 - acc: 0.6833
Epoch 478/500
900/900 [==============================] - 0s 78us/step - loss: 0.7533 - acc: 0.6711
Epoch 479/500
900/900 [==============================] - 0s 57us/step - loss: 0.7212 - acc: 0.6844
Epoch 480/500
900/900 [==============================] - 0s 95us/step - loss: 0.7178 - acc: 0.6933
Epoch 481/500
900/900 [==============================] - 0s 64us/step - loss: 0.7218 - acc: 0.6889
Epoch 482/500
900/900 [==============================] - 0s 80us/step - loss: 0.7108 - acc: 0.6956
Epoch 483/500
900/900 [==============================] - 0s 60us/step - loss: 0.7077 - acc: 0.7100
Epoch 484/500
900/900 [==============================] - 0s 98us/step - loss: 0.7481 - acc: 0.6911
Epoch 485/500
900/900 [==============================] - 0s 74us/step - loss: 0.7354 - acc: 0.6933
Epoch 486/500
900/900 [==============================] - 0s 74us/step - loss: 0.7351 - acc: 0.7044
Epoch 487/500
900/900 [==============================] - 0s 58us/step - loss: 0.7177 - acc: 0.7011
Epoch 488/500
900/900 [==============================] - 0s 86us/step - loss: 0.7134 - acc: 0.7067
Epoch 489/500
900/900 [==============================] - 0s 64us/step - loss: 0.7387 - acc: 0.6867
Epoch 490/500
900/900 [==============================] - 0s 89us/step - loss: 0.7157 - acc: 0.6989
Epoch 491/500
900/900 [==============================] - 0s 72us/step - loss: 0.7167 - acc: 0.7089
Epoch 492/500
900/900 [==============================] - 0s 79us/step - loss: 0.7254 - acc: 0.7022
Epoch 493/500
900/900 [==============================] - 0s 52us/step - loss: 0.7578 - acc: 0.6711
Epoch 494/500
900/900 [==============================] - 0s 68us/step - loss: 0.7325 - acc: 0.6856
Epoch 495/500
900/900 [==============================] - 0s 86us/step - loss: 0.7331 - acc: 0.6833
Epoch 496/500
900/900 [==============================] - 0s 72us/step - loss: 0.7395 - acc: 0.6844
Epoch 497/500
900/900 [==============================] - 0s 64us/step - loss: 0.7195 - acc: 0.6922
Epoch 498/500
900/900 [==============================] - 0s 76us/step - loss: 0.7136 - acc: 0.6933
Epoch 499/500
900/900 [==============================] - 0s 58us/step - loss: 0.7192 - acc: 0.6956
Epoch 500/500
900/900 [==============================] - 0s 86us/step - loss: 0.7187 - acc: 0.6967
Wall time: 37.6 s










    
Out[16]:








<tensorflow.python.keras._impl.keras.callbacks.History at 0x2111c984dd8>

















In [17]:



    


train_loss, train_accuracy = model.evaluate(X_train, y_train_categorical, batch_size=100)
train_accuracy



    


















    






900/900 [==============================] - 0s 67us/step










    
Out[17]:








0.7055555515819125

















In [18]:



    


test_loss, test_accuracy = model.evaluate(X_test, y_test_categorical, batch_size=100)
test_accuracy



    


















    






600/600 [==============================] - 0s 28us/step










    
Out[18]:








0.7133333285649618

















In [19]:



    


model.save('insurance.hdf5')

Look at all the different shapes for different kilometers per year

  • now we have three dimensions, so we need to set one to a certain number




In [20]:



    


kms_per_year = 20
plotPrediction(model, X_test[:, 1], X_test[:, 0],
               'Age', 'Max Speed', y_test,
                fixed = kms_per_year,
                title="Test Data Max Speed vs Age with Prediction, 20 km/year")



    


















    
























In [21]:



    


kms_per_year = 50
plotPrediction(model, X_test[:, 1], X_test[:, 0],
               'Age', 'Max Speed', y_test,
                fixed = kms_per_year,
                title="Test Data Max Speed vs Age with Prediction, 50 km/year")



    


















    
























In [22]:



    


kms_per_year = 5
plotPrediction(model, X_test[:, 1], X_test[:, 0],
               'Age', 'Max Speed', y_test,
                fixed = kms_per_year,
                title="Test Data Max Speed vs Age with Prediction, 5 km/year")



    


















    
























In [ ]:

Content source: DJCordhose/ai

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