A simple Sequential NN


In [ ]:
# Reference: http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense

In [10]:
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

In [11]:
# load the dataset
dataset = numpy.loadtxt("../data/pima-indians-diabetes.data", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]

In [17]:
# take a quick look at the data
print(pd.DataFrame(X).head(3))
print(pd.DataFrame(Y).head(3))


     0      1     2     3    4     5      6     7
0  6.0  148.0  72.0  35.0  0.0  33.6  0.627  50.0
1  1.0   85.0  66.0  29.0  0.0  26.6  0.351  31.0
2  8.0  183.0  64.0   0.0  0.0  23.3  0.672  32.0
     0
0  1.0
1  0.0
2  1.0

In [18]:
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))


Epoch 1/150
768/768 [==============================] - 0s - loss: 0.6835 - acc: 0.6120     
Epoch 2/150
768/768 [==============================] - 0s - loss: 0.6648 - acc: 0.6510     
Epoch 3/150
768/768 [==============================] - 0s - loss: 0.6521 - acc: 0.6510     
Epoch 4/150
768/768 [==============================] - 0s - loss: 0.6381 - acc: 0.6523     
Epoch 5/150
768/768 [==============================] - 0s - loss: 0.6188 - acc: 0.6680     
Epoch 6/150
768/768 [==============================] - 0s - loss: 0.6099 - acc: 0.6888     
Epoch 7/150
768/768 [==============================] - 0s - loss: 0.6048 - acc: 0.6784     
Epoch 8/150
768/768 [==============================] - 0s - loss: 0.5974 - acc: 0.6914     
Epoch 9/150
768/768 [==============================] - 0s - loss: 0.5942 - acc: 0.6901     
Epoch 10/150
768/768 [==============================] - 0s - loss: 0.5882 - acc: 0.6862     
Epoch 11/150
768/768 [==============================] - 0s - loss: 0.5879 - acc: 0.6953     
Epoch 12/150
768/768 [==============================] - 0s - loss: 0.5865 - acc: 0.6966     
Epoch 13/150
768/768 [==============================] - 0s - loss: 0.5837 - acc: 0.6823     
Epoch 14/150
768/768 [==============================] - 0s - loss: 0.5769 - acc: 0.7018     
Epoch 15/150
768/768 [==============================] - 0s - loss: 0.5738 - acc: 0.7005     
Epoch 16/150
768/768 [==============================] - 0s - loss: 0.5700 - acc: 0.6992     
Epoch 17/150
768/768 [==============================] - 0s - loss: 0.5808 - acc: 0.7031     
Epoch 18/150
768/768 [==============================] - 0s - loss: 0.5736 - acc: 0.6888     
Epoch 19/150
768/768 [==============================] - 0s - loss: 0.5732 - acc: 0.7044     
Epoch 20/150
768/768 [==============================] - 0s - loss: 0.5718 - acc: 0.7044     
Epoch 21/150
768/768 [==============================] - 0s - loss: 0.5694 - acc: 0.7096     
Epoch 22/150
768/768 [==============================] - 0s - loss: 0.5693 - acc: 0.7005     
Epoch 23/150
768/768 [==============================] - 0s - loss: 0.5664 - acc: 0.7018     
Epoch 24/150
768/768 [==============================] - 0s - loss: 0.5667 - acc: 0.7044     
Epoch 25/150
768/768 [==============================] - 0s - loss: 0.5663 - acc: 0.6927     
Epoch 26/150
768/768 [==============================] - 0s - loss: 0.5605 - acc: 0.7044     
Epoch 27/150
768/768 [==============================] - 0s - loss: 0.5611 - acc: 0.7161     
Epoch 28/150
768/768 [==============================] - 0s - loss: 0.5673 - acc: 0.7083     
Epoch 29/150
768/768 [==============================] - 0s - loss: 0.5625 - acc: 0.7174     
Epoch 30/150
768/768 [==============================] - 0s - loss: 0.5574 - acc: 0.7148     
Epoch 31/150
768/768 [==============================] - 0s - loss: 0.5610 - acc: 0.7057     
Epoch 32/150
768/768 [==============================] - 0s - loss: 0.5571 - acc: 0.7174     
Epoch 33/150
768/768 [==============================] - 0s - loss: 0.5533 - acc: 0.7109     
Epoch 34/150
768/768 [==============================] - 0s - loss: 0.5484 - acc: 0.7292     
Epoch 35/150
768/768 [==============================] - 0s - loss: 0.5521 - acc: 0.7161     
Epoch 36/150
768/768 [==============================] - 0s - loss: 0.5494 - acc: 0.7174     
Epoch 37/150
768/768 [==============================] - 0s - loss: 0.5517 - acc: 0.7227     
Epoch 38/150
768/768 [==============================] - 0s - loss: 0.5439 - acc: 0.7266     
Epoch 39/150
768/768 [==============================] - 0s - loss: 0.5462 - acc: 0.7240     
Epoch 40/150
768/768 [==============================] - 0s - loss: 0.5430 - acc: 0.7292     
Epoch 41/150
768/768 [==============================] - 0s - loss: 0.5455 - acc: 0.7305     
Epoch 42/150
768/768 [==============================] - 0s - loss: 0.5425 - acc: 0.7266     
Epoch 43/150
768/768 [==============================] - 0s - loss: 0.5469 - acc: 0.7135     
Epoch 44/150
768/768 [==============================] - 0s - loss: 0.5414 - acc: 0.7357     
Epoch 45/150
768/768 [==============================] - 0s - loss: 0.5424 - acc: 0.7240     
Epoch 46/150
768/768 [==============================] - 0s - loss: 0.5407 - acc: 0.7318     
Epoch 47/150
768/768 [==============================] - 0s - loss: 0.5358 - acc: 0.7266     
Epoch 48/150
768/768 [==============================] - 0s - loss: 0.5339 - acc: 0.7344     
Epoch 49/150
768/768 [==============================] - 0s - loss: 0.5322 - acc: 0.7331     
Epoch 50/150
768/768 [==============================] - 0s - loss: 0.5299 - acc: 0.7383     
Epoch 51/150
768/768 [==============================] - 0s - loss: 0.5314 - acc: 0.7422     
Epoch 52/150
768/768 [==============================] - 0s - loss: 0.5349 - acc: 0.7279     
Epoch 53/150
768/768 [==============================] - 0s - loss: 0.5314 - acc: 0.7448     
Epoch 54/150
768/768 [==============================] - 0s - loss: 0.5330 - acc: 0.7344     
Epoch 55/150
768/768 [==============================] - 0s - loss: 0.5287 - acc: 0.7461     
Epoch 56/150
768/768 [==============================] - 0s - loss: 0.5329 - acc: 0.7422     
Epoch 57/150
768/768 [==============================] - 0s - loss: 0.5282 - acc: 0.7383     
Epoch 58/150
768/768 [==============================] - 0s - loss: 0.5241 - acc: 0.7500     
Epoch 59/150
768/768 [==============================] - 0s - loss: 0.5247 - acc: 0.7448     
Epoch 60/150
768/768 [==============================] - 0s - loss: 0.5208 - acc: 0.7357     
Epoch 61/150
768/768 [==============================] - 0s - loss: 0.5225 - acc: 0.7435     
Epoch 62/150
768/768 [==============================] - 0s - loss: 0.5221 - acc: 0.7539     
Epoch 63/150
768/768 [==============================] - 0s - loss: 0.5186 - acc: 0.7422     
Epoch 64/150
768/768 [==============================] - 0s - loss: 0.5210 - acc: 0.7422     
Epoch 65/150
768/768 [==============================] - 0s - loss: 0.5174 - acc: 0.7461     
Epoch 66/150
768/768 [==============================] - 0s - loss: 0.5080 - acc: 0.7552     
Epoch 67/150
768/768 [==============================] - 0s - loss: 0.5179 - acc: 0.7474     
Epoch 68/150
768/768 [==============================] - 0s - loss: 0.5152 - acc: 0.7578     
Epoch 69/150
768/768 [==============================] - 0s - loss: 0.5147 - acc: 0.7500     
Epoch 70/150
768/768 [==============================] - 0s - loss: 0.5113 - acc: 0.7526     
Epoch 71/150
768/768 [==============================] - 0s - loss: 0.5164 - acc: 0.7513     
Epoch 72/150
768/768 [==============================] - 0s - loss: 0.5098 - acc: 0.7565     
Epoch 73/150
768/768 [==============================] - 0s - loss: 0.5145 - acc: 0.7500     
Epoch 74/150
768/768 [==============================] - 0s - loss: 0.5176 - acc: 0.7487     
Epoch 75/150
768/768 [==============================] - 0s - loss: 0.5131 - acc: 0.7474     
Epoch 76/150
768/768 [==============================] - 0s - loss: 0.5091 - acc: 0.7591     
Epoch 77/150
768/768 [==============================] - 0s - loss: 0.5150 - acc: 0.7565     
Epoch 78/150
768/768 [==============================] - 0s - loss: 0.5048 - acc: 0.7578     
Epoch 79/150
768/768 [==============================] - 0s - loss: 0.5062 - acc: 0.7422     
Epoch 80/150
768/768 [==============================] - 0s - loss: 0.5054 - acc: 0.7552     
Epoch 81/150
768/768 [==============================] - 0s - loss: 0.5032 - acc: 0.7526     
Epoch 82/150
768/768 [==============================] - 0s - loss: 0.5100 - acc: 0.7461     
Epoch 83/150
768/768 [==============================] - 0s - loss: 0.5019 - acc: 0.7552     
Epoch 84/150
768/768 [==============================] - 0s - loss: 0.5018 - acc: 0.7461     
Epoch 85/150
768/768 [==============================] - 0s - loss: 0.4949 - acc: 0.7682     
Epoch 86/150
768/768 [==============================] - 0s - loss: 0.5043 - acc: 0.7461     
Epoch 87/150
768/768 [==============================] - 0s - loss: 0.5021 - acc: 0.7747     
Epoch 88/150
768/768 [==============================] - 0s - loss: 0.4985 - acc: 0.7552     
Epoch 89/150
768/768 [==============================] - 0s - loss: 0.4922 - acc: 0.7669     
Epoch 90/150
768/768 [==============================] - 0s - loss: 0.4986 - acc: 0.7630     
Epoch 91/150
768/768 [==============================] - 0s - loss: 0.4960 - acc: 0.7656     
Epoch 92/150
768/768 [==============================] - 0s - loss: 0.4921 - acc: 0.7565     
Epoch 93/150
768/768 [==============================] - 0s - loss: 0.4988 - acc: 0.7591     
Epoch 94/150
768/768 [==============================] - 0s - loss: 0.5039 - acc: 0.7617     
Epoch 95/150
768/768 [==============================] - 0s - loss: 0.4910 - acc: 0.7552     
Epoch 96/150
768/768 [==============================] - 0s - loss: 0.4963 - acc: 0.7721     
Epoch 97/150
768/768 [==============================] - 0s - loss: 0.5029 - acc: 0.7539     
Epoch 98/150
768/768 [==============================] - 0s - loss: 0.4844 - acc: 0.7708     
Epoch 99/150
768/768 [==============================] - 0s - loss: 0.4891 - acc: 0.7799     
Epoch 100/150
768/768 [==============================] - 0s - loss: 0.4865 - acc: 0.7630     
Epoch 101/150
768/768 [==============================] - 0s - loss: 0.4833 - acc: 0.7721     
Epoch 102/150
768/768 [==============================] - 0s - loss: 0.4821 - acc: 0.7552     
Epoch 103/150
768/768 [==============================] - 0s - loss: 0.4861 - acc: 0.7721     
Epoch 104/150
768/768 [==============================] - 0s - loss: 0.4908 - acc: 0.7643     
Epoch 105/150
768/768 [==============================] - 0s - loss: 0.4817 - acc: 0.7747     
Epoch 106/150
768/768 [==============================] - 0s - loss: 0.4839 - acc: 0.7682     
Epoch 107/150
768/768 [==============================] - 0s - loss: 0.4824 - acc: 0.7565     
Epoch 108/150
768/768 [==============================] - 0s - loss: 0.4918 - acc: 0.7656     
Epoch 109/150
768/768 [==============================] - 0s - loss: 0.4767 - acc: 0.7786     
Epoch 110/150
768/768 [==============================] - 0s - loss: 0.4860 - acc: 0.7630     
Epoch 111/150
768/768 [==============================] - 0s - loss: 0.4744 - acc: 0.7812     
Epoch 112/150
768/768 [==============================] - 0s - loss: 0.4853 - acc: 0.7539     
Epoch 113/150
768/768 [==============================] - 0s - loss: 0.4737 - acc: 0.7721     
Epoch 114/150
768/768 [==============================] - 0s - loss: 0.4845 - acc: 0.7721     
Epoch 115/150
768/768 [==============================] - 0s - loss: 0.4815 - acc: 0.7513     
Epoch 116/150
768/768 [==============================] - 0s - loss: 0.4742 - acc: 0.7721     
Epoch 117/150
768/768 [==============================] - 0s - loss: 0.4771 - acc: 0.7760     
Epoch 118/150
768/768 [==============================] - 0s - loss: 0.4807 - acc: 0.7708     
Epoch 119/150
768/768 [==============================] - 0s - loss: 0.4849 - acc: 0.7474     
Epoch 120/150
768/768 [==============================] - 0s - loss: 0.4700 - acc: 0.7786     
Epoch 121/150
768/768 [==============================] - 0s - loss: 0.4786 - acc: 0.7643     
Epoch 122/150
768/768 [==============================] - 0s - loss: 0.4718 - acc: 0.7786     
Epoch 123/150
768/768 [==============================] - 0s - loss: 0.4826 - acc: 0.7565     
Epoch 124/150
768/768 [==============================] - 0s - loss: 0.4683 - acc: 0.7656     
Epoch 125/150
768/768 [==============================] - 0s - loss: 0.4767 - acc: 0.7604     
Epoch 126/150
768/768 [==============================] - 0s - loss: 0.4658 - acc: 0.7839     
Epoch 127/150
768/768 [==============================] - 0s - loss: 0.4684 - acc: 0.7643     
Epoch 128/150
768/768 [==============================] - 0s - loss: 0.4740 - acc: 0.7734     
Epoch 129/150
768/768 [==============================] - 0s - loss: 0.4685 - acc: 0.7734     
Epoch 130/150
768/768 [==============================] - 0s - loss: 0.4659 - acc: 0.7734     
Epoch 131/150
768/768 [==============================] - 0s - loss: 0.4725 - acc: 0.7747     
Epoch 132/150
768/768 [==============================] - 0s - loss: 0.4643 - acc: 0.7799     
Epoch 133/150
768/768 [==============================] - 0s - loss: 0.4716 - acc: 0.7721     
Epoch 134/150
768/768 [==============================] - 0s - loss: 0.4627 - acc: 0.7786     
Epoch 135/150
768/768 [==============================] - 0s - loss: 0.4672 - acc: 0.7708     
Epoch 136/150
768/768 [==============================] - 0s - loss: 0.4704 - acc: 0.7839     
Epoch 137/150
768/768 [==============================] - 0s - loss: 0.4635 - acc: 0.7734     
Epoch 138/150
768/768 [==============================] - 0s - loss: 0.4626 - acc: 0.7786     
Epoch 139/150
768/768 [==============================] - 0s - loss: 0.4638 - acc: 0.7695     
Epoch 140/150
768/768 [==============================] - 0s - loss: 0.4658 - acc: 0.7786     
Epoch 141/150
768/768 [==============================] - 0s - loss: 0.4577 - acc: 0.7839     
Epoch 142/150
768/768 [==============================] - 0s - loss: 0.4770 - acc: 0.7721     
Epoch 143/150
768/768 [==============================] - 0s - loss: 0.4623 - acc: 0.7773     
Epoch 144/150
768/768 [==============================] - 0s - loss: 0.4672 - acc: 0.7734     
Epoch 145/150
768/768 [==============================] - 0s - loss: 0.4547 - acc: 0.7812     
Epoch 146/150
768/768 [==============================] - 0s - loss: 0.4656 - acc: 0.7747     
Epoch 147/150
768/768 [==============================] - 0s - loss: 0.4558 - acc: 0.7799     
Epoch 148/150
768/768 [==============================] - 0s - loss: 0.4538 - acc: 0.7891     
Epoch 149/150
768/768 [==============================] - 0s - loss: 0.4605 - acc: 0.7747     
Epoch 150/150
768/768 [==============================] - 0s - loss: 0.4557 - acc: 0.7904     
608/768 [======================>.......] - ETA: 0sacc: 77.99%

In [19]:
# Predict on input values
predictions = model.predict(X)
# round predictions
rounded = [round(x) for x in predictions]
print(rounded)


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In [ ]: