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 [ ]:
Content source: pramitchoudhary/Experiments
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