In [1]:

    

from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)


    

    




Using TensorFlow backend.

In [2]:

    

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


    

In [3]:

    

# 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'))


    

    




/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:3: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(12, input_dim=8, activation="relu", kernel_initializer="uniform")`
  app.launch_new_instance()
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:4: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(8, activation="relu", kernel_initializer="uniform")`
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1, activation="sigmoid", kernel_initializer="uniform")`

In [4]:

    

# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])


    

In [6]:

    

# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)


    

    




Epoch 1/150
310/768 [===========>..................] - ETA: 0s - loss: 0.4703 - acc: 0.7516





    




/opt/conda/lib/python3.5/site-packages/keras/models.py:834: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.
  warnings.warn('The `nb_epoch` argument in `fit` '






    




768/768 [==============================] - 0s - loss: 0.4604 - acc: 0.7799     
Epoch 2/150
768/768 [==============================] - 0s - loss: 0.4514 - acc: 0.7813     
Epoch 3/150
768/768 [==============================] - 0s - loss: 0.4551 - acc: 0.7839     
Epoch 4/150
768/768 [==============================] - 0s - loss: 0.4581 - acc: 0.7813     
Epoch 5/150
768/768 [==============================] - 0s - loss: 0.4528 - acc: 0.7839     
Epoch 6/150
768/768 [==============================] - 0s - loss: 0.4599 - acc: 0.7773     
Epoch 7/150
768/768 [==============================] - 0s - loss: 0.4516 - acc: 0.7878     
Epoch 8/150
768/768 [==============================] - 0s - loss: 0.4522 - acc: 0.7813     
Epoch 9/150
768/768 [==============================] - 0s - loss: 0.4448 - acc: 0.7813     
Epoch 10/150
768/768 [==============================] - 0s - loss: 0.4538 - acc: 0.7865     
Epoch 11/150
768/768 [==============================] - 0s - loss: 0.4516 - acc: 0.7904     
Epoch 12/150
768/768 [==============================] - 0s - loss: 0.4487 - acc: 0.7799     
Epoch 13/150
768/768 [==============================] - 0s - loss: 0.4688 - acc: 0.7708     
Epoch 14/150
768/768 [==============================] - 0s - loss: 0.4513 - acc: 0.7839     
Epoch 15/150
768/768 [==============================] - 0s - loss: 0.4518 - acc: 0.7930     
Epoch 16/150
768/768 [==============================] - 0s - loss: 0.4526 - acc: 0.7904     
Epoch 17/150
768/768 [==============================] - 0s - loss: 0.4478 - acc: 0.7826     
Epoch 18/150
768/768 [==============================] - 0s - loss: 0.4483 - acc: 0.7943     
Epoch 19/150
768/768 [==============================] - 0s - loss: 0.4527 - acc: 0.7943     
Epoch 20/150
768/768 [==============================] - 0s - loss: 0.4492 - acc: 0.7891     
Epoch 21/150
768/768 [==============================] - 0s - loss: 0.4501 - acc: 0.7786     
Epoch 22/150
768/768 [==============================] - 0s - loss: 0.4438 - acc: 0.7865     
Epoch 23/150
768/768 [==============================] - 0s - loss: 0.4723 - acc: 0.7708     
Epoch 24/150
768/768 [==============================] - 0s - loss: 0.4538 - acc: 0.7813     
Epoch 25/150
768/768 [==============================] - 0s - loss: 0.4469 - acc: 0.8008     
Epoch 26/150
768/768 [==============================] - 0s - loss: 0.4554 - acc: 0.7891     
Epoch 27/150
768/768 [==============================] - 0s - loss: 0.4493 - acc: 0.7773     
Epoch 28/150
768/768 [==============================] - 0s - loss: 0.4476 - acc: 0.7917     
Epoch 29/150
768/768 [==============================] - 0s - loss: 0.4539 - acc: 0.7865     
Epoch 30/150
768/768 [==============================] - 0s - loss: 0.4594 - acc: 0.7799     
Epoch 31/150
768/768 [==============================] - 0s - loss: 0.4502 - acc: 0.7917     
Epoch 32/150
768/768 [==============================] - 0s - loss: 0.4452 - acc: 0.7813     
Epoch 33/150
768/768 [==============================] - 0s - loss: 0.4425 - acc: 0.7852     
Epoch 34/150
768/768 [==============================] - 0s - loss: 0.4542 - acc: 0.7747     
Epoch 35/150
768/768 [==============================] - 0s - loss: 0.4423 - acc: 0.7917     
Epoch 36/150
768/768 [==============================] - 0s - loss: 0.4443 - acc: 0.8008     
Epoch 37/150
768/768 [==============================] - 0s - loss: 0.4434 - acc: 0.7852     
Epoch 38/150
768/768 [==============================] - 0s - loss: 0.4397 - acc: 0.7943     
Epoch 39/150
768/768 [==============================] - 0s - loss: 0.4480 - acc: 0.7943     
Epoch 40/150
768/768 [==============================] - 0s - loss: 0.4460 - acc: 0.7878     
Epoch 41/150
768/768 [==============================] - 0s - loss: 0.4463 - acc: 0.7969     
Epoch 42/150
768/768 [==============================] - 0s - loss: 0.4487 - acc: 0.7799     
Epoch 43/150
768/768 [==============================] - 0s - loss: 0.4462 - acc: 0.7904     
Epoch 44/150
768/768 [==============================] - 0s - loss: 0.4432 - acc: 0.7917     
Epoch 45/150
768/768 [==============================] - 0s - loss: 0.4390 - acc: 0.7943     
Epoch 46/150
768/768 [==============================] - 0s - loss: 0.4460 - acc: 0.7760     
Epoch 47/150
768/768 [==============================] - 0s - loss: 0.4415 - acc: 0.7839     
Epoch 48/150
768/768 [==============================] - 0s - loss: 0.4561 - acc: 0.7760     
Epoch 49/150
768/768 [==============================] - 0s - loss: 0.4407 - acc: 0.7813     
Epoch 50/150
768/768 [==============================] - 0s - loss: 0.4387 - acc: 0.7839     
Epoch 51/150
768/768 [==============================] - 0s - loss: 0.4434 - acc: 0.7852     
Epoch 52/150
768/768 [==============================] - 0s - loss: 0.4453 - acc: 0.7891     
Epoch 53/150
768/768 [==============================] - 0s - loss: 0.4520 - acc: 0.7878     
Epoch 54/150
768/768 [==============================] - 0s - loss: 0.4452 - acc: 0.7904     
Epoch 55/150
768/768 [==============================] - 0s - loss: 0.4425 - acc: 0.7878     
Epoch 56/150
768/768 [==============================] - 0s - loss: 0.4367 - acc: 0.7943     
Epoch 57/150
768/768 [==============================] - 0s - loss: 0.4515 - acc: 0.7721     
Epoch 58/150
768/768 [==============================] - 0s - loss: 0.4386 - acc: 0.7917     
Epoch 59/150
768/768 [==============================] - 0s - loss: 0.4431 - acc: 0.7865     
Epoch 60/150
768/768 [==============================] - 0s - loss: 0.4404 - acc: 0.7839     
Epoch 61/150
768/768 [==============================] - 0s - loss: 0.4348 - acc: 0.7826     
Epoch 62/150
768/768 [==============================] - 0s - loss: 0.4416 - acc: 0.7956     
Epoch 63/150
768/768 [==============================] - 0s - loss: 0.4366 - acc: 0.8021     
Epoch 64/150
768/768 [==============================] - 0s - loss: 0.4402 - acc: 0.7943     
Epoch 65/150
768/768 [==============================] - 0s - loss: 0.4373 - acc: 0.7878     
Epoch 66/150
768/768 [==============================] - 0s - loss: 0.4487 - acc: 0.7865     
Epoch 67/150
768/768 [==============================] - 0s - loss: 0.4383 - acc: 0.7943     
Epoch 68/150
768/768 [==============================] - 0s - loss: 0.4311 - acc: 0.8021     
Epoch 69/150
768/768 [==============================] - 0s - loss: 0.4258 - acc: 0.8008     
Epoch 70/150
768/768 [==============================] - 0s - loss: 0.4311 - acc: 0.7891     
Epoch 71/150
768/768 [==============================] - 0s - loss: 0.4468 - acc: 0.7878     
Epoch 72/150
768/768 [==============================] - 0s - loss: 0.4495 - acc: 0.7813     
Epoch 73/150
768/768 [==============================] - 0s - loss: 0.4344 - acc: 0.7891     
Epoch 74/150
768/768 [==============================] - 0s - loss: 0.4343 - acc: 0.7969     
Epoch 75/150
768/768 [==============================] - 0s - loss: 0.4406 - acc: 0.7826     
Epoch 76/150
768/768 [==============================] - 0s - loss: 0.4484 - acc: 0.7852     
Epoch 77/150
768/768 [==============================] - 0s - loss: 0.4366 - acc: 0.7826     
Epoch 78/150
768/768 [==============================] - 0s - loss: 0.4408 - acc: 0.7865     
Epoch 79/150
768/768 [==============================] - 0s - loss: 0.4480 - acc: 0.7852     
Epoch 80/150
768/768 [==============================] - 0s - loss: 0.4510 - acc: 0.7813     
Epoch 81/150
768/768 [==============================] - 0s - loss: 0.4328 - acc: 0.7943     
Epoch 82/150
768/768 [==============================] - 0s - loss: 0.4362 - acc: 0.7917     
Epoch 83/150
768/768 [==============================] - 0s - loss: 0.4419 - acc: 0.7839     
Epoch 84/150
768/768 [==============================] - 0s - loss: 0.4425 - acc: 0.7813     
Epoch 85/150
768/768 [==============================] - 0s - loss: 0.4350 - acc: 0.7969     
Epoch 86/150
768/768 [==============================] - 0s - loss: 0.4322 - acc: 0.7930     
Epoch 87/150
768/768 [==============================] - 0s - loss: 0.4472 - acc: 0.7943     
Epoch 88/150
768/768 [==============================] - 0s - loss: 0.4386 - acc: 0.7917     
Epoch 89/150
768/768 [==============================] - 0s - loss: 0.4387 - acc: 0.7826     
Epoch 90/150
768/768 [==============================] - 0s - loss: 0.4366 - acc: 0.7904     
Epoch 91/150
768/768 [==============================] - 0s - loss: 0.4325 - acc: 0.7904     
Epoch 92/150
768/768 [==============================] - 0s - loss: 0.4445 - acc: 0.7839     
Epoch 93/150
768/768 [==============================] - 0s - loss: 0.4355 - acc: 0.7786     
Epoch 94/150
768/768 [==============================] - 0s - loss: 0.4529 - acc: 0.7799     
Epoch 95/150
768/768 [==============================] - 0s - loss: 0.4505 - acc: 0.7878     
Epoch 96/150
768/768 [==============================] - 0s - loss: 0.4391 - acc: 0.8008     
Epoch 97/150
768/768 [==============================] - 0s - loss: 0.4300 - acc: 0.7956     
Epoch 98/150
768/768 [==============================] - 0s - loss: 0.4389 - acc: 0.7799     
Epoch 99/150
768/768 [==============================] - 0s - loss: 0.4289 - acc: 0.7943     
Epoch 100/150
768/768 [==============================] - 0s - loss: 0.4425 - acc: 0.7995     
Epoch 101/150
768/768 [==============================] - 0s - loss: 0.4380 - acc: 0.7878     
Epoch 102/150
768/768 [==============================] - 0s - loss: 0.4343 - acc: 0.7799     
Epoch 103/150
768/768 [==============================] - 0s - loss: 0.4378 - acc: 0.7865     
Epoch 104/150
768/768 [==============================] - 0s - loss: 0.4377 - acc: 0.7956     
Epoch 105/150
768/768 [==============================] - 0s - loss: 0.4465 - acc: 0.7813     
Epoch 106/150
768/768 [==============================] - 0s - loss: 0.4218 - acc: 0.7930     
Epoch 107/150
768/768 [==============================] - 0s - loss: 0.4342 - acc: 0.7917     
Epoch 108/150
768/768 [==============================] - 0s - loss: 0.4349 - acc: 0.7891     
Epoch 109/150
768/768 [==============================] - 0s - loss: 0.4242 - acc: 0.7930     
Epoch 110/150
768/768 [==============================] - 0s - loss: 0.4401 - acc: 0.7721     
Epoch 111/150
768/768 [==============================] - 0s - loss: 0.4281 - acc: 0.7943     
Epoch 112/150
768/768 [==============================] - 0s - loss: 0.4234 - acc: 0.7943     
Epoch 113/150
768/768 [==============================] - 0s - loss: 0.4359 - acc: 0.8008     
Epoch 114/150
768/768 [==============================] - 0s - loss: 0.4282 - acc: 0.7943     
Epoch 115/150
768/768 [==============================] - 0s - loss: 0.4373 - acc: 0.7813     
Epoch 116/150
768/768 [==============================] - 0s - loss: 0.4297 - acc: 0.7956     
Epoch 117/150
768/768 [==============================] - 0s - loss: 0.4279 - acc: 0.7826     
Epoch 118/150
768/768 [==============================] - 0s - loss: 0.4285 - acc: 0.7904     
Epoch 119/150
768/768 [==============================] - 0s - loss: 0.4266 - acc: 0.7969     
Epoch 120/150
768/768 [==============================] - 0s - loss: 0.4340 - acc: 0.7852     
Epoch 121/150
768/768 [==============================] - 0s - loss: 0.4329 - acc: 0.7995     
Epoch 122/150
768/768 [==============================] - 0s - loss: 0.4339 - acc: 0.7930     
Epoch 123/150
768/768 [==============================] - 0s - loss: 0.4220 - acc: 0.7969     
Epoch 124/150
768/768 [==============================] - 0s - loss: 0.4434 - acc: 0.7852     
Epoch 125/150
768/768 [==============================] - 0s - loss: 0.4277 - acc: 0.7852     
Epoch 126/150
768/768 [==============================] - 0s - loss: 0.4273 - acc: 0.7982     
Epoch 127/150
768/768 [==============================] - 0s - loss: 0.4392 - acc: 0.7760     
Epoch 128/150
768/768 [==============================] - 0s - loss: 0.4328 - acc: 0.8021     
Epoch 129/150
768/768 [==============================] - 0s - loss: 0.4375 - acc: 0.7917     
Epoch 130/150
768/768 [==============================] - 0s - loss: 0.4299 - acc: 0.7969     
Epoch 131/150
768/768 [==============================] - 0s - loss: 0.4294 - acc: 0.7813     
Epoch 132/150
768/768 [==============================] - 0s - loss: 0.4325 - acc: 0.7982     
Epoch 133/150
768/768 [==============================] - 0s - loss: 0.4237 - acc: 0.7956     
Epoch 134/150
768/768 [==============================] - 0s - loss: 0.4284 - acc: 0.7969     
Epoch 135/150
768/768 [==============================] - 0s - loss: 0.4314 - acc: 0.7865     
Epoch 136/150
768/768 [==============================] - 0s - loss: 0.4342 - acc: 0.7878     
Epoch 137/150
768/768 [==============================] - 0s - loss: 0.4205 - acc: 0.7969     
Epoch 138/150
768/768 [==============================] - 0s - loss: 0.4441 - acc: 0.7865     
Epoch 139/150
768/768 [==============================] - 0s - loss: 0.4249 - acc: 0.7969     
Epoch 140/150
768/768 [==============================] - 0s - loss: 0.4262 - acc: 0.7930     
Epoch 141/150
768/768 [==============================] - 0s - loss: 0.4391 - acc: 0.7982     
Epoch 142/150
768/768 [==============================] - 0s - loss: 0.4253 - acc: 0.7891     
Epoch 143/150
768/768 [==============================] - 0s - loss: 0.4279 - acc: 0.7878     
Epoch 144/150
768/768 [==============================] - 0s - loss: 0.4299 - acc: 0.7917     
Epoch 145/150
768/768 [==============================] - 0s - loss: 0.4314 - acc: 0.7917     
Epoch 146/150
768/768 [==============================] - 0s - loss: 0.4316 - acc: 0.7839     
Epoch 147/150
768/768 [==============================] - 0s - loss: 0.4309 - acc: 0.7878     
Epoch 148/150
768/768 [==============================] - 0s - loss: 0.4276 - acc: 0.7969     
Epoch 149/150
768/768 [==============================] - 0s - loss: 0.4323 - acc: 0.7852     
Epoch 150/150
768/768 [==============================] - 0s - loss: 0.4252 - acc: 0.7917     






    
Out[6]:





<keras.callbacks.History at 0x7fd5b13889e8>

In [7]:

    

# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))


    

    




544/768 [====================>.........] - ETA: 0sacc: 80.34%

In [8]:

    

# calculate predictions
predictions = model.predict(X)
print(predictions)
# round predictions
rounded = [float(numpy.round(x)) for x in predictions]
print(rounded)


    

    




[[  6.69133127e-01]
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 [  6.37219846e-01]
 [  2.01348349e-01]
 [  5.76111138e-01]
 [  1.73783034e-01]
 [  8.15722525e-01]
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 [  8.83722544e-01]
 [  4.75948960e-01]
 [  4.82171088e-01]
 [  2.30581433e-01]
 [  1.03324257e-01]
 [  1.38909705e-02]
 [  9.16669548e-01]
 [  2.55967140e-01]
 [  1.26770377e-01]
 [  2.30245456e-01]
 [  7.74116740e-02]
 [  5.04455268e-01]
 [  3.29047322e-01]
 [  1.65348321e-01]
 [  3.73783708e-01]
 [  4.40197408e-01]
 [  8.51796150e-01]
 [  5.88658214e-01]
 [  9.21780705e-01]
 [  4.11899269e-01]
 [  3.29556800e-02]
 [  3.65873933e-01]
 [  5.38355336e-02]
 [  4.71399501e-02]
 [  1.70125216e-01]
 [  3.18676502e-01]
 [  3.27984124e-01]
 [  3.55091333e-01]
 [  2.56607115e-01]
 [  3.70088905e-01]
 [  5.93357682e-01]
 [  6.20962121e-04]
 [  1.13541283e-01]
 [  5.93073249e-01]
 [  5.00194728e-01]
 [  1.21848835e-02]
 [  8.41940939e-01]
 [  2.17973694e-01]
 [  9.42423880e-01]
 [  3.40437293e-01]
 [  9.48982239e-01]
 [  9.25409257e-01]
 [  3.44716683e-02]
 [  2.71657407e-01]
 [  2.07361747e-02]
 [  9.32211936e-01]
 [  2.83629090e-01]
 [  2.33667731e-01]
 [  2.42609277e-01]
 [  5.71690202e-02]
 [  1.71981499e-01]
 [  4.08073545e-01]
 [  4.49571371e-01]
 [  4.13742095e-01]
 [  3.19927722e-01]
 [  1.86247397e-02]
 [  8.08123350e-02]
 [  4.15818930e-01]
 [  1.99990690e-01]
 [  1.92325160e-01]
 [  5.75491562e-02]
 [  9.14961040e-01]
 [  8.75633284e-02]
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In [ ]: