In [3]:
# Following the tutorial from
# http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
data_file = "../data/pima-indians-diabetes.data"
In [4]:
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
# load pima indians dataset
dataset = np.loadtxt(data_file, delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
In [9]:
# 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'))
In [10]:
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)
Epoch 1/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 2/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 3/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 4/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 5/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 6/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 7/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 8/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 9/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 10/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 11/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 12/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 13/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 14/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 15/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 16/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 17/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 18/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 19/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 20/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 21/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 22/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 23/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 24/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 25/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 26/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 27/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 28/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 29/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 30/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 31/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 32/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 33/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 34/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 35/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 36/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 37/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 38/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 39/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 40/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 41/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 42/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 43/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 44/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 45/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 46/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 47/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 48/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 49/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 50/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 51/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 52/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 53/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 54/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 55/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 56/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 57/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 58/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 59/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 60/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 61/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 62/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 63/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 64/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 65/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 66/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 67/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 68/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 69/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 70/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 71/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 72/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 73/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 74/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 75/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 76/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 77/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 78/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 79/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 80/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 81/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 82/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 83/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 84/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 85/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 86/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 87/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 88/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 89/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 90/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 91/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 92/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 93/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 94/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 95/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 96/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 97/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 98/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 99/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 100/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 101/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 102/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 103/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 104/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 105/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 106/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 107/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 108/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 109/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 110/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 111/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 112/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 113/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 114/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 115/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 116/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 117/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 118/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 119/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 120/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 121/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 122/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 123/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 124/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 125/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 126/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 127/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 128/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 129/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 130/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 131/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 132/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 133/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 134/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 135/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 136/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 137/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 138/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 139/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 140/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 141/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 142/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 143/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 144/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 145/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 146/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 147/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 148/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 149/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Epoch 150/150
768/768 [==============================] - 0s - loss: 10.3792 - acc: 0.3490
Out[10]:
<keras.callbacks.History at 0x11259afd0>
In [11]:
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
32/768 [>.............................] - ETA: 0sacc: 34.90%
In [ ]:
Content source: thammegowda/notes
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