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In [ ]:



    


# https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/



    











In [2]:



    


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



    











In [6]:



    


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



    











In [7]:



    


# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))



    











In [8]:



    


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



    











In [9]:



    


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



    


















    






Epoch 1/150
768/768 [==============================] - 1s 2ms/step - loss: 3.7106 - acc: 0.5977
Epoch 2/150
768/768 [==============================] - 0s 358us/step - loss: 0.9376 - acc: 0.5924
Epoch 3/150
768/768 [==============================] - 0s 311us/step - loss: 0.7478 - acc: 0.6445
Epoch 4/150
768/768 [==============================] - 0s 224us/step - loss: 0.7121 - acc: 0.6549
Epoch 5/150
768/768 [==============================] - 0s 174us/step - loss: 0.6842 - acc: 0.6680
Epoch 6/150
768/768 [==============================] - 0s 257us/step - loss: 0.6522 - acc: 0.6797
Epoch 7/150
768/768 [==============================] - 0s 254us/step - loss: 0.6496 - acc: 0.6836
Epoch 8/150
768/768 [==============================] - 0s 307us/step - loss: 0.6380 - acc: 0.6875
Epoch 9/150
768/768 [==============================] - 0s 215us/step - loss: 0.6238 - acc: 0.6953
Epoch 10/150
768/768 [==============================] - 0s 184us/step - loss: 0.6288 - acc: 0.6771
Epoch 11/150
768/768 [==============================] - 0s 316us/step - loss: 0.6433 - acc: 0.6745
Epoch 12/150
768/768 [==============================] - 0s 273us/step - loss: 0.6400 - acc: 0.6732
Epoch 13/150
768/768 [==============================] - 0s 241us/step - loss: 0.6262 - acc: 0.6719
Epoch 14/150
768/768 [==============================] - 0s 302us/step - loss: 0.6179 - acc: 0.7018
Epoch 15/150
768/768 [==============================] - 0s 327us/step - loss: 0.6020 - acc: 0.6953
Epoch 16/150
768/768 [==============================] - 0s 244us/step - loss: 0.5877 - acc: 0.7018
Epoch 17/150
768/768 [==============================] - 0s 277us/step - loss: 0.5848 - acc: 0.6992
Epoch 18/150
768/768 [==============================] - 0s 202us/step - loss: 0.6008 - acc: 0.6849
Epoch 19/150
768/768 [==============================] - 0s 180us/step - loss: 0.5807 - acc: 0.7070
Epoch 20/150
768/768 [==============================] - 0s 275us/step - loss: 0.5811 - acc: 0.7174
Epoch 21/150
768/768 [==============================] - 0s 189us/step - loss: 0.5688 - acc: 0.7161
Epoch 22/150
768/768 [==============================] - 0s 214us/step - loss: 0.5824 - acc: 0.6966
Epoch 23/150
768/768 [==============================] - 0s 197us/step - loss: 0.5743 - acc: 0.7122
Epoch 24/150
768/768 [==============================] - 0s 172us/step - loss: 0.5677 - acc: 0.7344
Epoch 25/150
768/768 [==============================] - 0s 177us/step - loss: 0.5580 - acc: 0.7370
Epoch 26/150
768/768 [==============================] - 0s 197us/step - loss: 0.5708 - acc: 0.7031
Epoch 27/150
768/768 [==============================] - 0s 246us/step - loss: 0.5558 - acc: 0.7214
Epoch 28/150
768/768 [==============================] - 0s 288us/step - loss: 0.5559 - acc: 0.7344
Epoch 29/150
768/768 [==============================] - 0s 323us/step - loss: 0.5742 - acc: 0.7135
Epoch 30/150
768/768 [==============================] - 0s 188us/step - loss: 0.5613 - acc: 0.7214
Epoch 31/150
768/768 [==============================] - 0s 176us/step - loss: 0.5690 - acc: 0.7148
Epoch 32/150
768/768 [==============================] - 0s 171us/step - loss: 0.5655 - acc: 0.7096
Epoch 33/150
768/768 [==============================] - 0s 171us/step - loss: 0.5539 - acc: 0.7174
Epoch 34/150
768/768 [==============================] - 0s 176us/step - loss: 0.5528 - acc: 0.7305
Epoch 35/150
768/768 [==============================] - 0s 288us/step - loss: 0.5540 - acc: 0.7148
Epoch 36/150
768/768 [==============================] - 0s 224us/step - loss: 0.5627 - acc: 0.7096
Epoch 37/150
768/768 [==============================] - 0s 241us/step - loss: 0.5357 - acc: 0.7344
Epoch 38/150
768/768 [==============================] - 0s 301us/step - loss: 0.5459 - acc: 0.7135
Epoch 39/150
768/768 [==============================] - 0s 198us/step - loss: 0.5491 - acc: 0.7227
Epoch 40/150
768/768 [==============================] - 0s 216us/step - loss: 0.5494 - acc: 0.7174
Epoch 41/150
768/768 [==============================] - 0s 340us/step - loss: 0.5454 - acc: 0.7292
Epoch 42/150
768/768 [==============================] - 0s 293us/step - loss: 0.5388 - acc: 0.7396
Epoch 43/150
768/768 [==============================] - 0s 202us/step - loss: 0.5336 - acc: 0.7422
Epoch 44/150
768/768 [==============================] - 0s 329us/step - loss: 0.5353 - acc: 0.7448
Epoch 45/150
768/768 [==============================] - 0s 431us/step - loss: 0.5333 - acc: 0.7578
Epoch 46/150
768/768 [==============================] - 0s 346us/step - loss: 0.5293 - acc: 0.7578
Epoch 47/150
768/768 [==============================] - 0s 230us/step - loss: 0.5340 - acc: 0.7396
Epoch 48/150
768/768 [==============================] - 0s 234us/step - loss: 0.5353 - acc: 0.7370
Epoch 49/150
768/768 [==============================] - 0s 250us/step - loss: 0.5355 - acc: 0.7474
Epoch 50/150
768/768 [==============================] - 0s 251us/step - loss: 0.5275 - acc: 0.7409
Epoch 51/150
768/768 [==============================] - 0s 299us/step - loss: 0.5295 - acc: 0.7474
Epoch 52/150
768/768 [==============================] - 0s 255us/step - loss: 0.5306 - acc: 0.7422
Epoch 53/150
768/768 [==============================] - 0s 266us/step - loss: 0.5377 - acc: 0.7422
Epoch 54/150
768/768 [==============================] - 0s 234us/step - loss: 0.5384 - acc: 0.7279
Epoch 55/150
768/768 [==============================] - 0s 240us/step - loss: 0.5231 - acc: 0.7487
Epoch 56/150
768/768 [==============================] - 0s 237us/step - loss: 0.5281 - acc: 0.7435
Epoch 57/150
768/768 [==============================] - 0s 280us/step - loss: 0.5323 - acc: 0.7383
Epoch 58/150
768/768 [==============================] - 0s 242us/step - loss: 0.5233 - acc: 0.7539
Epoch 59/150
768/768 [==============================] - 0s 245us/step - loss: 0.5130 - acc: 0.7617
Epoch 60/150
768/768 [==============================] - 0s 238us/step - loss: 0.5341 - acc: 0.7370
Epoch 61/150
768/768 [==============================] - 0s 241us/step - loss: 0.5265 - acc: 0.7370
Epoch 62/150
768/768 [==============================] - 0s 232us/step - loss: 0.5177 - acc: 0.7487
Epoch 63/150
768/768 [==============================] - 0s 220us/step - loss: 0.5449 - acc: 0.7357
Epoch 64/150
768/768 [==============================] - 0s 260us/step - loss: 0.5319 - acc: 0.7422
Epoch 65/150
768/768 [==============================] - 0s 246us/step - loss: 0.5236 - acc: 0.7422
Epoch 66/150
768/768 [==============================] - 0s 294us/step - loss: 0.5078 - acc: 0.7487
Epoch 67/150
768/768 [==============================] - 0s 253us/step - loss: 0.5167 - acc: 0.7448
Epoch 68/150
768/768 [==============================] - 0s 253us/step - loss: 0.5143 - acc: 0.7526
Epoch 69/150
768/768 [==============================] - 0s 286us/step - loss: 0.5138 - acc: 0.7500
Epoch 70/150
768/768 [==============================] - 0s 264us/step - loss: 0.5377 - acc: 0.7240
Epoch 71/150
768/768 [==============================] - 0s 280us/step - loss: 0.5180 - acc: 0.7409
Epoch 72/150
768/768 [==============================] - 0s 219us/step - loss: 0.5176 - acc: 0.7448
Epoch 73/150
768/768 [==============================] - 0s 245us/step - loss: 0.5164 - acc: 0.7461
Epoch 74/150
768/768 [==============================] - 0s 221us/step - loss: 0.5108 - acc: 0.7604
Epoch 75/150
768/768 [==============================] - 0s 238us/step - loss: 0.5095 - acc: 0.7617
Epoch 76/150
768/768 [==============================] - 0s 232us/step - loss: 0.5119 - acc: 0.7513
Epoch 77/150
768/768 [==============================] - 0s 296us/step - loss: 0.5169 - acc: 0.7617
Epoch 78/150
768/768 [==============================] - 0s 316us/step - loss: 0.5131 - acc: 0.7474
Epoch 79/150
768/768 [==============================] - 0s 223us/step - loss: 0.5138 - acc: 0.7461
Epoch 80/150
768/768 [==============================] - 0s 259us/step - loss: 0.5105 - acc: 0.7565
Epoch 81/150
768/768 [==============================] - 0s 275us/step - loss: 0.5056 - acc: 0.7695
Epoch 82/150
768/768 [==============================] - 0s 228us/step - loss: 0.5060 - acc: 0.7513
Epoch 83/150
768/768 [==============================] - 0s 238us/step - loss: 0.5030 - acc: 0.7591
Epoch 84/150
768/768 [==============================] - 0s 228us/step - loss: 0.4995 - acc: 0.7526
Epoch 85/150
768/768 [==============================] - 0s 230us/step - loss: 0.5063 - acc: 0.7461
Epoch 86/150
768/768 [==============================] - 0s 210us/step - loss: 0.5064 - acc: 0.7474
Epoch 87/150
768/768 [==============================] - 0s 221us/step - loss: 0.4992 - acc: 0.7526
Epoch 88/150
768/768 [==============================] - 0s 250us/step - loss: 0.5010 - acc: 0.7643
Epoch 89/150
768/768 [==============================] - 0s 216us/step - loss: 0.5045 - acc: 0.7682
Epoch 90/150
768/768 [==============================] - 0s 233us/step - loss: 0.5102 - acc: 0.7513
Epoch 91/150
768/768 [==============================] - 0s 246us/step - loss: 0.5022 - acc: 0.7526
Epoch 92/150
768/768 [==============================] - 0s 286us/step - loss: 0.5057 - acc: 0.7396
Epoch 93/150
768/768 [==============================] - 0s 227us/step - loss: 0.4981 - acc: 0.7656
Epoch 94/150
768/768 [==============================] - 0s 227us/step - loss: 0.4992 - acc: 0.7656
Epoch 95/150
768/768 [==============================] - 0s 258us/step - loss: 0.5040 - acc: 0.7500
Epoch 96/150
768/768 [==============================] - 0s 212us/step - loss: 0.4908 - acc: 0.7669
Epoch 97/150
768/768 [==============================] - 0s 245us/step - loss: 0.5004 - acc: 0.7747
Epoch 98/150
768/768 [==============================] - 0s 210us/step - loss: 0.4905 - acc: 0.7617
Epoch 99/150
768/768 [==============================] - 0s 260us/step - loss: 0.4914 - acc: 0.7630
Epoch 100/150
768/768 [==============================] - 0s 232us/step - loss: 0.4845 - acc: 0.7773
Epoch 101/150
768/768 [==============================] - 0s 217us/step - loss: 0.4897 - acc: 0.7773
Epoch 102/150
768/768 [==============================] - 0s 225us/step - loss: 0.4984 - acc: 0.7578
Epoch 103/150
768/768 [==============================] - 0s 246us/step - loss: 0.4987 - acc: 0.7539
Epoch 104/150
768/768 [==============================] - 0s 212us/step - loss: 0.4918 - acc: 0.7839
Epoch 105/150
768/768 [==============================] - 0s 238us/step - loss: 0.5303 - acc: 0.7422
Epoch 106/150
768/768 [==============================] - 0s 215us/step - loss: 0.4976 - acc: 0.7656
Epoch 107/150
768/768 [==============================] - 0s 233us/step - loss: 0.4922 - acc: 0.7708
Epoch 108/150
768/768 [==============================] - 0s 293us/step - loss: 0.4982 - acc: 0.7695
Epoch 109/150
768/768 [==============================] - 0s 268us/step - loss: 0.4874 - acc: 0.7695
Epoch 110/150
768/768 [==============================] - 0s 194us/step - loss: 0.4906 - acc: 0.7682
Epoch 111/150
768/768 [==============================] - 0s 228us/step - loss: 0.4833 - acc: 0.7812
Epoch 112/150
768/768 [==============================] - 0s 249us/step - loss: 0.4916 - acc: 0.7773
Epoch 113/150
768/768 [==============================] - 0s 309us/step - loss: 0.4938 - acc: 0.7630
Epoch 114/150
768/768 [==============================] - 0s 358us/step - loss: 0.4911 - acc: 0.7604
Epoch 115/150
768/768 [==============================] - 0s 410us/step - loss: 0.4905 - acc: 0.7760
Epoch 116/150
768/768 [==============================] - 0s 327us/step - loss: 0.4944 - acc: 0.7721
Epoch 117/150
768/768 [==============================] - 0s 445us/step - loss: 0.4917 - acc: 0.7604
Epoch 118/150
768/768 [==============================] - 0s 293us/step - loss: 0.4894 - acc: 0.7826
Epoch 119/150
768/768 [==============================] - 0s 203us/step - loss: 0.4829 - acc: 0.7695
Epoch 120/150
768/768 [==============================] - 0s 307us/step - loss: 0.4927 - acc: 0.7786
Epoch 121/150
768/768 [==============================] - 0s 367us/step - loss: 0.4924 - acc: 0.7721
Epoch 122/150
768/768 [==============================] - 0s 194us/step - loss: 0.4862 - acc: 0.7721
Epoch 123/150
768/768 [==============================] - 0s 182us/step - loss: 0.4838 - acc: 0.7656
Epoch 124/150
768/768 [==============================] - 0s 181us/step - loss: 0.4831 - acc: 0.7708
Epoch 125/150
768/768 [==============================] - 0s 184us/step - loss: 0.4874 - acc: 0.7852
Epoch 126/150
768/768 [==============================] - 0s 181us/step - loss: 0.4817 - acc: 0.7786
Epoch 127/150
768/768 [==============================] - 0s 177us/step - loss: 0.4903 - acc: 0.7682
Epoch 128/150
768/768 [==============================] - 0s 172us/step - loss: 0.4721 - acc: 0.7786
Epoch 129/150
768/768 [==============================] - 0s 176us/step - loss: 0.4813 - acc: 0.7721
Epoch 130/150
768/768 [==============================] - 0s 172us/step - loss: 0.4749 - acc: 0.7865
Epoch 131/150
768/768 [==============================] - 0s 172us/step - loss: 0.4815 - acc: 0.7773
Epoch 132/150
768/768 [==============================] - 0s 174us/step - loss: 0.4805 - acc: 0.7839
Epoch 133/150
768/768 [==============================] - 0s 171us/step - loss: 0.4839 - acc: 0.7721
Epoch 134/150
768/768 [==============================] - 0s 174us/step - loss: 0.4837 - acc: 0.7734
Epoch 135/150
768/768 [==============================] - 0s 177us/step - loss: 0.4780 - acc: 0.7773
Epoch 136/150
768/768 [==============================] - 0s 172us/step - loss: 0.4739 - acc: 0.7786
Epoch 137/150
768/768 [==============================] - 0s 173us/step - loss: 0.4673 - acc: 0.7786
Epoch 138/150
768/768 [==============================] - 0s 172us/step - loss: 0.4806 - acc: 0.7839
Epoch 139/150
768/768 [==============================] - 0s 177us/step - loss: 0.4656 - acc: 0.7917
Epoch 140/150
768/768 [==============================] - 0s 172us/step - loss: 0.4834 - acc: 0.7773
Epoch 141/150
768/768 [==============================] - 0s 172us/step - loss: 0.4743 - acc: 0.7839
Epoch 142/150
768/768 [==============================] - 0s 176us/step - loss: 0.4836 - acc: 0.7708
Epoch 143/150
768/768 [==============================] - 0s 310us/step - loss: 0.4769 - acc: 0.7734
Epoch 144/150
768/768 [==============================] - 0s 333us/step - loss: 0.4772 - acc: 0.7747
Epoch 145/150
768/768 [==============================] - 0s 245us/step - loss: 0.4890 - acc: 0.7643
Epoch 146/150
768/768 [==============================] - 0s 229us/step - loss: 0.4942 - acc: 0.7669
Epoch 147/150
768/768 [==============================] - 0s 194us/step - loss: 0.4846 - acc: 0.7773
Epoch 148/150
768/768 [==============================] - 0s 180us/step - loss: 0.4715 - acc: 0.7773
Epoch 149/150
768/768 [==============================] - 0s 181us/step - loss: 0.4752 - acc: 0.7695
Epoch 150/150
768/768 [==============================] - 0s 184us/step - loss: 0.4776 - acc: 0.7721










    
Out[9]:








<keras.callbacks.History at 0x2683b99f630>

















In [10]:



    


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



    


















    






768/768 [==============================] - 0s 94us/step

acc: 79.82%

















In [12]:



    


#all in one cell  
# Create your first MLP in Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.data", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))



    


















    






Epoch 1/150
768/768 [==============================] - 1s 2ms/step - loss: 3.7104 - acc: 0.5977
Epoch 2/150
768/768 [==============================] - 0s 411us/step - loss: 0.9374 - acc: 0.5938
Epoch 3/150
768/768 [==============================] - 0s 250us/step - loss: 0.7478 - acc: 0.6445
Epoch 4/150
768/768 [==============================] - 0s 421us/step - loss: 0.7120 - acc: 0.6549
Epoch 5/150
768/768 [==============================] - 0s 333us/step - loss: 0.6839 - acc: 0.6667
Epoch 6/150
768/768 [==============================] - 0s 201us/step - loss: 0.6520 - acc: 0.6771
Epoch 7/150
768/768 [==============================] - 0s 174us/step - loss: 0.6505 - acc: 0.6810
Epoch 8/150
768/768 [==============================] - 0s 188us/step - loss: 0.6392 - acc: 0.6862
Epoch 9/150
768/768 [==============================] - 0s 211us/step - loss: 0.6249 - acc: 0.6953
Epoch 10/150
768/768 [==============================] - 0s 172us/step - loss: 0.6308 - acc: 0.6784
Epoch 11/150
768/768 [==============================] - 0s 178us/step - loss: 0.6498 - acc: 0.6719
Epoch 12/150
768/768 [==============================] - 0s 174us/step - loss: 0.6399 - acc: 0.6758
Epoch 13/150
768/768 [==============================] - 0s 197us/step - loss: 0.6252 - acc: 0.6745
Epoch 14/150
768/768 [==============================] - 0s 185us/step - loss: 0.6177 - acc: 0.7005
Epoch 15/150
768/768 [==============================] - 0s 260us/step - loss: 0.6019 - acc: 0.6953
Epoch 16/150
768/768 [==============================] - 0s 180us/step - loss: 0.5883 - acc: 0.7005
Epoch 17/150
768/768 [==============================] - 0s 171us/step - loss: 0.5838 - acc: 0.6992
Epoch 18/150
768/768 [==============================] - 0s 182us/step - loss: 0.6003 - acc: 0.6875
Epoch 19/150
768/768 [==============================] - 0s 313us/step - loss: 0.5797 - acc: 0.7135
Epoch 20/150
768/768 [==============================] - 0s 305us/step - loss: 0.5794 - acc: 0.7227
Epoch 21/150
768/768 [==============================] - 0s 264us/step - loss: 0.5690 - acc: 0.7148
Epoch 22/150
768/768 [==============================] - 0s 177us/step - loss: 0.5812 - acc: 0.7005
Epoch 23/150
768/768 [==============================] - 0s 171us/step - loss: 0.5739 - acc: 0.7135
Epoch 24/150
768/768 [==============================] - 0s 172us/step - loss: 0.5681 - acc: 0.7331
Epoch 25/150
768/768 [==============================] - 0s 173us/step - loss: 0.5573 - acc: 0.7357
Epoch 26/150
768/768 [==============================] - 0s 172us/step - loss: 0.5707 - acc: 0.7018
Epoch 27/150
768/768 [==============================] - 0s 171us/step - loss: 0.5557 - acc: 0.7253
Epoch 28/150
768/768 [==============================] - 0s 173us/step - loss: 0.5553 - acc: 0.7318
Epoch 29/150
768/768 [==============================] - 0s 188us/step - loss: 0.5738 - acc: 0.7201
Epoch 30/150
768/768 [==============================] - 0s 172us/step - loss: 0.5611 - acc: 0.7227
Epoch 31/150
768/768 [==============================] - 0s 172us/step - loss: 0.5681 - acc: 0.7174
Epoch 32/150
768/768 [==============================] - 0s 177us/step - loss: 0.5637 - acc: 0.7161
Epoch 33/150
768/768 [==============================] - 0s 174us/step - loss: 0.5515 - acc: 0.7214
Epoch 34/150
768/768 [==============================] - 0s 173us/step - loss: 0.5510 - acc: 0.7331
Epoch 35/150
768/768 [==============================] - 0s 178us/step - loss: 0.5508 - acc: 0.7240
Epoch 36/150
768/768 [==============================] - 0s 335us/step - loss: 0.5597 - acc: 0.7057
Epoch 37/150
768/768 [==============================] - 0s 220us/step - loss: 0.5371 - acc: 0.7331
Epoch 38/150
768/768 [==============================] - 0s 177us/step - loss: 0.5406 - acc: 0.7227
Epoch 39/150
768/768 [==============================] - 0s 171us/step - loss: 0.5447 - acc: 0.7214
Epoch 40/150
768/768 [==============================] - 0s 174us/step - loss: 0.5439 - acc: 0.7240
Epoch 41/150
768/768 [==============================] - 0s 290us/step - loss: 0.5435 - acc: 0.7357
Epoch 42/150
768/768 [==============================] - 0s 223us/step - loss: 0.5363 - acc: 0.7370
Epoch 43/150
768/768 [==============================] - 0s 210us/step - loss: 0.5320 - acc: 0.7513
Epoch 44/150
768/768 [==============================] - 0s 174us/step - loss: 0.5325 - acc: 0.7396
Epoch 45/150
768/768 [==============================] - 0s 174us/step - loss: 0.5308 - acc: 0.7539
Epoch 46/150
768/768 [==============================] - 0s 210us/step - loss: 0.5292 - acc: 0.7500
Epoch 47/150
768/768 [==============================] - 0s 197us/step - loss: 0.5329 - acc: 0.7357
Epoch 48/150
768/768 [==============================] - 0s 220us/step - loss: 0.5326 - acc: 0.7448
Epoch 49/150
768/768 [==============================] - 0s 276us/step - loss: 0.5327 - acc: 0.7500
Epoch 50/150
768/768 [==============================] - 0s 182us/step - loss: 0.5270 - acc: 0.7396
Epoch 51/150
768/768 [==============================] - 0s 276us/step - loss: 0.5271 - acc: 0.7500
Epoch 52/150
768/768 [==============================] - 0s 346us/step - loss: 0.5286 - acc: 0.7448
Epoch 53/150
768/768 [==============================] - 0s 240us/step - loss: 0.5378 - acc: 0.7435
Epoch 54/150
768/768 [==============================] - 0s 174us/step - loss: 0.5365 - acc: 0.7318
Epoch 55/150
768/768 [==============================] - 0s 172us/step - loss: 0.5221 - acc: 0.7500
Epoch 56/150
768/768 [==============================] - 0s 172us/step - loss: 0.5292 - acc: 0.7409
Epoch 57/150
768/768 [==============================] - 0s 309us/step - loss: 0.5305 - acc: 0.7370
Epoch 58/150
768/768 [==============================] - 0s 331us/step - loss: 0.5231 - acc: 0.7513
Epoch 59/150
768/768 [==============================] - 0s 302us/step - loss: 0.5121 - acc: 0.7630
Epoch 60/150
768/768 [==============================] - 0s 357us/step - loss: 0.5331 - acc: 0.7331
Epoch 61/150
768/768 [==============================] - 0s 448us/step - loss: 0.5277 - acc: 0.7383
Epoch 62/150
768/768 [==============================] - 0s 290us/step - loss: 0.5173 - acc: 0.7565
Epoch 63/150
768/768 [==============================] - 0s 258us/step - loss: 0.5453 - acc: 0.7331
Epoch 64/150
768/768 [==============================] - 0s 241us/step - loss: 0.5307 - acc: 0.7448
Epoch 65/150
768/768 [==============================] - 0s 228us/step - loss: 0.5197 - acc: 0.7474
Epoch 66/150
768/768 [==============================] - 0s 257us/step - loss: 0.5057 - acc: 0.7500
Epoch 67/150
768/768 [==============================] - 0s 247us/step - loss: 0.5159 - acc: 0.7422
Epoch 68/150
768/768 [==============================] - 0s 212us/step - loss: 0.5139 - acc: 0.7565
Epoch 69/150
768/768 [==============================] - 0s 257us/step - loss: 0.5119 - acc: 0.7513
Epoch 70/150
768/768 [==============================] - 0s 266us/step - loss: 0.5364 - acc: 0.7188
Epoch 71/150
768/768 [==============================] - 0s 233us/step - loss: 0.5171 - acc: 0.7396
Epoch 72/150
768/768 [==============================] - 0s 241us/step - loss: 0.5171 - acc: 0.7513
Epoch 73/150
768/768 [==============================] - 0s 246us/step - loss: 0.5161 - acc: 0.7500
Epoch 74/150
768/768 [==============================] - 0s 224us/step - loss: 0.5096 - acc: 0.7604
Epoch 75/150
768/768 [==============================] - 0s 259us/step - loss: 0.5089 - acc: 0.7578
Epoch 76/150
768/768 [==============================] - 0s 228us/step - loss: 0.5100 - acc: 0.7526
Epoch 77/150
768/768 [==============================] - 0s 238us/step - loss: 0.5152 - acc: 0.7604
Epoch 78/150
768/768 [==============================] - 0s 297us/step - loss: 0.5117 - acc: 0.7500
Epoch 79/150
768/768 [==============================] - 0s 267us/step - loss: 0.5129 - acc: 0.7448
Epoch 80/150
768/768 [==============================] - 0s 225us/step - loss: 0.5107 - acc: 0.7578
Epoch 81/150
768/768 [==============================] - 0s 266us/step - loss: 0.5062 - acc: 0.7669
Epoch 82/150
768/768 [==============================] - 0s 270us/step - loss: 0.5038 - acc: 0.7539
Epoch 83/150
768/768 [==============================] - 0s 241us/step - loss: 0.4990 - acc: 0.7591
Epoch 84/150
768/768 [==============================] - 0s 258us/step - loss: 0.4976 - acc: 0.7591
Epoch 85/150
768/768 [==============================] - 0s 237us/step - loss: 0.5046 - acc: 0.7487
Epoch 86/150
768/768 [==============================] - 0s 228us/step - loss: 0.5052 - acc: 0.7487
Epoch 87/150
768/768 [==============================] - 0s 232us/step - loss: 0.4980 - acc: 0.7565
Epoch 88/150
768/768 [==============================] - 0s 309us/step - loss: 0.5011 - acc: 0.7604
Epoch 89/150
768/768 [==============================] - 0s 281us/step - loss: 0.5046 - acc: 0.7734
Epoch 90/150
768/768 [==============================] - 0s 220us/step - loss: 0.5077 - acc: 0.7552
Epoch 91/150
768/768 [==============================] - 0s 236us/step - loss: 0.5025 - acc: 0.7565
Epoch 92/150
768/768 [==============================] - 0s 227us/step - loss: 0.5046 - acc: 0.7448
Epoch 93/150
768/768 [==============================] - 0s 238us/step - loss: 0.4970 - acc: 0.7721
Epoch 94/150
768/768 [==============================] - 0s 230us/step - loss: 0.4990 - acc: 0.7656
Epoch 95/150
768/768 [==============================] - 0s 251us/step - loss: 0.5025 - acc: 0.7500
Epoch 96/150
768/768 [==============================] - 0s 236us/step - loss: 0.4905 - acc: 0.7695
Epoch 97/150
768/768 [==============================] - 0s 233us/step - loss: 0.4975 - acc: 0.7747
Epoch 98/150
768/768 [==============================] - 0s 260us/step - loss: 0.4887 - acc: 0.7656
Epoch 99/150
768/768 [==============================] - 0s 234us/step - loss: 0.4900 - acc: 0.7721
Epoch 100/150
768/768 [==============================] - 0s 216us/step - loss: 0.4846 - acc: 0.7760
Epoch 101/150
768/768 [==============================] - 0s 201us/step - loss: 0.4900 - acc: 0.7773
Epoch 102/150
768/768 [==============================] - 0s 221us/step - loss: 0.4988 - acc: 0.7552
Epoch 103/150
768/768 [==============================] - 0s 232us/step - loss: 0.4997 - acc: 0.7565
Epoch 104/150
768/768 [==============================] - 0s 246us/step - loss: 0.4911 - acc: 0.7865
Epoch 105/150
768/768 [==============================] - 0s 249us/step - loss: 0.5291 - acc: 0.7487
Epoch 106/150
768/768 [==============================] - 0s 233us/step - loss: 0.4943 - acc: 0.7747
Epoch 107/150
768/768 [==============================] - 0s 206us/step - loss: 0.4912 - acc: 0.7721
Epoch 108/150
768/768 [==============================] - 0s 225us/step - loss: 0.5003 - acc: 0.7630
Epoch 109/150
768/768 [==============================] - 0s 305us/step - loss: 0.4852 - acc: 0.7669
Epoch 110/150
768/768 [==============================] - 0s 193us/step - loss: 0.4900 - acc: 0.7656
Epoch 111/150
768/768 [==============================] - 0s 250us/step - loss: 0.4838 - acc: 0.7786
Epoch 112/150
768/768 [==============================] - 0s 241us/step - loss: 0.4958 - acc: 0.7708
Epoch 113/150
768/768 [==============================] - 0s 240us/step - loss: 0.4955 - acc: 0.7604
Epoch 114/150
768/768 [==============================] - 0s 227us/step - loss: 0.4927 - acc: 0.7604
Epoch 115/150
768/768 [==============================] - 0s 199us/step - loss: 0.4912 - acc: 0.7695
Epoch 116/150
768/768 [==============================] - 0s 207us/step - loss: 0.4928 - acc: 0.7721
Epoch 117/150
768/768 [==============================] - 0s 199us/step - loss: 0.4901 - acc: 0.7604
Epoch 118/150
768/768 [==============================] - 0s 186us/step - loss: 0.4889 - acc: 0.7786
Epoch 119/150
768/768 [==============================] - 0s 223us/step - loss: 0.4811 - acc: 0.7630
Epoch 120/150
768/768 [==============================] - 0s 225us/step - loss: 0.4934 - acc: 0.7721
Epoch 121/150
768/768 [==============================] - 0s 185us/step - loss: 0.4924 - acc: 0.7734
Epoch 122/150
768/768 [==============================] - 0s 216us/step - loss: 0.4843 - acc: 0.7826
Epoch 123/150
768/768 [==============================] - 0s 198us/step - loss: 0.4804 - acc: 0.7682
Epoch 124/150
768/768 [==============================] - 0s 211us/step - loss: 0.4831 - acc: 0.7760
Epoch 125/150
768/768 [==============================] - 0s 199us/step - loss: 0.4878 - acc: 0.7812
Epoch 126/150
768/768 [==============================] - 0s 227us/step - loss: 0.4795 - acc: 0.7826
Epoch 127/150
768/768 [==============================] - 0s 199us/step - loss: 0.4900 - acc: 0.7682
Epoch 128/150
768/768 [==============================] - 0s 211us/step - loss: 0.4723 - acc: 0.7721
Epoch 129/150
768/768 [==============================] - 0s 207us/step - loss: 0.4819 - acc: 0.7695
Epoch 130/150
768/768 [==============================] - 0s 219us/step - loss: 0.4749 - acc: 0.7878
Epoch 131/150
768/768 [==============================] - 0s 227us/step - loss: 0.4827 - acc: 0.7656
Epoch 132/150
768/768 [==============================] - 0s 228us/step - loss: 0.4809 - acc: 0.7839
Epoch 133/150
768/768 [==============================] - 0s 216us/step - loss: 0.4828 - acc: 0.7708
Epoch 134/150
768/768 [==============================] - 0s 210us/step - loss: 0.4847 - acc: 0.7747
Epoch 135/150
768/768 [==============================] - 0s 219us/step - loss: 0.4776 - acc: 0.7747
Epoch 136/150
768/768 [==============================] - 0s 201us/step - loss: 0.4738 - acc: 0.7786
Epoch 137/150
768/768 [==============================] - 0s 215us/step - loss: 0.4691 - acc: 0.7773
Epoch 138/150
768/768 [==============================] - 0s 214us/step - loss: 0.4804 - acc: 0.7812
Epoch 139/150
768/768 [==============================] - 0s 228us/step - loss: 0.4651 - acc: 0.7930
Epoch 140/150
768/768 [==============================] - 0s 199us/step - loss: 0.4825 - acc: 0.7826
Epoch 141/150
768/768 [==============================] - 0s 214us/step - loss: 0.4743 - acc: 0.7799
Epoch 142/150
768/768 [==============================] - 0s 249us/step - loss: 0.4843 - acc: 0.7721
Epoch 143/150
768/768 [==============================] - 0s 193us/step - loss: 0.4758 - acc: 0.7734
Epoch 144/150
768/768 [==============================] - 0s 214us/step - loss: 0.4767 - acc: 0.7734
Epoch 145/150
768/768 [==============================] - 0s 220us/step - loss: 0.4900 - acc: 0.7630
Epoch 146/150
768/768 [==============================] - 0s 216us/step - loss: 0.4935 - acc: 0.7669
Epoch 147/150
768/768 [==============================] - 0s 221us/step - loss: 0.4839 - acc: 0.7747
Epoch 148/150
768/768 [==============================] - 0s 220us/step - loss: 0.4724 - acc: 0.7695
Epoch 149/150
768/768 [==============================] - 0s 221us/step - loss: 0.4742 - acc: 0.7682
Epoch 150/150
768/768 [==============================] - 0s 233us/step - loss: 0.4776 - acc: 0.7695
768/768 [==============================] - 0s 134us/step

acc: 79.43%

















In [16]:



    


# Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.data", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10,  verbose=2)
# calculate predictions
predictions = model.predict(X)
# round predictions
rounded = [round(x[0]) for x in predictions]
print(rounded)



    


















    






Epoch 1/150
 - 1s - loss: 0.6771 - acc: 0.6510
Epoch 2/150
 - 0s - loss: 0.6586 - acc: 0.6510
Epoch 3/150
 - 0s - loss: 0.6470 - acc: 0.6510
Epoch 4/150
 - 0s - loss: 0.6393 - acc: 0.6510
Epoch 5/150
 - 0s - loss: 0.6320 - acc: 0.6510
Epoch 6/150
 - 0s - loss: 0.6188 - acc: 0.6510
Epoch 7/150
 - 0s - loss: 0.6194 - acc: 0.6510
Epoch 8/150
 - 0s - loss: 0.6135 - acc: 0.6510
Epoch 9/150
 - 0s - loss: 0.6087 - acc: 0.6510
Epoch 10/150
 - 0s - loss: 0.6164 - acc: 0.6510
Epoch 11/150
 - 0s - loss: 0.6052 - acc: 0.6510
Epoch 12/150
 - 0s - loss: 0.6034 - acc: 0.6510
Epoch 13/150
 - 0s - loss: 0.6004 - acc: 0.6510
Epoch 14/150
 - 0s - loss: 0.6033 - acc: 0.6510
Epoch 15/150
 - 0s - loss: 0.5989 - acc: 0.6510
Epoch 16/150
 - 0s - loss: 0.6000 - acc: 0.6510
Epoch 17/150
 - 0s - loss: 0.5995 - acc: 0.6510
Epoch 18/150
 - 0s - loss: 0.6007 - acc: 0.6510
Epoch 19/150
 - 0s - loss: 0.5972 - acc: 0.6510
Epoch 20/150
 - 0s - loss: 0.5982 - acc: 0.6510
Epoch 21/150
 - 0s - loss: 0.5950 - acc: 0.6510
Epoch 22/150
 - 0s - loss: 0.5936 - acc: 0.6510
Epoch 23/150
 - 0s - loss: 0.5930 - acc: 0.6510
Epoch 24/150
 - 0s - loss: 0.5989 - acc: 0.6510
Epoch 25/150
 - 0s - loss: 0.5956 - acc: 0.6510
Epoch 26/150
 - 0s - loss: 0.6006 - acc: 0.6510
Epoch 27/150
 - 0s - loss: 0.5949 - acc: 0.6510
Epoch 28/150
 - 0s - loss: 0.5905 - acc: 0.6510
Epoch 29/150
 - 0s - loss: 0.5927 - acc: 0.6510
Epoch 30/150
 - 0s - loss: 0.5909 - acc: 0.6510
Epoch 31/150
 - 0s - loss: 0.5900 - acc: 0.6510
Epoch 32/150
 - 0s - loss: 0.5903 - acc: 0.6510
Epoch 33/150
 - 0s - loss: 0.5844 - acc: 0.6510
Epoch 34/150
 - 0s - loss: 0.5894 - acc: 0.6510
Epoch 35/150
 - 0s - loss: 0.5916 - acc: 0.6510
Epoch 36/150
 - 0s - loss: 0.5834 - acc: 0.6510
Epoch 37/150
 - 0s - loss: 0.5824 - acc: 0.6510
Epoch 38/150
 - 0s - loss: 0.5923 - acc: 0.6510
Epoch 39/150
 - 0s - loss: 0.5833 - acc: 0.6471
Epoch 40/150
 - 0s - loss: 0.5869 - acc: 0.6693
Epoch 41/150
 - 0s - loss: 0.5820 - acc: 0.6953
Epoch 42/150
 - 0s - loss: 0.5807 - acc: 0.7070
Epoch 43/150
 - 0s - loss: 0.5787 - acc: 0.7122
Epoch 44/150
 - 0s - loss: 0.5865 - acc: 0.7031
Epoch 45/150
 - 0s - loss: 0.5788 - acc: 0.7096
Epoch 46/150
 - 0s - loss: 0.5774 - acc: 0.7018
Epoch 47/150
 - 0s - loss: 0.5782 - acc: 0.7148
Epoch 48/150
 - 0s - loss: 0.5752 - acc: 0.7070
Epoch 49/150
 - 0s - loss: 0.5744 - acc: 0.7122
Epoch 50/150
 - 0s - loss: 0.5740 - acc: 0.7174
Epoch 51/150
 - 0s - loss: 0.5731 - acc: 0.7174
Epoch 52/150
 - 0s - loss: 0.5706 - acc: 0.7135
Epoch 53/150
 - 0s - loss: 0.5729 - acc: 0.7122
Epoch 54/150
 - 0s - loss: 0.5707 - acc: 0.7096
Epoch 55/150
 - 0s - loss: 0.5728 - acc: 0.7057
Epoch 56/150
 - 0s - loss: 0.5710 - acc: 0.7109
Epoch 57/150
 - 0s - loss: 0.5678 - acc: 0.7083
Epoch 58/150
 - 0s - loss: 0.5725 - acc: 0.7096
Epoch 59/150
 - 0s - loss: 0.5668 - acc: 0.7044
Epoch 60/150
 - 0s - loss: 0.5690 - acc: 0.7057
Epoch 61/150
 - 0s - loss: 0.5662 - acc: 0.7083
Epoch 62/150
 - 0s - loss: 0.5680 - acc: 0.7201
Epoch 63/150
 - 0s - loss: 0.5712 - acc: 0.7096
Epoch 64/150
 - 0s - loss: 0.5658 - acc: 0.7174
Epoch 65/150
 - 0s - loss: 0.5630 - acc: 0.7109
Epoch 66/150
 - 0s - loss: 0.5588 - acc: 0.7135
Epoch 67/150
 - 0s - loss: 0.5586 - acc: 0.7135
Epoch 68/150
 - 0s - loss: 0.5603 - acc: 0.7083
Epoch 69/150
 - 0s - loss: 0.5559 - acc: 0.7279
Epoch 70/150
 - 0s - loss: 0.5609 - acc: 0.7109
Epoch 71/150
 - 0s - loss: 0.5571 - acc: 0.7044
Epoch 72/150
 - 0s - loss: 0.5556 - acc: 0.7096
Epoch 73/150
 - 0s - loss: 0.5501 - acc: 0.7148
Epoch 74/150
 - 0s - loss: 0.5577 - acc: 0.6992
Epoch 75/150
 - 0s - loss: 0.5540 - acc: 0.7201
Epoch 76/150
 - 0s - loss: 0.5508 - acc: 0.7227
Epoch 77/150
 - 0s - loss: 0.5504 - acc: 0.7253
Epoch 78/150
 - 0s - loss: 0.5465 - acc: 0.7292
Epoch 79/150
 - 0s - loss: 0.5498 - acc: 0.7174
Epoch 80/150
 - 0s - loss: 0.5448 - acc: 0.7266
Epoch 81/150
 - 0s - loss: 0.5427 - acc: 0.7305
Epoch 82/150
 - 0s - loss: 0.5529 - acc: 0.7174
Epoch 83/150
 - 0s - loss: 0.5524 - acc: 0.7109
Epoch 84/150
 - 0s - loss: 0.5451 - acc: 0.7187
Epoch 85/150
 - 0s - loss: 0.5465 - acc: 0.7201
Epoch 86/150
 - 0s - loss: 0.5498 - acc: 0.7266
Epoch 87/150
 - 0s - loss: 0.5405 - acc: 0.7253
Epoch 88/150
 - 0s - loss: 0.5399 - acc: 0.7201
Epoch 89/150
 - 0s - loss: 0.5583 - acc: 0.7318
Epoch 90/150
 - 0s - loss: 0.5416 - acc: 0.7253
Epoch 91/150
 - 0s - loss: 0.5391 - acc: 0.7266
Epoch 92/150
 - 0s - loss: 0.5405 - acc: 0.7266
Epoch 93/150
 - 0s - loss: 0.5402 - acc: 0.7174
Epoch 94/150
 - 0s - loss: 0.5392 - acc: 0.7357
Epoch 95/150
 - 0s - loss: 0.5359 - acc: 0.7214
Epoch 96/150
 - 0s - loss: 0.5413 - acc: 0.7357
Epoch 97/150
 - 0s - loss: 0.5401 - acc: 0.7266
Epoch 98/150
 - 0s - loss: 0.5332 - acc: 0.7305
Epoch 99/150
 - 0s - loss: 0.5274 - acc: 0.7409
Epoch 100/150
 - 0s - loss: 0.5339 - acc: 0.7279
Epoch 101/150
 - 0s - loss: 0.5313 - acc: 0.7279
Epoch 102/150
 - 0s - loss: 0.5323 - acc: 0.7409
Epoch 103/150
 - 0s - loss: 0.5404 - acc: 0.7227
Epoch 104/150
 - 0s - loss: 0.5351 - acc: 0.7344
Epoch 105/150
 - 0s - loss: 0.5287 - acc: 0.7318
Epoch 106/150
 - 0s - loss: 0.5291 - acc: 0.7305
Epoch 107/150
 - 0s - loss: 0.5335 - acc: 0.7409
Epoch 108/150
 - 0s - loss: 0.5314 - acc: 0.7331
Epoch 109/150
 - 0s - loss: 0.5269 - acc: 0.7383
Epoch 110/150
 - 0s - loss: 0.5249 - acc: 0.7422
Epoch 111/150
 - 0s - loss: 0.5323 - acc: 0.7383
Epoch 112/150
 - 0s - loss: 0.5241 - acc: 0.7396
Epoch 113/150
 - 0s - loss: 0.5236 - acc: 0.7409
Epoch 114/150
 - 0s - loss: 0.5257 - acc: 0.7435
Epoch 115/150
 - 0s - loss: 0.5204 - acc: 0.7435
Epoch 116/150
 - 0s - loss: 0.5213 - acc: 0.7422
Epoch 117/150
 - 0s - loss: 0.5189 - acc: 0.7461
Epoch 118/150
 - 0s - loss: 0.5240 - acc: 0.7435
Epoch 119/150
 - 0s - loss: 0.5125 - acc: 0.7448
Epoch 120/150
 - 0s - loss: 0.5154 - acc: 0.7435
Epoch 121/150
 - 0s - loss: 0.5159 - acc: 0.7565
Epoch 122/150
 - 0s - loss: 0.5131 - acc: 0.7578
Epoch 123/150
 - 0s - loss: 0.5104 - acc: 0.7474
Epoch 124/150
 - 0s - loss: 0.5059 - acc: 0.7617
Epoch 125/150
 - 0s - loss: 0.5067 - acc: 0.7448
Epoch 126/150
 - 0s - loss: 0.5088 - acc: 0.7227
Epoch 127/150
 - 0s - loss: 0.5109 - acc: 0.7539
Epoch 128/150
 - 0s - loss: 0.5037 - acc: 0.7708
Epoch 129/150
 - 0s - loss: 0.5129 - acc: 0.7591
Epoch 130/150
 - 0s - loss: 0.4990 - acc: 0.7656
Epoch 131/150
 - 0s - loss: 0.4970 - acc: 0.7617
Epoch 132/150
 - 0s - loss: 0.4951 - acc: 0.7656
Epoch 133/150
 - 0s - loss: 0.5020 - acc: 0.7565
Epoch 134/150
 - 0s - loss: 0.5000 - acc: 0.7721
Epoch 135/150
 - 0s - loss: 0.4927 - acc: 0.7617
Epoch 136/150
 - 0s - loss: 0.4975 - acc: 0.7578
Epoch 137/150
 - 0s - loss: 0.5046 - acc: 0.7643
Epoch 138/150
 - 0s - loss: 0.4963 - acc: 0.7643
Epoch 139/150
 - 0s - loss: 0.4869 - acc: 0.7643
Epoch 140/150
 - 0s - loss: 0.4884 - acc: 0.7591
Epoch 141/150
 - 0s - loss: 0.4879 - acc: 0.7630
Epoch 142/150
 - 0s - loss: 0.4911 - acc: 0.7617
Epoch 143/150
 - 0s - loss: 0.4841 - acc: 0.7721
Epoch 144/150
 - 0s - loss: 0.4856 - acc: 0.7708
Epoch 145/150
 - 0s - loss: 0.4869 - acc: 0.7760
Epoch 146/150
 - 0s - loss: 0.4883 - acc: 0.7682
Epoch 147/150
 - 0s - loss: 0.4831 - acc: 0.7747
Epoch 148/150
 - 0s - loss: 0.4880 - acc: 0.7852
Epoch 149/150
 - 0s - loss: 0.4779 - acc: 0.7721
Epoch 150/150
 - 0s - loss: 0.4756 - acc: 0.7669
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In [ ]:

Content source: tschinz/iPython_Workspace

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