In [1]:

    

!wget http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data


    

    




--2018-01-02 15:13:11--  http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data
Resolving archive.ics.uci.edu... 128.195.10.249
Connecting to archive.ics.uci.edu|128.195.10.249|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 23279 (23K) [text/plain]
Saving to: ‘pima-indians-diabetes.data’

pima-indians-diabet 100%[===================>]  22.73K  --.-KB/s    in 0.1s    

2018-01-02 15:13:12 (166 KB/s) - ‘pima-indians-diabetes.data’ saved [23279/23279]

In [16]:

    

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
import numpy as np

seed = 7
np.random.seed(seed)

# load pima indians dataset
dataset = np.loadtxt('pima-indians-diabetes.data', delimiter=',')
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'))

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

filepath = 'weights-improvement-{epoch:03d}-{val_acc:.2f}.hdf5'
checkpoint = ModelCheckpoint(filepath,
                             monitor='val_acc',
                             verbose=1,
                             save_best_only=True,
                             mode='max')
callbacks_list = [checkpoint]
model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10,
          callbacks=callbacks_list, verbose=0)


    

    




Epoch 00000: val_acc improved from -inf to 0.67323, saving model to weights-improvement-000-0.67.hdf5
Epoch 00001: val_acc did not improve
Epoch 00002: val_acc did not improve
Epoch 00003: val_acc improved from 0.67323 to 0.69291, saving model to weights-improvement-003-0.69.hdf5
Epoch 00004: val_acc improved from 0.69291 to 0.70866, saving model to weights-improvement-004-0.71.hdf5
Epoch 00005: val_acc did not improve
Epoch 00006: val_acc did not improve
Epoch 00007: val_acc did not improve
Epoch 00008: val_acc did not improve
Epoch 00009: val_acc improved from 0.70866 to 0.70866, saving model to weights-improvement-009-0.71.hdf5
Epoch 00010: val_acc did not improve
Epoch 00011: val_acc improved from 0.70866 to 0.71654, saving model to weights-improvement-011-0.72.hdf5
Epoch 00012: val_acc did not improve
Epoch 00013: val_acc improved from 0.71654 to 0.71654, saving model to weights-improvement-013-0.72.hdf5
Epoch 00014: val_acc did not improve
Epoch 00015: val_acc did not improve
Epoch 00016: val_acc did not improve
Epoch 00017: val_acc improved from 0.71654 to 0.72835, saving model to weights-improvement-017-0.73.hdf5
Epoch 00018: val_acc did not improve
Epoch 00019: val_acc did not improve
Epoch 00020: val_acc did not improve
Epoch 00021: val_acc did not improve
Epoch 00022: val_acc did not improve
Epoch 00023: val_acc did not improve
Epoch 00024: val_acc did not improve
Epoch 00025: val_acc did not improve
Epoch 00026: val_acc did not improve
Epoch 00027: val_acc did not improve
Epoch 00028: val_acc did not improve
Epoch 00029: val_acc improved from 0.72835 to 0.73622, saving model to weights-improvement-029-0.74.hdf5
Epoch 00030: val_acc did not improve
Epoch 00031: val_acc did not improve
Epoch 00032: val_acc did not improve
Epoch 00033: val_acc did not improve
Epoch 00034: val_acc did not improve
Epoch 00035: val_acc did not improve
Epoch 00036: val_acc improved from 0.73622 to 0.73622, saving model to weights-improvement-036-0.74.hdf5
Epoch 00037: val_acc improved from 0.73622 to 0.74016, saving model to weights-improvement-037-0.74.hdf5
Epoch 00038: val_acc improved from 0.74016 to 0.74409, saving model to weights-improvement-038-0.74.hdf5
Epoch 00039: val_acc did not improve
Epoch 00040: val_acc did not improve
Epoch 00041: val_acc did not improve
Epoch 00042: val_acc did not improve
Epoch 00043: val_acc did not improve
Epoch 00044: val_acc did not improve
Epoch 00045: val_acc did not improve
Epoch 00046: val_acc did not improve
Epoch 00047: val_acc improved from 0.74409 to 0.74409, saving model to weights-improvement-047-0.74.hdf5
Epoch 00048: val_acc did not improve
Epoch 00049: val_acc did not improve
Epoch 00050: val_acc did not improve
Epoch 00051: val_acc did not improve
Epoch 00052: val_acc improved from 0.74409 to 0.74803, saving model to weights-improvement-052-0.75.hdf5
Epoch 00053: val_acc did not improve
Epoch 00054: val_acc did not improve
Epoch 00055: val_acc did not improve
Epoch 00056: val_acc did not improve
Epoch 00057: val_acc did not improve
Epoch 00058: val_acc did not improve
Epoch 00059: val_acc did not improve
Epoch 00060: val_acc did not improve
Epoch 00061: val_acc did not improve
Epoch 00062: val_acc improved from 0.74803 to 0.75197, saving model to weights-improvement-062-0.75.hdf5
Epoch 00063: val_acc did not improve
Epoch 00064: val_acc did not improve
Epoch 00065: val_acc did not improve
Epoch 00066: val_acc did not improve
Epoch 00067: val_acc did not improve
Epoch 00068: val_acc did not improve
Epoch 00069: val_acc did not improve
Epoch 00070: val_acc did not improve
Epoch 00071: val_acc improved from 0.75197 to 0.76378, saving model to weights-improvement-071-0.76.hdf5
Epoch 00072: val_acc did not improve
Epoch 00073: val_acc did not improve
Epoch 00074: val_acc did not improve
Epoch 00075: val_acc did not improve
Epoch 00076: val_acc did not improve
Epoch 00077: val_acc improved from 0.76378 to 0.76772, saving model to weights-improvement-077-0.77.hdf5
Epoch 00078: val_acc did not improve
Epoch 00079: val_acc did not improve
Epoch 00080: val_acc did not improve
Epoch 00081: val_acc did not improve
Epoch 00082: val_acc did not improve
Epoch 00083: val_acc improved from 0.76772 to 0.77165, saving model to weights-improvement-083-0.77.hdf5
Epoch 00084: val_acc did not improve
Epoch 00085: val_acc did not improve
Epoch 00086: val_acc did not improve
Epoch 00087: val_acc did not improve
Epoch 00088: val_acc did not improve
Epoch 00089: val_acc did not improve
Epoch 00090: val_acc did not improve
Epoch 00091: val_acc did not improve
Epoch 00092: val_acc did not improve
Epoch 00093: val_acc did not improve
Epoch 00094: val_acc did not improve
Epoch 00095: val_acc improved from 0.77165 to 0.77559, saving model to weights-improvement-095-0.78.hdf5
Epoch 00096: val_acc did not improve
Epoch 00097: val_acc did not improve
Epoch 00098: val_acc did not improve
Epoch 00099: val_acc did not improve
Epoch 00100: val_acc improved from 0.77559 to 0.77559, saving model to weights-improvement-100-0.78.hdf5
Epoch 00101: val_acc improved from 0.77559 to 0.77953, saving model to weights-improvement-101-0.78.hdf5
Epoch 00102: val_acc did not improve
Epoch 00103: val_acc did not improve
Epoch 00104: val_acc did not improve
Epoch 00105: val_acc improved from 0.77953 to 0.77953, saving model to weights-improvement-105-0.78.hdf5
Epoch 00106: val_acc did not improve
Epoch 00107: val_acc did not improve
Epoch 00108: val_acc did not improve
Epoch 00109: val_acc did not improve
Epoch 00110: val_acc did not improve
Epoch 00111: val_acc did not improve
Epoch 00112: val_acc did not improve
Epoch 00113: val_acc improved from 0.77953 to 0.78740, saving model to weights-improvement-113-0.79.hdf5
Epoch 00114: val_acc did not improve
Epoch 00115: val_acc did not improve
Epoch 00116: val_acc did not improve
Epoch 00117: val_acc did not improve
Epoch 00118: val_acc did not improve
Epoch 00119: val_acc did not improve
Epoch 00120: val_acc did not improve
Epoch 00121: val_acc did not improve
Epoch 00122: val_acc did not improve
Epoch 00123: val_acc did not improve
Epoch 00124: val_acc did not improve
Epoch 00125: val_acc did not improve
Epoch 00126: val_acc did not improve
Epoch 00127: val_acc did not improve
Epoch 00128: val_acc did not improve
Epoch 00129: val_acc did not improve
Epoch 00130: val_acc did not improve
Epoch 00131: val_acc did not improve
Epoch 00132: val_acc did not improve
Epoch 00133: val_acc did not improve
Epoch 00134: val_acc did not improve
Epoch 00135: val_acc did not improve
Epoch 00136: val_acc did not improve
Epoch 00137: val_acc did not improve
Epoch 00138: val_acc did not improve
Epoch 00139: val_acc did not improve
Epoch 00140: val_acc did not improve
Epoch 00141: val_acc improved from 0.78740 to 0.79134, saving model to weights-improvement-141-0.79.hdf5
Epoch 00142: val_acc improved from 0.79134 to 0.79528, saving model to weights-improvement-142-0.80.hdf5
Epoch 00143: val_acc did not improve
Epoch 00144: val_acc did not improve
Epoch 00145: val_acc did not improve
Epoch 00146: val_acc did not improve
Epoch 00147: val_acc did not improve
Epoch 00148: val_acc did not improve
Epoch 00149: val_acc did not improve






    
Out[16]:





<keras.callbacks.History at 0x31c038e10>

In [17]:

    

ls *.hdf5


    

    




weights-improvement-000-0.67.hdf5  weights-improvement-052-0.75.hdf5
weights-improvement-003-0.69.hdf5  weights-improvement-062-0.75.hdf5
weights-improvement-004-0.71.hdf5  weights-improvement-071-0.76.hdf5
weights-improvement-009-0.71.hdf5  weights-improvement-077-0.77.hdf5
weights-improvement-011-0.72.hdf5  weights-improvement-083-0.77.hdf5
weights-improvement-013-0.72.hdf5  weights-improvement-095-0.78.hdf5
weights-improvement-017-0.73.hdf5  weights-improvement-100-0.78.hdf5
weights-improvement-029-0.74.hdf5  weights-improvement-101-0.78.hdf5
weights-improvement-036-0.74.hdf5  weights-improvement-105-0.78.hdf5
weights-improvement-037-0.74.hdf5  weights-improvement-113-0.79.hdf5
weights-improvement-038-0.74.hdf5  weights-improvement-141-0.79.hdf5
weights-improvement-047-0.74.hdf5  weights-improvement-142-0.80.hdf5

In [19]:

    

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
import numpy as np

seed = 7
np.random.seed(seed)

# load pima indians dataset
dataset = np.loadtxt('pima-indians-diabetes.data', delimiter=',')
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'))

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

filepath = 'weights.best.hdf5'
checkpoint = ModelCheckpoint(filepath,
                             monitor='val_acc',
                             verbose=1,
                             save_best_only=True,
                             mode='max')
callbacks_list = [checkpoint]
model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10,
          callbacks=callbacks_list, verbose=0)


    

    




Epoch 00000: val_acc improved from -inf to 0.67323, saving model to weights.best.hdf5
Epoch 00001: val_acc did not improve
Epoch 00002: val_acc did not improve
Epoch 00003: val_acc did not improve
Epoch 00004: val_acc improved from 0.67323 to 0.70472, saving model to weights.best.hdf5
Epoch 00005: val_acc did not improve
Epoch 00006: val_acc did not improve
Epoch 00007: val_acc did not improve
Epoch 00008: val_acc did not improve
Epoch 00009: val_acc did not improve
Epoch 00010: val_acc did not improve
Epoch 00011: val_acc improved from 0.70472 to 0.71260, saving model to weights.best.hdf5
Epoch 00012: val_acc did not improve
Epoch 00013: val_acc improved from 0.71260 to 0.72047, saving model to weights.best.hdf5
Epoch 00014: val_acc did not improve
Epoch 00015: val_acc did not improve
Epoch 00016: val_acc did not improve
Epoch 00017: val_acc did not improve
Epoch 00018: val_acc did not improve
Epoch 00019: val_acc did not improve
Epoch 00020: val_acc did not improve
Epoch 00021: val_acc did not improve
Epoch 00022: val_acc did not improve
Epoch 00023: val_acc did not improve
Epoch 00024: val_acc did not improve
Epoch 00025: val_acc did not improve
Epoch 00026: val_acc did not improve
Epoch 00027: val_acc did not improve
Epoch 00028: val_acc improved from 0.72047 to 0.72835, saving model to weights.best.hdf5
Epoch 00029: val_acc did not improve
Epoch 00030: val_acc did not improve
Epoch 00031: val_acc did not improve
Epoch 00032: val_acc did not improve
Epoch 00033: val_acc did not improve
Epoch 00034: val_acc did not improve
Epoch 00035: val_acc did not improve
Epoch 00036: val_acc did not improve
Epoch 00037: val_acc improved from 0.72835 to 0.74409, saving model to weights.best.hdf5
Epoch 00038: val_acc improved from 0.74409 to 0.75591, saving model to weights.best.hdf5
Epoch 00039: val_acc did not improve
Epoch 00040: val_acc did not improve
Epoch 00041: val_acc did not improve
Epoch 00042: val_acc did not improve
Epoch 00043: val_acc did not improve
Epoch 00044: val_acc did not improve
Epoch 00045: val_acc did not improve
Epoch 00046: val_acc did not improve
Epoch 00047: val_acc did not improve
Epoch 00048: val_acc did not improve
Epoch 00049: val_acc did not improve
Epoch 00050: val_acc did not improve
Epoch 00051: val_acc did not improve
Epoch 00052: val_acc did not improve
Epoch 00053: val_acc did not improve
Epoch 00054: val_acc did not improve
Epoch 00055: val_acc did not improve
Epoch 00056: val_acc did not improve
Epoch 00057: val_acc did not improve
Epoch 00058: val_acc did not improve
Epoch 00059: val_acc did not improve
Epoch 00060: val_acc did not improve
Epoch 00061: val_acc did not improve
Epoch 00062: val_acc did not improve
Epoch 00063: val_acc did not improve
Epoch 00064: val_acc did not improve
Epoch 00065: val_acc improved from 0.75591 to 0.76772, saving model to weights.best.hdf5
Epoch 00066: val_acc did not improve
Epoch 00067: val_acc did not improve
Epoch 00068: val_acc did not improve
Epoch 00069: val_acc did not improve
Epoch 00070: val_acc did not improve
Epoch 00071: val_acc improved from 0.76772 to 0.77165, saving model to weights.best.hdf5
Epoch 00072: val_acc did not improve
Epoch 00073: val_acc did not improve
Epoch 00074: val_acc did not improve
Epoch 00075: val_acc did not improve
Epoch 00076: val_acc did not improve
Epoch 00077: val_acc improved from 0.77165 to 0.77559, saving model to weights.best.hdf5
Epoch 00078: val_acc did not improve
Epoch 00079: val_acc did not improve
Epoch 00080: val_acc did not improve
Epoch 00081: val_acc did not improve
Epoch 00082: val_acc did not improve
Epoch 00083: val_acc improved from 0.77559 to 0.77953, saving model to weights.best.hdf5
Epoch 00084: val_acc did not improve
Epoch 00085: val_acc did not improve
Epoch 00086: val_acc did not improve
Epoch 00087: val_acc did not improve
Epoch 00088: val_acc did not improve
Epoch 00089: val_acc did not improve
Epoch 00090: val_acc did not improve
Epoch 00091: val_acc did not improve
Epoch 00092: val_acc did not improve
Epoch 00093: val_acc did not improve
Epoch 00094: val_acc did not improve
Epoch 00095: val_acc did not improve
Epoch 00096: val_acc did not improve
Epoch 00097: val_acc did not improve
Epoch 00098: val_acc did not improve
Epoch 00099: val_acc did not improve
Epoch 00100: val_acc did not improve
Epoch 00101: val_acc did not improve
Epoch 00102: val_acc did not improve
Epoch 00103: val_acc improved from 0.77953 to 0.77953, saving model to weights.best.hdf5
Epoch 00104: val_acc did not improve
Epoch 00105: val_acc did not improve
Epoch 00106: val_acc did not improve
Epoch 00107: val_acc did not improve
Epoch 00108: val_acc improved from 0.77953 to 0.78740, saving model to weights.best.hdf5
Epoch 00109: val_acc did not improve
Epoch 00110: val_acc did not improve
Epoch 00111: val_acc did not improve
Epoch 00112: val_acc did not improve
Epoch 00113: val_acc did not improve
Epoch 00114: val_acc improved from 0.78740 to 0.79528, saving model to weights.best.hdf5
Epoch 00115: val_acc did not improve
Epoch 00116: val_acc did not improve
Epoch 00117: val_acc did not improve
Epoch 00118: val_acc did not improve
Epoch 00119: val_acc did not improve
Epoch 00120: val_acc did not improve
Epoch 00121: val_acc did not improve
Epoch 00122: val_acc did not improve
Epoch 00123: val_acc did not improve
Epoch 00124: val_acc did not improve
Epoch 00125: val_acc did not improve
Epoch 00126: val_acc improved from 0.79528 to 0.79528, saving model to weights.best.hdf5
Epoch 00127: val_acc did not improve
Epoch 00128: val_acc did not improve
Epoch 00129: val_acc did not improve
Epoch 00130: val_acc did not improve
Epoch 00131: val_acc did not improve
Epoch 00132: val_acc did not improve
Epoch 00133: val_acc did not improve
Epoch 00134: val_acc did not improve
Epoch 00135: val_acc did not improve
Epoch 00136: val_acc did not improve
Epoch 00137: val_acc did not improve
Epoch 00138: val_acc did not improve
Epoch 00139: val_acc did not improve
Epoch 00140: val_acc did not improve
Epoch 00141: val_acc did not improve
Epoch 00142: val_acc did not improve
Epoch 00143: val_acc did not improve
Epoch 00144: val_acc improved from 0.79528 to 0.80709, saving model to weights.best.hdf5
Epoch 00145: val_acc did not improve
Epoch 00146: val_acc did not improve
Epoch 00147: val_acc did not improve
Epoch 00148: val_acc did not improve
Epoch 00149: val_acc did not improve






    
Out[19]:





<keras.callbacks.History at 0x31c3c0c50>

In [33]:

    

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
import numpy as np

seed = 7
np.random.seed(seed)

# 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'))
# model.load_weights('weights.best.hdf5')

# モデル構造も含まれるので下記でOK
model = load_model('weights.best.hdf5')

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

dataset = np.loadtxt('pima-indians-diabetes.data', delimiter=',')
X = dataset[:, 0:8]
Y = dataset[:, 8]
scores = model.evaluate(X, Y, verbose=0)
print('%s: %.2f%%' % (model.metrics_names[1], scores[1] * 100))


    

    




acc: 77.99%