dsafsdaf

whatever.py

from keras.models import Sequential
from keras.layers import Dense

#rm /Users/kevinperko/anaconda/lib/python2.7/site-packages/sklearn/utils/random.so
from sklearn.model_selection import train_test_split, StratifiedKFold
import numpy

numpy.random.seed(7)

#seed = 7

dataset = numpy.loadtxt("/Users/kevinperko/Downloads/pima-indians-diabetes.csv",delimiter=",")

X = dataset[:,0:8]
Y = dataset[:,8]

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

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

print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

#automatic validation
model.fit(X,Y, validation_split=0.33, epochs=150, batch_size=10)

#manual validation
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=seed)
model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs=150, batch_size=10)

kfold = StratifiedKFold(n_splits = 10, shuffle=True, random_state = seed)
cvscores = []

for train,test in kfold.split(X,Y):
	model = Sequential()
	model.add(Dense(12, input_dim=8, activation='relu'))
	model.add(Dense(8, activation='relu'))
	model.add(Dense(1, activation='sigmoid'))
	#something
	model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
	#something else
	model.fit(X[train], Y[train], epochs = 150, batch_size=10, verbose=0)
	scores = model.evaluate(X[test], Y[test], verbose=0)
	#something more
	print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
	cvscores.append(scores[1]*100)

print("%.2f%% (+/- %.2f%%" % (numpy.mean(cvscores), numpy.std(cvscores)))


#new model
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
import numpy

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

seed = 7
numpy.random.seed(seed)
dataset = numpy.loadtxt("/Users/kevinperko/Downloads/pima-indians-diabetes.csv",delimiter=",")

X = dataset[:,0:8]
Y = dataset[:,8]

model = KerasClassifier(build_fn=create_model, epochs = 150, batch_size=10, verbose=0)
kfold = StratifiedKFold(n_splits = 10, shuffle=True, random_state = seed)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())