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

dataset = numpy.loadtxt("../file_trainning", delimiter=",")
X = dataset[:, 1:785]
Y = dataset[:, 0]


model = Sequential()
model.add(Dense(12, input_dim=784, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, Y, epochs=1, batch_size=10)
scores = model.evaluate(X,Y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
predictions = model.predict(X)
rounded = [round(x[0]) for x in predictions]
print(rounded)