neural_network

gistfile1.txt

from keras.models import Sequential
from keras.layers import Dense
from sklearn import datasets
from tensorflow import keras
# define the keras model
model = Sequential()
model.add(Dense(12, input_dim=12, activation='relu')) # 12 neurons and expecting 8 columns x
model.add(Dense(8, activation='relu'))#8 neurons with activation function rectified
model.add(Dense(8, activation='relu'))#8 neurons with activation function rectified
model.add(Dense(1, activation='sigmoid')) #1 neuron with sigmoid activation
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(x_train, y_train, epochs = 100)
model.evaluate(x_test, y_test, verbose = True)[1]