Skopt Tutorial - Baseline NN

baseline_nn.py

# the usual imports for a vanilla nueral net
import keras
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(16, input_shape=input_shape, activation='relu',name = 'input_layer'))
model.add(Dense(16, activation='relu', name="hidden_layer"))

model.add(Dense(10,activation='softmax',name="output_layer"))
model.compile(optimizer = 'adam', loss='categorical_crossentropy', metrics=["accuracy"])

#model.summary prints a description of the model
#model.summary()

#model history is stored as "blackbox". 
blackbox = model.fit(X_train, y_train, batch_size=128, epochs =3, validation_split=.15)

accuracy = model.evaluate(X_test,y_test)[1]
print(accuracy)