# importing requirements | |
from keras.layers import Dense | |
from keras.models import Sequential | |
from keras.optimizers import adam | |
# alpha = 0.001 as given in the lr parameter in adam() optimizer | |
# build the model | |
model_alpha1 = Sequential() | |
model_alpha1.add(Dense(50, input_dim=2, activation='relu')) | |
model_alpha1.add(Dense(3, activation='softmax')) | |
# compile the model | |
opt_alpha1 = adam(lr=0.001) | |
model_alpha1.compile(loss='kullback_leibler_divergence', optimizer=opt_alpha1, metrics=['accuracy']) | |
# fit the model | |
# dummy_Y is the one-hot encoded | |
# history_alpha1 is used to score the validation and accuracy scores for plotting | |
history_alpha1 = model_alpha1.fit(dataX, dummy_Y, validation_data=(dataX, dummy_Y), epochs=200, verbose=0) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment