Conv((1, 1, 16), **conv),
Pooling(8, op="avg"),
Activation(Softmax())
]
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
mlp = Model(layers=layers)
# configure callbacks
callbacks = Callbacks(mlp)
def do_nothing(_):
pass
callbacks.callbacks = []
callbacks.on_train_begin = do_nothing
callbacks.on_epoch_end = do_nothing
mlp.fit(train_set,
optimizer=opt_gdm,
num_epochs=num_epochs,
cost=cost,
callbacks=callbacks)
opt_metric = 1.0 - mlp.eval(valid_set, metric=Misclassification())
print('Metric = {}'.format(opt_metric))
conn.experiments(experiment.id).observations().create(
suggestion=suggestion.id,
value=float(opt_metric[0]),
)
