.format(*params))
if params in final_train_perfs and params in final_val_perfs:
print("Skipping: already have results")
continue
save_path = os.path.join(
base_save_dir,
"lr{};epochs{};dropout{};dense_layers{};dense_layer_units{};batch_size{}"
.format(*params))
if not os.path.exists(save_path):
os.makedirs(save_path)
av_train_perf = {"acc": 0, "prec": 0, "rec": 0, "f1": 0}
av_val_perf = {"acc": 0, "prec": 0, "rec": 0, "f1": 0}
for i in range(args.ensemble_size):
print("Building model")
model = ShallowNet(Xs["train"].shape[1], dropout, dense_layers,
dense_layer_units, args.weights)
model.compile(optimizer=Adam(lr=lr), loss="binary_crossentropy")
print("Model built")
history = model.fit(
X=Xs["train"],
y=ys["train"],
batch_size=batch_size,
nb_epoch=epochs,
verbose=1,
validation_data=(Xs["val"], ys["val"]),
shuffle=True,
show_accuracy=True,
callbacks=[
LearningRateScheduler(lambda e: lr_schedule(epochs, lr, e))
])
