def run(bs, path, lr, ks, num_layer):
fold=1
for X_train, Y_train, X_val, Y_val, val_cat in zip(training_data,
training_label,
validation_data,
validation_label,
validation_cate_label):
print("Fold "+str(fold))
model = tools.create_model(lr, bs, ks, num_layer)
inner_path = path+"/fold_"+str(fold)
if not os.path.exists(inner_path):
os.makedirs(inner_path)
early_stop = EarlyStopping(patience=20)
history = model.fit(x = X_train,
y = Y_train,
epochs=80,
validation_data=(X_val, Y_val),
callbacks=[early_stop],
batch_size=bs,
verbose=0)
evaluation = model.evaluate(x = X_val, y = Y_val)
validation_prediction = model.predict_classes(X_val, batch_size=bs)
score = f1_score(val_cat, validation_prediction, average=None)
tools.show_plot(inner_path, history)
tools.write_file(inner_path+"/readme.txt", evaluation, score, model)
fold = fold + 1
del model
fold = fold + 1
del model
## main()
base_path = "5_fold_10/"
if not os.path.exists(base_path):
os.makedirs(base_path)
# index=1
# with open(base_path+"Cartesian_product.txt", "w+") as writer:
# for ks, num_layer, bs, lr in itertools.product(kernel_size, num_layers, batch_size, learning_rate):
# writer.write(str(index)+":"+"\n")
# writer.write('ks={}, num_layer={}, bs={}, lr={}'.format(ks, num_layer, bs, lr)+"\n")
# index = index + 1
# del index
index=41
for ks, num_layer, bs, lr in itertools.product(kernel_size, num_layers, batch_size, learning_rate):
print("Index "+str(index))
path = base_path+str(index)
if not os.path.exists(path):
os.makedirs(path)
model = tools.create_model(lr, bs, ks, num_layer)
if model:
run(bs, path, lr, ks, num_layer)
else:
overflow_model = overflow_model + 1
del model
index = index + 1
print("Training finished! Overflow model: ", overflow_model)
