def cross_fold(X, Y, n, *model_args, **model_kwargs):
num_examples = len(Y)
# shuffle data first
idx = np.arange(num_examples)
np.random.shuffle(idx)
# split into n sets
splits = np.split(idx, n)
for i in range(n):
# get train/test sets
idx_test = splits[i]
tr1 = splits[:i]
tr2 = splits[i + 1:]
tr1.extend(tr2)
idx_train = np.concatenate(tr1)
X_train = X[idx_train, :]
X_test = X[idx_test, :]
Y_train = Y[idx_train]
Y_test = Y[idx_test]
# create new model
model = NeuralNet(*model_args, **model_kwargs)
# train
num_epochs = model.fit(X_train, Y_train)
# compare
out = model.predict(X_test)
print(i, model.score(X_train, Y_train), num_epochs)
def run_blobs(n=5):
X, Y = get_blob_sets(n)
net = NeuralNet(4,
20,
6,
a_func=Sigmoid,
validation_set=(X[2:4], Y[2:4]),
multi_vsets=True)
num_epochs = net.fit(X[:2], Y[:2], True)
score = net.score(X[-1], Y[-1], multi_sets=False)
print("accuracy:", score)
print("epochs:", num_epochs)
if __name__ == '__main__':
args = parse_args()
train = get_sets(args.train, args.dir, args.ext, "Training")
test = get_sets(args.test, args.dir, args.ext, "Test")
validation = get_sets(args.validation, args.dir, args.ext, "Validation")
model = NeuralNet(args.num_features,
args.num_hidden,
args.num_classes,
validation_set=validation,
multi_vsets=True,
max_epochs=args.max_epochs,
patience=args.patience,
learning_rate=args.learning_rate)
num_epochs = model.fit(train[0], train[1], True)
score = model.score(test[0], test[1], multi_sets=True)
print("accuracy:", score)
print("epochs:", num_epochs)
if args.out:
with open(args.out, 'a') as _f:
print("Training Sets:",
args.train,
"Validation Sets:",
args.validation,
"Test Sets:",
args.test,
file=_f)
print("hidden nodes:", args.num_hidden, file=_f)
print("learning rate:",
args.learning_rate,