def TwoLayerNetDemo(reg=0.0):
data = get_CIFAR10_data(9000, 1000)
model = TwoLayerNet(reg=reg)
solver = Solver(model, data, update_rule='sgd',
optim_config={'learning_rate': 1e-3, },
lr_decay=0.95, num_epochs=10,
batch_size=100, print_every=100)
solver.train()
X_test = data['X_test']
y_test = data['y_test']
num_samples = y_test.shape[0]
acc = solver.predict(X_test, y_test, num_samples)
print ["Accuracy", acc]
def ThreeLayerConvNetDemo(batch_size=32, num_filters=9, use_batchnorm=False,
weight_scale=1e-2, reg=0.0, update_rule='sgd'):
data = get_CIFAR10_data(1000, 100)
hidden_dims = [100, 50]
model = ThreeLayerConvNet(num_filters=num_filters)
solver = Solver(model, data, update_rule=update_rule,
optim_config={'learning_rate': 1e-3, },
lr_decay=0.95, num_epochs=10,
batch_size=batch_size, print_every=100)
solver.train()
X_test = data['X_test'][1:100]
y_test = data['y_test'][1:100]
num_samples = y_test.shape[0]
acc = solver.predict(X_test, y_test, num_samples)
print ["Accuracy", acc]
def FullyConnectedNetDemo(dropout=0.5, use_batchnorm=True, HeReLU=False,
weight_scale=1e-2, reg=0.0, update_rule='adam',
num_epochs=10):
data = get_CIFAR10_data(19000, 1000)
hidden_dims = [100, 50]
model = FullyConnectedNet(hidden_dims=hidden_dims,
weight_scale=weight_scale,
use_batchnorm=use_batchnorm,
HeReLU=False, reg=reg)
solver = Solver(model, data, update_rule=update_rule,
optim_config={'learning_rate': 1e-3, },
lr_decay=0.95, num_epochs=num_epochs,
batch_size=100, print_every=100)
solver.train()
X_test = data['X_test']
y_test = data['y_test']
num_samples = y_test.shape[0]
acc = solver.predict(X_test, y_test, num_samples)
print ["Accuracy", acc]
