def main():
# Create model.
model = TwoLayerNet()
# Create data iterators for training and testing sets.
data = get_CIFAR10_data('cifar-10-batches-py')
train_dataiter = NDArrayIter(data=data['X_train'],
label=data['y_train'],
batch_size=batch_size,
shuffle=True)
test_dataiter = NDArrayIter(data=data['X_test'],
label=data['y_test'],
batch_size=batch_size,
shuffle=False)
# Create solver.
solver = Solver(model,
train_dataiter,
test_dataiter,
num_epochs=5,
init_rule='gaussian',
init_config={
'stdvar': 0.001
},
update_rule='sgd_momentum',
optim_config={
'learning_rate': 1e-4,
'momentum': 0.9
},
verbose=True,
print_every=20)
solver.init()
solver.train()
train_acc = solver.check_accuracy(
train_dataiter, num_samples=solver.train_acc_num_samples)
# a bug-free mlp should reach around 60% train acc
assert (train_acc >= 0.45)
def main():
# Create model.
model = ConvolutionNet()
# Create data iterators for training and testing sets.
data = get_CIFAR10_data('cifar-10-batches-py')
train_dataiter = NDArrayIter(data=data['X_train'],
label=data['y_train'],
batch_size=batch_size,
shuffle=True)
test_dataiter = NDArrayIter(data=data['X_test'],
label=data['y_test'],
batch_size=batch_size,
shuffle=False)
# Create solver.
solver = Solver(model,
train_dataiter,
test_dataiter,
num_epochs=1,
init_rule='gaussian',
init_config={
'stdvar': 0.001
},
update_rule='sgd_momentum',
optim_config={
'learning_rate': 1e-3,
'momentum': 0.9
},
verbose=True,
print_every=20)
# Initialize model parameters.
solver.init()
# Train!
solver.train()
train_acc = solver.check_accuracy(
train_dataiter, num_samples=solver.train_acc_num_samples)
# a normal cnn should reach 50% train acc
assert (train_acc >= 0.40)
def main():
# Create model.
model = ConvolutionNet()
# Create data iterators for training and testing sets.
data = get_CIFAR10_data('cifar-10-batches-py')
train_dataiter = NDArrayIter(data=data['X_train'],
label=data['y_train'],
batch_size=batch_size,
shuffle=True)
test_dataiter = NDArrayIter(data=data['X_test'],
label=data['y_test'],
batch_size=batch_size,
shuffle=False)
# Create solver.
solver = Solver(model,
train_dataiter,
test_dataiter,
num_epochs=1,
init_rule='gaussian',
init_config={'stdvar': 0.001},
update_rule='sgd_momentum',
optim_config={
'learning_rate': 1e-3,
'momentum': 0.9
},
verbose=True,
print_every=20)
# Initialize model parameters.
solver.init()
# Train!
solver.train()
train_acc = solver.check_accuracy(
train_dataiter, num_samples=solver.train_acc_num_samples)
# a normal cnn should reach 50% train acc
assert (train_acc >= 0.40)
