def train_overfit_net(save_net = False):
# get CIFAR10 data
data = get_CIFAR10_data()
# subsample the data. 50 draws
indices_to_select = np.random.randint(0, len(data["X_train"]), 50)
# extract the samples
data["X_train"] = data["X_train"][indices_to_select]
data["y_train"] = data["y_train"][indices_to_select]
# intialize net
model = FullyConnectedNet([50],
input_dim = 32*32*3,
num_classes = 10,
dropout = 0,
reg = 0.0,
weight_scale = 1e-2)
# initialize solver
solver = Solver(model,data,
update_rule = 'sgd',
optim_config = {'learning_rate': 5e-4},
lr_decay = 0.85,
num_epochs = 20,
batch_size = 5,
print_every = 100)
# train the net
solver.train()
if save_net:
# test the net and save its training, validation and testing accuracies.
# get training accuracy
train_acc = str.format("{0:.2f}", solver.check_accuracy(data["X_train"], data["y_train"]) * 100) + "\%"
# get validation accuracy
val_acc = str.format("{0:.2f}", solver.best_val_acc * 100) + "\%"
# get testing accuracy
test_acc = str.format("{0:.2f}", solver.check_accuracy(data["X_test"], data["y_test"]) * 100) + "\%"
text = "Accuracies: " + train_acc + " training, " + val_acc + " validation \& " + test_acc + " testing."
# write to file
append_to_file("nets/overfit_net/info.tex", text, mode = "w")
# save net info
save_net_info("nets/overfit_net", solver)
def train_cifar10_net(save_net=False):
"""
Uses a Solver instance to train a TwoLayerNet that achieves at least 50%
accuracy on the validation set.
"""
# get CIFAR10 data
data = get_CIFAR10_data()
# intialize net
model = FullyConnectedNet([100],
input_dim=32 * 32 * 3,
num_classes=10,
dropout=0,
reg=0.0)
# initialize solver
solver = Solver(model,
data,
update_rule='sgd',
optim_config={'learning_rate': 1e-3},
lr_decay=0.95,
num_epochs=20,
batch_size=100,
print_every=100)
# train the net
solver.train()
if save_net:
# test the net and save its training, validation and testing accuracies.
# get training accuracy
train_acc = str.format(
"{0:.2f}",
solver.check_accuracy(data["X_train"], data["y_train"]) *
100) + "\%"
# get validation accuracy
val_acc = str.format("{0:.2f}", solver.best_val_acc * 100) + "\%"
# get testing accuracy
test_acc = str.format(
"{0:.2f}",
solver.check_accuracy(data["X_test"], data["y_test"]) * 100) + "\%"
text = "Accuracies: " + train_acc + " training, " + val_acc + " validation \& " + test_acc + " testing."
# write to file
append_to_file("nets/train_net/info.tex", text, mode="w")
# save net info
save_net_info("nets/train_net", solver)
model.mean_image = data['mean_image']
lr = 0.013614446824659357
solver = Solver(model,
data,
update_rule='sgd_momentum',
optim_config={
'learning_rate': lr,
},
lr_decay=0.8,
num_epochs=100,
batch_size=70,
print_every=100,
checkpoint_name="intermediate")
solver.train()
acc1 = solver.check_accuracy(data['X_val'], data['y_val'])
acc2 = solver.check_accuracy(data['X_train'], data['y_train'])
#val_acc = solver.best_val_acc
#acc = max(solver.train_acc_history)
#json_log = open("output_test.json", mode='wt', buffering=1)
#json_log.write(json.dumps({'Learning Rate': lr,'accuracy': acc, 'val_acc': val_acc,}) + '\n')
##############################################################################
# END OF YOUR CODE #
##############################################################################
import matplotlib.pyplot as plt
plt.subplot(2, 1, 1)
plt.title("Training loss")
plt.plot(solver.loss_history, "o")
num_classes=10,
dtype=np.float64)
solver = Solver(model,
data,
update_rule='sgd',
optim_config={
'learning_rate': 1e-3,
},
lr_decay=0.85,
num_epochs=30,
batch_size=65,
print_every=1000)
solver.train()
acc = solver.check_accuracy(data['X_train'], data['y_train'])
print("Training accuracy {} on {} examples".format(acc, len(data['X_train'])))
acc = solver.check_accuracy(data['X_val'], data['y_val'])
print("validation accuracy {} on {} examples".format(acc, len(data['X_val'])))
acc = solver.check_accuracy(data['X_test'], data['y_test'])
print("Test accuracy {} on {} examples".format(acc, len(data['X_test'])))
import matplotlib.pyplot as plt
plt.subplot(2, 1, 1)
plt.title("Training loss")
plt.plot(solver.loss_history, "o")
plt.xlabel('Iteration')
plt.subplot(2, 1, 2)
plt.title('Accuracy')
plt.plot(solver.train_acc_history, '-o', label='train')
plt.plot(solver.val_acc_history, '-o', label='val')