def run(save_loc="cnn/ONN",
loss=Loss.MSE,
OD=10,
gradient=Gradient.APPROXIMATE):
print("\n----- Running {} -----".format(os.path.basename(__file__)))
####################################################
# Configure datasets.
####################################################
dataset = Dataset.MNIST
if dataset != Dataset.MNIST:
save_loc += "_{}".format(str(dataset).split(".")[-1])
batch_size_train = 64
batch_size_test = 1000
####################################################
# Configure Networks.
####################################################
sat_abs_nl_args = {
'I_sat': 1,
'OD': OD,
'encoding': Encoding.AMPLITUDE,
'gradient': gradient
}
SANL = lambda: SatAbsNL(**sat_abs_nl_args)
if loss == Loss.MSE:
output = None
loss_str = "mse"
elif loss == Loss.CCE:
output = lambda: nn.LogSoftmax(-1)
loss_str = "nll"
else:
raise ValueError("Unrecognised loss :", loss)
net_args = {
'n_ch_conv': [32, 64],
'kernel_size_conv': [5, 5],
'n_in_fc': 1024,
'n_hid_fc': [128],
'activation_conv': [SANL, SANL],
'activation_fc': SANL,
'dropout': lambda: nn.Dropout(0.4),
'conv_args': {
'stride': 1,
'padding': 0,
'bias': False
},
'pool_conv': lambda: nn.AvgPool2d(kernel_size=2, stride=2),
'n_out': 10 if dataset != Dataset.EMNIST else 47,
'bias_fc': False,
'output': output
}
####################################################
# Train classifiers
####################################################
n_seeds = 5
losses = {}
corrects = {}
valid_scores = {}
for i in range(n_seeds):
lab = 'seed{}'.format(i)
network = ConvNet(**net_args)
train_loader, test_loader, validation_loader = get_dataset_loaders(
dataset=dataset,
train_batch=batch_size_train,
test_batch=batch_size_test,
unroll_img=False,
max_value=15 if OD > 10 else 5,
get_validation=True)
classifier = Classifier(
network,
train_loader,
test_loader,
n_epochs=30 if dataset == Dataset.MNIST else 40,
learning_rate=5e-4,
init_weight_mean=0.,
init_weight_std=0.01,
init_conv_weight_std=0.1,
loss=loss_str,
weight_range=None,
weight_normalisation=weight_norm.NONE,
log_interval=25,
n_test_per_epoch=0,
save_path=os.path.join(save_loc, lab))
train_losses, test_correct = classifier.train()
losses[lab] = train_losses
corrects[lab] = test_correct
####################################################
# Validation
####################################################
classifier.load(classifier.network_save_path)
valid_loss, valid_correct = classifier.validate(validation_loader)
print("Validation accuracy : {:.2f}%".format(
100. * valid_correct / len(validation_loader.dataset)))
valid_scores[lab] = 100. * valid_correct / len(
validation_loader.dataset)
validation_save_path = os.path.join(classifier.save_path,
"validation_score.pkl")
with open(validation_save_path, 'wb+') as output:
pickle.dump(np.array([valid_loss, valid_correct]), output,
pickle.HIGHEST_PROTOCOL)
print('Validation scores saved to {}'.format(validation_save_path))
print("Validation scores are:")
for lab, score in valid_scores.items():
print("\t{} : {:.2f}%".format(lab, score))
####################################################
# Plot results
####################################################
fig_fname = os.path.join(save_loc, "training_performance")
with plt.style.context('seaborn-paper', after_reset=True):
fig, (ax1, ax2) = plt.subplots(1,
2,
figsize=(7, 2.5),
gridspec_kw={'wspace': 0.3})
window = 25
avg_mask = np.ones(window) / window
for lab, data in losses.items():
ax1.plot(np.convolve(data[:, 0], avg_mask, 'valid'),
np.convolve(data[:, 1], avg_mask, 'valid'),
label=lab,
linewidth=0.75,
alpha=0.8)
ax1.legend()
ax1.set_xlabel("Epoch")
ax1.set_ylabel("Losses")
for lab, data in corrects.items():
ax2.plot(data[:, 0],
data[:, 1] / len(test_loader.dataset),
label=lab)
print("{}: Best score {}/{}".format(lab, np.max(data),
len(test_loader)))
ax2.legend()
ax2.set_xlabel("Epoch")
ax2.set_ylabel("Accuracy")
plt.savefig(fig_fname + ".png", bbox_inches='tight')
plt.savefig(fig_fname + ".pdf", bbox_inches='tight')
def run(save_loc="mlp/ANN", n_hid=2, loss=Loss.CCE, activation=nn.ReLU):
print("\n----- Running {} -----".format(os.path.basename(__file__)))
####################################################
# Configure datasets.
####################################################
dataset = Dataset.MNIST
if dataset != Dataset.MNIST:
save_loc += "_{}".format(str(dataset).split(".")[-1])
batch_size_train = 64
batch_size_test = 1000
input_scaling = 1
####################################################
# Configure Networks.
####################################################
if loss == Loss.MSE:
output = None
loss_str = "mse"
elif loss == Loss.CCE:
output = lambda: nn.LogSoftmax(-1)
loss_str = "nll"
else:
raise ValueError("Unrecognised loss :", loss)
default_net_args = {
'n_hid': [128] * n_hid,
'n_in': 784,
'n_out': 10 if dataset != Dataset.EMNIST else 47,
'activation': activation,
'output': output,
}
####################################################
# Train classifiers
####################################################
n_seeds = 5
losses = {}
corrects = {}
valid_scores = {}
for i in range(n_seeds):
lab = 'seed{}'.format(i)
network = LinNet(**default_net_args)
train_loader, test_loader, validation_loader = get_dataset_loaders(
dataset=dataset,
train_batch=batch_size_train,
test_batch=batch_size_test,
unroll_img=True,
max_value=input_scaling,
get_validation=True)
classifier = Classifier(network,
train_loader,
test_loader,
n_epochs=50,
learning_rate=5e-4,
init_weight_mean=0,
init_weight_std=0.1,
loss=loss_str,
weight_range=None,
weight_normalisation=weight_norm.NONE,
log_interval=25,
n_test_per_epoch=0,
save_path=os.path.join(save_loc, lab))
train_losses, test_correct = classifier.train()
losses[lab] = train_losses
corrects[lab] = test_correct
####################################################
# Validation
####################################################
classifier.load(classifier.network_save_path)
valid_loss, valid_correct = classifier.validate(validation_loader)
print("Validation accuracy : {:.2f}%".format(
100. * valid_correct / len(validation_loader.dataset)))
valid_scores[lab] = 100. * valid_correct / len(
validation_loader.dataset)
validation_save_path = os.path.join(classifier.save_path,
"validation_score.pkl")
with open(validation_save_path, 'wb+') as output:
pickle.dump(np.array([valid_loss, valid_correct]), output,
pickle.HIGHEST_PROTOCOL)
print('Validation scores saved to {}'.format(validation_save_path))
print("Validation scores are:")
for lab, score in valid_scores.items():
print("\t{} : {:.2f}%".format(lab, score))
####################################################
# Plot results
####################################################
fig_fname = os.path.join(save_loc, "training_performance")
with plt.style.context('seaborn-paper', after_reset=True):
fig, (ax1, ax2) = plt.subplots(1,
2,
figsize=(7, 2.5),
gridspec_kw={'wspace': 0.3})
window = 25
avg_mask = np.ones(window) / window
for lab, data in losses.items():
ax1.plot(np.convolve(data[:, 0], avg_mask, 'valid'),
np.convolve(data[:, 1], avg_mask, 'valid'),
label=lab,
linewidth=0.75,
alpha=0.8)
ax1.legend()
ax1.set_xlabel("Epoch")
ax1.set_ylabel("Losses")
for lab, data in corrects.items():
ax2.plot(data[:, 0],
data[:, 1] / len(test_loader.dataset),
label=lab)
print("{}: Best score {}/{}".format(lab, np.max(data),
len(test_loader)))
ax2.legend()
ax2.set_xlabel("Epoch")
ax2.set_ylabel("Accuracy")
plt.savefig(fig_fname + ".png", bbox_inches='tight')
plt.savefig(fig_fname + ".pdf", bbox_inches='tight')
