def draw_feature(path='train_log/models/best-accuracy',
scale=1,
data_set='fashion_mnist'):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
checkpoint = torch.load(path, map_location=device)
data_loader_train, data_loader_test, data_train, data_test = load_dataset(
data_set=data_set)
net = Model(n_feature)
net.load_state_dict(checkpoint['model_state_dict'])
exact_list = ['feature']
feature_extractor = FeatureExtractor(net, exact_list)
feature_extractor.to(device)
# get weight
weight = checkpoint['model_state_dict']['pred.weight'].to('cpu').data
weight_norm = weight / torch.norm(weight, dim=1, keepdim=True)
print("weight_norm: ", torch.norm(weight, dim=1))
# get feature
features = []
labels = []
for data in data_loader_train:
X_train, y_train = data
X_train = Variable(X_train).to(device)
outputs = feature_extractor(X_train)['feature'].data
features.append(outputs)
labels.append(y_train)
features = torch.cat(features, dim=0).to('cpu').data
features_norm = features / torch.norm(features, dim=1, keepdim=True)
features = features.numpy()
features_norm = features_norm.numpy()
labels = torch.cat(labels, dim=0).to('cpu').data.numpy()
# draw features
label_list = get_label_list(data_set)
plt.figure(1, figsize=(20, 20))
plt.subplot(221)
for i in range(10):
plt.plot([0, scale * weight[i, 0]], [0, scale * weight[i, 1]],
color=color_list[i])
feature = features[labels == i]
plt.scatter(feature[:, 0],
feature[:, 1],
c=color_list[i],
marker='.',
label=label_list[i],
s=1)
plt.legend()
plt.subplot(223)
for i in range(10):
plt.plot([0, weight_norm[i, 0]], [0, weight_norm[i, 1]],
color=color_list[i])
feature = features_norm[labels == i]
plt.scatter(feature[:, 0],
feature[:, 1],
c=color_list[i],
marker='.',
label=label_list[i],
s=1)
plt.legend()
# get feature
features = []
labels = []
for data in data_loader_test:
X_test, y_test = data
X_test = Variable(X_test).to(device)
outputs = feature_extractor(X_test)['feature'].data
features.append(outputs)
labels.append(y_test)
features = torch.cat(features, dim=0).to('cpu').data
features_norm = features / torch.norm(features, dim=1, keepdim=True)
features = features.numpy()
features_norm = features_norm.numpy()
labels = torch.cat(labels, dim=0).to('cpu').data.numpy()
plt.subplot(222)
for i in range(10):
plt.plot([0, scale * weight[i, 0]], [0, scale * weight[i, 1]],
color=color_list[i])
feature = features[labels == i]
plt.scatter(feature[:, 0],
feature[:, 1],
c=color_list[i],
marker='.',
label=label_list[i],
s=1)
plt.legend()
plt.subplot(224)
for i in range(10):
plt.plot([0, weight_norm[i, 0]], [0, weight_norm[i, 1]],
color=color_list[i])
feature = features_norm[labels == i]
plt.scatter(feature[:, 0],
feature[:, 1],
c=color_list[i],
marker='.',
label=label_list[i],
s=1)
plt.legend()
title = os.path.basename(os.getcwd()) + '-' + os.path.basename(path)
plt.suptitle(title)
fname = 'train_log/feature-{}'.format(os.path.basename(path))
figname = 'train_log/{}.png'.format(fname)
os.remove(figname) if os.path.exists(figname) else None
plt.savefig(fname)
plt.close('all')
def draw_curve(curv_state='train_log/curve/curve_info',
root='.',
data_set='fashion_mnist'):
if isinstance(curv_state, dict):
curv_state = curv_state
else:
root = os.path.dirname(curv_state)
curv_state = torch.load(curv_state)
label_list = get_label_list(data_set)
title = os.path.basename(os.getcwd())
with change_dir(root):
plt.figure(1, figsize=(10, 20))
plt.subplot(411)
plt.plot(curv_state['train_epoch']['loss'],
linewidth=1.0,
label='train')
plt.plot(curv_state['val_epoch']['loss'], linewidth=1.0, label='val')
plt.legend()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.subplot(412)
plt.plot(curv_state['train_epoch']['accuracy'],
linewidth=1.0,
label='train')
plt.plot(curv_state['val_epoch']['accuracy'],
linewidth=1.0,
label='val')
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.legend()
plt.subplot(413)
train_acc_cls = np.array(curv_state['train_epoch']['acc_cls'])
for i in range(10):
plt.plot(train_acc_cls[:, i], linewidth=1.0, label=label_list[i])
plt.legend()
plt.xlabel('epoch')
plt.ylabel('train-acc')
plt.subplot(414)
val_acc_cls = np.array(curv_state['val_epoch']['acc_cls'])
for i in range(10):
plt.plot(val_acc_cls[:, i], linewidth=1.0, label=label_list[i])
plt.legend()
plt.xlabel('epoch')
plt.ylabel('test-acc')
plt.suptitle(title)
plt.savefig('curve-epoch')
# plt.show()
plt.figure(2, figsize=(10, 10))
plt.subplot(211)
plt.plot(curv_state['train_step']['loss'],
linewidth=1.0,
label='train')
plt.xlabel('step')
plt.ylabel('loss')
plt.legend()
plt.subplot(212)
plt.plot(curv_state['train_step']['accuracy'],
linewidth=1.0,
label='train')
plt.xlabel('step')
plt.ylabel('accuravy')
plt.legend()
plt.suptitle(title)
plt.savefig('curve-step')
plt.close('all')
