def showDataSet(dataMat, labelMat):
data_plus = []
for i in range(len(group)):
data_plus.append(dataMat[i])
data_plus_np = np.array(data_plus) # 转换为numpy矩阵
plt.scatter(np.transpose(data_plus_np)[0],
np.transpose(data_plus_np)[1]) # 散点图
plt.show()
def get_hist(data, col):
[columndates, orderdates] = column_and_order_dates(data, col)
[difference_dates, date_values] = days_difference(columndates, orderdates,
31)
plt.hist(x=difference_dates, bins=100)
plt.xlabel('Day')
plt.ylabel('Amount')
plt.show()
print(date_values)
def show_images(images, labels, preds):
plt.figure(figsize=(8, 4))
for i, image in enumerate(images):
plt.subplot(1, 6, i + 1, xticks=[], yticks=[])
image = image.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image = image * std + mean
image = np.clip(image, 0., 1.)
plt.imshow(image)
col = 'green'
if preds[i] != labels[i]:
col = 'red'
plt.xlabel(f'{class_names[int(labels[i].numpy())]}')
plt.ylabel(f'{class_names[int(preds[i].numpy())]}', color=col)
plt.tight_layout()
plt.show()
if torch.cuda.is_available():
model.cuda()
for epoch in range(epoches): #循环epoch，设置学习率
if epoch in [epoches * 0.25, epoches * 0.5]:
for param_group in optimizier.param_groups:
param_group['lr'] *= 0.1
for img, _ in train_data:
img = img.view(img.size(0), -1)
img = Variable(img.cuda())
#forward
_, output = model(img) #把数据放入模型
loss = criterion(output, img) #损失
#backward
optimizier.zero_grad()
loss.backward()
optimizier.step()
print('epoch=', epoch, loss.data.float())
for param_group in optimizier.param_groups:
print(param_group['lr'])
if (epoch + 1) % 5 == 0:
pic = to_img(output.cpu().data)
if not os.path.exists('./simple_autoencoder'): #判断该路径是否存在
os.mkdir(pic, './simple_autoencoder/image_{}.png'.format(
epoch + 1)) #如果不存在则将处理后的数据放入该路径
code = Variable(torch.FloatTensor([1.19, -3.36, 2.06]).cuda())
decode = model.decoder(code) #解码
decode_img = to_img(decode).squeeze()
decode_img = decode_img.data.cpu().numpy() * 255
plt.imshow(decode_img.astype('uint8'), cmap='gray')
plt.show()