encode与decode

import torchfrom torch import nnimport numpy as npimport matplotlib.pyplot as pltimport torch.utils.data as Dataimport torchvisionfrom mpl_toolkits.mplot3d import Axes3D ???#画3D图from matplotlib import cm# Hyper ParametersEPOCH=10BATCH_SIZE=64LR = 0.005 # learning rateDOWNLOAD_MNIST=FalseN_TEST_IMG=5train_data=torchvision.datasets.MNIST( ???root=‘./mnist/‘, ???train=True, ???transform=torchvision.transforms.ToTensor(), ???download=DOWNLOAD_MNIST)train_loader=Data.DataLoader(dataset=train_data,batch_size=BATCH_SIZE,shuffle=True)class AutoEncoder(nn.Module): ???def __init__(self): ???????super(AutoEncoder, self).__init__() ???????self.encoder = nn.Sequential( ???????????nn.Linear(28 * 28, 128), ???????????nn.Tanh(), ???????????nn.Linear(128,64), ???????????nn.Tanh(), ???????????nn.Linear(64, 12), ???????????# nn.Tanh(), ???????????# nn.Linear(12, 3), ???????) ???????self.decoder=nn.Sequential( ???????????# nn.Linear(3,12), ???????????# nn.Tanh(), ???????????nn.Linear(12, 64), ???????????nn.Tanh(), ???????????nn.Linear(64, 128), ???????????nn.Tanh(), ???????????nn.Linear(128, 28*28), ???????????nn.Sigmoid() ???????) ???def forward(self, x ): ??????encoder=self.encoder(x) ??????decoder=self.decoder(encoder) ??????return ?encoder,decoderAutoEncoder = AutoEncoder()# print(AutoEncoder)optimizer = torch.optim.Adam(AutoEncoder.parameters(), lr=LR) ?# optimize all cnn parametersloss_func = nn.MSELoss()f,a=plt.subplots(2,N_TEST_IMG,figsize=(5,2))plt.ion() ?# continuously plotview_data=train_data.train_data[:N_TEST_IMG].view(-1,28*28).type(torch.FloatTensor)/255for i in range(N_TEST_IMG): ???a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap=‘gray‘) ???a[0][i].set_xticks(()) ???a[0][i].set_yticks(())for epoch in range(EPOCH): ???for step,(x,b_label) in enumerate(train_loader): ???????b_x=x.view(-1,28*28) ???????b_y=x.view(-1,28*28) ???????encoded, decoded = AutoEncoder(b_x) ???????loss=loss_func(decoded,b_y) ???????optimizer.zero_grad() ???????loss.backward() ???????optimizer.step() ???????if step%100==0: ???????????print(‘Epoch:|‘,epoch,‘train loss:%0.4f‘%loss.data.numpy()) ???????????_,decoded_data=AutoEncoder(view_data) ???????????for i in range(N_TEST_IMG): ???????????????a[1][i].clear() ???????????????a[1][i].imshow(np.reshape(decoded.data.numpy()[i],(28,28)),cmap=‘gray‘) ???????????????a[1][i].set_xticks(()) ???????????????a[1][i].set_yticks(()) ???????????plt.draw() ???????????plt.pause(0.05)plt.ioff()plt.show()view_data=train_data.train_data[:200].view(-1,28*28).type(torch.FloatTensor)/255encoded_data,_=AutoEncoder(view_data)fig=plt.figure(2)ax=Axes3D(fig)X,Y,Z=encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()values=train_data.train_labels[:200].numpy()for x,y,z ,s in zip(X,Y,Z,values): ???c=cm.rainbow(int(255*s/9)) ???ax.text(x,y,z,s,backgroundcolor=c)ax.set_xlim(X.min(),X.max())ax.set_ylim(Y.min(),Y.max())ax.set_zlim(Z.min(),Z.max())plt.show()