import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np

EPOCH = 10
BATCH_SIZE = 64
LR = 0.005         # learning rate
DOWNLOAD_MNIST = False
N_TEST_IMG = 5


train_data = torchvision.datasets.MNIST(
    root = './mnist',
    train=True,
    transform=torchvision.transforms.ToTensor(),
)

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,decoder

autoencoder = AutoEncoder()
optimizer = torch.optim.Adam(autoencoder.parameters(),lr = LR)
loss_func = nn.MSELoss()


f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
plt.ion()

view_data = Variable(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)

for 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,y) in enumerate(train_loader):
        b_x = Variable(x.view(-1,28*28))
        b_y = Variable(x.view(-1,28*28))
        b_label = Variable(y)

        encoder,decoder = autoencoder(b_x)

        loss = loss_func(decoder,b_y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if step % 100 == 0:
            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0])

            # plotting decoded image (second row)
            _, decoded_data = autoencoder(view_data)
            for i in range(N_TEST_IMG):
                a[1][i].clear()
                a[1][i].imshow(np.reshape(decoded_data.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 = Variable(train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.)
encoded_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()
