【PyTorch】生成对抗网络/GAN(generative adversarial network)

1 模型介绍

GAN(generative adversarial network)自2014年被提出以来就引起广泛关注，下面是来自百度百科的词条内容：

生成式对抗网络（GAN, Generative Adversarial Networks ）是一种深度学习模型，是近年来复杂分布上无监督学习最具前景的方法之一。模型通过框架中（至少）两个模块：生成模型（Generative Model）和判别模型（Discriminative Model）的互相博弈学习产生相当好的输出。原始 GAN 理论中，并不要求 G 和 D 都是神经网络，只需要是能拟合相应生成和判别的函数即可。但实用中一般均使用深度神经网络作为 G 和 D 。一个优秀的GAN应用需要有良好的训练方法，否则可能由于神经网络模型的自由性而导致输出不理想。

Ian J. Goodfellow等人发表的论文链接：https://arxiv.org/abs/1406.2661

2 具体代码

# generative adversarial network(GAN)
import os 
import torch
import torchvision 
import torch.nn as nn 
from torchvision import transforms
from torchvision.utils import save_image

# Device configuration 
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyper-parameters
latent_size = 64
hidden_size = 256
image_size = 784
num_epochs = 200
batch_size = 100
sample_dir = 'samples'

# Create a directory if not exists
if not os.path.exists(sample_dir):
    os.makedirs(sample_dir)

# Image processing, 3 for RGB channels, 1 for greyscale channels
# transform = transforms.Compose([
#   transforms.ToTensor(),
#   transforms.Normalize(mean=(0.5, 0.5, 0.5),   
#   std=(0.5, 0.5, 0.5))])

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])])

# MNIST dataset
mnist = torchvision.datasets.MNIST(
    root='data/',
    train=True,
    transform=transform,
    download=True)
# Data loader
data_loader = torch.utils.data.DataLoader(
    dataset=mnist,
    batch_size=batch_size, 
    shuffle=True)

# Discriminator
D = nn.Sequential(
    nn.Linear(image_size, hidden_size),
    nn.LeakyReLU(0.2),
    nn.Linear(hidden_size, hidden_size),
    nn.LeakyReLU(0.2),
    nn.Linear(hidden_size, 1),
    nn.Sigmoid())

# Generator 
G = nn.Sequential(
    nn.Linear(latent_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, image_size),
    nn.Tanh())

# Device setting
D = D.to(device)
G = G.to(device)

# Binary cross entropy loss and optimizer
criterion = nn.BCELoss()
d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0002)
g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0002)

def denorm(x):
    out = (x + 1) / 2
    return out.clamp(0, 1)

def reset_grad():
    d_optimizer.zero_grad()
    g_optimizer.zero_grad()

# Start training
total_step = len(data_loader)
for epoch in range(num_epochs):
    for i, (images, _) in enumerate(data_loader):
        images = images.reshape(batch_size, -1).to(device)        

        # Create the labels which are later used as input for the BCE loss
        real_labels = torch.ones(batch_size, 1).to(device)
        fake_labels = torch.zeros(batch_size, 1).to(device)

        # Train the discriminator           
        # Compute BCE_Loss using real images where BCE_Loss(x, y): - y * log(D(x)) - (1-y) * log(1 - D(x))
        # Second term of the loss is always zero since real_labels == 1
        outputs = D(images)
        d_loss_real = criterion(outputs, real_labels)
        real_score = outputs

        # Compute BCELoss using fake images
        # First term of the loss is always zero since fake_labels == 0
        z = torch.randn(batch_size, latent_size).to(device)
        fake_images = G(z)
        outputs = D(fake_images)
        d_loss_fake = criterion(outputs, fake_labels)
        fake_score = outputs

        # Backprop and optimize
        d_loss = d_loss_real + d_loss_fake
        reset_grad()
        d_loss.backward()
        d_optimizer.step()

        #  Train the generator 
        # Compute loss with fake images
        z = torch.randn(batch_size, latent_size).to(device)
        fake_images = G(z)
        outputs = D(fake_images)

        # We train G to maximize log(D(G(z)) instead of minimizing log(1-D(G(z)))
        # For the reason, see the last paragraph of section 3. https://arxiv.org/pdf/1406.2661.pdf
        g_loss = criterion(outputs, real_labels)
        
        # Backprop and optimize
        reset_grad()
        g_loss.backward()
        g_optimizer.step()

        if (i+1) % 200 == 0:
            print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}' 
                  .format(
                      epoch, 
                      num_epochs, 
                      i+1, 
                      total_step, 
                      d_loss.item(), 
                      g_loss.item(), 
                      real_score.mean().item(), fake_score.mean().item()))
        
    # Save real images
    if (epoch+1) == 1:
        images = images.reshape(images.size(0), 1, 28, 28)
        save_image(denorm(images), os.path.join(sample_dir, 'real_images.png'))
    
    # Save sampled images
    fake_images = fake_images.reshape(fake_images.size(0), 1, 28, 28)
    save_image(denorm(fake_images), os.path.join(sample_dir, 'fake_images-{}.png'.format(epoch+1)))

# Save the model checkpoints 
torch.save(G.state_dict(), 'G.ckpt')
torch.save(D.state_dict(), 'D.ckpt')

3 程序输出

训练过程就是不断增强生成模型G的能力，达到“以假乱真”的效果。从训练输出可以看出d_loss增大、D(x)降低，说明判别模型D越来越分辨不出real image和fake image；g_loss下降、D(G(z))增大，说明生成模型G生成的fake image越来越接近real image。
模型每步都保存了生成的fake image，这里展示其中几个epoch（0，50，100，150，199），以及展示real image。
– epoch = 0

– epoch = 50

– epoch = 100

– epoch = 150

– epoch = 199

– real image

Epoch [0/200], Step [200/600], d_loss: 0.0470, g_loss: 4.4273, D(x): 0.99, D(G(z)): 0.04
Epoch [0/200], Step [400/600], d_loss: 0.1453, g_loss: 6.1361, D(x): 0.96, D(G(z)): 0.07
Epoch [0/200], Step [600/600], d_loss: 0.0254, g_loss: 5.2651, D(x): 0.99, D(G(z)): 0.02
Epoch [1/200], Step [200/600], d_loss: 0.0161, g_loss: 6.4054, D(x): 0.99, D(G(z)): 0.01
Epoch [1/200], Step [400/600], d_loss: 0.2298, g_loss: 4.2960, D(x): 0.94, D(G(z)): 0.08
Epoch [1/200], Step [600/600], d_loss: 0.4214, g_loss: 4.9261, D(x): 0.93, D(G(z)): 0.23
Epoch [2/200], Step [200/600], d_loss: 0.0552, g_loss: 4.5034, D(x): 0.98, D(G(z)): 0.03
Epoch [2/200], Step [400/600], d_loss: 0.0684, g_loss: 5.3159, D(x): 0.97, D(G(z)): 0.04
Epoch [2/200], Step [600/600], d_loss: 0.3504, g_loss: 2.7364, D(x): 0.85, D(G(z)): 0.09
Epoch [3/200], Step [200/600], d_loss: 0.8830, g_loss: 3.0232, D(x): 0.69, D(G(z)): 0.14
Epoch [3/200], Step [400/600], d_loss: 0.2208, g_loss: 4.2910, D(x): 0.93, D(G(z)): 0.06
Epoch [3/200], Step [600/600], d_loss: 0.4205, g_loss: 2.9028, D(x): 0.85, D(G(z)): 0.11
Epoch [4/200], Step [200/600], d_loss: 0.2909, g_loss: 3.5080, D(x): 0.96, D(G(z)): 0.20
Epoch [4/200], Step [400/600], d_loss: 0.0816, g_loss: 5.0248, D(x): 0.97, D(G(z)): 0.03
Epoch [4/200], Step [600/600], d_loss: 0.2410, g_loss: 4.5674, D(x): 0.92, D(G(z)): 0.07
Epoch [5/200], Step [200/600], d_loss: 0.4174, g_loss: 3.5316, D(x): 0.89, D(G(z)): 0.12
Epoch [5/200], Step [400/600], d_loss: 0.3851, g_loss: 4.3338, D(x): 0.87, D(G(z)): 0.07
Epoch [5/200], Step [600/600], d_loss: 0.4061, g_loss: 4.1645, D(x): 0.89, D(G(z)): 0.09
Epoch [6/200], Step [200/600], d_loss: 0.1964, g_loss: 5.1655, D(x): 0.91, D(G(z)): 0.03
Epoch [6/200], Step [400/600], d_loss: 0.2616, g_loss: 3.4105, D(x): 0.92, D(G(z)): 0.08
Epoch [6/200], Step [600/600], d_loss: 0.2332, g_loss: 3.8159, D(x): 0.94, D(G(z)): 0.10
Epoch [7/200], Step [200/600], d_loss: 0.1943, g_loss: 3.9958, D(x): 0.92, D(G(z)): 0.06
Epoch [7/200], Step [400/600], d_loss: 0.0882, g_loss: 5.2199, D(x): 0.96, D(G(z)): 0.04
Epoch [7/200], Step [600/600], d_loss: 0.0824, g_loss: 4.9151, D(x): 0.96, D(G(z)): 0.02
Epoch [8/200], Step [200/600], d_loss: 0.3849, g_loss: 4.6934, D(x): 0.86, D(G(z)): 0.02
Epoch [8/200], Step [400/600], d_loss: 0.2212, g_loss: 3.4482, D(x): 0.96, D(G(z)): 0.11
Epoch [8/200], Step [600/600], d_loss: 0.1743, g_loss: 5.7623, D(x): 0.96, D(G(z)): 0.07
Epoch [9/200], Step [200/600], d_loss: 0.2993, g_loss: 4.2877, D(x): 0.95, D(G(z)): 0.10
Epoch [9/200], Step [400/600], d_loss: 0.1218, g_loss: 5.1353, D(x): 0.95, D(G(z)): 0.02
Epoch [9/200], Step [600/600], d_loss: 0.4142, g_loss: 3.8948, D(x): 0.88, D(G(z)): 0.07
Epoch [10/200], Step [200/600], d_loss: 0.1586, g_loss: 4.3726, D(x): 0.98, D(G(z)): 0.09
Epoch [10/200], Step [400/600], d_loss: 0.1054, g_loss: 5.0776, D(x): 0.96, D(G(z)): 0.02
Epoch [10/200], Step [600/600], d_loss: 0.2254, g_loss: 5.7384, D(x): 0.91, D(G(z)): 0.01
Epoch [11/200], Step [200/600], d_loss: 0.0880, g_loss: 4.9024, D(x): 0.96, D(G(z)): 0.04
Epoch [11/200], Step [400/600], d_loss: 0.2936, g_loss: 2.4484, D(x): 0.93, D(G(z)): 0.08
Epoch [11/200], Step [600/600], d_loss: 0.1335, g_loss: 4.1263, D(x): 0.96, D(G(z)): 0.05
Epoch [12/200], Step [200/600], d_loss: 0.1299, g_loss: 5.0566, D(x): 0.95, D(G(z)): 0.02
Epoch [12/200], Step [400/600], d_loss: 0.0839, g_loss: 4.9301, D(x): 0.96, D(G(z)): 0.02
Epoch [12/200], Step [600/600], d_loss: 0.1568, g_loss: 4.9410, D(x): 0.96, D(G(z)): 0.06
Epoch [13/200], Step [200/600], d_loss: 0.0838, g_loss: 4.4768, D(x): 0.98, D(G(z)): 0.02
Epoch [13/200], Step [400/600], d_loss: 0.2606, g_loss: 5.2801, D(x): 0.97, D(G(z)): 0.15
Epoch [13/200], Step [600/600], d_loss: 0.1078, g_loss: 6.9736, D(x): 0.95, D(G(z)): 0.02
Epoch [14/200], Step [200/600], d_loss: 0.2917, g_loss: 7.4783, D(x): 0.89, D(G(z)): 0.00
Epoch [14/200], Step [400/600], d_loss: 0.1246, g_loss: 5.6440, D(x): 0.99, D(G(z)): 0.06
Epoch [14/200], Step [600/600], d_loss: 0.1669, g_loss: 4.6825, D(x): 0.97, D(G(z)): 0.08
Epoch [15/200], Step [200/600], d_loss: 0.0684, g_loss: 5.4430, D(x): 0.96, D(G(z)): 0.01
Epoch [15/200], Step [400/600], d_loss: 0.1261, g_loss: 4.1301, D(x): 0.97, D(G(z)): 0.05
Epoch [15/200], Step [600/600], d_loss: 0.3928, g_loss: 5.0515, D(x): 0.89, D(G(z)): 0.04
Epoch [16/200], Step [200/600], d_loss: 0.0822, g_loss: 5.1707, D(x): 0.97, D(G(z)): 0.03
Epoch [16/200], Step [400/600], d_loss: 0.1735, g_loss: 5.7974, D(x): 0.92, D(G(z)): 0.02
Epoch [16/200], Step [600/600], d_loss: 0.1906, g_loss: 4.7995, D(x): 0.98, D(G(z)): 0.12
Epoch [17/200], Step [200/600], d_loss: 0.3929, g_loss: 2.9602, D(x): 0.90, D(G(z)): 0.12
Epoch [17/200], Step [400/600], d_loss: 0.3379, g_loss: 4.5416, D(x): 0.92, D(G(z)): 0.13
Epoch [17/200], Step [600/600], d_loss: 0.3320, g_loss: 4.0064, D(x): 0.90, D(G(z)): 0.05
Epoch [18/200], Step [200/600], d_loss: 0.1296, g_loss: 4.5214, D(x): 0.99, D(G(z)): 0.09
Epoch [18/200], Step [400/600], d_loss: 0.1229, g_loss: 4.5100, D(x): 0.93, D(G(z)): 0.02
Epoch [18/200], Step [600/600], d_loss: 0.1938, g_loss: 4.3949, D(x): 0.92, D(G(z)): 0.04
Epoch [19/200], Step [200/600], d_loss: 0.5453, g_loss: 4.8068, D(x): 0.98, D(G(z)): 0.26
Epoch [19/200], Step [400/600], d_loss: 0.2208, g_loss: 4.7370, D(x): 0.95, D(G(z)): 0.07
Epoch [19/200], Step [600/600], d_loss: 0.0719, g_loss: 5.3687, D(x): 0.98, D(G(z)): 0.05
Epoch [20/200], Step [200/600], d_loss: 0.2448, g_loss: 5.9713, D(x): 0.92, D(G(z)): 0.04
Epoch [20/200], Step [400/600], d_loss: 0.1789, g_loss: 6.3533, D(x): 0.96, D(G(z)): 0.08
Epoch [20/200], Step [600/600], d_loss: 0.1881, g_loss: 5.5990, D(x): 0.95, D(G(z)): 0.07
Epoch [21/200], Step [200/600], d_loss: 0.1302, g_loss: 6.8167, D(x): 0.96, D(G(z)): 0.06
Epoch [21/200], Step [400/600], d_loss: 0.1292, g_loss: 6.2911, D(x): 0.96, D(G(z)): 0.05
Epoch [21/200], Step [600/600], d_loss: 0.3090, g_loss: 4.1408, D(x): 0.90, D(G(z)): 0.09
Epoch [22/200], Step [200/600], d_loss: 0.2777, g_loss: 5.8237, D(x): 0.95, D(G(z)): 0.13
Epoch [22/200], Step [400/600], d_loss: 0.1448, g_loss: 4.4736, D(x): 0.95, D(G(z)): 0.04
Epoch [22/200], Step [600/600], d_loss: 0.5411, g_loss: 5.5632, D(x): 0.82, D(G(z)): 0.03
Epoch [23/200], Step [200/600], d_loss: 0.2966, g_loss: 5.2273, D(x): 0.92, D(G(z)): 0.10
Epoch [23/200], Step [400/600], d_loss: 0.2568, g_loss: 3.5231, D(x): 0.90, D(G(z)): 0.05
Epoch [23/200], Step [600/600], d_loss: 0.3062, g_loss: 4.3809, D(x): 0.91, D(G(z)): 0.12
Epoch [24/200], Step [200/600], d_loss: 0.2343, g_loss: 4.5202, D(x): 0.89, D(G(z)): 0.03
Epoch [24/200], Step [400/600], d_loss: 0.1924, g_loss: 3.9855, D(x): 0.95, D(G(z)): 0.09
Epoch [24/200], Step [600/600], d_loss: 0.4676, g_loss: 5.2467, D(x): 0.83, D(G(z)): 0.03
Epoch [25/200], Step [200/600], d_loss: 0.2428, g_loss: 3.3750, D(x): 0.93, D(G(z)): 0.09
Epoch [25/200], Step [400/600], d_loss: 0.4260, g_loss: 4.1176, D(x): 0.86, D(G(z)): 0.11
Epoch [25/200], Step [600/600], d_loss: 0.3781, g_loss: 3.8905, D(x): 0.87, D(G(z)): 0.10
Epoch [26/200], Step [200/600], d_loss: 0.3581, g_loss: 4.3727, D(x): 0.90, D(G(z)): 0.08
Epoch [26/200], Step [400/600], d_loss: 0.3745, g_loss: 4.4046, D(x): 0.90, D(G(z)): 0.10
Epoch [26/200], Step [600/600], d_loss: 0.2697, g_loss: 3.4132, D(x): 0.93, D(G(z)): 0.11
Epoch [27/200], Step [200/600], d_loss: 0.3638, g_loss: 4.0925, D(x): 0.90, D(G(z)): 0.10
Epoch [27/200], Step [400/600], d_loss: 0.2662, g_loss: 3.7483, D(x): 0.92, D(G(z)): 0.09
Epoch [27/200], Step [600/600], d_loss: 0.3261, g_loss: 2.8968, D(x): 0.97, D(G(z)): 0.19
Epoch [28/200], Step [200/600], d_loss: 0.4217, g_loss: 2.8956, D(x): 0.90, D(G(z)): 0.12
Epoch [28/200], Step [400/600], d_loss: 0.6127, g_loss: 3.2918, D(x): 0.94, D(G(z)): 0.30
Epoch [28/200], Step [600/600], d_loss: 0.2736, g_loss: 3.9999, D(x): 0.94, D(G(z)): 0.13
Epoch [29/200], Step [200/600], d_loss: 0.2467, g_loss: 4.0165, D(x): 0.96, D(G(z)): 0.12
Epoch [29/200], Step [400/600], d_loss: 0.4403, g_loss: 3.2785, D(x): 0.91, D(G(z)): 0.16
Epoch [29/200], Step [600/600], d_loss: 0.3136, g_loss: 3.7659, D(x): 0.91, D(G(z)): 0.11
Epoch [30/200], Step [200/600], d_loss: 0.3471, g_loss: 2.9367, D(x): 0.90, D(G(z)): 0.13
Epoch [30/200], Step [400/600], d_loss: 0.3247, g_loss: 3.4481, D(x): 0.92, D(G(z)): 0.14
Epoch [30/200], Step [600/600], d_loss: 0.5462, g_loss: 2.6313, D(x): 0.87, D(G(z)): 0.20
Epoch [31/200], Step [200/600], d_loss: 0.5558, g_loss: 3.1563, D(x): 0.83, D(G(z)): 0.11
Epoch [31/200], Step [400/600], d_loss: 0.4707, g_loss: 2.9219, D(x): 0.85, D(G(z)): 0.12
Epoch [31/200], Step [600/600], d_loss: 0.5093, g_loss: 4.5862, D(x): 0.80, D(G(z)): 0.04
Epoch [32/200], Step [200/600], d_loss: 0.4759, g_loss: 3.3408, D(x): 0.87, D(G(z)): 0.15
Epoch [32/200], Step [400/600], d_loss: 0.3769, g_loss: 2.8612, D(x): 0.85, D(G(z)): 0.08
Epoch [32/200], Step [600/600], d_loss: 0.5780, g_loss: 1.8472, D(x): 0.83, D(G(z)): 0.18
Epoch [33/200], Step [200/600], d_loss: 0.5509, g_loss: 2.8013, D(x): 0.87, D(G(z)): 0.20
Epoch [33/200], Step [400/600], d_loss: 0.3934, g_loss: 3.4992, D(x): 0.84, D(G(z)): 0.06
Epoch [33/200], Step [600/600], d_loss: 0.3825, g_loss: 2.5781, D(x): 0.91, D(G(z)): 0.18
Epoch [34/200], Step [200/600], d_loss: 0.6687, g_loss: 3.3864, D(x): 0.78, D(G(z)): 0.15
Epoch [34/200], Step [400/600], d_loss: 0.5931, g_loss: 1.7835, D(x): 0.86, D(G(z)): 0.23
Epoch [34/200], Step [600/600], d_loss: 0.4132, g_loss: 2.6908, D(x): 0.86, D(G(z)): 0.13
Epoch [35/200], Step [200/600], d_loss: 0.4721, g_loss: 3.1934, D(x): 0.84, D(G(z)): 0.10
Epoch [35/200], Step [400/600], d_loss: 0.6144, g_loss: 3.2895, D(x): 0.88, D(G(z)): 0.24
Epoch [35/200], Step [600/600], d_loss: 0.2995, g_loss: 3.3611, D(x): 0.91, D(G(z)): 0.13
Epoch [36/200], Step [200/600], d_loss: 0.6170, g_loss: 2.6114, D(x): 0.80, D(G(z)): 0.15
Epoch [36/200], Step [400/600], d_loss: 0.6916, g_loss: 2.7102, D(x): 0.79, D(G(z)): 0.18
Epoch [36/200], Step [600/600], d_loss: 0.6275, g_loss: 3.8490, D(x): 0.78, D(G(z)): 0.11
Epoch [37/200], Step [200/600], d_loss: 0.3609, g_loss: 3.4735, D(x): 0.91, D(G(z)): 0.15
Epoch [37/200], Step [400/600], d_loss: 0.5123, g_loss: 3.4943, D(x): 0.78, D(G(z)): 0.08
Epoch [37/200], Step [600/600], d_loss: 0.7515, g_loss: 2.9656, D(x): 0.80, D(G(z)): 0.19
Epoch [38/200], Step [200/600], d_loss: 0.4069, g_loss: 3.1408, D(x): 0.93, D(G(z)): 0.21
Epoch [38/200], Step [400/600], d_loss: 0.6983, g_loss: 3.0050, D(x): 0.80, D(G(z)): 0.19
Epoch [38/200], Step [600/600], d_loss: 0.6511, g_loss: 3.3372, D(x): 0.82, D(G(z)): 0.16
Epoch [39/200], Step [200/600], d_loss: 0.5633, g_loss: 3.1432, D(x): 0.86, D(G(z)): 0.21
Epoch [39/200], Step [400/600], d_loss: 0.5349, g_loss: 4.1383, D(x): 0.84, D(G(z)): 0.17
Epoch [39/200], Step [600/600], d_loss: 0.5642, g_loss: 2.8495, D(x): 0.79, D(G(z)): 0.14
Epoch [40/200], Step [200/600], d_loss: 0.4702, g_loss: 4.4767, D(x): 0.88, D(G(z)): 0.17
Epoch [40/200], Step [400/600], d_loss: 0.5330, g_loss: 2.8938, D(x): 0.85, D(G(z)): 0.18
Epoch [40/200], Step [600/600], d_loss: 0.4769, g_loss: 3.6471, D(x): 0.84, D(G(z)): 0.14
Epoch [41/200], Step [200/600], d_loss: 0.5435, g_loss: 2.7885, D(x): 0.85, D(G(z)): 0.16
Epoch [41/200], Step [400/600], d_loss: 0.6488, g_loss: 3.1947, D(x): 0.81, D(G(z)): 0.21
Epoch [41/200], Step [600/600], d_loss: 0.4791, g_loss: 3.1396, D(x): 0.87, D(G(z)): 0.18
Epoch [42/200], Step [200/600], d_loss: 0.5799, g_loss: 2.7526, D(x): 0.83, D(G(z)): 0.23
Epoch [42/200], Step [400/600], d_loss: 0.5212, g_loss: 2.5783, D(x): 0.81, D(G(z)): 0.14
Epoch [42/200], Step [600/600], d_loss: 0.8305, g_loss: 2.5786, D(x): 0.79, D(G(z)): 0.26
Epoch [43/200], Step [200/600], d_loss: 0.4356, g_loss: 2.7466, D(x): 0.90, D(G(z)): 0.20
Epoch [43/200], Step [400/600], d_loss: 0.3823, g_loss: 4.8288, D(x): 0.84, D(G(z)): 0.09
Epoch [43/200], Step [600/600], d_loss: 0.5135, g_loss: 2.4546, D(x): 0.87, D(G(z)): 0.20
Epoch [44/200], Step [200/600], d_loss: 0.5855, g_loss: 2.9187, D(x): 0.82, D(G(z)): 0.21
Epoch [44/200], Step [400/600], d_loss: 0.4671, g_loss: 2.7824, D(x): 0.84, D(G(z)): 0.17
Epoch [44/200], Step [600/600], d_loss: 0.5226, g_loss: 2.8698, D(x): 0.94, D(G(z)): 0.28
Epoch [45/200], Step [200/600], d_loss: 0.4413, g_loss: 3.1040, D(x): 0.84, D(G(z)): 0.13
Epoch [45/200], Step [400/600], d_loss: 0.4432, g_loss: 2.1684, D(x): 0.84, D(G(z)): 0.13
Epoch [45/200], Step [600/600], d_loss: 0.7264, g_loss: 1.9863, D(x): 0.77, D(G(z)): 0.17
Epoch [46/200], Step [200/600], d_loss: 0.5404, g_loss: 2.6853, D(x): 0.80, D(G(z)): 0.12
Epoch [46/200], Step [400/600], d_loss: 0.6838, g_loss: 2.1826, D(x): 0.84, D(G(z)): 0.25
Epoch [46/200], Step [600/600], d_loss: 0.5273, g_loss: 3.0864, D(x): 0.84, D(G(z)): 0.16
Epoch [47/200], Step [200/600], d_loss: 0.4595, g_loss: 2.7024, D(x): 0.83, D(G(z)): 0.14
Epoch [47/200], Step [400/600], d_loss: 0.5056, g_loss: 2.7431, D(x): 0.83, D(G(z)): 0.19
Epoch [47/200], Step [600/600], d_loss: 0.7370, g_loss: 2.7867, D(x): 0.77, D(G(z)): 0.17
Epoch [48/200], Step [200/600], d_loss: 0.4393, g_loss: 2.7028, D(x): 0.88, D(G(z)): 0.18
Epoch [48/200], Step [400/600], d_loss: 0.5673, g_loss: 2.7980, D(x): 0.92, D(G(z)): 0.29
Epoch [48/200], Step [600/600], d_loss: 0.5334, g_loss: 2.1539, D(x): 0.85, D(G(z)): 0.22
Epoch [49/200], Step [200/600], d_loss: 0.5661, g_loss: 3.0253, D(x): 0.78, D(G(z)): 0.10
Epoch [49/200], Step [400/600], d_loss: 0.4719, g_loss: 2.5743, D(x): 0.86, D(G(z)): 0.17
Epoch [49/200], Step [600/600], d_loss: 0.6065, g_loss: 2.5545, D(x): 0.82, D(G(z)): 0.22
Epoch [50/200], Step [200/600], d_loss: 0.4225, g_loss: 2.4486, D(x): 0.84, D(G(z)): 0.15
Epoch [50/200], Step [400/600], d_loss: 0.6071, g_loss: 2.4751, D(x): 0.76, D(G(z)): 0.09
Epoch [50/200], Step [600/600], d_loss: 0.6256, g_loss: 2.4212, D(x): 0.80, D(G(z)): 0.20
Epoch [51/200], Step [200/600], d_loss: 0.5850, g_loss: 3.3030, D(x): 0.77, D(G(z)): 0.13
Epoch [51/200], Step [400/600], d_loss: 0.4128, g_loss: 2.2547, D(x): 0.85, D(G(z)): 0.14
Epoch [51/200], Step [600/600], d_loss: 0.5473, g_loss: 2.4636, D(x): 0.85, D(G(z)): 0.23
Epoch [52/200], Step [200/600], d_loss: 0.6625, g_loss: 1.8152, D(x): 0.80, D(G(z)): 0.24
Epoch [52/200], Step [400/600], d_loss: 0.7375, g_loss: 3.1472, D(x): 0.88, D(G(z)): 0.34
Epoch [52/200], Step [600/600], d_loss: 0.6016, g_loss: 2.0477, D(x): 0.76, D(G(z)): 0.15
Epoch [53/200], Step [200/600], d_loss: 0.8320, g_loss: 2.4250, D(x): 0.71, D(G(z)): 0.20
Epoch [53/200], Step [400/600], d_loss: 0.6171, g_loss: 2.1886, D(x): 0.77, D(G(z)): 0.19
Epoch [53/200], Step [600/600], d_loss: 0.7682, g_loss: 1.6478, D(x): 0.74, D(G(z)): 0.21
Epoch [54/200], Step [200/600], d_loss: 0.5863, g_loss: 2.3504, D(x): 0.85, D(G(z)): 0.24
Epoch [54/200], Step [400/600], d_loss: 0.8063, g_loss: 2.3055, D(x): 0.72, D(G(z)): 0.21
Epoch [54/200], Step [600/600], d_loss: 0.7454, g_loss: 2.2996, D(x): 0.74, D(G(z)): 0.16
Epoch [55/200], Step [200/600], d_loss: 0.5397, g_loss: 2.3880, D(x): 0.83, D(G(z)): 0.19
Epoch [55/200], Step [400/600], d_loss: 0.7615, g_loss: 2.0757, D(x): 0.80, D(G(z)): 0.28
Epoch [55/200], Step [600/600], d_loss: 0.4954, g_loss: 2.7377, D(x): 0.85, D(G(z)): 0.21
Epoch [56/200], Step [200/600], d_loss: 0.5220, g_loss: 2.6000, D(x): 0.87, D(G(z)): 0.24
Epoch [56/200], Step [400/600], d_loss: 0.7056, g_loss: 1.6327, D(x): 0.81, D(G(z)): 0.28
Epoch [56/200], Step [600/600], d_loss: 0.7858, g_loss: 2.8862, D(x): 0.82, D(G(z)): 0.28
Epoch [57/200], Step [200/600], d_loss: 0.4511, g_loss: 2.6597, D(x): 0.88, D(G(z)): 0.20
Epoch [57/200], Step [400/600], d_loss: 0.5671, g_loss: 2.5462, D(x): 0.79, D(G(z)): 0.17
Epoch [57/200], Step [600/600], d_loss: 0.5693, g_loss: 2.0140, D(x): 0.80, D(G(z)): 0.18
Epoch [58/200], Step [200/600], d_loss: 0.4825, g_loss: 2.1138, D(x): 0.87, D(G(z)): 0.22
Epoch [58/200], Step [400/600], d_loss: 0.4156, g_loss: 3.4756, D(x): 0.91, D(G(z)): 0.19
Epoch [58/200], Step [600/600], d_loss: 0.5624, g_loss: 2.8075, D(x): 0.79, D(G(z)): 0.15
Epoch [59/200], Step [200/600], d_loss: 0.6324, g_loss: 2.9195, D(x): 0.85, D(G(z)): 0.21
Epoch [59/200], Step [400/600], d_loss: 0.6008, g_loss: 2.3756, D(x): 0.83, D(G(z)): 0.23
Epoch [59/200], Step [600/600], d_loss: 0.4797, g_loss: 2.4052, D(x): 0.84, D(G(z)): 0.16
Epoch [60/200], Step [200/600], d_loss: 0.7230, g_loss: 2.6456, D(x): 0.73, D(G(z)): 0.15
Epoch [60/200], Step [400/600], d_loss: 0.7388, g_loss: 1.8178, D(x): 0.79, D(G(z)): 0.26
Epoch [60/200], Step [600/600], d_loss: 0.4551, g_loss: 2.7809, D(x): 0.87, D(G(z)): 0.18
Epoch [61/200], Step [200/600], d_loss: 0.4204, g_loss: 3.1016, D(x): 0.89, D(G(z)): 0.17
Epoch [61/200], Step [400/600], d_loss: 0.5638, g_loss: 2.5622, D(x): 0.83, D(G(z)): 0.19
Epoch [61/200], Step [600/600], d_loss: 0.6783, g_loss: 3.2631, D(x): 0.76, D(G(z)): 0.16
Epoch [62/200], Step [200/600], d_loss: 0.4386, g_loss: 2.8836, D(x): 0.82, D(G(z)): 0.12
Epoch [62/200], Step [400/600], d_loss: 0.6454, g_loss: 2.2365, D(x): 0.81, D(G(z)): 0.21
Epoch [62/200], Step [600/600], d_loss: 0.6741, g_loss: 2.0690, D(x): 0.85, D(G(z)): 0.29
Epoch [63/200], Step [200/600], d_loss: 0.5414, g_loss: 2.1266, D(x): 0.77, D(G(z)): 0.13
Epoch [63/200], Step [400/600], d_loss: 0.7652, g_loss: 2.1745, D(x): 0.72, D(G(z)): 0.21
Epoch [63/200], Step [600/600], d_loss: 0.7641, g_loss: 2.1227, D(x): 0.82, D(G(z)): 0.30
Epoch [64/200], Step [200/600], d_loss: 0.6524, g_loss: 2.6566, D(x): 0.77, D(G(z)): 0.17
Epoch [64/200], Step [400/600], d_loss: 0.8171, g_loss: 2.5850, D(x): 0.69, D(G(z)): 0.18
Epoch [64/200], Step [600/600], d_loss: 0.6750, g_loss: 2.3300, D(x): 0.74, D(G(z)): 0.17
Epoch [65/200], Step [200/600], d_loss: 0.7582, g_loss: 1.9702, D(x): 0.76, D(G(z)): 0.24
Epoch [65/200], Step [400/600], d_loss: 0.9523, g_loss: 2.4954, D(x): 0.70, D(G(z)): 0.24
Epoch [65/200], Step [600/600], d_loss: 0.6429, g_loss: 1.7366, D(x): 0.77, D(G(z)): 0.20
Epoch [66/200], Step [200/600], d_loss: 0.7050, g_loss: 2.5913, D(x): 0.70, D(G(z)): 0.15
Epoch [66/200], Step [400/600], d_loss: 0.5947, g_loss: 2.0411, D(x): 0.85, D(G(z)): 0.25
Epoch [66/200], Step [600/600], d_loss: 0.4883, g_loss: 2.6249, D(x): 0.84, D(G(z)): 0.17
Epoch [67/200], Step [200/600], d_loss: 0.8298, g_loss: 1.3956, D(x): 0.77, D(G(z)): 0.30
Epoch [67/200], Step [400/600], d_loss: 0.7305, g_loss: 2.7201, D(x): 0.75, D(G(z)): 0.18
Epoch [67/200], Step [600/600], d_loss: 0.5734, g_loss: 2.0877, D(x): 0.84, D(G(z)): 0.26
Epoch [68/200], Step [200/600], d_loss: 0.5154, g_loss: 2.9730, D(x): 0.78, D(G(z)): 0.14
Epoch [68/200], Step [400/600], d_loss: 0.8208, g_loss: 2.4197, D(x): 0.68, D(G(z)): 0.15
Epoch [68/200], Step [600/600], d_loss: 0.7707, g_loss: 2.4385, D(x): 0.77, D(G(z)): 0.23
Epoch [69/200], Step [200/600], d_loss: 0.7308, g_loss: 2.4949, D(x): 0.77, D(G(z)): 0.25
Epoch [69/200], Step [400/600], d_loss: 0.6022, g_loss: 1.7119, D(x): 0.80, D(G(z)): 0.19
Epoch [69/200], Step [600/600], d_loss: 0.7737, g_loss: 2.0528, D(x): 0.76, D(G(z)): 0.23
Epoch [70/200], Step [200/600], d_loss: 0.6713, g_loss: 2.1575, D(x): 0.74, D(G(z)): 0.18
Epoch [70/200], Step [400/600], d_loss: 0.4558, g_loss: 2.6740, D(x): 0.81, D(G(z)): 0.14
Epoch [70/200], Step [600/600], d_loss: 0.6664, g_loss: 2.3781, D(x): 0.82, D(G(z)): 0.26
Epoch [71/200], Step [200/600], d_loss: 0.4208, g_loss: 2.7306, D(x): 0.84, D(G(z)): 0.13
Epoch [71/200], Step [400/600], d_loss: 0.5696, g_loss: 2.3525, D(x): 0.86, D(G(z)): 0.28
Epoch [71/200], Step [600/600], d_loss: 0.6642, g_loss: 2.1666, D(x): 0.89, D(G(z)): 0.34
Epoch [72/200], Step [200/600], d_loss: 0.7516, g_loss: 2.1152, D(x): 0.85, D(G(z)): 0.34
Epoch [72/200], Step [400/600], d_loss: 0.8580, g_loss: 2.2404, D(x): 0.76, D(G(z)): 0.28
Epoch [72/200], Step [600/600], d_loss: 0.8393, g_loss: 1.7819, D(x): 0.85, D(G(z)): 0.37
Epoch [73/200], Step [200/600], d_loss: 0.5924, g_loss: 2.6861, D(x): 0.76, D(G(z)): 0.17
Epoch [73/200], Step [400/600], d_loss: 0.7924, g_loss: 2.5014, D(x): 0.80, D(G(z)): 0.30
Epoch [73/200], Step [600/600], d_loss: 0.8842, g_loss: 2.0168, D(x): 0.75, D(G(z)): 0.31
Epoch [74/200], Step [200/600], d_loss: 0.6369, g_loss: 2.6035, D(x): 0.77, D(G(z)): 0.16
Epoch [74/200], Step [400/600], d_loss: 1.0137, g_loss: 1.9856, D(x): 0.74, D(G(z)): 0.31
Epoch [74/200], Step [600/600], d_loss: 0.6401, g_loss: 2.8239, D(x): 0.83, D(G(z)): 0.24
Epoch [75/200], Step [200/600], d_loss: 0.7222, g_loss: 2.6499, D(x): 0.76, D(G(z)): 0.21
Epoch [75/200], Step [400/600], d_loss: 0.5535, g_loss: 2.6794, D(x): 0.76, D(G(z)): 0.16
Epoch [75/200], Step [600/600], d_loss: 0.7484, g_loss: 1.8749, D(x): 0.76, D(G(z)): 0.24
Epoch [76/200], Step [200/600], d_loss: 0.6172, g_loss: 2.1332, D(x): 0.84, D(G(z)): 0.26
Epoch [76/200], Step [400/600], d_loss: 0.9073, g_loss: 1.7258, D(x): 0.74, D(G(z)): 0.32
Epoch [76/200], Step [600/600], d_loss: 0.6465, g_loss: 3.3903, D(x): 0.74, D(G(z)): 0.15
Epoch [77/200], Step [200/600], d_loss: 0.7583, g_loss: 2.1551, D(x): 0.78, D(G(z)): 0.25
Epoch [77/200], Step [400/600], d_loss: 0.6474, g_loss: 1.8975, D(x): 0.80, D(G(z)): 0.21
Epoch [77/200], Step [600/600], d_loss: 0.8241, g_loss: 1.8567, D(x): 0.68, D(G(z)): 0.20
Epoch [78/200], Step [200/600], d_loss: 0.6767, g_loss: 2.6657, D(x): 0.71, D(G(z)): 0.16
Epoch [78/200], Step [400/600], d_loss: 0.5965, g_loss: 2.0876, D(x): 0.82, D(G(z)): 0.23
Epoch [78/200], Step [600/600], d_loss: 0.8346, g_loss: 1.7815, D(x): 0.80, D(G(z)): 0.34
Epoch [79/200], Step [200/600], d_loss: 0.9473, g_loss: 1.3489, D(x): 0.77, D(G(z)): 0.35
Epoch [79/200], Step [400/600], d_loss: 0.6000, g_loss: 1.9143, D(x): 0.81, D(G(z)): 0.24
Epoch [79/200], Step [600/600], d_loss: 0.6208, g_loss: 2.1870, D(x): 0.75, D(G(z)): 0.18
Epoch [80/200], Step [200/600], d_loss: 0.7634, g_loss: 2.1038, D(x): 0.73, D(G(z)): 0.19
Epoch [80/200], Step [400/600], d_loss: 0.6876, g_loss: 2.3661, D(x): 0.78, D(G(z)): 0.25
Epoch [80/200], Step [600/600], d_loss: 0.7142, g_loss: 2.4693, D(x): 0.78, D(G(z)): 0.25
Epoch [81/200], Step [200/600], d_loss: 0.6182, g_loss: 1.9420, D(x): 0.83, D(G(z)): 0.24
Epoch [81/200], Step [400/600], d_loss: 0.6391, g_loss: 2.0722, D(x): 0.76, D(G(z)): 0.19
Epoch [81/200], Step [600/600], d_loss: 0.7186, g_loss: 2.5009, D(x): 0.70, D(G(z)): 0.15
Epoch [82/200], Step [200/600], d_loss: 0.7566, g_loss: 2.4882, D(x): 0.77, D(G(z)): 0.25
Epoch [82/200], Step [400/600], d_loss: 0.8851, g_loss: 2.2362, D(x): 0.77, D(G(z)): 0.33
Epoch [82/200], Step [600/600], d_loss: 0.7781, g_loss: 1.6748, D(x): 0.79, D(G(z)): 0.30
Epoch [83/200], Step [200/600], d_loss: 0.7610, g_loss: 2.0682, D(x): 0.80, D(G(z)): 0.28
Epoch [83/200], Step [400/600], d_loss: 0.5442, g_loss: 1.9091, D(x): 0.84, D(G(z)): 0.24
Epoch [83/200], Step [600/600], d_loss: 0.9009, g_loss: 1.7996, D(x): 0.77, D(G(z)): 0.30
Epoch [84/200], Step [200/600], d_loss: 0.5958, g_loss: 2.4650, D(x): 0.81, D(G(z)): 0.22
Epoch [84/200], Step [400/600], d_loss: 0.6394, g_loss: 1.7314, D(x): 0.78, D(G(z)): 0.24
Epoch [84/200], Step [600/600], d_loss: 0.7294, g_loss: 2.2409, D(x): 0.80, D(G(z)): 0.29
Epoch [85/200], Step [200/600], d_loss: 1.0039, g_loss: 2.1785, D(x): 0.76, D(G(z)): 0.36
Epoch [85/200], Step [400/600], d_loss: 0.7370, g_loss: 1.9103, D(x): 0.83, D(G(z)): 0.33
Epoch [85/200], Step [600/600], d_loss: 0.6967, g_loss: 2.4991, D(x): 0.75, D(G(z)): 0.22
Epoch [86/200], Step [200/600], d_loss: 0.6571, g_loss: 1.6934, D(x): 0.85, D(G(z)): 0.31
Epoch [86/200], Step [400/600], d_loss: 0.9010, g_loss: 1.7845, D(x): 0.67, D(G(z)): 0.21
Epoch [86/200], Step [600/600], d_loss: 0.7478, g_loss: 1.8386, D(x): 0.78, D(G(z)): 0.26
Epoch [87/200], Step [200/600], d_loss: 0.7735, g_loss: 1.5609, D(x): 0.80, D(G(z)): 0.30
Epoch [87/200], Step [400/600], d_loss: 0.8374, g_loss: 1.3501, D(x): 0.73, D(G(z)): 0.26
Epoch [87/200], Step [600/600], d_loss: 0.8446, g_loss: 1.9913, D(x): 0.75, D(G(z)): 0.28
Epoch [88/200], Step [200/600], d_loss: 0.7278, g_loss: 2.1532, D(x): 0.71, D(G(z)): 0.16
Epoch [88/200], Step [400/600], d_loss: 0.7137, g_loss: 2.1165, D(x): 0.69, D(G(z)): 0.18
Epoch [88/200], Step [600/600], d_loss: 0.8887, g_loss: 1.2378, D(x): 0.70, D(G(z)): 0.28
Epoch [89/200], Step [200/600], d_loss: 0.8917, g_loss: 1.9705, D(x): 0.69, D(G(z)): 0.26
Epoch [89/200], Step [400/600], d_loss: 0.7657, g_loss: 2.4673, D(x): 0.77, D(G(z)): 0.26
Epoch [89/200], Step [600/600], d_loss: 0.6419, g_loss: 1.9880, D(x): 0.82, D(G(z)): 0.27
Epoch [90/200], Step [200/600], d_loss: 0.7286, g_loss: 1.9025, D(x): 0.78, D(G(z)): 0.23
Epoch [90/200], Step [400/600], d_loss: 0.5856, g_loss: 1.6288, D(x): 0.84, D(G(z)): 0.25
Epoch [90/200], Step [600/600], d_loss: 0.6936, g_loss: 2.2498, D(x): 0.81, D(G(z)): 0.26
Epoch [91/200], Step [200/600], d_loss: 0.7015, g_loss: 2.2918, D(x): 0.70, D(G(z)): 0.18
Epoch [91/200], Step [400/600], d_loss: 0.6961, g_loss: 1.4990, D(x): 0.76, D(G(z)): 0.24
Epoch [91/200], Step [600/600], d_loss: 1.0444, g_loss: 1.7366, D(x): 0.72, D(G(z)): 0.33
Epoch [92/200], Step [200/600], d_loss: 0.8074, g_loss: 2.1368, D(x): 0.67, D(G(z)): 0.20
Epoch [92/200], Step [400/600], d_loss: 1.0336, g_loss: 1.5150, D(x): 0.67, D(G(z)): 0.29
Epoch [92/200], Step [600/600], d_loss: 0.7923, g_loss: 1.6682, D(x): 0.71, D(G(z)): 0.22
Epoch [93/200], Step [200/600], d_loss: 0.9705, g_loss: 1.5378, D(x): 0.85, D(G(z)): 0.44
Epoch [93/200], Step [400/600], d_loss: 0.9318, g_loss: 1.4094, D(x): 0.74, D(G(z)): 0.34
Epoch [93/200], Step [600/600], d_loss: 0.8385, g_loss: 2.1395, D(x): 0.69, D(G(z)): 0.18
Epoch [94/200], Step [200/600], d_loss: 0.8333, g_loss: 1.7765, D(x): 0.67, D(G(z)): 0.19
Epoch [94/200], Step [400/600], d_loss: 0.7310, g_loss: 2.2643, D(x): 0.73, D(G(z)): 0.21
Epoch [94/200], Step [600/600], d_loss: 0.8712, g_loss: 2.3476, D(x): 0.68, D(G(z)): 0.22
Epoch [95/200], Step [200/600], d_loss: 0.6457, g_loss: 1.8868, D(x): 0.78, D(G(z)): 0.24
Epoch [95/200], Step [400/600], d_loss: 0.8926, g_loss: 1.7284, D(x): 0.72, D(G(z)): 0.30
Epoch [95/200], Step [600/600], d_loss: 0.7586, g_loss: 1.8511, D(x): 0.75, D(G(z)): 0.27
Epoch [96/200], Step [200/600], d_loss: 0.6954, g_loss: 2.1118, D(x): 0.76, D(G(z)): 0.20
Epoch [96/200], Step [400/600], d_loss: 0.9447, g_loss: 1.9479, D(x): 0.69, D(G(z)): 0.26
Epoch [96/200], Step [600/600], d_loss: 0.8760, g_loss: 1.6268, D(x): 0.69, D(G(z)): 0.26
Epoch [97/200], Step [200/600], d_loss: 0.8981, g_loss: 1.8473, D(x): 0.70, D(G(z)): 0.26
Epoch [97/200], Step [400/600], d_loss: 0.8313, g_loss: 2.1134, D(x): 0.79, D(G(z)): 0.34
Epoch [97/200], Step [600/600], d_loss: 0.9799, g_loss: 1.7327, D(x): 0.77, D(G(z)): 0.38
Epoch [98/200], Step [200/600], d_loss: 0.7928, g_loss: 2.4119, D(x): 0.73, D(G(z)): 0.23
Epoch [98/200], Step [400/600], d_loss: 0.7950, g_loss: 1.7916, D(x): 0.77, D(G(z)): 0.27
Epoch [98/200], Step [600/600], d_loss: 0.9023, g_loss: 1.8960, D(x): 0.77, D(G(z)): 0.34
Epoch [99/200], Step [200/600], d_loss: 0.7460, g_loss: 1.4753, D(x): 0.77, D(G(z)): 0.25
Epoch [99/200], Step [400/600], d_loss: 1.0268, g_loss: 2.0465, D(x): 0.67, D(G(z)): 0.28
Epoch [99/200], Step [600/600], d_loss: 1.0442, g_loss: 1.4376, D(x): 0.71, D(G(z)): 0.31
Epoch [100/200], Step [200/600], d_loss: 0.9897, g_loss: 1.7333, D(x): 0.66, D(G(z)): 0.29
Epoch [100/200], Step [400/600], d_loss: 0.7949, g_loss: 2.5213, D(x): 0.73, D(G(z)): 0.26
Epoch [100/200], Step [600/600], d_loss: 0.8020, g_loss: 1.8205, D(x): 0.80, D(G(z)): 0.34
Epoch [101/200], Step [200/600], d_loss: 0.8511, g_loss: 1.7354, D(x): 0.68, D(G(z)): 0.23
Epoch [101/200], Step [400/600], d_loss: 0.6992, g_loss: 1.7517, D(x): 0.77, D(G(z)): 0.25
Epoch [101/200], Step [600/600], d_loss: 0.7008, g_loss: 1.9080, D(x): 0.75, D(G(z)): 0.24
Epoch [102/200], Step [200/600], d_loss: 0.9531, g_loss: 1.8946, D(x): 0.67, D(G(z)): 0.28
Epoch [102/200], Step [400/600], d_loss: 0.7998, g_loss: 2.2541, D(x): 0.77, D(G(z)): 0.30
Epoch [102/200], Step [600/600], d_loss: 0.7117, g_loss: 1.7053, D(x): 0.78, D(G(z)): 0.24
Epoch [103/200], Step [200/600], d_loss: 0.8539, g_loss: 1.6787, D(x): 0.73, D(G(z)): 0.28
Epoch [103/200], Step [400/600], d_loss: 0.9104, g_loss: 2.1986, D(x): 0.81, D(G(z)): 0.39
Epoch [103/200], Step [600/600], d_loss: 0.9180, g_loss: 2.4920, D(x): 0.68, D(G(z)): 0.25
Epoch [104/200], Step [200/600], d_loss: 0.9855, g_loss: 1.8069, D(x): 0.59, D(G(z)): 0.17
Epoch [104/200], Step [400/600], d_loss: 0.8452, g_loss: 2.0168, D(x): 0.70, D(G(z)): 0.24
Epoch [104/200], Step [600/600], d_loss: 0.9090, g_loss: 1.9037, D(x): 0.72, D(G(z)): 0.30
Epoch [105/200], Step [200/600], d_loss: 0.9647, g_loss: 1.5274, D(x): 0.64, D(G(z)): 0.26
Epoch [105/200], Step [400/600], d_loss: 0.7125, g_loss: 1.8630, D(x): 0.75, D(G(z)): 0.22
Epoch [105/200], Step [600/600], d_loss: 0.7479, g_loss: 1.8036, D(x): 0.79, D(G(z)): 0.31
Epoch [106/200], Step [200/600], d_loss: 0.9350, g_loss: 1.9197, D(x): 0.73, D(G(z)): 0.33
Epoch [106/200], Step [400/600], d_loss: 0.8513, g_loss: 1.5831, D(x): 0.79, D(G(z)): 0.32
Epoch [106/200], Step [600/600], d_loss: 0.8434, g_loss: 2.5236, D(x): 0.68, D(G(z)): 0.19
Epoch [107/200], Step [200/600], d_loss: 0.9225, g_loss: 1.6804, D(x): 0.78, D(G(z)): 0.39
Epoch [107/200], Step [400/600], d_loss: 0.8651, g_loss: 1.8469, D(x): 0.74, D(G(z)): 0.29
Epoch [107/200], Step [600/600], d_loss: 1.0709, g_loss: 1.8910, D(x): 0.61, D(G(z)): 0.26
Epoch [108/200], Step [200/600], d_loss: 0.8335, g_loss: 1.8592, D(x): 0.75, D(G(z)): 0.31
Epoch [108/200], Step [400/600], d_loss: 0.8644, g_loss: 1.8383, D(x): 0.65, D(G(z)): 0.20
Epoch [108/200], Step [600/600], d_loss: 0.7784, g_loss: 2.1106, D(x): 0.81, D(G(z)): 0.30
Epoch [109/200], Step [200/600], d_loss: 0.7713, g_loss: 1.8474, D(x): 0.78, D(G(z)): 0.30
Epoch [109/200], Step [400/600], d_loss: 0.6287, g_loss: 2.1118, D(x): 0.77, D(G(z)): 0.20
Epoch [109/200], Step [600/600], d_loss: 0.8158, g_loss: 1.9784, D(x): 0.68, D(G(z)): 0.21
Epoch [110/200], Step [200/600], d_loss: 0.8989, g_loss: 1.6065, D(x): 0.76, D(G(z)): 0.35
Epoch [110/200], Step [400/600], d_loss: 0.8740, g_loss: 1.4742, D(x): 0.71, D(G(z)): 0.30
Epoch [110/200], Step [600/600], d_loss: 0.6629, g_loss: 2.0034, D(x): 0.84, D(G(z)): 0.30
Epoch [111/200], Step [200/600], d_loss: 0.8124, g_loss: 1.7153, D(x): 0.80, D(G(z)): 0.34
Epoch [111/200], Step [400/600], d_loss: 0.8479, g_loss: 2.0981, D(x): 0.68, D(G(z)): 0.20
Epoch [111/200], Step [600/600], d_loss: 0.7136, g_loss: 1.8688, D(x): 0.72, D(G(z)): 0.21
Epoch [112/200], Step [200/600], d_loss: 0.7248, g_loss: 1.7079, D(x): 0.71, D(G(z)): 0.20
Epoch [112/200], Step [400/600], d_loss: 0.9196, g_loss: 1.7992, D(x): 0.64, D(G(z)): 0.20
Epoch [112/200], Step [600/600], d_loss: 0.6023, g_loss: 1.9134, D(x): 0.77, D(G(z)): 0.19
Epoch [113/200], Step [200/600], d_loss: 0.9980, g_loss: 1.7837, D(x): 0.70, D(G(z)): 0.31
Epoch [113/200], Step [400/600], d_loss: 0.9119, g_loss: 1.4296, D(x): 0.74, D(G(z)): 0.33
Epoch [113/200], Step [600/600], d_loss: 0.8514, g_loss: 1.8519, D(x): 0.72, D(G(z)): 0.24
Epoch [114/200], Step [200/600], d_loss: 0.9286, g_loss: 1.6051, D(x): 0.74, D(G(z)): 0.35
Epoch [114/200], Step [400/600], d_loss: 0.8345, g_loss: 1.9047, D(x): 0.72, D(G(z)): 0.28
Epoch [114/200], Step [600/600], d_loss: 0.7357, g_loss: 1.6866, D(x): 0.76, D(G(z)): 0.26
Epoch [115/200], Step [200/600], d_loss: 1.1178, g_loss: 1.8709, D(x): 0.73, D(G(z)): 0.39
Epoch [115/200], Step [400/600], d_loss: 0.9369, g_loss: 1.4690, D(x): 0.70, D(G(z)): 0.31
Epoch [115/200], Step [600/600], d_loss: 0.6943, g_loss: 1.7425, D(x): 0.77, D(G(z)): 0.24
Epoch [116/200], Step [200/600], d_loss: 0.7689, g_loss: 1.6584, D(x): 0.78, D(G(z)): 0.30
Epoch [116/200], Step [400/600], d_loss: 0.8996, g_loss: 1.3530, D(x): 0.80, D(G(z)): 0.38
Epoch [116/200], Step [600/600], d_loss: 0.9487, g_loss: 1.3110, D(x): 0.79, D(G(z)): 0.41
Epoch [117/200], Step [200/600], d_loss: 0.7204, g_loss: 2.5085, D(x): 0.81, D(G(z)): 0.27
Epoch [117/200], Step [400/600], d_loss: 0.9537, g_loss: 1.8315, D(x): 0.67, D(G(z)): 0.26
Epoch [117/200], Step [600/600], d_loss: 0.6763, g_loss: 1.7682, D(x): 0.80, D(G(z)): 0.27
Epoch [118/200], Step [200/600], d_loss: 0.9276, g_loss: 1.7115, D(x): 0.66, D(G(z)): 0.22
Epoch [118/200], Step [400/600], d_loss: 0.9136, g_loss: 1.4024, D(x): 0.73, D(G(z)): 0.31
Epoch [118/200], Step [600/600], d_loss: 0.8714, g_loss: 1.6280, D(x): 0.80, D(G(z)): 0.37
Epoch [119/200], Step [200/600], d_loss: 0.8546, g_loss: 1.5792, D(x): 0.73, D(G(z)): 0.31
Epoch [119/200], Step [400/600], d_loss: 0.7035, g_loss: 1.8312, D(x): 0.71, D(G(z)): 0.19
Epoch [119/200], Step [600/600], d_loss: 0.8878, g_loss: 1.7036, D(x): 0.69, D(G(z)): 0.25
Epoch [120/200], Step [200/600], d_loss: 0.9592, g_loss: 1.3375, D(x): 0.61, D(G(z)): 0.21
Epoch [120/200], Step [400/600], d_loss: 0.9011, g_loss: 2.0829, D(x): 0.66, D(G(z)): 0.22
Epoch [120/200], Step [600/600], d_loss: 0.8830, g_loss: 1.5800, D(x): 0.73, D(G(z)): 0.31
Epoch [121/200], Step [200/600], d_loss: 1.0095, g_loss: 1.6000, D(x): 0.64, D(G(z)): 0.31
Epoch [121/200], Step [400/600], d_loss: 0.7881, g_loss: 1.6243, D(x): 0.71, D(G(z)): 0.21
Epoch [121/200], Step [600/600], d_loss: 0.8295, g_loss: 1.6557, D(x): 0.71, D(G(z)): 0.24
Epoch [122/200], Step [200/600], d_loss: 0.9404, g_loss: 1.3748, D(x): 0.60, D(G(z)): 0.18
Epoch [122/200], Step [400/600], d_loss: 0.8459, g_loss: 2.3138, D(x): 0.66, D(G(z)): 0.19
Epoch [122/200], Step [600/600], d_loss: 0.9420, g_loss: 1.5667, D(x): 0.67, D(G(z)): 0.25
Epoch [123/200], Step [200/600], d_loss: 1.1139, g_loss: 1.4878, D(x): 0.56, D(G(z)): 0.20
Epoch [123/200], Step [400/600], d_loss: 0.7282, g_loss: 1.6294, D(x): 0.73, D(G(z)): 0.24
Epoch [123/200], Step [600/600], d_loss: 0.8722, g_loss: 1.2667, D(x): 0.68, D(G(z)): 0.26
Epoch [124/200], Step [200/600], d_loss: 0.7568, g_loss: 1.9428, D(x): 0.67, D(G(z)): 0.21
Epoch [124/200], Step [400/600], d_loss: 0.8482, g_loss: 2.0735, D(x): 0.72, D(G(z)): 0.30
Epoch [124/200], Step [600/600], d_loss: 0.9920, g_loss: 1.4466, D(x): 0.72, D(G(z)): 0.33
Epoch [125/200], Step [200/600], d_loss: 1.0061, g_loss: 2.2152, D(x): 0.64, D(G(z)): 0.27
Epoch [125/200], Step [400/600], d_loss: 1.0078, g_loss: 1.6272, D(x): 0.74, D(G(z)): 0.37
Epoch [125/200], Step [600/600], d_loss: 0.9771, g_loss: 2.1177, D(x): 0.71, D(G(z)): 0.29
Epoch [126/200], Step [200/600], d_loss: 1.0673, g_loss: 1.3473, D(x): 0.62, D(G(z)): 0.31
Epoch [126/200], Step [400/600], d_loss: 1.0108, g_loss: 1.8402, D(x): 0.67, D(G(z)): 0.30
Epoch [126/200], Step [600/600], d_loss: 0.9529, g_loss: 1.7117, D(x): 0.77, D(G(z)): 0.38
Epoch [127/200], Step [200/600], d_loss: 0.9988, g_loss: 1.5464, D(x): 0.71, D(G(z)): 0.35
Epoch [127/200], Step [400/600], d_loss: 0.7894, g_loss: 1.7470, D(x): 0.78, D(G(z)): 0.33
Epoch [127/200], Step [600/600], d_loss: 0.6126, g_loss: 2.2699, D(x): 0.79, D(G(z)): 0.23
Epoch [128/200], Step [200/600], d_loss: 0.8969, g_loss: 1.7038, D(x): 0.76, D(G(z)): 0.36
Epoch [128/200], Step [400/600], d_loss: 1.0108, g_loss: 1.3678, D(x): 0.67, D(G(z)): 0.33
Epoch [128/200], Step [600/600], d_loss: 0.8283, g_loss: 1.8264, D(x): 0.74, D(G(z)): 0.30
Epoch [129/200], Step [200/600], d_loss: 0.7680, g_loss: 1.9022, D(x): 0.69, D(G(z)): 0.18
Epoch [129/200], Step [400/600], d_loss: 0.9077, g_loss: 1.5271, D(x): 0.76, D(G(z)): 0.36
Epoch [129/200], Step [600/600], d_loss: 0.7863, g_loss: 1.5050, D(x): 0.75, D(G(z)): 0.30
Epoch [130/200], Step [200/600], d_loss: 0.7647, g_loss: 1.6246, D(x): 0.76, D(G(z)): 0.29
Epoch [130/200], Step [400/600], d_loss: 1.0322, g_loss: 1.4377, D(x): 0.82, D(G(z)): 0.44
Epoch [130/200], Step [600/600], d_loss: 0.9320, g_loss: 2.2156, D(x): 0.67, D(G(z)): 0.24
Epoch [131/200], Step [200/600], d_loss: 1.0010, g_loss: 1.8664, D(x): 0.65, D(G(z)): 0.29
Epoch [131/200], Step [400/600], d_loss: 0.8542, g_loss: 1.8232, D(x): 0.70, D(G(z)): 0.22
Epoch [131/200], Step [600/600], d_loss: 0.7486, g_loss: 1.7270, D(x): 0.78, D(G(z)): 0.28
Epoch [132/200], Step [200/600], d_loss: 0.9719, g_loss: 1.2860, D(x): 0.68, D(G(z)): 0.29
Epoch [132/200], Step [400/600], d_loss: 0.9132, g_loss: 1.5629, D(x): 0.65, D(G(z)): 0.24
Epoch [132/200], Step [600/600], d_loss: 0.9548, g_loss: 1.8049, D(x): 0.68, D(G(z)): 0.29
Epoch [133/200], Step [200/600], d_loss: 0.6619, g_loss: 1.7538, D(x): 0.76, D(G(z)): 0.23
Epoch [133/200], Step [400/600], d_loss: 1.0047, g_loss: 1.5824, D(x): 0.74, D(G(z)): 0.37
Epoch [133/200], Step [600/600], d_loss: 0.8980, g_loss: 1.5372, D(x): 0.70, D(G(z)): 0.29
Epoch [134/200], Step [200/600], d_loss: 0.9166, g_loss: 1.2988, D(x): 0.80, D(G(z)): 0.37
Epoch [134/200], Step [400/600], d_loss: 0.9848, g_loss: 1.6209, D(x): 0.72, D(G(z)): 0.35
Epoch [134/200], Step [600/600], d_loss: 0.8396, g_loss: 1.5322, D(x): 0.69, D(G(z)): 0.28
Epoch [135/200], Step [200/600], d_loss: 0.8331, g_loss: 1.5928, D(x): 0.68, D(G(z)): 0.24
Epoch [135/200], Step [400/600], d_loss: 0.9271, g_loss: 1.7751, D(x): 0.68, D(G(z)): 0.28
Epoch [135/200], Step [600/600], d_loss: 1.2802, g_loss: 1.3046, D(x): 0.63, D(G(z)): 0.37
Epoch [136/200], Step [200/600], d_loss: 1.0288, g_loss: 1.6774, D(x): 0.68, D(G(z)): 0.32
Epoch [136/200], Step [400/600], d_loss: 0.9027, g_loss: 1.6579, D(x): 0.77, D(G(z)): 0.37
Epoch [136/200], Step [600/600], d_loss: 1.0080, g_loss: 1.3883, D(x): 0.67, D(G(z)): 0.30
Epoch [137/200], Step [200/600], d_loss: 0.9043, g_loss: 1.5809, D(x): 0.69, D(G(z)): 0.30
Epoch [137/200], Step [400/600], d_loss: 0.8684, g_loss: 1.7851, D(x): 0.76, D(G(z)): 0.31
Epoch [137/200], Step [600/600], d_loss: 0.9694, g_loss: 1.5669, D(x): 0.71, D(G(z)): 0.33
Epoch [138/200], Step [200/600], d_loss: 0.7400, g_loss: 1.6188, D(x): 0.72, D(G(z)): 0.24
Epoch [138/200], Step [400/600], d_loss: 0.9084, g_loss: 1.6095, D(x): 0.67, D(G(z)): 0.27
Epoch [138/200], Step [600/600], d_loss: 0.8807, g_loss: 1.4123, D(x): 0.72, D(G(z)): 0.30
Epoch [139/200], Step [200/600], d_loss: 0.6726, g_loss: 1.3809, D(x): 0.78, D(G(z)): 0.25
Epoch [139/200], Step [400/600], d_loss: 0.9256, g_loss: 1.1885, D(x): 0.73, D(G(z)): 0.33
Epoch [139/200], Step [600/600], d_loss: 0.9302, g_loss: 1.2736, D(x): 0.69, D(G(z)): 0.28
Epoch [140/200], Step [200/600], d_loss: 0.9735, g_loss: 1.3551, D(x): 0.69, D(G(z)): 0.31
Epoch [140/200], Step [400/600], d_loss: 1.0635, g_loss: 1.8120, D(x): 0.65, D(G(z)): 0.27
Epoch [140/200], Step [600/600], d_loss: 0.9437, g_loss: 1.2334, D(x): 0.69, D(G(z)): 0.33
Epoch [141/200], Step [200/600], d_loss: 0.9152, g_loss: 1.8172, D(x): 0.64, D(G(z)): 0.26
Epoch [141/200], Step [400/600], d_loss: 0.8368, g_loss: 1.5303, D(x): 0.71, D(G(z)): 0.26
Epoch [141/200], Step [600/600], d_loss: 0.8082, g_loss: 1.7512, D(x): 0.73, D(G(z)): 0.30
Epoch [142/200], Step [200/600], d_loss: 1.0856, g_loss: 1.4494, D(x): 0.64, D(G(z)): 0.31
Epoch [142/200], Step [400/600], d_loss: 1.0798, g_loss: 1.6213, D(x): 0.61, D(G(z)): 0.29
Epoch [142/200], Step [600/600], d_loss: 1.0647, g_loss: 1.6840, D(x): 0.72, D(G(z)): 0.37
Epoch [143/200], Step [200/600], d_loss: 1.0429, g_loss: 1.7174, D(x): 0.73, D(G(z)): 0.38
Epoch [143/200], Step [400/600], d_loss: 0.7951, g_loss: 1.7997, D(x): 0.68, D(G(z)): 0.23
Epoch [143/200], Step [600/600], d_loss: 1.0584, g_loss: 1.6945, D(x): 0.63, D(G(z)): 0.31
Epoch [144/200], Step [200/600], d_loss: 0.8060, g_loss: 2.1261, D(x): 0.70, D(G(z)): 0.24
Epoch [144/200], Step [400/600], d_loss: 0.8945, g_loss: 1.5272, D(x): 0.70, D(G(z)): 0.28
Epoch [144/200], Step [600/600], d_loss: 0.7505, g_loss: 1.8432, D(x): 0.75, D(G(z)): 0.28
Epoch [145/200], Step [200/600], d_loss: 1.1672, g_loss: 1.6065, D(x): 0.66, D(G(z)): 0.35
Epoch [145/200], Step [400/600], d_loss: 0.8520, g_loss: 1.4128, D(x): 0.82, D(G(z)): 0.37
Epoch [145/200], Step [600/600], d_loss: 0.8785, g_loss: 1.4892, D(x): 0.75, D(G(z)): 0.33
Epoch [146/200], Step [200/600], d_loss: 0.8565, g_loss: 1.3905, D(x): 0.75, D(G(z)): 0.31
Epoch [146/200], Step [400/600], d_loss: 1.0247, g_loss: 1.2543, D(x): 0.70, D(G(z)): 0.38
Epoch [146/200], Step [600/600], d_loss: 0.9116, g_loss: 1.5564, D(x): 0.61, D(G(z)): 0.20
Epoch [147/200], Step [200/600], d_loss: 0.9788, g_loss: 1.6320, D(x): 0.67, D(G(z)): 0.32
Epoch [147/200], Step [400/600], d_loss: 1.0233, g_loss: 1.2688, D(x): 0.64, D(G(z)): 0.31
Epoch [147/200], Step [600/600], d_loss: 0.8218, g_loss: 1.3464, D(x): 0.73, D(G(z)): 0.30
Epoch [148/200], Step [200/600], d_loss: 0.8826, g_loss: 1.8604, D(x): 0.69, D(G(z)): 0.29
Epoch [148/200], Step [400/600], d_loss: 0.8574, g_loss: 1.2975, D(x): 0.72, D(G(z)): 0.31
Epoch [148/200], Step [600/600], d_loss: 0.8590, g_loss: 1.2500, D(x): 0.71, D(G(z)): 0.29
Epoch [149/200], Step [200/600], d_loss: 0.9476, g_loss: 1.3186, D(x): 0.70, D(G(z)): 0.32
Epoch [149/200], Step [400/600], d_loss: 1.1153, g_loss: 1.4313, D(x): 0.66, D(G(z)): 0.36
Epoch [149/200], Step [600/600], d_loss: 0.9207, g_loss: 1.5918, D(x): 0.64, D(G(z)): 0.24
Epoch [150/200], Step [200/600], d_loss: 1.0620, g_loss: 1.9464, D(x): 0.67, D(G(z)): 0.36
Epoch [150/200], Step [400/600], d_loss: 0.8902, g_loss: 2.1322, D(x): 0.62, D(G(z)): 0.18
Epoch [150/200], Step [600/600], d_loss: 0.8853, g_loss: 1.5706, D(x): 0.71, D(G(z)): 0.30
Epoch [151/200], Step [200/600], d_loss: 1.1444, g_loss: 1.7974, D(x): 0.60, D(G(z)): 0.30
Epoch [151/200], Step [400/600], d_loss: 0.9993, g_loss: 1.4988, D(x): 0.63, D(G(z)): 0.26
Epoch [151/200], Step [600/600], d_loss: 1.0538, g_loss: 1.8101, D(x): 0.63, D(G(z)): 0.26
Epoch [152/200], Step [200/600], d_loss: 0.8820, g_loss: 1.8347, D(x): 0.76, D(G(z)): 0.33
Epoch [152/200], Step [400/600], d_loss: 0.9782, g_loss: 1.5429, D(x): 0.63, D(G(z)): 0.26
Epoch [152/200], Step [600/600], d_loss: 1.2152, g_loss: 1.7726, D(x): 0.60, D(G(z)): 0.33
Epoch [153/200], Step [200/600], d_loss: 0.9680, g_loss: 1.2081, D(x): 0.68, D(G(z)): 0.33
Epoch [153/200], Step [400/600], d_loss: 0.9256, g_loss: 1.6799, D(x): 0.69, D(G(z)): 0.30
Epoch [153/200], Step [600/600], d_loss: 0.9704, g_loss: 1.2176, D(x): 0.76, D(G(z)): 0.38
Epoch [154/200], Step [200/600], d_loss: 1.0162, g_loss: 1.3491, D(x): 0.72, D(G(z)): 0.36
Epoch [154/200], Step [400/600], d_loss: 0.9492, g_loss: 2.0773, D(x): 0.62, D(G(z)): 0.21
Epoch [154/200], Step [600/600], d_loss: 0.8634, g_loss: 1.4710, D(x): 0.71, D(G(z)): 0.29
Epoch [155/200], Step [200/600], d_loss: 0.8810, g_loss: 1.6961, D(x): 0.69, D(G(z)): 0.25
Epoch [155/200], Step [400/600], d_loss: 1.0829, g_loss: 1.4673, D(x): 0.58, D(G(z)): 0.25
Epoch [155/200], Step [600/600], d_loss: 1.0839, g_loss: 1.4223, D(x): 0.73, D(G(z)): 0.40
Epoch [156/200], Step [200/600], d_loss: 1.1851, g_loss: 1.6687, D(x): 0.63, D(G(z)): 0.37
Epoch [156/200], Step [400/600], d_loss: 0.9716, g_loss: 1.6617, D(x): 0.64, D(G(z)): 0.28
Epoch [156/200], Step [600/600], d_loss: 0.8191, g_loss: 1.7810, D(x): 0.69, D(G(z)): 0.24
Epoch [157/200], Step [200/600], d_loss: 1.0335, g_loss: 1.3579, D(x): 0.61, D(G(z)): 0.24
Epoch [157/200], Step [400/600], d_loss: 1.0019, g_loss: 1.3144, D(x): 0.71, D(G(z)): 0.35
Epoch [157/200], Step [600/600], d_loss: 0.8015, g_loss: 1.4819, D(x): 0.75, D(G(z)): 0.31
Epoch [158/200], Step [200/600], d_loss: 0.9141, g_loss: 1.5450, D(x): 0.73, D(G(z)): 0.32
Epoch [158/200], Step [400/600], d_loss: 1.0250, g_loss: 1.6567, D(x): 0.70, D(G(z)): 0.34
Epoch [158/200], Step [600/600], d_loss: 1.1062, g_loss: 1.2812, D(x): 0.72, D(G(z)): 0.41
Epoch [159/200], Step [200/600], d_loss: 0.9579, g_loss: 1.7507, D(x): 0.65, D(G(z)): 0.29
Epoch [159/200], Step [400/600], d_loss: 1.0090, g_loss: 1.6796, D(x): 0.66, D(G(z)): 0.30
Epoch [159/200], Step [600/600], d_loss: 0.9404, g_loss: 1.4110, D(x): 0.62, D(G(z)): 0.24
Epoch [160/200], Step [200/600], d_loss: 0.8459, g_loss: 1.5923, D(x): 0.79, D(G(z)): 0.35
Epoch [160/200], Step [400/600], d_loss: 1.0983, g_loss: 1.5172, D(x): 0.61, D(G(z)): 0.30
Epoch [160/200], Step [600/600], d_loss: 0.9830, g_loss: 1.4812, D(x): 0.69, D(G(z)): 0.35
Epoch [161/200], Step [200/600], d_loss: 0.8541, g_loss: 1.5836, D(x): 0.66, D(G(z)): 0.25
Epoch [161/200], Step [400/600], d_loss: 1.0266, g_loss: 1.3839, D(x): 0.65, D(G(z)): 0.32
Epoch [161/200], Step [600/600], d_loss: 0.9394, g_loss: 1.6997, D(x): 0.70, D(G(z)): 0.33
Epoch [162/200], Step [200/600], d_loss: 0.8215, g_loss: 1.5584, D(x): 0.71, D(G(z)): 0.29
Epoch [162/200], Step [400/600], d_loss: 0.9102, g_loss: 1.5626, D(x): 0.71, D(G(z)): 0.33
Epoch [162/200], Step [600/600], d_loss: 0.9159, g_loss: 1.8538, D(x): 0.71, D(G(z)): 0.31
Epoch [163/200], Step [200/600], d_loss: 1.0573, g_loss: 1.7526, D(x): 0.78, D(G(z)): 0.45
Epoch [163/200], Step [400/600], d_loss: 0.7909, g_loss: 1.6003, D(x): 0.68, D(G(z)): 0.23
Epoch [163/200], Step [600/600], d_loss: 0.9629, g_loss: 1.4818, D(x): 0.73, D(G(z)): 0.35
Epoch [164/200], Step [200/600], d_loss: 1.0584, g_loss: 1.8487, D(x): 0.62, D(G(z)): 0.29
Epoch [164/200], Step [400/600], d_loss: 0.9867, g_loss: 1.7461, D(x): 0.71, D(G(z)): 0.36
Epoch [164/200], Step [600/600], d_loss: 1.0074, g_loss: 1.5841, D(x): 0.68, D(G(z)): 0.31
Epoch [165/200], Step [200/600], d_loss: 0.9651, g_loss: 1.5523, D(x): 0.72, D(G(z)): 0.35
Epoch [165/200], Step [400/600], d_loss: 0.8979, g_loss: 1.5189, D(x): 0.66, D(G(z)): 0.23
Epoch [165/200], Step [600/600], d_loss: 0.8604, g_loss: 1.4859, D(x): 0.79, D(G(z)): 0.35
Epoch [166/200], Step [200/600], d_loss: 0.9645, g_loss: 1.7473, D(x): 0.61, D(G(z)): 0.23
Epoch [166/200], Step [400/600], d_loss: 0.6825, g_loss: 1.3785, D(x): 0.84, D(G(z)): 0.33
Epoch [166/200], Step [600/600], d_loss: 0.9094, g_loss: 1.3634, D(x): 0.73, D(G(z)): 0.34
Epoch [167/200], Step [200/600], d_loss: 0.8262, g_loss: 1.6001, D(x): 0.72, D(G(z)): 0.30
Epoch [167/200], Step [400/600], d_loss: 0.9909, g_loss: 1.9477, D(x): 0.55, D(G(z)): 0.18
Epoch [167/200], Step [600/600], d_loss: 1.0432, g_loss: 1.3547, D(x): 0.60, D(G(z)): 0.26
Epoch [168/200], Step [200/600], d_loss: 0.9359, g_loss: 2.0457, D(x): 0.70, D(G(z)): 0.32
Epoch [168/200], Step [400/600], d_loss: 1.0142, g_loss: 1.6429, D(x): 0.66, D(G(z)): 0.32
Epoch [168/200], Step [600/600], d_loss: 0.9667, g_loss: 1.3652, D(x): 0.75, D(G(z)): 0.39
Epoch [169/200], Step [200/600], d_loss: 0.8659, g_loss: 1.8797, D(x): 0.66, D(G(z)): 0.23
Epoch [169/200], Step [400/600], d_loss: 0.9469, g_loss: 1.5848, D(x): 0.77, D(G(z)): 0.39
Epoch [169/200], Step [600/600], d_loss: 0.9253, g_loss: 1.6434, D(x): 0.67, D(G(z)): 0.27
Epoch [170/200], Step [200/600], d_loss: 0.9876, g_loss: 1.5204, D(x): 0.73, D(G(z)): 0.40
Epoch [170/200], Step [400/600], d_loss: 1.0286, g_loss: 1.3180, D(x): 0.64, D(G(z)): 0.31
Epoch [170/200], Step [600/600], d_loss: 1.0189, g_loss: 1.3023, D(x): 0.68, D(G(z)): 0.33
Epoch [171/200], Step [200/600], d_loss: 0.9299, g_loss: 1.4886, D(x): 0.77, D(G(z)): 0.36
Epoch [171/200], Step [400/600], d_loss: 0.8501, g_loss: 1.1939, D(x): 0.72, D(G(z)): 0.31
Epoch [171/200], Step [600/600], d_loss: 0.9355, g_loss: 1.5083, D(x): 0.61, D(G(z)): 0.25
Epoch [172/200], Step [200/600], d_loss: 0.9333, g_loss: 1.3659, D(x): 0.76, D(G(z)): 0.37
Epoch [172/200], Step [400/600], d_loss: 0.8872, g_loss: 1.5996, D(x): 0.69, D(G(z)): 0.27
Epoch [172/200], Step [600/600], d_loss: 0.9234, g_loss: 1.5076, D(x): 0.64, D(G(z)): 0.23
Epoch [173/200], Step [200/600], d_loss: 0.8251, g_loss: 1.9171, D(x): 0.72, D(G(z)): 0.26
Epoch [173/200], Step [400/600], d_loss: 1.1377, g_loss: 1.5771, D(x): 0.57, D(G(z)): 0.28
Epoch [173/200], Step [600/600], d_loss: 0.9027, g_loss: 1.7397, D(x): 0.65, D(G(z)): 0.27
Epoch [174/200], Step [200/600], d_loss: 0.8281, g_loss: 1.6004, D(x): 0.68, D(G(z)): 0.26
Epoch [174/200], Step [400/600], d_loss: 0.8857, g_loss: 1.7043, D(x): 0.72, D(G(z)): 0.33
Epoch [174/200], Step [600/600], d_loss: 0.9295, g_loss: 1.8799, D(x): 0.68, D(G(z)): 0.28
Epoch [175/200], Step [200/600], d_loss: 0.9017, g_loss: 1.7627, D(x): 0.70, D(G(z)): 0.30
Epoch [175/200], Step [400/600], d_loss: 0.8745, g_loss: 1.3993, D(x): 0.68, D(G(z)): 0.28
Epoch [175/200], Step [600/600], d_loss: 1.0171, g_loss: 1.4064, D(x): 0.74, D(G(z)): 0.40
Epoch [176/200], Step [200/600], d_loss: 0.8911, g_loss: 1.4087, D(x): 0.78, D(G(z)): 0.38
Epoch [176/200], Step [400/600], d_loss: 1.0525, g_loss: 1.3220, D(x): 0.67, D(G(z)): 0.34
Epoch [176/200], Step [600/600], d_loss: 0.7854, g_loss: 2.0379, D(x): 0.68, D(G(z)): 0.22
Epoch [177/200], Step [200/600], d_loss: 0.9156, g_loss: 1.9224, D(x): 0.73, D(G(z)): 0.34
Epoch [177/200], Step [400/600], d_loss: 0.7522, g_loss: 1.7134, D(x): 0.68, D(G(z)): 0.20
Epoch [177/200], Step [600/600], d_loss: 0.8522, g_loss: 1.3662, D(x): 0.70, D(G(z)): 0.27
Epoch [178/200], Step [200/600], d_loss: 0.9038, g_loss: 1.3921, D(x): 0.70, D(G(z)): 0.27
Epoch [178/200], Step [400/600], d_loss: 0.9785, g_loss: 1.6295, D(x): 0.68, D(G(z)): 0.33
Epoch [178/200], Step [600/600], d_loss: 0.9072, g_loss: 1.6979, D(x): 0.69, D(G(z)): 0.31
Epoch [179/200], Step [200/600], d_loss: 1.0421, g_loss: 2.0187, D(x): 0.65, D(G(z)): 0.31
Epoch [179/200], Step [400/600], d_loss: 0.8482, g_loss: 1.6220, D(x): 0.74, D(G(z)): 0.31
Epoch [179/200], Step [600/600], d_loss: 0.9346, g_loss: 1.7184, D(x): 0.66, D(G(z)): 0.28
Epoch [180/200], Step [200/600], d_loss: 0.9424, g_loss: 1.6355, D(x): 0.65, D(G(z)): 0.26
Epoch [180/200], Step [400/600], d_loss: 0.9139, g_loss: 1.5548, D(x): 0.70, D(G(z)): 0.31
Epoch [180/200], Step [600/600], d_loss: 0.7622, g_loss: 1.4710, D(x): 0.72, D(G(z)): 0.27
Epoch [181/200], Step [200/600], d_loss: 1.1145, g_loss: 1.4237, D(x): 0.66, D(G(z)): 0.36
Epoch [181/200], Step [400/600], d_loss: 1.0776, g_loss: 1.4094, D(x): 0.75, D(G(z)): 0.40
Epoch [181/200], Step [600/600], d_loss: 0.7935, g_loss: 1.5159, D(x): 0.72, D(G(z)): 0.26
Epoch [182/200], Step [200/600], d_loss: 0.7814, g_loss: 1.3784, D(x): 0.75, D(G(z)): 0.28
Epoch [182/200], Step [400/600], d_loss: 1.0844, g_loss: 1.1643, D(x): 0.66, D(G(z)): 0.37
Epoch [182/200], Step [600/600], d_loss: 0.8495, g_loss: 1.8156, D(x): 0.69, D(G(z)): 0.28
Epoch [183/200], Step [200/600], d_loss: 1.0465, g_loss: 1.5846, D(x): 0.66, D(G(z)): 0.34
Epoch [183/200], Step [400/600], d_loss: 1.0113, g_loss: 1.5419, D(x): 0.66, D(G(z)): 0.31
Epoch [183/200], Step [600/600], d_loss: 0.9660, g_loss: 1.3313, D(x): 0.75, D(G(z)): 0.38
Epoch [184/200], Step [200/600], d_loss: 0.9482, g_loss: 1.5999, D(x): 0.68, D(G(z)): 0.29
Epoch [184/200], Step [400/600], d_loss: 0.9748, g_loss: 1.3257, D(x): 0.66, D(G(z)): 0.29
Epoch [184/200], Step [600/600], d_loss: 1.0018, g_loss: 1.5651, D(x): 0.70, D(G(z)): 0.35
Epoch [185/200], Step [200/600], d_loss: 0.9115, g_loss: 1.6923, D(x): 0.71, D(G(z)): 0.32
Epoch [185/200], Step [400/600], d_loss: 0.7879, g_loss: 1.7067, D(x): 0.70, D(G(z)): 0.26
Epoch [185/200], Step [600/600], d_loss: 0.9752, g_loss: 1.6181, D(x): 0.65, D(G(z)): 0.27
Epoch [186/200], Step [200/600], d_loss: 0.9705, g_loss: 1.5116, D(x): 0.65, D(G(z)): 0.29
Epoch [186/200], Step [400/600], d_loss: 0.8693, g_loss: 1.5713, D(x): 0.75, D(G(z)): 0.35
Epoch [186/200], Step [600/600], d_loss: 1.0797, g_loss: 1.4829, D(x): 0.68, D(G(z)): 0.38
Epoch [187/200], Step [200/600], d_loss: 0.8853, g_loss: 1.6581, D(x): 0.70, D(G(z)): 0.30
Epoch [187/200], Step [400/600], d_loss: 0.8808, g_loss: 1.7821, D(x): 0.72, D(G(z)): 0.33
Epoch [187/200], Step [600/600], d_loss: 0.9180, g_loss: 1.7416, D(x): 0.74, D(G(z)): 0.35
Epoch [188/200], Step [200/600], d_loss: 0.8069, g_loss: 1.4954, D(x): 0.73, D(G(z)): 0.29
Epoch [188/200], Step [400/600], d_loss: 0.9400, g_loss: 1.8438, D(x): 0.69, D(G(z)): 0.29
Epoch [188/200], Step [600/600], d_loss: 0.9805, g_loss: 1.5522, D(x): 0.72, D(G(z)): 0.35
Epoch [189/200], Step [200/600], d_loss: 0.8633, g_loss: 1.7176, D(x): 0.69, D(G(z)): 0.30
Epoch [189/200], Step [400/600], d_loss: 0.8756, g_loss: 1.5109, D(x): 0.71, D(G(z)): 0.30
Epoch [189/200], Step [600/600], d_loss: 1.1062, g_loss: 1.7254, D(x): 0.64, D(G(z)): 0.32
Epoch [190/200], Step [200/600], d_loss: 0.9787, g_loss: 1.3468, D(x): 0.70, D(G(z)): 0.35
Epoch [190/200], Step [400/600], d_loss: 0.7695, g_loss: 1.5876, D(x): 0.70, D(G(z)): 0.22
Epoch [190/200], Step [600/600], d_loss: 0.9003, g_loss: 1.5295, D(x): 0.72, D(G(z)): 0.35
Epoch [191/200], Step [200/600], d_loss: 1.0498, g_loss: 1.5651, D(x): 0.57, D(G(z)): 0.24
Epoch [191/200], Step [400/600], d_loss: 0.9835, g_loss: 1.5292, D(x): 0.62, D(G(z)): 0.26
Epoch [191/200], Step [600/600], d_loss: 1.0058, g_loss: 1.8599, D(x): 0.61, D(G(z)): 0.26
Epoch [192/200], Step [200/600], d_loss: 1.0540, g_loss: 1.4902, D(x): 0.62, D(G(z)): 0.32
Epoch [192/200], Step [400/600], d_loss: 0.9754, g_loss: 1.3995, D(x): 0.70, D(G(z)): 0.37
Epoch [192/200], Step [600/600], d_loss: 0.9655, g_loss: 1.3887, D(x): 0.68, D(G(z)): 0.31
Epoch [193/200], Step [200/600], d_loss: 0.9888, g_loss: 1.6682, D(x): 0.63, D(G(z)): 0.26
Epoch [193/200], Step [400/600], d_loss: 0.8445, g_loss: 1.5723, D(x): 0.73, D(G(z)): 0.33
Epoch [193/200], Step [600/600], d_loss: 0.9094, g_loss: 1.8187, D(x): 0.69, D(G(z)): 0.30
Epoch [194/200], Step [200/600], d_loss: 0.9689, g_loss: 1.7502, D(x): 0.61, D(G(z)): 0.23
Epoch [194/200], Step [400/600], d_loss: 0.8786, g_loss: 1.3475, D(x): 0.75, D(G(z)): 0.35
Epoch [194/200], Step [600/600], d_loss: 0.8869, g_loss: 2.3168, D(x): 0.67, D(G(z)): 0.26
Epoch [195/200], Step [200/600], d_loss: 0.9529, g_loss: 1.9417, D(x): 0.71, D(G(z)): 0.33
Epoch [195/200], Step [400/600], d_loss: 1.0048, g_loss: 1.2613, D(x): 0.75, D(G(z)): 0.40
Epoch [195/200], Step [600/600], d_loss: 0.9013, g_loss: 1.4521, D(x): 0.66, D(G(z)): 0.30
Epoch [196/200], Step [200/600], d_loss: 0.8513, g_loss: 1.5400, D(x): 0.74, D(G(z)): 0.29
Epoch [196/200], Step [400/600], d_loss: 0.9727, g_loss: 1.3992, D(x): 0.64, D(G(z)): 0.30
Epoch [196/200], Step [600/600], d_loss: 0.9113, g_loss: 1.4926, D(x): 0.77, D(G(z)): 0.40
Epoch [197/200], Step [200/600], d_loss: 0.9602, g_loss: 1.4885, D(x): 0.68, D(G(z)): 0.32
Epoch [197/200], Step [400/600], d_loss: 1.0403, g_loss: 1.8077, D(x): 0.65, D(G(z)): 0.32
Epoch [197/200], Step [600/600], d_loss: 0.8746, g_loss: 1.8007, D(x): 0.74, D(G(z)): 0.33
Epoch [198/200], Step [200/600], d_loss: 0.9678, g_loss: 1.2999, D(x): 0.62, D(G(z)): 0.26
Epoch [198/200], Step [400/600], d_loss: 0.9882, g_loss: 1.3104, D(x): 0.71, D(G(z)): 0.37
Epoch [198/200], Step [600/600], d_loss: 0.9346, g_loss: 1.3032, D(x): 0.74, D(G(z)): 0.39
Epoch [199/200], Step [200/600], d_loss: 0.9317, g_loss: 1.5710, D(x): 0.69, D(G(z)): 0.31
Epoch [199/200], Step [400/600], d_loss: 1.0818, g_loss: 1.3135, D(x): 0.56, D(G(z)): 0.22
Epoch [199/200], Step [600/600], d_loss: 0.8854, g_loss: 1.0944, D(x): 0.69, D(G(z)): 0.31

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【Python】成功解决ModuleNotFoundError: No module named ‘torchinfo‘ 高斯小哥 BUG解决方案合集 python pytorch 新手入门学习 debug
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SWIFT环境配置及大模型微调实践 weixin_43870390 swift 开发语言 ios
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Win环境下安装 torch==1.1.0 JOYCE_Leo16 Bug记录深度学习 python 人工智能 pytorch
问题描述复现很多模型的时候，会遇到torch版本不一致问题，尤其是torch1.1.0一直都在安装错误，试了很多方法都没用。解决方案在默认环境中安装torch：pipinstallhttps://download.pytorch.org/whl/cu90/torch-1.1.0-cp36-cp36m-win_amd64.whlpipinstallhttps://download.pytorch.o
最新ChatGPT支持下的PyTorch机器学习与深度学习 zkzhzy ChatGPT 机器学习 python 机器学习深度学习 pytorch chatgpt 数据分析人工智能
近年来，随着AlphaGo、无人驾驶汽车、医学影像智慧辅助诊疗、ImageNet竞赛等热点事件的发生，人工智能迎来了新一轮的发展浪潮。尤其是深度学习技术，在许多行业都取得了颠覆性的成果。另外，近年来，Pytorch深度学习框架受到越来越多科研人员的关注和喜爱。郁磊（副教授）主要从事AI人工智能、大语言模型及软件开发、生理系统建模与仿真、生物医学信号处理，具有丰富的科研经验，主编《MATLAB智能算
【PyTorch】成功解决ModuleNotFoundError: No module named ‘torch’ 高斯小哥 PyTorch零基础入门教程 pytorch 人工智能 python conda debug 深度学习机器学习
【PyTorch】成功解决ModuleNotFoundError:Nomodulenamed‘torch’个人主页：高斯小哥高质量专栏：Matplotlib之旅：零基础精通数据可视化、Python基础【高质量合集】、PyTorch零基础入门教程希望得到您的订阅和支持~创作高质量博文(平均质量分92+)，分享更多关于深度学习、PyTorch、Python领域的优质内容！（希望得到您的关注~）文章目录
今日无更新我的昵称违规了
学校的一个会忙得昏天黑地。明天有自己的一个发表，还要准备PPT，根据原来的改改就好……这周真的是有点繁杂了，搞定之后连着四五月份要写两篇论文，再加上五月底的课程论文还有紧接着的文献综述，看样子要疯……现在梳理一下自己手里的锤子：转到Pytorch，使用AllenNLP了解Transformer、了解LSTM了解jieba等分词工具了解Gensim等NLP处理工具接下来要做的：基于AllenNLP搞
PyTorch学习笔记之基础函数篇（四）熊猫Devin 深度学习之PyTorch pytorch 学习笔记
文章目录2.8torch.logspace函数讲解2.9torch.ones函数2.10torch.rand函数2.11torch.randn函数2.12torch.zeros函数2.8torch.logspace函数讲解torch.logspace函数在PyTorch中用于生成一个在对数尺度上均匀分布的张量（tensor）。这意味着张量中的元素是按照对数间隔排列的，而不是线性间隔。这对于创建在数
加速 PyTorch 模型预测常见方法梳理 samoyan pytorch pytorch 人工智能 python
目录1.使用GPU加速2.批量推理3.使用半精度浮点数(FP16)4.禁用梯度计算5.模型简化与量化6.使用TorchScript7.模型并行和数据并行结论在使用PyTorch进行模型预测时，可以通过多种方法来加快推理速度。以下是一些加速模型预测的常用方法，但注意有些模型直接使用下面方法会出错，大家谨慎使用：1.使用GPU加速如果您有可用的GPU资源，确保您的模型在GPU上运行，因为GPU提供了比
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机器学习是人工智能的一个重要分支，它通过让计算机系统利用数据自我学习来改进任务执行的能力。在机器学习领域，有许多成熟的框架被广泛使用，这些框架提供了构建和训练机器学习模型的工具。以下是一些常用的机器学习框架：TensorFlow：由Google开发，是一个开源的软件库，用于数据流编程，广泛应用于各类机器学习任务。它支持分布式计算，能够在大规模数据集上训练复杂的模型。PyTorch：由Faceboo
pytorch中张量变换函数 weixin_42924890 pytorch 人工智能 python
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深度学习pytorch——索引与切片 Echo-J AI 深度学习 pytorch 人工智能
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Pytorch nn.Module 霖大侠 pytorch 人工智能 python 深度学习 cnn 神经网络卷积神经网络
一、torch.nn简介torch.nn是PyTorch中用于构建神经网络的模块。它提供了一系列的类和函数，用于定义神经网络的各种层、损失函数、优化器等。torch.nn提供的类：Module:所有神经网络模型的基类，用于定义自定义神经网络模型。Linear:线性层，进行线性变换。Conv2d:二维卷积层。RNN,LSTM,GRU:循环神经网络层，分别对应简单RNN、长短时记忆网络（LSTM）、门
【深度学习笔记】1 数据操作 RIKI_1 深度学习深度学习笔记人工智能
注：本文为《动手学深度学习》开源内容，仅为个人学习记录，无抄袭搬运意图数据操作在深度学习中，我们通常会频繁地对数据进行操作。作为动手学深度学习的基础，本节将介绍如何对内存中的数据进行操作。在PyTorch中，torch.Tensor是存储和变换数据的主要工具。如果你之前用过NumPy，你会发现Tensor和NumPy的多维数组非常类似。然而，Tensor提供GPU计算和自动求梯度等更多功能，这些使
【python】成功解决ModuleNotFoundError: No module named ‘tensorboardX‘ 高斯小哥 BUG解决方案合集 python 学习 debug
【python】成功解决ModuleNotFoundError:Nomodulenamed‘tensorboardX’个人主页：高斯小哥高质量专栏：Matplotlib之旅：零基础精通数据可视化、Python基础【高质量合集】、PyTorch零基础入门教程希望得到您的订阅和支持~创作高质量博文(平均质量分92+)，分享更多关于深度学习、PyTorch、Python领域的优质内容！（希望得到您的关注
KGCN---pytorch代码（1）---data_loader sweet_Mary 推荐算法 python 推荐算法 pytorch 人工智能机器学习深度学习
代码：importpandasaspdimportnumpyasnpfromsklearn.preprocessingimportLabelEncoderfromsklearn.model_selectionimporttrain_test_splitimportrandomclassDataLoader:'''DataLoaderclasswhichmakesdatasetfortraining
【Python】成功解决AttributeError: ‘MyClass‘ object has no attribute ‘my_attribute‘ 高斯小哥 BUG解决方案合集 python 新手入门学习 debug
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PyTorch 实现图像卷积和反卷积操作及代码算法channel pytorch 人工智能 python 深度学习机器学习
你好，我是郭震在深度学习中，尤其是在处理图像相关任务时，卷积和反卷积（转置卷积）都是非常核心的概念。它们在神经网络中扮演着重要的角色，但用途和工作原理有所不同。以下是对传统卷积和反卷积的介绍，以及它们在PyTorch中的应用示例。传统卷积(nn.Conv2d)用途传统卷积通常用于特征提取。在处理图像时，通过应用卷积核（也称为滤波器）来扫描输入图像或特征映射，可以有效地识别图像中的局部特征（如边缘、
beam search原理与常见实现，与直接sample的区别 samoyan LLM 面试 transformer 面试
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PyTorch：深度学习的革命性框架奔强的程序学习
在深度学习领域，PyTorch已经成为了一个革命性的框架。自2016年开源以来，它以其高效、灵活和易于使用的特性，迅速获得了研究者和开发者的青睐。PyTorch不仅简化了深度学习的复杂流程，还提供了丰富的功能和强大的扩展性，使得深度学习的应用更加广泛和深入。下面将从PyTorch的核心特性、应用场景、生态系统、与其他框架的比较以及未来发展等方面，对PyTorch进行详细介绍。一、核心特性动态计算图
不同框架表示图像时维度顺序的区别：pytorch、keras&tf、opencv、numpy、PIL 蓝海渔夫 python pytorch pytorch keras opencv pillow python 人工智能 numpy
在PyTorch、Keras、OpenCV、NumPy和PIL这几个框架中，它们在表示图像时的维度存储顺序有所不同。下面我将逐一解释每个框架中图像维度的存储顺序：1，PyTorch:PyTorch中图像的维度顺序通常遵循[N,C,H,W]的格式，也就是channelfirst格式，其中：C代表通道数（channels），例如RGB图像有3个通道。N代表批量大小（batchsize），即一次处理的图
PyTorch训练，TensorRT部署的简要步骤（采用ONNX中转的方式）赛先生.AI TensorRT pytorch 人工智能 TensorRT ONNX
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Ubuntu20.04安装并配置vscode double_c之科研 vscode ubuntu
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真快！几分钟就把视频语音识别为文本了，不到10行代码诗者才子酒中仙音视频语音识别人工智能
虽然已经很简单了，但是对于程序员来说还是不够简洁，毕竟程序员都很“懒”，Whisper虽说安装和调用已经很简单了，但还是需要独立安装PyTorch、ffmpeg甚至Rust。将音视频文件中的音频转为文字内容，这个需求放到两年前还不大好实现，但是放到今天，几分钟就解决了。听说有的公司为了抓取训练数据，已经把抖音、快手这些短视频平台上的视频扒了个遍，然后将其中的音频提取成文本，用作大数据模型的训练语料
pytorch 介绍以及常用工具包展示 R0ot pytorch 人工智能 python
1.引言1.1背景：神经网络和深度学习的崛起介绍神经网络和深度学习在计算机科学和人工智能中的重要性。1.2PyTorch简介：张量计算框架的演进回顾PyTorch作为张量计算框架的发展历程。强调其灵活性、动态计算图和深度学习社区的支持。2.PyTorch基础2.1张量：PyTorch的核心数据结构创建和操作张量的基本操作，如加法、乘法等。张量的自动微分功能，介绍autograd模块。2.2动态计算
LSTM进行字符级文本生成（pytorch实现）山川而川-R lstm pytorch 人工智能
文章目录基于pytorch的LSTM进行字符集文本生成前言一、数据集二、代码实现1.到入库和LSTM进行模型构建2.数据预处理函数3.训练函数4.预测函数5.文本生成函数6.主函数完整代码总结前言本文介绍了机器学习中深度学习的内容使用pytorch构建LSTM模型进行字符级文本生成任务一、数据集https://download.csdn.net/download/qq_52785473/78428
使用TensorRT在PyTorch项目中加速深度学习推理从零开始学习人工智能深度学习 pytorch 人工智能
在PyTorch项目中使用TensorRT进行深度学习推理通常涉及以下步骤：模型训练：首先，在PyTorch中训练你的深度学习模型。模型导出：训练完成后，将模型从PyTorch导出为ONNX（OpenNeuralNetworkExchange）格式。ONNX是一种用于表示深度学习模型的开放格式，它使得模型可以在不同的深度学习框架之间互操作。模型优化：使用TensorRT优化ONNX模型。Tenso
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