DCGAN，即深度卷積生成對抗網絡（Deep Convolutional Generative Adversarial Network），是一種深度學習模型，由Ian Goodfellow等人在2014年提出。DCGAN在生成對抗網絡（GAN）的基礎上，引入了深度卷積神經網絡（CNN）的結構，用于生成高質量、高分辨率的圖像。

DCGAN的原理可以概括為兩個主要部分：生成器（Generator）和判別器（Discriminator）。生成器和判別器都是深度卷積神經網絡，它們通過對抗過程互相博弈，最終達到納什均衡。

1. 生成器（Generator）：生成器的輸入是一個隨機的噪聲向量，通過一系列的卷積、反卷積、批標準化（Batch Normalization）和激活函數（如ReLU、Tanh等）操作，生成一個與真實圖像具有相同尺寸的圖像。生成器的目標是生成盡可能逼真的圖像，以欺騙判別器。

2. 判別器（Discriminator）：判別器的輸入是一個圖像，它通過一系列的卷積、批標準化和激活函數操作，判斷輸入圖像是真實圖像還是生成器生成的假圖像。判別器的目標是能夠準確地區分真實圖像和假圖像。

在訓練過程中，生成器和判別器交替進行優化。生成器嘗試生成逼真的圖像，而判別器嘗試更好地識別真實圖像和假圖像。這個過程可以看作是一種博弈，生成器和判別器在不斷的迭代過程中提高自己的性能。最終，當生成器和判別器達到納什均衡時，生成器能夠生成高質量的逼真圖像，判別器無法準確地區分真實圖像和假圖像。

DCGAN在計算機視覺領域有廣泛的應用，如圖像生成、圖像修復、圖像轉換等。通過調整網絡結構和訓練策略，DCGAN還可以應用于其他領域，如自然語言處理、音頻生成等。

數據準備與處理

%%capture captured_output
# 實驗環境已經預裝了mindspore==2.2.14，如需更換mindspore版本，可更改下面mindspore的版本號
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14
from download import downloadurl = "https://download.mindspore.cn/dataset/Faces/faces.zip"path = download(url, "./faces", kind="zip", replace=True)
import numpy as np
import mindspore.dataset as ds
import mindspore.dataset.vision as visiondef create_dataset_imagenet(dataset_path):"""數據加載"""dataset = ds.ImageFolderDataset(dataset_path,num_parallel_workers=4,shuffle=True,decode=True)# 數據增強操作transforms = [vision.Resize(image_size),vision.CenterCrop(image_size),vision.HWC2CHW(),lambda x: ((x / 255).astype("float32"))]# 數據映射操作dataset = dataset.project('image')dataset = dataset.map(transforms, 'image')# 批量操作dataset = dataset.batch(batch_size)return datasetdataset = create_dataset_imagenet('./faces')
import matplotlib.pyplot as pltdef plot_data(data):# 可視化部分訓練數據plt.figure(figsize=(10, 3), dpi=140)for i, image in enumerate(data[0][:30], 1):plt.subplot(3, 10, i)plt.axis("off")plt.imshow(image.transpose(1, 2, 0))plt.show()sample_data = next(dataset.create_tuple_iterator(output_numpy=True))
plot_data(sample_data)

構造網絡

生成器

import mindspore as ms
from mindspore import nn, ops
from mindspore.common.initializer import Normalweight_init = Normal(mean=0, sigma=0.02)
gamma_init = Normal(mean=1, sigma=0.02)class Generator(nn.Cell):"""DCGAN網絡生成器"""def __init__(self):super(Generator, self).__init__()self.generator = nn.SequentialCell(nn.Conv2dTranspose(nz, ngf * 8, 4, 1, 'valid', weight_init=weight_init),nn.BatchNorm2d(ngf * 8, gamma_init=gamma_init),nn.ReLU(),nn.Conv2dTranspose(ngf * 8, ngf * 4, 4, 2, 'pad', 1, weight_init=weight_init),nn.BatchNorm2d(ngf * 4, gamma_init=gamma_init),nn.ReLU(),nn.Conv2dTranspose(ngf * 4, ngf * 2, 4, 2, 'pad', 1, weight_init=weight_init),nn.BatchNorm2d(ngf * 2, gamma_init=gamma_init),nn.ReLU(),nn.Conv2dTranspose(ngf * 2, ngf, 4, 2, 'pad', 1, weight_init=weight_init),nn.BatchNorm2d(ngf, gamma_init=gamma_init),nn.ReLU(),nn.Conv2dTranspose(ngf, nc, 4, 2, 'pad', 1, weight_init=weight_init),nn.Tanh())def construct(self, x):return self.generator(x)generator = Generator()

判別器

class Discriminator(nn.Cell):"""DCGAN網絡判別器"""def __init__(self):super(Discriminator, self).__init__()self.discriminator = nn.SequentialCell(nn.Conv2d(nc, ndf, 4, 2, 'pad', 1, weight_init=weight_init),nn.LeakyReLU(0.2),nn.Conv2d(ndf, ndf * 2, 4, 2, 'pad', 1, weight_init=weight_init),nn.BatchNorm2d(ngf * 2, gamma_init=gamma_init),nn.LeakyReLU(0.2),nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 'pad', 1, weight_init=weight_init),nn.BatchNorm2d(ngf * 4, gamma_init=gamma_init),nn.LeakyReLU(0.2),nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 'pad', 1, weight_init=weight_init),nn.BatchNorm2d(ngf * 8, gamma_init=gamma_init),nn.LeakyReLU(0.2),nn.Conv2d(ndf * 8, 1, 4, 1, 'valid', weight_init=weight_init),)self.adv_layer = nn.Sigmoid()def construct(self, x):out = self.discriminator(x)out = out.reshape(out.shape[0], -1)return self.adv_layer(out)discriminator = Discriminator()

模型訓練

損失函數

# 定義損失函數
adversarial_loss = nn.BCELoss(reduction='mean')

優化器

# 為生成器和判別器設置優化器
optimizer_D = nn.Adam(discriminator.trainable_params(), learning_rate=lr, beta1=beta1)
optimizer_G = nn.Adam(generator.trainable_params(), learning_rate=lr, beta1=beta1)
optimizer_G.update_parameters_name('optim_g.')
optimizer_D.update_parameters_name('optim_d.')

訓練模型

訓練分為兩個主要部分：訓練判別器和訓練生成器。

def generator_forward(real_imgs, valid):# 將噪聲采樣為發生器的輸入z = ops.standard_normal((real_imgs.shape[0], nz, 1, 1))# 生成一批圖像gen_imgs = generator(z)# 損失衡量發生器繞過判別器的能力g_loss = adversarial_loss(discriminator(gen_imgs), valid)return g_loss, gen_imgsdef discriminator_forward(real_imgs, gen_imgs, valid, fake):# 衡量鑒別器從生成的樣本中對真實樣本進行分類的能力real_loss = adversarial_loss(discriminator(real_imgs), valid)fake_loss = adversarial_loss(discriminator(gen_imgs), fake)d_loss = (real_loss + fake_loss) / 2return d_lossgrad_generator_fn = ms.value_and_grad(generator_forward, None,optimizer_G.parameters,has_aux=True)
grad_discriminator_fn = ms.value_and_grad(discriminator_forward, None,optimizer_D.parameters)@ms.jit
def train_step(imgs):valid = ops.ones((imgs.shape[0], 1), mindspore.float32)fake = ops.zeros((imgs.shape[0], 1), mindspore.float32)(g_loss, gen_imgs), g_grads = grad_generator_fn(imgs, valid)optimizer_G(g_grads)d_loss, d_grads = grad_discriminator_fn(imgs, gen_imgs, valid, fake)optimizer_D(d_grads)return g_loss, d_loss, gen_imgs
import mindsporeG_losses = []
D_losses = []
image_list = []total = dataset.get_dataset_size()
for epoch in range(num_epochs):generator.set_train()discriminator.set_train()# 為每輪訓練讀入數據for i, (imgs, ) in enumerate(dataset.create_tuple_iterator()):g_loss, d_loss, gen_imgs = train_step(imgs)if i % 100 == 0 or i == total - 1:# 輸出訓練記錄print('[%2d/%d][%3d/%d]   Loss_D:%7.4f  Loss_G:%7.4f' % (epoch + 1, num_epochs, i + 1, total, d_loss.asnumpy(), g_loss.asnumpy()))D_losses.append(d_loss.asnumpy())G_losses.append(g_loss.asnumpy())# 每個epoch結束后，使用生成器生成一組圖片generator.set_train(False)fixed_noise = ops.standard_normal((batch_size, nz, 1, 1))img = generator(fixed_noise)image_list.append(img.transpose(0, 2, 3, 1).asnumpy())# 保存網絡模型參數為ckpt文件mindspore.save_checkpoint(generator, "./generator.ckpt")mindspore.save_checkpoint(discriminator, "./discriminator.ckpt")
plt.figure(figsize=(10, 5))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(G_losses, label="G", color='blue')
plt.plot(D_losses, label="D", color='orange')
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()import matplotlib.pyplot as plt
import matplotlib.animation as animationdef showGif(image_list):show_list = []fig = plt.figure(figsize=(8, 3), dpi=120)for epoch in range(len(image_list)):images = []for i in range(3):row = np.concatenate((image_list[epoch][i * 8:(i + 1) * 8]), axis=1)images.append(row)img = np.clip(np.concatenate((images[:]), axis=0), 0, 1)plt.axis("off")show_list.append([plt.imshow(img)])ani = animation.ArtistAnimation(fig, show_list, interval=1000, repeat_delay=1000, blit=True)ani.save('./dcgan.gif', writer='pillow', fps=1)showGif(image_list)