GAN原理
生成对抗网络GAN由生成器和判别器两部分组成：
- 判别器是常规的神经网络分类器，一半时间判别器接收来自训练数据中的真实图像，另一半时间收到来自生成器中的虚假图像。训练判别器使得对于真实图像，它输出的概率值接近1，而对于虚假图像则接近0
- 生成器与判别器正好相反，通过训练，它输出判别器赋值概率接近1的图像。生成器需要产生更加真实的输出，从而欺骗判别器
- 在GAN中要同时使用两个优化器，分别用来最小化判别器和生成器的损失
Batch Normalization
Batch Normalization是DCGAN(Deep Covolutional GAN)中常用的技术，它可以使网络训练得更快，允许更大的学习率，使更多的激活函数变得有效，并且使得参数更易初始化，BN一般用于激活函数使用之前。以图片数据为例，这里简单介绍一下BN的计算过程（参照Tensorflow和Keras中的API）。记训练数据$X$的维数为($N_{batch}$, $N_{height}$, $N_{width}$, $N_{channel}$)，批次均值和批次方差分别为$$mu_{c}=frac{1}{N_bN_hN_w} sum_{i=1}^{N_b} sum_{j=1}^{N_h}sum_{k=1}^{N_w}X_{ijkc} ext{ }, ext{ }sigma_{c}^{2}=frac{1}{N_bN_hN_w} sum_{i=1}^{N_b} sum_{j=1}^{N_h}sum_{k=1}^{N_w}left(X_{ijkc}-mu_{c}
ight)^{2} ext{ }, ext{其中}c=1,2,cdots,N_c$$则BN的输出为$$Y_{ijkc}=gamma hat{X}_{ijkc}+eta, ext{ where }hat{X}_{ijkc}=frac{X_{ijkc}-mu_{c}}{sqrt{sigma_{c}^{2}+epsilon}}$$其中$epsilon$是一个很小的正值（例如0.001），$gamma$和$eta$均为可训练参数。最后用$mu_{c}$和$sigma_{c}^{2}$更新总体的均值和方差，总体均值和方差在检验网络和进行预测时使用：$$hat{mu}_c= au hat{mu}_{c}+(1- au) mu_{c} ext{ }, ext{ }hat{sigma}_{c}^{2}= auhat{sigma}_{c}^{2}+(1- au) sigma_{c}^{2} ext{ }, ext{其中}c=1,2,cdots,N_c$$其中$hat{mu}_c$和$hat{sigma}_{c}^{2}$的初始值为0和1，$ au$可取为0.99
DCGAN应用示例
使用的数据集为the Street View House Numbers(SVHN) dataset，目标是由虚假图像（随机噪音）生成数字图像，具体代码如下所示：
- 数据处理
View Codeimport pickle as pkl import matplotlib.pyplot as plt import numpy as np from scipy.io import loadmat import tensorflow as tf ### 读取数据 data_dir = 'data/' trainset = loadmat(data_dir + 'svhntrain_32x32.mat') testset = loadmat(data_dir + 'svhntest_32x32.mat') #the same scale as tanh activation function def scale(x, feature_range=(-1, 1)): # scale to (0, 1) x = ((x - x.min())/(255 - x.min())) # scale to feature_range min, max = feature_range x = x * (max - min) + min return x ### 数据准备 class Dataset: def __init__(self, train, test, val_frac=0.5, shuffle=False, scale_func=None): split_idx = int(len(test['y'])*(1 - val_frac)) self.test_x, self.valid_x = test['X'][:,:,:,:split_idx], test['X'][:,:,:,split_idx:] self.test_y, self.valid_y = test['y'][:split_idx], test['y'][split_idx:] self.train_x, self.train_y = train['X'], train['y'] ###(32,32,3,:) to (:,32,32,3) self.train_x = np.rollaxis(self.train_x, 3) self.valid_x = np.rollaxis(self.valid_x, 3) self.test_x = np.rollaxis(self.test_x, 3) if scale_func is None: self.scaler = scale else: self.scaler = scale_func self.shuffle = shuffle def batches(self, batch_size): if self.shuffle: idx = np.arange(len(self.train_x)) np.random.shuffle(idx) self.train_x = self.train_x[idx] self.train_y = self.train_y[idx] n_batches = len(self.train_y)//batch_size for ii in range(0, len(self.train_y), batch_size): x = self.train_x[ii:ii+batch_size] y = self.train_y[ii:ii+batch_size] yield self.scaler(x), y ### 创建数据集 dataset = Dataset(trainset, testset)
- 搭建网络
- 模型输入
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def model_inputs(real_dim, z_dim): inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='input_real') inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z') return inputs_real, inputs_z
- 搭建生成器Generator
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### Generator def generator(z, output_dim, reuse=False, alpha=0.2, training=True): with tf.variable_scope('generator', reuse=reuse): x1 = tf.layers.dense(z, 4*4*512) #First fully connected layer x1 = tf.reshape(x1, (-1, 4, 4, 512)) #Reshape it to start the convolutional stack x1 = tf.layers.batch_normalization(x1, training=training) x1 = tf.maximum(alpha * x1, x1) #leaky relu, 4x4x512 now # transpose convolution > batch norm > leaky ReLU x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same') #with zero padding x2 = tf.layers.batch_normalization(x2, training=training) x2 = tf.maximum(alpha * x2, x2) #8x8x256 now # transpose convolution > batch norm > leaky ReLU x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same') x3 = tf.layers.batch_normalization(x3, training=training) x3 = tf.maximum(alpha * x3, x3) #16x16x128 now # output layer logits = tf.layers.conv2d_transpose(x3, output_dim, 5, strides=2, padding='same') #32x32x3 now out = tf.tanh(logits) return out
- 搭建判别器Discriminator
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### Discriminator def discriminator(x, reuse=False, training=True, alpha=0.2): with tf.variable_scope('discriminator', reuse=reuse): x1 = tf.layers.conv2d(x, 64, 5, strides=2, padding='same') #Input layer is 32x32x3 relu1 = tf.maximum(alpha * x1, x1) #16x16x64 # convolution > batch norm > leaky ReLU x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same') bn2 = tf.layers.batch_normalization(x2, training=training) relu2 = tf.maximum(alpha * bn2, bn2) #8x8x128 # convolution > batch norm > leaky ReLU x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same') bn3 = tf.layers.batch_normalization(x3, training=training) relu3 = tf.maximum(alpha * bn3, bn3) #4x4x256 # Flatten it flat = tf.reshape(relu3, (-1, 4*4*256)) logits = tf.layers.dense(flat, 1) out = tf.sigmoid(logits) return out, logits
- 搭建GAN并计算损失函数
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### Create GAN and Compute Model Loss ### input_real: Images from the real dataset ### input_z: Z input(noise) ### output_dim: The number of channels in the output image def model_loss(input_real, input_z, output_dim, training=True, alpha=0.2, smooth=0.1): g_model = generator(input_z, output_dim, alpha=alpha, training=training) d_model_real, d_logits_real = discriminator(input_real, training=training, alpha=alpha) # reuse=True: reuse the variables instead of creating new ones if we build the graph again d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, training=training, alpha=alpha) # real and fake loss d_loss_real = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1-smooth)) #label smoothing d_loss_real = tf.reduce_mean(d_loss_real) d_loss_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)) d_loss_fake = tf.reduce_mean(d_loss_fake) ### discriminator and generator loss d_loss = d_loss_real + d_loss_fake g_loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)) g_loss = tf.reduce_mean(g_loss) return d_loss, g_loss, g_model
- 优化器
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### Optimizer ### beta1: The exponential decay rate for the 1st moment in the optimizer def model_opt(d_loss, g_loss, learning_rate, beta1): # Get weights and bias to update t_vars = tf.trainable_variables() d_vars = [var for var in t_vars if var.name.startswith('discriminator')] g_vars = [var for var in t_vars if var.name.startswith('generator')] # Optimize with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): #update population mean and variance d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars) g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars) return d_train_opt, g_train_opt
- 封装GAN
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### Final GAN class GAN: def __init__(self, real_size, z_size, learning_rate, alpha=0.2, smooth=0.1, beta1=0.5): tf.reset_default_graph() self.input_real, self.input_z = model_inputs(real_size, z_size) self.training = tf.placeholder_with_default(True, (), "train_status") self.d_loss, self.g_loss, self.samples = model_loss(self.input_real, self.input_z, real_size[2], training=self.training, alpha=alpha, smooth=smooth) self.d_opt, self.g_opt = model_opt(self.d_loss, self.g_loss, learning_rate, beta1)
- 模型输入
- 训练网络
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def train(net, dataset, epochs, batch_size, print_every=10, show_every=100): saver = tf.train.Saver() sample_z = np.random.uniform(-1, 1, size=(72, z_size)) #samples for generator to generate(for plotting) samples, losses = [], [] steps = 0 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for e in range(epochs): for x, y in dataset.batches(batch_size): steps += 1 ### sample random noise for Generator batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size)) ### run optimizers _, _ = sess.run([net.d_opt, net.g_opt], feed_dict={net.input_real:x, net.input_z:batch_z}) ### get the losses and print them out if steps % print_every == 0: train_loss_d = net.d_loss.eval({net.input_z: batch_z, net.input_real: x}) train_loss_g = net.g_loss.eval({net.input_z: batch_z}) print("Epoch {}/{}...".format(e+1, epochs), "Discriminator Loss: {:.4f}...".format(train_loss_d), "Generator Loss: {:.4f}".format(train_loss_g)) losses.append((train_loss_d, train_loss_g)) #save losses to view after training ### save generated samples if steps % show_every == 0: # training=False: the batch normalization layers will use the population statistics rather than the batch statistics gen_samples = sess.run(net.samples, feed_dict={net.input_z: sample_z, net.training: False}) samples.append(gen_samples) saver.save(sess, './checkpoints/generator.ckpt') with open('samples.pkl', 'wb') as f: pkl.dump(samples, f) return losses, samples ### Hyperparameters real_size = (32,32,3) z_size = 100 learning_rate = 0.0002 batch_size = 128 epochs = 25 alpha = 0.2 smooth = 0.1 beta1 = 0.5 ### Create and Train the network net = GAN(real_size, z_size, learning_rate, alpha=alpha, smooth=smooth, beta1=beta1) losses, samples = train(net, dataset, epochs, batch_size)
- 最终结果可视化
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### Visualize def view_samples(sample, nrows, ncols, figsize=(5,5)): #the number of the sample=nrows*ncols fig, axes = plt.subplots(figsize=figsize, nrows=nrows, ncols=ncols, sharey=True, sharex=True) for ax, img in zip(axes.flatten(), sample): ax.axis('off') img = ((img - img.min())*255 / (img.max() - img.min())).astype(np.uint8) ax.set_adjustable('box-forced') im = ax.imshow(img, aspect='equal') plt.subplots_adjust(wspace=0, hspace=0) return fig, axes view_samples(samples[-1], 6, 12, figsize=(10,5))
最终生成的图像如下图所示
GAN应用于半监督学习
使用的数据集同上，为了建立一个半监督学习的情景，这里仅使用前1000个训练数据的标签，并且将GAN的判别器由二分类变为多分类，针对此数据，共分为11类（10个真实数字和虚假图像）。代码的整体结构和上一部分相同，这里仅注释有改动的部分，针对该网络更为细节的改进参考文章Improved Techniques for Training GANs以及对应的github仓库。
- 数据处理
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import pickle as pkl import matplotlib.pyplot as plt import numpy as np from scipy.io import loadmat import tensorflow as tf data_dir = 'data/' trainset = loadmat(data_dir + 'svhntrain_32x32.mat') testset = loadmat(data_dir + 'svhntest_32x32.mat') def scale(x, feature_range=(-1, 1)): x = ((x - x.min())/(255 - x.min())) min, max = feature_range x = x * (max - min) + min return x class Dataset: def __init__(self, train, test, val_frac=0.5, shuffle=True, scale_func=None): split_idx = int(len(test['y'])*(1 - val_frac)) self.test_x, self.valid_x = test['X'][:,:,:,:split_idx], test['X'][:,:,:,split_idx:] self.test_y, self.valid_y = test['y'][:split_idx], test['y'][split_idx:] self.train_x, self.train_y = train['X'], train['y'] ################### # For the purpose of semi-supervised learn, pretend that there are only 1000 labels # Use this mask to say which labels will allow to use self.label_mask = np.zeros_like(self.train_y) self.label_mask[0:1000] = 1 ################### self.train_x = np.rollaxis(self.train_x, 3) self.valid_x = np.rollaxis(self.valid_x, 3) self.test_x = np.rollaxis(self.test_x, 3) if scale_func is None: self.scaler = scale else: self.scaler = scale_func self.train_x = self.scaler(self.train_x) self.valid_x = self.scaler(self.valid_x) self.test_x = self.scaler(self.test_x) self.shuffle = shuffle def batches(self, batch_size, which_set="train"): ################### # Semi-supervised learn need both train data and validation(test) data # Semi-supervised learn need both images and labels ################### x_name = which_set + "_x" y_name = which_set + "_y" num_examples = len(getattr(self, y_name)) if self.shuffle: idx = np.arange(num_examples) np.random.shuffle(idx) setattr(self, x_name, getattr(self, x_name)[idx]) setattr(self, y_name, getattr(self, y_name)[idx]) if which_set == "train": self.label_mask = self.label_mask[idx] dataset_x = getattr(self, x_name) dataset_y = getattr(self, y_name) for ii in range(0, num_examples, batch_size): x = dataset_x[ii:ii+batch_size] y = dataset_y[ii:ii+batch_size] if which_set == "train": ################### # When use the data for training, need to include the label mask # Pretend don't have access to some of the labels yield x, y, self.label_mask[ii:ii+batch_size] ################### else: yield x, y dataset = Dataset(trainset, testset)
- 搭建网络
- 模型输入
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def model_inputs(real_dim, z_dim): inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='input_real') inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z') ################### # Add placeholders for labels and label masks y = tf.placeholder(tf.int32, (None), name='y') label_mask = tf.placeholder(tf.int32, (None), name='label_mask') ################### return inputs_real, inputs_z, y, label_mask
- 搭建生成器Generator
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### Generator def generator(z, output_dim, reuse=False, alpha=0.2, training=True, size_mult=128): with tf.variable_scope('generator', reuse=reuse): x1 = tf.layers.dense(z, 4 * 4 * size_mult * 4) x1 = tf.reshape(x1, (-1, 4, 4, size_mult * 4)) x1 = tf.layers.batch_normalization(x1, training=training) x1 = tf.maximum(alpha * x1, x1) #(:,4,4,4*size_mult) x2 = tf.layers.conv2d_transpose(x1, size_mult * 2, 5, strides=2, padding='same') x2 = tf.layers.batch_normalization(x2, training=training) x2 = tf.maximum(alpha * x2, x2) #(:,8,8,2*size_mult) x3 = tf.layers.conv2d_transpose(x2, size_mult, 5, strides=2, padding='same') x3 = tf.layers.batch_normalization(x3, training=training) x3 = tf.maximum(alpha * x3, x3) #(:,16,16,size_mult) logits = tf.layers.conv2d_transpose(x3, output_dim, 5, strides=2, padding='same') #(:,32,32,3) out = tf.tanh(logits) return out
- 搭建判别器Discriminator
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### Discriminator ################### ### Add dropout layer to reduce overfitting since only 1000 labelled samples exist ### 10 class classification(10 digits) and set [fake logit=0] ################### def discriminator(x, reuse=False, training=True, alpha=0.2, drop_rate=0., num_classes=10, size_mult=64): with tf.variable_scope('discriminator', reuse=reuse): # Add dropout layer x = tf.layers.dropout(x, rate=drop_rate/2.5) #Input layer (:,32,32,3) ################### x1 = tf.layers.conv2d(x, size_mult, 3, strides=2, padding='same') relu1 = tf.maximum(alpha * x1, x1) # Add dropout layer relu1 = tf.layers.dropout(relu1, rate=drop_rate) #(:,16,16,size_mult) ################### x2 = tf.layers.conv2d(relu1, size_mult, 3, strides=2, padding='same') bn2 = tf.layers.batch_normalization(x2, training=training) relu2 = tf.maximum(alpha * x2, x2) #(:,8,8,size_mult) ################### x3 = tf.layers.conv2d(relu2, size_mult, 3, strides=2, padding='same') bn3 = tf.layers.batch_normalization(x3, training=training) relu3 = tf.maximum(alpha * bn3, bn3) # Add dropout layer relu3 = tf.layers.dropout(relu3, rate=drop_rate) #(:,4,4,size_mult) ################### x4 = tf.layers.conv2d(relu3, 2 * size_mult, 3, strides=1, padding='same') bn4 = tf.layers.batch_normalization(x4, training=training) relu4 = tf.maximum(alpha * bn4, bn4) #(:,4,4,2*size_mult) ################### x5 = tf.layers.conv2d(relu4, 2 * size_mult, 3, strides=1, padding='same') bn5 = tf.layers.batch_normalization(x5, training=training) relu5 = tf.maximum(alpha * bn5, bn5) #(:,4,4,2*size_mult) ################### x6 = tf.layers.conv2d(relu5, 2 * size_mult, 3, strides=1, padding='valid') # This layer is used for the feature matching loss, don't use batch normalization on this layer # See the function model_loss for the feature matching loss relu6 = tf.maximum(alpha * x6, x6) #(:,2,2,2*size_mult) ################### # Flatten by global average pooling features = tf.reduce_mean(relu6, (1, 2)) #(:,2*size_mult) # Multi-classification class_logits = tf.layers.dense(features, num_classes) #(:,10) out = tf.nn.softmax(class_logits) ################### # Split real and fake logits for classifying real and fake real_class_logits = class_logits fake_class_logits = 0. # Set gan_logits such that P(input is real | input) = sigmoid(gan_logits) # For Numerical stability, use this trick: log sum_i exp a_i = m + log sum_i exp(a_i - m), m = max_i a_i mx = tf.reduce_max(real_class_logits, 1, keepdims=True) #(:,1) stable_real_class_logits = real_class_logits - mx #minus the largest real logit for each sample, (:,10) gan_logits = tf.log(tf.reduce_sum(tf.exp(stable_real_class_logits), 1)) + tf.squeeze(mx) - fake_class_logits #(number of samples,) ################### return out, class_logits, gan_logits, features
- 搭建GAN并计算损失函数
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### Create GAN and Compute Model Loss def model_loss(input_real, input_z, output_dim, y, num_classes, label_mask, g_size_mult, d_size_mult, training=True, alpha=0.2, drop_rate=0.): g_model = generator(input_z, output_dim, alpha=alpha, size_mult=g_size_mult, training=training) d_on_real = discriminator(input_real, alpha=alpha, drop_rate=drop_rate, size_mult=d_size_mult, training=training) d_on_fake = discriminator(g_model, reuse=True, alpha=alpha, drop_rate=drop_rate, size_mult=d_size_mult, training=training) out_real, class_logits_real, gan_logits_real, features_real = d_on_real out_fake, class_logits_fake, gan_logits_fake, features_fake = d_on_fake ################### # Compute the loss for the discriminator # 1. The loss for the GAN problem, minimize the cross-entropy for the binary # real-vs-fake classification problem # 2. The loss for the SVHN digit classification problem, where minimize the # cross-entropy(use the labels) for the multi-class softmax d_loss_real = tf.nn.sigmoid_cross_entropy_with_logits(logits=gan_logits_real, labels=tf.ones_like(gan_logits_real)*0.9) # label smoothing d_loss_real = tf.reduce_mean(d_loss_real) d_loss_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits=gan_logits_fake, labels=tf.zeros_like(gan_logits_fake)) d_loss_fake = tf.reduce_mean(d_loss_fake) y = tf.squeeze(y) #labels class_cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=class_logits_real, labels=tf.one_hot(y, class_logits_real.get_shape()[1], dtype=tf.float32)) # Use label_mask to ignore the examples pretending unlabeled for the semi-supervised problem class_cross_entropy = tf.squeeze(class_cross_entropy) label_mask = tf.squeeze(tf.to_float(label_mask)) d_loss_class = tf.reduce_sum(label_mask * class_cross_entropy) / tf.maximum(1., tf.reduce_sum(label_mask)) d_loss = d_loss_class + d_loss_real + d_loss_fake ################### # Compute the loss for the generator # Set the loss to the "feature matching" loss invented by Tim Salimans at OpenAI # This loss is the mean absolute difference between the real features and the fake features # This loss works better for semi-supervised learnings than the traditional generator loss real_moments = tf.reduce_mean(features_real, axis=0) fake_moments = tf.reduce_mean(features_fake, axis=0) g_loss = tf.reduce_mean(tf.abs(real_moments - fake_moments)) ################### pred_class = tf.cast(tf.argmax(class_logits_real, 1), tf.int32) eq = tf.equal(y, pred_class) correct = tf.reduce_sum(tf.to_float(eq)) masked_correct = tf.reduce_sum(label_mask * tf.to_float(eq)) return d_loss, g_loss, correct, masked_correct, g_model
- 优化器
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### Optimizer def model_opt(d_loss, g_loss, learning_rate, beta1): t_vars = tf.trainable_variables() d_vars = [var for var in t_vars if var.name.startswith('discriminator')] g_vars = [var for var in t_vars if var.name.startswith('generator')] with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars) g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars) return d_train_opt, g_train_opt
- 封装GAN
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### Final GAN class GAN: def __init__(self, real_size, z_size, g_size_mult=32, d_size_mult=64, num_classes=10, alpha=0.2, beta1=0.5): tf.reset_default_graph() ################### # The dropout rate and learning rate self.drop_rate = tf.placeholder_with_default(.6, (), "drop_rate") self.learning_rate = tf.placeholder(tf.float32, None, "learning_rate") ################### self.input_real, self.input_z, self.y, self.label_mask = model_inputs(real_size, z_size) self.training = tf.placeholder_with_default(True, (), "train_status") loss_results = model_loss(self.input_real, self.input_z, real_size[2], self.y, num_classes, self.label_mask, g_size_mult, d_size_mult, self.training, alpha, self.drop_rate) self.d_loss, self.g_loss, self.correct, self.masked_correct, self.samples = loss_results self.d_opt, self.g_opt = model_opt(self.d_loss, self.g_loss, self.learning_rate, beta1)
- 模型输入
- 训练网络
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def train(net, dataset, epochs, batch_size, learning_rate): saver = tf.train.Saver() sample_z = np.random.normal(0, 1, size=(50, z_size)) samples, train_accuracies, test_accuracies = [], [], [] steps = 0 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for e in range(epochs): print("Epoch",e) num_examples = 0 num_correct = 0 for x, y, label_mask in dataset.batches(batch_size): steps += 1 num_examples += label_mask.sum() batch_z = np.random.normal(0, 1, size=(batch_size, z_size)) _, _, correct = sess.run([net.d_opt, net.g_opt, net.masked_correct], feed_dict={net.input_real: x, net.input_z: batch_z, net.y: y, net.label_mask: label_mask, net.learning_rate: learning_rate}) num_correct += correct ################### # At the end of the epoch: # compute train accuracy(only for labeled[masked] images) # shrink learning rate train_accuracy = num_correct / float(num_examples) print(" Classifier train accuracy: ", train_accuracy) learning_rate *= 0.9 ################### # At the end of the epoch: compute test accuracy num_examples = 0 num_correct = 0 for x, y in dataset.batches(batch_size, which_set="test"): num_examples += x.shape[0] correct = sess.run(net.correct, feed_dict={net.input_real: x, net.y: y, net.drop_rate: 0., net.training: False}) num_correct += correct test_accuracy = num_correct / float(num_examples) print(" Classifier test accuracy", test_accuracy) ################### # Save history of accuracies to view after training train_accuracies.append(train_accuracy) test_accuracies.append(test_accuracy) ################### gen_samples = sess.run(net.samples, feed_dict={net.input_z: sample_z, net.training: False}) samples.append(gen_samples) saver.save(sess, './checkpoints/generator.ckpt') with open('samples.pkl', 'wb') as f: pkl.dump(samples, f) return train_accuracies, test_accuracies, samples real_size = (32,32,3) z_size = 100 learning_rate = 0.0003 batch_size = 128 epochs = 20 net = GAN(real_size, z_size) train_accuracies, test_accuracies, samples = train(net, dataset, epochs, batch_size, learning_rate)
- 最终结果
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# Plot accuracies fig, ax = plt.subplots(figsize=(10,5)) plt.plot(train_accuracies, label='Train', alpha=0.5) plt.plot(test_accuracies, label='Test', alpha=0.5) ax.set_xticks(range(epochs)) plt.title("Accuracy(Final Test: {0}%)".format(int(round(test_accuracies[-1]*100)))) plt.legend()
