簡單易用 TensorFlow 代碼集,GAN通用框架、函數

來源：GitHub    作者：Junho Kim

今天為大家推薦一個實用的GitHub項目：TensorFlow-Cookbook。 這是一個易用的TensorFlow代碼集，包含了對GAN有用的一些通用架構和函數。

今天為大家推薦一個實用的GitHub項目：TensorFlow-Cookbook。

這是一個易用的TensorFlow代碼集，作者是來自韓國的AI研究科學家Junho Kim，內容涵蓋了譜歸一化卷積、部分卷積、pixel shuffle、幾種歸一化函數、 tf-datasetAPI，等等。

作者表示，這個repo包含了對GAN有用的一些通用架構和函數。

項目正在進行中，作者將持續為其他領域添加有用的代碼，目前正在添加的是 tf-Eager mode的代碼。歡迎提交pull requests和issues。

Github地址 : 

https://github.com/taki0112/Tensorflow-Cookbook

Import

ops.py

operations

from ops import *

utils.py

image processing

from utils import *

Network template

def network(x, is_training=True, reuse=False, scope="network"):    with tf.variable_scope(scope, reuse=reuse):        x = conv(...)                ...                return logit

使用DatasetAPI向網絡插入數據

Image_Data_Class = ImageData(img_size, img_ch, augment_flag)trainA = trainA.map(Image_Data_Class.image_processing, num_parallel_calls=16)trainA = trainA.shuffle(buffer_size=10000).prefetch(buffer_size=batch_size).batch(batch_size).repeat()trainA_iterator = trainA.make_one_shot_iterator()data_A = trainA_iterator.get_next()logit = network(data_A)

Option

padding='SAME'

pad_type

sn

Ra

loss_func

gan

lsgan

hinge

wgan

wgan-gp

dragan

注意權重(Weight)

weight_init = tf.truncated_normal_initializer(mean=0.0, stddev=0.02)weight_regularizer = tf.contrib.layers.l2_regularizer(0.0001)weight_regularizer_fully = tf.contrib.layers.l2_regularizer(0.0001)

初始化(Initialization)

Xavier : tf.contrib.layers.xavier_initializer()

He : tf.contrib.layers.variance_scaling_initializer()

Normal : tf.random_normal_initializer(mean=0.0, stddev=0.02)

Truncated_normal : tf.truncated_normal_initializer(mean=0.0, stddev=0.02)

Orthogonal : tf.orthogonal_initializer(1.0) / # if relu = sqrt(2), the others = 1.0

正則化(Regularization)basic conv

x = conv(x, channels=64, kernel=3, stride=2, pad=1, pad_type='reflect', use_bias=True, sn=True, scope='conv')

partial conv (NVIDIA Partial Convolution)

x = partial_conv(x, channels=64, kernel=3, stride=2, use_bias=True, padding='SAME', sn=True, scope='partial_conv')

dilated conv

x = dilate_conv(x, channels=64, kernel=3, rate=2, use_bias=True, padding='SAME', sn=True, scope='dilate_conv')

basic deconv

x = deconv(x, channels=64, kernel=3, stride=2, padding='SAME', use_bias=True, sn=True, scope='deconv')

Fully-connected

x = fully_conneted(x, units=64, use_bias=True, sn=True, scope='fully_connected')

Pixel shuffle

x = conv_pixel_shuffle_down(x, scale_factor=2, use_bias=True, sn=True, scope='pixel_shuffle_down')x = conv_pixel_shuffle_up(x, scale_factor=2, use_bias=True, sn=True, scope='pixel_shuffle_up')

down ===> [height, width] -> [height // scale_factor, width // scale_factor]

up ===> [height, width] -> [height * scale_factor, width * scale_factor]

residual block

x = resblock(x, channels=64, is_training=is_training, use_bias=True, sn=True, scope='residual_block')x = resblock_down(x, channels=64, is_training=is_training, use_bias=True, sn=True, scope='residual_block_down')x = resblock_up(x, channels=64, is_training=is_training, use_bias=True, sn=True, scope='residual_block_up')

down ===> [height, width] -> [height // 2, width // 2]

up ===> [height, width] -> [height * 2, width * 2]

attention block

x = self_attention(x, channels=64, use_bias=True, sn=True, scope='self_attention')x = self_attention_with_pooling(x, channels=64, use_bias=True, sn=True, scope='self_attention_version_2')x = squeeze_excitation(x, channels=64, ratio=16, use_bias=True, sn=True, scope='squeeze_excitation')x = convolution_block_attention(x, channels=64, ratio=16, use_bias=True, sn=True, scope='convolution_block_attention')

Normalization

x = batch_norm(x, is_training=is_training, scope='batch_norm')x = instance_norm(x, scope='instance_norm')x = layer_norm(x, scope='layer_norm')x = group_norm(x, groups=32, scope='group_norm')x = pixel_norm(x)x = batch_instance_norm(x, scope='batch_instance_norm')x = condition_batch_norm(x, z, is_training=is_training, scope='condition_batch_norm'):x = adaptive_instance_norm(x, gamma, beta):

https://github.com/taki0112/BigGAN-Tensorflow

  https://github.com/taki0112/MUNIT-Tensorflow

Activation

x = relu(x)x = lrelu(x, alpha=0.2)x = tanh(x)x = sigmoid(x)x = swish(x)

Pooling & Resize

x = up_sample(x, scale_factor=2)x = max_pooling(x, pool_size=2)x = avg_pooling(x, pool_size=2)x = global_max_pooling(x)x = global_avg_pooling(x)x = flatten(x)x = hw_flatten(x)

classification loss

loss, accuracy = classification_loss(logit, label)

pixel loss

loss = L1_loss(x, y)loss = L2_loss(x, y)loss = huber_loss(x, y)loss = histogram_loss(x, y)

gan loss

d_loss = discriminator_loss(Ra=True, loss_func='wgan-gp', real=real_logit, fake=fake_logit)g_loss = generator_loss(Ra=True, loss_func='wgan_gp', real=real_logit, fake=fake_logit)

https://github.com/taki0112/BigGAN-Tensorflow/blob/master/BigGAN_512.py#L180

kl-divergence (z ~ N(0, 1))

loss = kl_loss(mean, logvar)

Author

Junho Kim

Github地址 : 

https://github.com/taki0112/Tensorflow-Cookbook

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