# Tensorflow-Cookbook

**Repository Path**: deeplearningrepos/Tensorflow-Cookbook

## Basic Information

- **Project Name**: Tensorflow-Cookbook
- **Description**: Simple Tensorflow Cookbook for easy-to-use
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-30
- **Last Updated**: 2021-08-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">
  <img src="./assets/tf-cook.png" height = '300px'>
</div>


# [Web page](http://bit.ly/jhkim_tf_cookbook)
# [Tensorflow 2 Cookbook](https://github.com/taki0112/Tensorflow2-Cookbook)

## Contributions
In now, this repo contains general architectures and functions that are useful for the GAN and classificstion.

I will continue to add useful things to other areas.

Also, your pull requests and issues are always welcome.

And write what you want to implement on the issue. I'll implement it.

# How to use
## Import
* `ops.py`
  * **operations**
  * from ops import *
* `utils.py`
  * **image processing**
  * from utils import *
  
## Network template
```python
def network(x, is_training=True, reuse=False, scope="network"):
    with tf.variable_scope(scope, reuse=reuse):
        x = conv(...)
        
        ...
        
        return logit
```

## Insert data to network using DatasetAPI
```python
Image_Data_Class = ImageData(img_size, img_ch, augment_flag)

trainA_dataset = ['./dataset/cat/trainA/a.jpg', 
                  './dataset/cat/trainA/b.png', 
                  './dataset/cat/trainA/c.jpeg', 
                  ...]
trainA = tf.data.Dataset.from_tensor_slices(trainA_dataset)
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)
```
* See [this](https://github.com/taki0112/Tensorflow-DatasetAPI) for more information.

## Option
* `padding='SAME'`
  * pad = ceil[ (kernel - stride) / 2 ]
* `pad_type`
  * 'zero' or 'reflect'
* `sn`
  * use [spectral_normalization](https://arxiv.org/pdf/1802.05957.pdf) or not

## Caution
* If you don't want to share variable, **set all scope names differently.**

---
## Weight
```python
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)
```
### Initialization
* `Xavier` : tf.contrib.layers.xavier_initializer()
  ```python
  
    USE """tf.contrib.layers.variance_scaling_initializer()"""
    
    if uniform :
      factor = gain * gain
      mode = 'FAN_AVG'
    else :
      factor = (gain * gain) / 1.3
      mode = 'FAN_AVG'
  ```
* `He` : tf.contrib.layers.variance_scaling_initializer()
  ```python
    if uniform :
      factor = gain * gain
      mode = 'FAN_IN'
    else :
      factor = (gain * gain) / 1.3
      mode = 'FAN_OUT'
  ```
* `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

### Regularization
* `l2_decay` : tf.contrib.layers.l2_regularizer(0.0001)
* `orthogonal_regularizer` : orthogonal_regularizer(0.0001) & orthogonal_regularizer_fully(0.0001)

## Convolution
### basic conv
```python
x = conv(x, channels=64, kernel=3, stride=2, pad=1, pad_type='reflect', use_bias=True, sn=True, scope='conv')
```
<div align="center">
  <img src="https://github.com/vdumoulin/conv_arithmetic/raw/master/gif/padding_strides.gif" width = '300px'>
</div>

### partial conv (NVIDIA [Partial Convolution](https://github.com/NVIDIA/partialconv))
```python
x = partial_conv(x, channels=64, kernel=3, stride=2, use_bias=True, padding='SAME', sn=True, scope='partial_conv')
```

![p_conv](https://github.com/taki0112/partial_conv-Tensorflow/raw/master/assets/partial_conv.png)
![p_result](https://github.com/taki0112/partial_conv-Tensorflow/raw/master/assets/classification.png)

### dilated conv
```python
x = dilate_conv(x, channels=64, kernel=3, rate=2, use_bias=True, padding='VALID', sn=True, scope='dilate_conv')
```
<div align="center">
  <img src="https://github.com/vdumoulin/conv_arithmetic/raw/master/gif/dilation.gif" width = '300px'>
</div>

---

## Deconvolution
### basic deconv
```python
x = deconv(x, channels=64, kernel=3, stride=1, padding='SAME', use_bias=True, sn=True, scope='deconv')
```
<div align="center">
  <img src="https://github.com/vdumoulin/conv_arithmetic/raw/master/gif/padding_strides_transposed.gif" width = '300px'>
</div>

---

## Fully-connected
```python
x = fully_connected(x, units=64, use_bias=True, sn=True, scope='fully_connected')
```

<div align="center">
  <img src="https://stanford.edu/~shervine/teaching/cs-230/illustrations/fully-connected.png">
</div>

---

## Pixel shuffle
```python
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**]

![pixel_shuffle](./assets/pixel_shuffle.png)


---

## Block
### residual block
```python
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**]
<div align="center">
  <img src="https://cdn-images-1.medium.com/max/1600/1*FqmD91PvbH7NKCnQWFJxvg.png">
</div>

### dense block
```python
x = denseblock(x, channels=64, n_db=6, is_training=is_training, use_bias=True, sn=True, scope='denseblock')
```
* `n_db` ===> The number of dense-block
<div align="center">
  <img src="https://github.com/taki0112/Densenet-Tensorflow/raw/master/assests/Denseblock.JPG" height = '400px'>
</div>

### residual-dense block
```python
x = res_denseblock(x, channels=64, n_rdb=20, n_rdb_conv=6, is_training=is_training, use_bias=True, sn=True, scope='res_denseblock')
```
* `n_rdb` ===> The number of RDB
* `n_rdb_conv` ===> per RDB conv layer

<div align="center">
  <img src=./assets/compare.png height = '400px'>
  <img src=./assets/rdn.png height = '350px' width='800px'>
  <img src=./assets/rdb.png height = '250px' width='650px'>
</div>

### attention block
```python
x = self_attention(x, use_bias=True, sn=True, scope='self_attention')
x = self_attention_with_pooling(x, use_bias=True, sn=True, scope='self_attention_version_2')

x = squeeze_excitation(x, ratio=16, use_bias=True, sn=True, scope='squeeze_excitation')

x = convolution_block_attention(x, ratio=16, use_bias=True, sn=True, scope='convolution_block_attention')

x = global_context_block(x, use_bias=True, sn=True, scope='gc_block')

x = srm_block(x, use_bias=False, is_training=is_training, scope='srm_block')
```

<div align="center">
  <img src="https://github.com/taki0112/Self-Attention-GAN-Tensorflow/raw/master/assests/framework.PNG">
</div>

---

<div align="center">
  <img src="https://github.com/hujie-frank/SENet/blob/master/figures/SE-Inception-module.jpg" width="420">
  <img src="https://github.com/hujie-frank/SENet/blob/master/figures/SE-ResNet-module.jpg"  width="420">
</div>

---


<div align="center">
  <img src="https://bloglunit.files.wordpress.com/2018/08/screen-shot-2018-08-22-at-8-42-27-pm.png?w=2800">
  <img src="https://bloglunit.files.wordpress.com/2018/08/screen-shot-2018-08-22-at-8-47-09-pm.png?w=2800">
</div>


---

<div align="center">
  <img src=./assets/gcb.png>
</div>

---

<div align="center">
  <img src=./assets/srm.png height='350' width='500'>
</div>

---

## Normalization
```python
x = batch_norm(x, is_training=is_training, scope='batch_norm')
x = layer_norm(x, scope='layer_norm')
x = instance_norm(x, scope='instance_norm')
x = group_norm(x, groups=32, scope='group_norm')

x = pixel_norm(x)

x = batch_instance_norm(x, scope='batch_instance_norm')
x = layer_instance_norm(x, scope='layer_instance_norm')
x = switch_norm(x, scope='switch_norm')

x = condition_batch_norm(x, z, is_training=is_training, scope='condition_batch_norm'):

x = adaptive_instance_norm(x, gamma, beta)
x = adaptive_layer_instance_norm(x, gamma, beta, smoothing=True, scope='adaLIN')

```
* See [this](https://github.com/taki0112/BigGAN-Tensorflow) for how to use `condition_batch_norm`
* See [this](https://github.com/taki0112/MUNIT-Tensorflow) for how to use `adaptive_instance_norm`
* See [this](https://github.com/taki0112/UGATIT) for how to use `adaptive_layer_instance_norm` & `layer_instance_norm`

<div align="center">
  <img src="https://github.com/taki0112/Group_Normalization-Tensorflow/raw/master/assests/norm.png">
</div>


<div align="center">
  <img src="https://github.com/taki0112/Switchable_Normalization-Tensorflow/raw/master/assests/teaser.png">
</div>

---

## Activation
```python
x = relu(x)
x = lrelu(x, alpha=0.2)
x = tanh(x)
x = sigmoid(x)
x = swish(x)
x = elu(x)
```

---

## Pooling & Resize
```python
x = nearest_up_sample(x, scale_factor=2)
x = bilinear_up_sample(x, scale_factor=2)
x = nearest_down_sample(x, scale_factor=2)
x = bilinear_down_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)
```

---

## Loss
### classification loss
```python
loss, accuracy = classification_loss(logit, label)

loss = dice_loss(n_classes=10, logit, label)
```

### regularization loss
```python
g_reg_loss = regularization_loss('generator')
d_reg_loss = regularization_loss('discriminator')
```

* If you want to use `regularizer`, then you should write it

### pixel loss
```python
loss = L1_loss(x, y)
loss = L2_loss(x, y)
loss = huber_loss(x, y)
loss = histogram_loss(x, y)

loss = gram_style_loss(x, y)

loss = color_consistency_loss(x, y)
```
* `histogram_loss` means the difference in the color distribution of the image pixel values.
* `gram_style_loss` means the difference between the styles using gram matrix.
* `color_consistency_loss` means the color difference between the generated image and the input image.

### gan loss
```python
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)
```
* `Ra`
  * use [relativistic gan](https://arxiv.org/pdf/1807.00734.pdf) or not
* `loss_func`
  * gan
  * lsgan
  * hinge
  * wgan-gp
  * dragan
* See [this](https://github.com/taki0112/BigGAN-Tensorflow/blob/master/BigGAN_512.py#L180) for how to use `gradient_penalty`

<div align="center">
  <img src=./assets/relativistic.png>
</div>

### [vdb loss](https://arxiv.org/abs/1810.00821)
```python
d_bottleneck_loss = vdb_loss(real_mu, real_logvar, i_c) + vdb_loss(fake_mu, fake_logvar, i_c)
```

### kl-divergence (z ~ N(0, 1))
```python
loss = kl_loss(mean, logvar)
```

---

## Author
[Junho Kim](http://bit.ly/jhkim_ai)