https://github.com/taki0112/densenet-tensorflow
Simple Tensorflow implementation of Densenet using Cifar10, MNIST
https://github.com/taki0112/densenet-tensorflow
densenet densenet-tensorflow tensorflow
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Simple Tensorflow implementation of Densenet using Cifar10, MNIST
- Host: GitHub
- URL: https://github.com/taki0112/densenet-tensorflow
- Owner: taki0112
- License: mit
- Created: 2017-08-07T02:44:26.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-03-04T09:04:40.000Z (almost 7 years ago)
- Last Synced: 2025-05-25T12:05:15.902Z (7 months ago)
- Topics: densenet, densenet-tensorflow, tensorflow
- Language: Python
- Homepage:
- Size: 991 KB
- Stars: 507
- Watchers: 25
- Forks: 195
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Densenet-Tensorflow
Tensorflow implementation of [Densenet](https://arxiv.org/abs/1608.06993) using **Cifar10, MNIST**
* The code that implements *this paper* is ***Densenet.py***
* There is a *slight difference*, I used ***AdamOptimizer***
If you want to see the ***original author's code*** or ***other implementations***, please refer to this [link](https://github.com/liuzhuang13/DenseNet)
## Requirements
* Tensorflow 1.x
* Python 3.x
* tflearn (If you are easy to use ***global average pooling***, you should install ***tflearn***
```bash
However, I implemented it using tf.layers, so don't worry
```
## Issue
* I used ***tf.contrib.layers.batch_norm***
```python
def Batch_Normalization(x, training, scope):
with arg_scope([batch_norm],
scope=scope,
updates_collections=None,
decay=0.9,
center=True,
scale=True,
zero_debias_moving_mean=True) :
return tf.cond(training,
lambda : batch_norm(inputs=x, is_training=training, reuse=None),
lambda : batch_norm(inputs=x, is_training=training, reuse=True))
```
* If not enough GPU memory, Please edit the code
```python
with tf.Session() as sess : NO
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess : OK
```
* https://github.com/taki0112/Densenet-Tensorflow/issues/10
## Idea
### What is the "Global Average Pooling" ?
```python
def Global_Average_Pooling(x, stride=1) :
width = np.shape(x)[1]
height = np.shape(x)[2]
pool_size = [width, height]
return tf.layers.average_pooling2d(inputs=x, pool_size=pool_size, strides=stride)
# The stride value does not matter
````
* If you use tflearn, please refer to this [link](http://tflearn.org/layers/conv/#global-average-pooling)
```python
def Global_Average_Pooling(x):
return tflearn.layers.conv.global_avg_pool(x, name='Global_avg_pooling')
```
### What is the "Dense Connectivity" ?
### What is the "Densenet Architecture" ?
```python
def Dense_net(self, input_x):
x = conv_layer(input_x, filter=2 * self.filters, kernel=[7,7], stride=2, layer_name='conv0')
x = Max_Pooling(x, pool_size=[3,3], stride=2)
x = self.dense_block(input_x=x, nb_layers=6, layer_name='dense_1')
x = self.transition_layer(x, scope='trans_1')
x = self.dense_block(input_x=x, nb_layers=12, layer_name='dense_2')
x = self.transition_layer(x, scope='trans_2')
x = self.dense_block(input_x=x, nb_layers=48, layer_name='dense_3')
x = self.transition_layer(x, scope='trans_3')
x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_final')
x = Batch_Normalization(x, training=self.training, scope='linear_batch')
x = Relu(x)
x = Global_Average_Pooling(x)
x = Linear(x)
return x
```
### What is the "Dense Block" ?
```python
def dense_block(self, input_x, nb_layers, layer_name):
with tf.name_scope(layer_name):
layers_concat = list()
layers_concat.append(input_x)
x = self.bottleneck_layer(input_x, scope=layer_name + '_bottleN_' + str(0))
layers_concat.append(x)
for i in range(nb_layers - 1):
x = Concatenation(layers_concat)
x = self.bottleneck_layer(x, scope=layer_name + '_bottleN_' + str(i + 1))
layers_concat.append(x)
x = Concatenation(layers_concat)
return x
```
### What is the "Bottleneck Layer" ?
```python
def bottleneck_layer(self, x, scope):
with tf.name_scope(scope):
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
x = conv_layer(x, filter=4 * self.filters, kernel=[1,1], layer_name=scope+'_conv1')
x = Drop_out(x, rate=dropout_rate, training=self.training)
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch2')
x = Relu(x)
x = conv_layer(x, filter=self.filters, kernel=[3,3], layer_name=scope+'_conv2')
x = Drop_out(x, rate=dropout_rate, training=self.training)
return x
```
### What is the "Transition Layer" ?
```python
def transition_layer(self, x, scope):
with tf.name_scope(scope):
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
x = conv_layer(x, filter=self.filters, kernel=[1,1], layer_name=scope+'_conv1')
x = Drop_out(x, rate=dropout_rate, training=self.training)
x = Average_pooling(x, pool_size=[2,2], stride=2)
return x
```
## Compare Structure (CNN, ResNet, DenseNet)
## Results
* (***MNIST***) The highest test accuracy is ***99.2%*** (This result does ***not use dropout***)
* The number of dense block layers is fixed to ***4***
```python
for i in range(self.nb_blocks) :
# original : 6 -> 12 -> 48
x = self.dense_block(input_x=x, nb_layers=4, layer_name='dense_'+str(i))
x = self.transition_layer(x, scope='trans_'+str(i))
```
### CIFAR-10
### CIFAR-100
### Image Net
## Related works
* [ResNeXt-Tensorflow](https://github.com/taki0112/ResNeXt-Tensorflow)
* [SENet-Tensorflow](https://github.com/taki0112/SENet-Tensorflow)
* [ResNet-Tensorflow](https://github.com/taki0112/ResNet-Tensorflow)
## References
* [Korean](https://www.youtube.com/watch?v=fe2Vn0mwALI)
* [English](https://www.youtube.com/watch?v=-W6y8xnd--U)
* [Classification Datasets Results](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html)
## Author
Junho Kim