
# Symbolic Layers

While you can use other symbolic libraries,
tensorpack also contains a small collection of common model primitives,
such as conv/deconv, fc, bn, pooling layers.
Using the tensorpack implementations, you can also benefit from `argscope` and `LinearWrap` to
simplify the code.

Note that these layers were written because there were no other alternatives back at that time.
In the future we may shift the implementation to `tf.layers` because they will be better maintained.
You can start using `tf.layers` today as long as it fits your need.

### argscope and LinearWrap
`argscope` gives you a context with default arguments.
`LinearWrap` is a syntax sugar to simplify building "linear structure" models.

The following code:
```python
with argscope(Conv2D, out_channel=32, kernel_shape=3, nl=tf.nn.relu):
  l = (LinearWrap(image)  # the starting brace is only for line-breaking
       .Conv2D('conv0')
       .MaxPooling('pool0', 2)
       .Conv2D('conv1', padding='SAME')
       .Conv2D('conv2', kernel_shape=5)
       .FullyConnected('fc0', 512, nl=tf.nn.relu)
       .Dropout('dropout', 0.5)
       .tf.multiply(0.5)
       .apply(func, *args, **kwargs)
       .FullyConnected('fc1', out_dim=10, nl=tf.identity)())
```
is equivalent to:
```
l = Conv2D('conv0', image, 32, 3, nl=tf.nn.relu)
l = MaxPooling('pool0', l, 2)
l = Conv2D('conv1', l, 32, 3, padding='SAME', nl=tf.nn.relu)
l = Conv2D('conv2', l, 32, 5, nl=tf.nn.relu)
l = FullyConnected('fc0', l, 512, nl=tf.nn.relu)
l = Dropout('dropout', l, 0.5)
l = tf.multiply(l, 0.5)
l = func(l, *args, **kwargs)
l = FullyConnected('fc1', l, 10, nl=tf.identity)
```

### Access Internal Variables:

Access the variables like this:
```python
l = Conv2D('conv1', l, 32, 3)
print(l.variables.W)
print(l.variables.b)
```
The names are documented in API documentation.
Note that this method doesn't work with LinearWrap, and cannot access the variables created by an activation function.

### Use Models outside Tensorpack

You can use tensorpack models alone as a simple symbolic function library.
To do this, just enter a [TowerContext](../modules/tfutils.html#tensorpack.tfutils.TowerContext)
when you define your model:
```python
with TowerContext('', is_training=True):
  # call any tensorpack layer
```

Some layers (in particular ``BatchNorm``) has different train/test time behavior which is controlled
by ``TowerContext``. If you need to use the tensorpack version of them in test time, you'll need to create the ops for them under another context.
```python
# Open a `reuse=True` variable scope here if you're sharing variables, then:
with TowerContext('some_name_or_empty_string', is_training=False):
  # build the graph again
```

### Use Other Symbolic Libraries within Tensorpack

When defining the model you can construct the graph using whatever library you feel comfortable with.

Usually, slim/tflearn/tensorlayer are just symbolic functions, calling them is nothing different
from calling `tf.add`. You may need to be careful how regularizations/BN updates are supposed
to be handled in those libraries, though.

It is a bit different to use sonnet/Keras.
sonnet/Keras manages the variable scope by their own model classes, and calling their symbolic functions
always creates new variable scope. See the [Keras example](../examples/mnist-keras.py) for how to use it within tensorpack.
The support is only preliminary for now.
