# TensorFlow-Slim

TF-Slim is a lightweight library for defining, training and evaluating complex
models in TensorFlow. Components of tf-slim can be freely mixed with native
tensorflow, as well as other frameworks, such as tf.contrib.learn.

## Usage
```python
import tensorflow.contrib.slim as slim
```

## Why TF-Slim?

TF-Slim is a library that makes building, training and evaluation neural
networks simple:

* Allows the user to define models much more compactly by eliminating
boilerplate code. This is accomplished through the use of
[argument scoping](https://www.tensorflow.org/code/tensorflow/contrib/framework/python/ops/arg_scope.py)
and numerous high level
[layers](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
and
[variables](https://www.tensorflow.org/code/tensorflow/contrib/framework/python/ops/variables.py).
These tools increase readability and maintainability, reduce the likelihood
of an error from copy-and-pasting hyperparameter values and simplifies
hyperparameter tuning.
* Makes developing models simple by providing commonly used
[regularizers](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/regularizers.py).
* Several widely used computer vision models (e.g., VGG, AlexNet) have been
developed in slim, and are
[available](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/nets/)
to users. These can either be used as black boxes, or can be extended in various
ways, e.g., by adding "multiple heads" to different internal layers.
* Slim makes it easy to extend complex models, and to warm start training
algorithms by using pieces of pre-existing model checkpoints.

## What are the various components of TF-Slim?

TF-Slim is composed of several parts which were design to exist independently.
These include the following main pieces (explained in detail below).

* [arg_scope](https://www.tensorflow.org/code/tensorflow/contrib/framework/python/ops/arg_scope.py):
provides a new scope named `arg_scope` that allows a user to define default
arguments for specific operations within that scope.
* [data](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/data/):
contains TF-slim's
[dataset](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/data/dataset.py)
definition,
[data providers](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/data/data_provider.py),
[parallel_reader](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/data/parallel_reader.py),
and
[decoding](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/data/data_decoder.py)
utilities.
* [evaluation](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/evaluation.py):
contains routines for evaluating models.
* [layers](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py):
contains high level layers for building models using tensorflow.
* [learning](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/learning.py):
contains routines for training models.
* [losses](https://www.tensorflow.org/code/tensorflow/contrib/losses/python/losses/loss_ops.py):
contains commonly used loss functions.
* [metrics](https://www.tensorflow.org/code/tensorflow/contrib/metrics/python/ops/metric_ops.py):
contains popular evaluation metrics.
* [nets](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/nets/):
contains popular network definitions such as
[VGG](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/nets/vgg.py)
and
[AlexNet](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/nets/alexnet.py)
models.
* [queues](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/queues.py):
provides a context manager for easily and safely starting and closing
QueueRunners.
* [regularizers](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/regularizers.py):
contains weight regularizers.
* [variables](https://www.tensorflow.org/code/tensorflow/contrib/framework/python/ops/variables.py):
provides convenience wrappers for variable creation and manipulation.

## Defining Models

Models can be succinctly defined using TF-Slim by combining its variables,
layers and scopes. Each of these elements are defined below.

### Variables

Creating
[`Variables`](https://www.tensorflow.org/how_tos/variables/index.html)
in native tensorflow requires either a predefined value or an initialization
mechanism (e.g. randomly sampled from a Gaussian). Furthermore, if a variable
needs to be created
on a specific device, such as a GPU, the specification must be
[made explicit](https://www.tensorflow.org/how_tos/using_gpu/index.html).
To alleviate the code required for variable creation, TF-Slim provides a set
of thin wrapper functions in
[variables.py](https://www.tensorflow.org/code/tensorflow/contrib/framework/python/ops/variables.py)
which allow callers to easily define variables.

For example, to create a `weight` variable, initialize it using a truncated
normal distribution, regularize it with an `l2_loss` and place it on the `CPU`,
one need only declare the following:

```python
weights = slim.variable('weights',
                             shape=[10, 10, 3 , 3],
                             initializer=tf.truncated_normal_initializer(stddev=0.1),
                             regularizer=slim.l2_regularizer(0.05),
                             device='/CPU:0')
```

Note that in native TensorFlow, there are two types of variables: regular
variables and local (transient) variables. The vast majority of variables are
regular variables: once created, they can be saved to disk using a
[saver](https://www.tensorflow.org/versions/r0.11/api_docs/python/state_ops.html#Saver).
Local variables are those variables that only exist for the duration of a
session and are not saved to disk.

TF-Slim further differentiates variables by defining *model variables*, which
are variables that represent parameters of a model. Model variables are
trained or fine-tuned during learning and are loaded
from a checkpoint during evaluation or inference. Examples include the variables
created by a `slim.fully_connected` or `slim.conv2d` layer. Non-model variables
are all other variables that are used during learning or evaluation but are not
required for actually performing inference. For example, the `global_step` is
a variable using during learning and evaluation but it is not actually part of
the model. Similarly, moving average variables might mirror model variables,
but the moving averages are not themselves model variables.

Both model variables and regular variables can be easily created and retrieved
via TF-Slim:

```python
# Model Variables
weights = slim.model_variable('weights',
                              shape=[10, 10, 3 , 3],
                              initializer=tf.truncated_normal_initializer(stddev=0.1),
                              regularizer=slim.l2_regularizer(0.05),
                              device='/CPU:0')
model_variables = slim.get_model_variables()

# Regular variables
my_var = slim.variable('my_var',
                       shape=[20, 1],
                       initializer=tf.zeros_initializer())
regular_variables_and_model_variables = slim.get_variables()
```

How does this work? When you create a model variable via TF-Slim's layers or
directly via the `slim.model_variable` function, TF-Slim adds the variable to
a the `tf.GraphKeys.MODEL_VARIABLES` collection. What if you have your own
custom layers or variable creation routine but still want TF-Slim to manage or
be aware of your model variables? TF-Slim provides a convenience function for
adding the model variable to its collection:

```python
my_model_variable = CreateViaCustomCode()

# Letting TF-Slim know about the additional variable.
slim.add_model_variable(my_model_variable)
```


### Layers

While the set of TensorFlow operations is quite extensive, developers of
neural networks typically think of models in terms of higher level concepts
like "layers", "losses", "metrics", and "networks". A layer,
such as a Convolutional Layer, a Fully Connected Layer or a BatchNorm Layer
are more abstract than a single TensorFlow operation and typically involve
several operations. Furthermore, a layer usually (but not always) has
variables (tunable parameters) associated with it, unlike more primitive
operations. For example, a Convolutional Layer in a neural network
is composed of several low level operations:

1. Creating the weight and bias variables
2. Convolving the weights with the input from the previous layer
3. Adding the biases to the result of the convolution.
4. Applying an activation function.

Using only plain TensorFlow code, this can be rather laborious:

```python
input = ...
with tf.name_scope('conv1_1') as scope:
  kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,
                                           stddev=1e-1), name='weights')
  conv = tf.nn.conv2d(input, kernel, [1, 1, 1, 1], padding='SAME')
  biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
                       trainable=True, name='biases')
  bias = tf.nn.bias_add(conv, biases)
  conv1 = tf.nn.relu(bias, name=scope)
```

To alleviate the need to duplicate this code repeatedly, TF-Slim provides a
number of convenient operations defined at the more abstract level of
neural network layers. For example, compare the code above to an invocation
of the corresponding TF-Slim code:

```python
input = ...
net = slim.conv2d(input, 128, [3, 3], scope='conv1_1')
```

TF-Slim provides standard implementations for numerous components for building
neural networks. These include:

Layer | TF-Slim
------- | --------
BiasAdd  | [slim.bias_add](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
BatchNorm  | [slim.batch_norm](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
Conv2d | [slim.conv2d](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
Conv2dInPlane | [slim.conv2d_in_plane](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
Conv2dTranspose (Deconv) | [slim.conv2d_transpose](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
FullyConnected | [slim.fully_connected](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
AvgPool2D | [slim.avg_pool2d](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
Dropout| [slim.dropout](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
Flatten | [slim.flatten](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
MaxPool2D | [slim.max_pool2d](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
OneHotEncoding | [slim.one_hot_encoding](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
SeparableConv2 | [slim.separable_conv2d](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
UnitNorm | [slim.unit_norm](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)

TF-Slim also provides two meta-operations called `repeat` and `stack` that
allow users to repeatedly perform the same operation. For example, consider the
following snippet from the
[VGG](https://www.robots.ox.ac.uk/~vgg/research/very_deep/) network whose layers
perform several convolutions in a row between pooling layers:

```python
net = ...
net = slim.conv2d(net, 256, [3, 3], scope='conv3_1')
net = slim.conv2d(net, 256, [3, 3], scope='conv3_2')
net = slim.conv2d(net, 256, [3, 3], scope='conv3_3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
```

One way to reduce this code duplication would be via a `for` loop:

```python
net = ...
for i in range(3):
  net = slim.conv2d(net, 256, [3, 3], scope='conv3_' % (i+1))
net = slim.max_pool2d(net, [2, 2], scope='pool3')
```

This can be made even cleaner by using TF-Slim's `repeat` operation:

```python
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool(net, [2, 2], scope='pool2')
```

Notice that the `slim.repeat` not only applies the same argument in-line, it
also is smart enough to unroll the scopes such that the scopes assigned to each
subsequent call of `slim.conv2d` are appended with an underscore and iteration
number. More concretely, the scopes in the example above would be named
'conv3/conv3_1', 'conv3/conv3_2' and 'conv3/conv3_3'.

Furthermore, TF-Slim's `slim.stack` operator allows a caller to repeatedly apply
the same operation with different arguments to create a *stack* or tower of
layers. `slim.stack` also creates a new `tf.variable_scope` for each
operation created. For example, a simple way to create a Multi-Layer Perceptron
(MLP):

```python
# Verbose way:
x = slim.fully_connected(x, 32, scope='fc/fc_1')
x = slim.fully_connected(x, 64, scope='fc/fc_2')
x = slim.fully_connected(x, 128, scope='fc/fc_3')

# Equivalent, TF-Slim way using slim.stack:
slim.stack(x, slim.fully_connected, [32, 64, 128], scope='fc')
```

In this example, `slim.stack` calls `slim.fully_connected` three times passing
the output of one invocation of the function to the next. However, the number of
hidden units in each invocation changes from 32 to 64 to 128. Similarly, one
can use stack to simplify a tower of multiple convolutions:

```python
# Verbose way:
x = slim.conv2d(x, 32, [3, 3], scope='core/core_1')
x = slim.conv2d(x, 32, [1, 1], scope='core/core_2')
x = slim.conv2d(x, 64, [3, 3], scope='core/core_3')
x = slim.conv2d(x, 64, [1, 1], scope='core/core_4')

# Using stack:
slim.stack(x, slim.conv2d, [(32, [3, 3]), (32, [1, 1]), (64, [3, 3]), (64, [1, 1])], scope='core')
```

### Scopes

In addition to the types of scope mechanisms in TensorFlow
([name_scope](https://www.tensorflow.org/api_docs/python/framework.html#name_scope),
[variable_scope](https://www.tensorflow.org/api_docs/python/state_layers.html#variable_scope),
TF-Slim adds a new scoping mechanism called
[arg_scope](https://www.tensorflow.org/code/tensorflow/contrib/framework/python/ops/arg_scope.py).
This new scope allows a user to specify one or more operations and a set of
arguments which will be passed to each of the operations defined in the
`arg_scope`. This functionality is best illustrated by example. Consider the
following code snippet:


```python
net = slim.conv2d(inputs, 64, [11, 11], 4, padding='SAME',
                  weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
                  weights_regularizer=slim.l2_regularizer(0.0005), scope='conv1')
net = slim.conv2d(net, 128, [11, 11], padding='VALID',
                  weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
                  weights_regularizer=slim.l2_regularizer(0.0005), scope='conv2')
net = slim.conv2d(net, 256, [11, 11], padding='SAME',
                  weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
                  weights_regularizer=slim.l2_regularizer(0.0005), scope='conv3')
```

It should be clear that these three convolution layers share many of the same
hyperparameters. Two have the same padding, all three have the same
weights_initializer and weight_regularizer. This code is hard to read and
contains a lot of repeated values that should be factored out. One solution
would be to specify default values using variables:

```python
padding = 'SAME'
initializer = tf.truncated_normal_initializer(stddev=0.01)
regularizer = slim.l2_regularizer(0.0005)
net = slim.conv2d(inputs, 64, [11, 11], 4,
                  padding=padding,
                  weights_initializer=initializer,
                  weights_regularizer=regularizer,
                  scope='conv1')
net = slim.conv2d(net, 128, [11, 11],
                  padding='VALID',
                  weights_initializer=initializer,
                  weights_regularizer=regularizer,
                  scope='conv2')
net = slim.conv2d(net, 256, [11, 11],
                  padding=padding,
                  weights_initializer=initializer,
                  weights_regularizer=regularizer,
                  scope='conv3')
```

This solution ensures that all three convolutions share the exact same parameter
values but doesn't reduce completely the code clutter. By using an `arg_scope`,
we can both ensure that each layer uses the same values and simplify the code:

```python
  with slim.arg_scope([slim.conv2d], padding='SAME',
                      weights_initializer=tf.truncated_normal_initializer(stddev=0.01)
                      weights_regularizer=slim.l2_regularizer(0.0005)):
    net = slim.conv2d(inputs, 64, [11, 11], scope='conv1')
    net = slim.conv2d(net, 128, [11, 11], padding='VALID', scope='conv2')
    net = slim.conv2d(net, 256, [11, 11], scope='conv3')
```

As the example illustrates, the use of arg_scope makes the code cleaner,
simpler and easier to maintain. Notice that while argument values are specifed
in the arg_scope, they can be overwritten locally. In particular, while
the padding argument has been set to 'SAME', the second convolution overrides
it with the value of 'VALID'.

One can also nest `arg_scopes` and use multiple operations in the same scope.
For example:

```python
with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      activation_fn=tf.nn.relu,
                      weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
                      weights_regularizer=slim.l2_regularizer(0.0005)):
  with slim.arg_scope([slim.conv2d], stride=1, padding='SAME'):
    net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
    net = slim.conv2d(net, 256, [5, 5],
                      weights_initializer=tf.truncated_normal_initializer(stddev=0.03),
                      scope='conv2')
    net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc')
```

In this example, the first `arg_scope` applies the same `weights_initializer`
and `weights_regularizer` arguments to the `conv2d` and `fully_connected` layers
in its scope. In the second `arg_scope`, additional default arguments to
`conv2d` only are specified.

### Working Example: Specifying the VGG16 Layers

By combining TF-Slim Variables, Operations and scopes, we can write a normally
very complex network with very few lines of code. For example, the entire
[VGG](https://www.robots.ox.ac.uk/~vgg/research/very_deep/) architecture can be
defined with just the following snippet:

```python
def vgg16(inputs):
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      activation_fn=tf.nn.relu,
                      weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
                      weights_regularizer=slim.l2_regularizer(0.0005)):
    net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
    net = slim.max_pool2d(net, [2, 2], scope='pool1')
    net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
    net = slim.max_pool2d(net, [2, 2], scope='pool2')
    net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
    net = slim.max_pool2d(net, [2, 2], scope='pool3')
    net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
    net = slim.max_pool2d(net, [2, 2], scope='pool4')
    net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
    net = slim.max_pool2d(net, [2, 2], scope='pool5')
    net = slim.fully_connected(net, 4096, scope='fc6')
    net = slim.dropout(net, 0.5, scope='dropout6')
    net = slim.fully_connected(net, 4096, scope='fc7')
    net = slim.dropout(net, 0.5, scope='dropout7')
    net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc8')
  return net
```

## Training Models

Training Tensorflow models requires a model, a loss function, the gradient
computation and a training routine that iteratively computes the gradients
of the model weights relative to the loss and updates the weights accordingly.
TF-Slim provides both common loss functions and a set of helper functions
that run the training and evaluation routines.

### Losses

The loss function defines a quantity that we want to minimize. For
classification problems, this is typically the cross entropy between the true
distribution and the predicted probability distribution across
classes. For regression problems, this is often the sum-of-squares differences
between the predicted and true values.

Certain models, such as multi-task
learning models, require the use of multiple loss functions simultaneously. In
other words, the loss function ultimately being minimized is the sum of various
other loss functions. For example, consider a model that predicts both
the type of scene in an image as well as the depth from the
camera of each pixel. This model's loss function would be the sum of the
classification loss and depth prediction loss.

TF-Slim provides an easy-to-use mechanism for defining and keeping track of
loss functions via the
[losses](https://www.tensorflow.org/code/tensorflow/contrib/losses/python/losses/loss_ops.py)
module. Consider the simple case where we want to train the VGG network:


```python
import tensorflow as tf
vgg = tf.contrib.slim.nets.vgg

# Load the images and labels.
images, labels = ...

# Create the model.
predictions = vgg.vgg16(images)

# Define the loss functions and get the total loss.
loss = slim.losses.softmax_cross_entropy(predictions, labels)
```

In this example, we start by creating the model (using TF-Slim's VGG
implementation), and add the standard classification loss. Now, lets turn
to the case where we have a multi-task model that produces multiple outputs:


```python
# Load the images and labels.
images, scene_labels, depth_labels = ...

# Create the model.
scene_predictions, depth_predictions = CreateMultiTaskModel(images)

# Define the loss functions and get the total loss.
classification_loss = slim.losses.softmax_cross_entropy(scene_predictions, scene_labels)
sum_of_squares_loss = slim.losses.sum_of_squares(depth_predictions, depth_labels)

# The following two lines have the same effect:
total_loss = classification_loss + sum_of_squares_loss
total_loss = slim.losses.get_total_loss(add_regularization_losses=False)
```

In this example, we have two losses which we add by calling
`slim.losses.softmax_cross_entropy` and `slim.losses.sum_of_squares`. We can
obtain the total loss by adding them together (`total_loss`) or by calling
`slim.losses.get_total_loss()`. How did this work?
When you create a loss function via TF-Slim, TF-Slim adds the loss to a
special TensorFlow collection of loss functions. This enables you to either
manage the total loss manually, or allow TF-Slim to manage them for you.

What if you want to let TF-Slim manage the losses for you but have a custom loss
function?
[loss_ops.py](https://www.tensorflow.org/code/tensorflow/contrib/losses/python/losses/loss_ops.py)
also has a function that adds this loss to TF-Slims collection. For example:

```python
# Load the images and labels.
images, scene_labels, depth_labels, pose_labels = ...

# Create the model.
scene_predictions, depth_predictions, pose_predictions = CreateMultiTaskModel(images)

# Define the loss functions and get the total loss.
classification_loss = slim.losses.softmax_cross_entropy(scene_predictions, scene_labels)
sum_of_squares_loss = slim.losses.sum_of_squares(depth_predictions, depth_labels)
pose_loss = MyCustomLossFunction(pose_predictions, pose_labels)
slim.losses.add_loss(pose_loss) # Letting TF-Slim know about the additional loss.

# The following two ways to compute the total loss are equivalent:
regularization_loss = tf.add_n(slim.losses.get_regularization_losses())
total_loss1 = classification_loss + sum_of_squares_loss + pose_loss + regularization_loss

# (Regularization Loss is included in the total loss by default).
total_loss2 = slim.losses.get_total_loss()
```
In this example, we can again either produce the total loss function manually
or let TF-Slim know about the additional loss and let TF-Slim handle the losses.

### Training Loop

TF-Slim provides a simple but powerful set of tools for training models
found in
[learning.py](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/learning.py).
These include a Train function that repeatedly measures the loss, computes
gradients and saves the model to disk, as well as several convenience functions
for manipulating gradients. For example, once we've
specified the model, the loss function and the optimization scheme, we can
call `slim.learning.create_train_op` and `slim.learning.train` to perform the
optimization:

```python
g = tf.Graph()

# Create the model and specify the losses...
...

total_loss = slim.losses.get_total_loss()
optimizer = tf.train.GradientDescentOptimizer(learning_rate)

# create_train_op ensures that each time we ask for the loss, the update_ops
# are run and the gradients being computed are applied too.
train_op = slim.learning.create_train_op(total_loss, optimizer)
logdir = ... # Where checkpoints are stored.

slim.learning.train(
    train_op,
    logdir,
    number_of_steps=1000,
    save_summaries_secs=300,
    save_interval_secs=600):
```

In this example, `slim.learning.train` is provided with the `train_op` which is
used to (a) compute the loss and (b) apply the gradient step. `logdir` specifies
the directory where the checkpoints and event files are stored. We can limit the
number of gradient steps taken to any number. In this case, we've asked for
`1000` steps to be taken. Finally, `save_summaries_secs=300` indicates that
we'll compute summaries every 5 minutes and `save_interval_secs=600` indicates
that we'll save a model checkpoint every 10 minutes.

### Working Example: Training the VGG16 Model

To illustrate this, lets
examine the following sample of training the VGG network:

```python
import tensorflow as tf

slim = tf.contrib.slim
vgg = tf.contrib.slim.nets.vgg

...

train_log_dir = ...
if not tf.gfile.Exists(train_log_dir):
  tf.gfile.MakeDirs(train_log_dir)

with tf.Graph().as_default():
  # Set up the data loading:
  images, labels = ...

  # Define the model:
  predictions = vgg.vgg16(images, is_training=True)

  # Specify the loss function:
  slim.losses.softmax_cross_entropy(predictions, labels)

  total_loss = slim.losses.get_total_loss()
  tf.summary.scalar('losses/total_loss', total_loss)

  # Specify the optimization scheme:
  optimizer = tf.train.GradientDescentOptimizer(learning_rate=.001)

  # create_train_op that ensures that when we evaluate it to get the loss,
  # the update_ops are done and the gradient updates are computed.
  train_tensor = slim.learning.create_train_op(total_loss, optimizer)

  # Actually runs training.
  slim.learning.train(train_tensor, train_log_dir)
```

## Fine-Tuning Existing Models

### Brief Recap on Restoring Variables from a Checkpoint

After a model has been trained, it can be restored using `tf.train.Saver()`
which restores `Variables` from a given checkpoint. For many cases,
`tf.train.Saver()` provides a simple mechanism to restore all or just a
few variables.

```python
# Create some variables.
v1 = tf.Variable(..., name="v1")
v2 = tf.Variable(..., name="v2")
...
# Add ops to restore all the variables.
restorer = tf.train.Saver()

# Add ops to restore some variables.
restorer = tf.train.Saver([v1, v2])

# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")
  # Do some work with the model
  ...
```

See [Restoring Variables](https://www.tensorflow.org/how_tos/variables/index.html#restoring-variables)
and
[Choosing which Variables to Save and Restore](https://www.tensorflow.org/how_tos/variables/index.html#choosing-which-variables-to-save-and-restore)
sections of the [Variables](https://www.tensorflow.org/how_tos/variables/index.html)
page for more details.

### Partially Restoring Models

It is often desirable to fine-tune a pre-trained model on an entirely new
dataset or even a new task. In these situations, one can use TF-Slim's
helper functions to select a subset of variables to restore:

```python
# Create some variables.
v1 = slim.variable(name="v1", ...)
v2 = slim.variable(name="nested/v2", ...)
...

# Get list of variables to restore (which contains only 'v2'). These are all
# equivalent methods:
variables_to_restore = slim.get_variables_by_name("v2")
# or
variables_to_restore = slim.get_variables_by_suffix("2")
# or
variables_to_restore = slim.get_variables(scope="nested")
# or
variables_to_restore = slim.get_variables_to_restore(include=["nested"])
# or
variables_to_restore = slim.get_variables_to_restore(exclude=["v1"])

# Create the saver which will be used to restore the variables.
restorer = tf.train.Saver(variables_to_restore)

with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")
  # Do some work with the model
  ...
```

### Restoring models with different variable names

When restoring variables from a checkpoint, the `Saver`
locates the variable names in a checkpoint file and maps them to variables in
the current graph. Above, we created a saver by passing to it a list of
variables. In this case, the names of the variables to locate in the checkpoint
file were implicitly obtained from each provided variable's `var.op.name`.

This works well when the variable names in the checkpoint file match those in
the graph. However, sometimes, we want to restore a model from a checkpoint
whose variables have different names those in the current graph. In this case,
we must provide the `Saver` a dictionary that maps from each checkpoint variable
name to each graph variable. Consider the following example where the checkpoint
variables names are obtained via a simple function:

```python
# Assuming than 'conv1/weights' should be restored from 'vgg16/conv1/weights'
def name_in_checkpoint(var):
  return 'vgg16/' + var.op.name

# Assuming than 'conv1/weights' and 'conv1/bias' should be restored from 'conv1/params1' and 'conv1/params2'
def name_in_checkpoint(var):
  if "weights" in var.op.name:
    return var.op.name.replace("weights", "params1")
  if "bias" in var.op.name:
    return var.op.name.replace("bias", "params2")

variables_to_restore = slim.get_model_variables()
variables_to_restore = {name_in_checkpoint(var):var for var in variables_to_restore}
restorer = tf.train.Saver(variables_to_restore)

with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")
```

### Fine-Tuning a Model on a different task

Consider the case where we have a pre-trained VGG16 model. The model was
trained on the ImageNet dataset, which has 1000 classes. However, we would
like to apply it to the Pascal VOC dataset which has only 20 classes. To
do so, we can initialize our new model using the values of the pre-trained
model excluding the final layer:

```python
# Load the Pascal VOC data
image, label = MyPascalVocDataLoader(...)
images, labels = tf.train.batch([image, label], batch_size=32)

# Create the model
predictions = vgg.vgg_16(images)

train_op = slim.learning.create_train_op(...)

# Specify where the Model, trained on ImageNet, was saved.
model_path = '/path/to/pre_trained_on_imagenet.checkpoint'

# Specify where the new model will live:
log_dir = '/path/to/my_pascal_model_dir/'

# Restore only the convolutional layers:
variables_to_restore = slim.get_variables_to_restore(exclude=['fc6', 'fc7', 'fc8'])
init_fn = assign_from_checkpoint_fn(model_path, variables_to_restore)

# Start training.
slim.learning.train(train_op, log_dir, init_fn=init_fn)
```

## Evaluating Models.

Once we've trained a model (or even while the model is busy training) we'd like
to see how well the model performs in practice. This is accomplished by picking
a set of evaluation metrics, which will grade the models performance, and the
evaluation code which actually loads the data, performs inference, compares the
results to the ground truth and records the evaluation scores. This step may be
performed once or repeated periodically.

### Metrics

We define a metric to be a performance measure that is not a loss function
(losses are directly optimized during training), but which we are still
interested in for the purpose of evaluating our model.
For example, we might want to minimize log loss, but our metrics of interest
might be F1 score (test accuracy), or Intersection Over Union score (which are not
differentiable, and therefore cannot be used as losses).

TF-Slim provides a set of metric operations that makes evaluating models
easy. Abstractly, computing the value of a metric can be divided into three
parts:

1. Initialization: initialize the variables used to compute the metrics.
2. Aggregation: perform operations (sums, etc) used to compute the metrics.
3. Finalization: (optionally) perform any final operation to compute metric
values. For example, computing means, mins, maxes, etc.

For example, to compute `mean_absolute_error`, two variables, a `count` and
`total` variable are *initialized* to zero. During *aggregation*, we observed
some set of predictions and labels, compute their absolute differences and add
the total to `total`. Each time we observe another value,
`count` is incremented. Finally, during *finalization*, `total` is divided
by `count` to obtain the mean.

The following example demonstrates the API for declaring metrics. Because
metrics are often evaluated on a test set which is different from the training
set (upon which the loss is computed), we'll assume we're using test data:

```python
images, labels = LoadTestData(...)
predictions = MyModel(images)

mae_value_op, mae_update_op = slim.metrics.streaming_mean_absolute_error(predictions, labels)
mre_value_op, mre_update_op = slim.metrics.streaming_mean_relative_error(predictions, labels, labels)
pl_value_op, pl_update_op = slim.metrics.percentage_less(mean_relative_errors, 0.3)
```

As the example illustrates, the creation of a metric returns two values:
a *value_op* and an *update_op*. The value_op is an idempotent operation that
returns the current value of the metric. The update_op is an operation that
performs the *aggregation* step mentioned above as well as returning the value
of the metric.

Keeping track of each `value_op` and `update_op` can be laborious. To deal with
this, TF-Slim provides two convenience functions:

```python

# Aggregates the value and update ops in two lists:
value_ops, update_ops = slim.metrics.aggregate_metrics(
    slim.metrics.streaming_mean_absolute_error(predictions, labels),
    slim.metrics.streaming_mean_squared_error(predictions, labels))

# Aggregates the value and update ops in two dictionaries:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
    "eval/mean_absolute_error": slim.metrics.streaming_mean_absolute_error(predictions, labels),
    "eval/mean_squared_error": slim.metrics.streaming_mean_squared_error(predictions, labels),
})

```

### Working example: Tracking Multiple Metrics

Putting it all together:

```python
import tensorflow as tf

slim = tf.contrib.slim
vgg = tf.contrib.slim.nets.vgg


# Load the data
images, labels = load_data(...)

# Define the network
predictions = vgg.vgg_16(images)

# Choose the metrics to compute:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
    "eval/mean_absolute_error": slim.metrics.streaming_mean_absolute_error(predictions, labels),
    "eval/mean_squared_error": slim.metrics.streaming_mean_squared_error(predictions, labels),
})

# Evaluate the model using 1000 batches of data:
num_batches = 1000

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  sess.run(tf.local_variables_initializer())

  for batch_id in range(num_batches):
    sess.run(names_to_updates.values())

  metric_values = sess.run(name_to_values.values())
  for metric, value in zip(names_to_values.keys(), metric_values):
    print('Metric %s has value: %f' % (metric, value))
```

Note that
[metric_ops.py](https://www.tensorflow.org/code/tensorflow/contrib/metrics/python/ops/metric_ops.py)
can be used in isolation without using either
[layers.py](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/layers.py)
or
[loss_ops.py](https://www.tensorflow.org/code/tensorflow/contrib/losses/python/losses/loss_ops.py)

### Evaluation Loop

TF-Slim provides an evaluation module
([evaluation.py](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/evaluation.py)),
which contains helper functions for writing model evaluation scripts using
metrics from
the [metric_ops.py](https://www.tensorflow.org/code/tensorflow/contrib/metrics/python/ops/metric_ops.py)
module. These include a function for periodically running evaluations,
evaluating
metrics over batches of data and printing and summarizing metric results. For
example:

```python
import tensorflow as tf

slim = tf.contrib.slim

# Load the data
images, labels = load_data(...)

# Define the network
predictions = MyModel(images)

# Choose the metrics to compute:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
    'accuracy': slim.metrics.accuracy(predictions, labels),
    'precision': slim.metrics.precision(predictions, labels),
    'recall': slim.metrics.recall(mean_relative_errors, 0.3),
})

# Create the summary ops such that they also print out to std output:
summary_ops = []
for metric_name, metric_value in names_to_values.iteritems():
  op = tf.summary.scalar(metric_name, metric_value)
  op = tf.Print(op, [metric_value], metric_name)
  summary_ops.append(op)

num_examples = 10000
batch_size = 32
num_batches = math.ceil(num_examples / float(batch_size))

# Setup the global step.
slim.get_or_create_global_step()

output_dir = ... # Where the summaries are stored.
eval_interval_secs = ... # How often to run the evaluation.
slim.evaluation.evaluation_loop(
    'local',
    checkpoint_dir,
    log_dir,
    num_evals=num_batches,
    eval_op=names_to_updates.values(),
    summary_op=tf.summary.merge(summary_ops),
    eval_interval_secs=eval_interval_secs)
```

## Authors
Sergio Guadarrama and Nathan Silberman
