)
# grads_and_vars is a list of tuples (gradient, variable). Do whatever you
# need to the 'gradient' part, for example cap them, etc.
capped_grads_and_vars = [(MyCapper(gv[0]), gv[1])) for gv in grads_and_vars]
# Ask the optimizer to apply the capped gradients.
opt.apply_gradients(capped_grads_and_vars)
```
- - -
#### tf.train.Optimizer.__init__(use_locking, name) {#Optimizer.__init__}
Create a new Optimizer.
This must be called by the constructors of subclasses.
##### Args:
* use_locking: Bool. If True apply use locks to prevent concurrent updates
to variables.
* name: A non-empty string. The name to use for accumulators created
for the optimizer.
##### Raises:
* ValueError: if name is malformed.
- - -
#### tf.train.Optimizer.minimize(loss, global_step=None, var_list=None, gate_gradients=1, name=None) {#Optimizer.minimize}
Add operations to minimize 'loss' by updating 'var_list'.
This method simply combines calls compute_gradients() and
apply_gradients(). If you want to process the gradient before applying them
call compute_gradients() and apply_gradients() explicitly instead of using
this function.
##### Args:
* loss: A Tensor containing the value to minimize.
* global_step: Optional Variable to increment by one after the
variables have been updated.
* var_list: Optional list of variables.Variable to update to minimize
'loss'. Defaults to the list of variables collected in the graph
under the key GraphKeys.TRAINABLE_VARIABLES.
* gate_gradients: How to gate the computation of gradients. Can be
GATE_NONE, GATE_OP, or GATE_GRAPH.
* name: Optional name for the returned operation.
##### Returns:
An Operation that updates the variables in 'var_list'. If 'global_step'
was not None, that operation also increments global_step.
##### Raises:
* ValueError: if some of the variables are not variables.Variable objects.
- - -
#### tf.train.Optimizer.compute_gradients(loss, var_list=None, gate_gradients=1) {#Optimizer.compute_gradients}
Compute gradients of "loss" for the variables in "var_list".
This is the first part of minimize(). It returns a list
of (gradient, variable) pairs where "gradient" is the gradient
for "variable". Note that "gradient" can be a Tensor, a
IndexedSlices, or None if there is no gradient for the
given variable.
##### Args:
* loss: A Tensor containing the value to minimize.
* var_list: Optional list of variables.Variable to update to minimize
"loss". Defaults to the list of variables collected in the graph
under the key GraphKey.TRAINABLE_VARIABLES.
* gate_gradients: How to gate the computation of gradients. Can be
GATE_NONE, GATE_OP, or GATE_GRAPH.
##### Returns:
A list of (gradient, variable) pairs.
##### Raises:
* TypeError: If var_list contains anything else than variables.Variable.
* ValueError: If some arguments are invalid.
- - -
#### tf.train.Optimizer.apply_gradients(grads_and_vars, global_step=None, name=None) {#Optimizer.apply_gradients}
Apply gradients to variables.
This is the second part of minimize(). It returns an Operation that
applies gradients.
##### Args:
* grads_and_vars: List of (gradient, variable) pairs as returned by
compute_gradients().
* global_step: Optional Variable to increment by one after the
variables have been updated.
* name: Optional name for the returned operation. Default to the
name passed to the Optimizer constructor.
##### Returns:
An Operation that applies the specified gradients. If 'global_step'
was not None, that operation also increments global_step.
##### Raises:
* TypeError: if grads_and_vars is malformed.
### Gating Gradients {#AUTOGENERATED-gating-gradients}
Both `minimize()` and `compute_gradients()` accept a `gate_gradient` argument
that controls the degree of parallelism during the application of the
gradients.
The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
GATE_NONE: Compute and apply gradients in parallel. This provides the
maximum parallelism in execution, at the cost of some non-reproducibility in
the results. For example the two gradients of MatMul depend on the input
values: With `GATE_NONE` one of the gradients could be applied to one of the
inputs _before_ the other gradient is computed resulting in non-reproducible
results.
GATE_OP: For each Op, make sure all gradients are computed before they
are used. This prevents race conditions for Ops that generate gradients for
multiple inputs where the gradients depend on the inputs.
GATE_GRAPH: Make sure all gradients for all variables are computed
before any one of them is used. This provides the least parallelism but can
be useful if you want to process all gradients before applying any of them.
### Slots {#AUTOGENERATED-slots}
Some optimizer subclasses, such as `MomentumOptimizer` and `AdagradOptimizer`
allocate and manage additional variables associated with the variables to
train. These are called Slots. Slots have names and you can ask the
optimizer for the names of the slots that it uses. Once you have a slot name
you can ask the optimizer for the variable it created to hold the slot value.
This can be useful if you want to log debug a training algorithm, report stats
about the slots, etc.
- - -
#### tf.train.Optimizer.get_slot_names() {#Optimizer.get_slot_names}
Return a list of the names of slots created by the Optimizer.
See get_slot().
##### Returns:
A list of strings.
- - -
#### tf.train.Optimizer.get_slot(var, name) {#Optimizer.get_slot}
Return a slot named "name" created for "var" by the Optimizer.
Some Optimizer subclasses use additional variables. For example
Momentum and Adagrad use variables to accumulate updates. This method
gives access to these Variables if for some reason you need them.
Use get_slot_names() to get the list of slot names created by the Optimizer.
##### Args:
* var: A variable passed to minimize() or apply_gradients().
* name: A string.
##### Returns:
The Variable for the slot if it was created, None otherwise.
- - -
### class tf.train.GradientDescentOptimizer {#GradientDescentOptimizer}
Optimizer that implements the gradient descent algorithm.
- - -
#### tf.train.GradientDescentOptimizer.__init__(learning_rate, use_locking=False, name='GradientDescent') {#GradientDescentOptimizer.__init__}
Construct a new gradient descent optimizer.
##### Args:
* learning_rate: A Tensor or a floating point value. The learning
rate to use.
* use_locking: If True use locks for update operation.s
* name: Optional name prefix for the operations created when applying
gradients. Defaults to "GradientDescent".
- - -
### class tf.train.AdagradOptimizer {#AdagradOptimizer}
Optimizer that implements the Adagrad algorithm.
- - -
#### tf.train.AdagradOptimizer.__init__(learning_rate, initial_accumulator_value=0.1, use_locking=False, name='Adagrad') {#AdagradOptimizer.__init__}
Construct a new Adagrad optimizer.
##### Args:
* learning_rate: A `Tensor` or a floating point value. The learning rate.
* initial_accumulator_value: A floating point value.
Starting value for the accumulators, must be positive.
* use_locking: If `True` use locks for update operations.
* name: Optional name prefix for the operations created when applying
gradients. Defaults to "Adagrad".
##### Raises:
* ValueError: If the initial_accumulator_value is invalid.
- - -
### class tf.train.MomentumOptimizer {#MomentumOptimizer}
Optimizer that implements the Momentum algorithm.
- - -
#### tf.train.MomentumOptimizer.__init__(learning_rate, momentum, use_locking=False, name='Momentum') {#MomentumOptimizer.__init__}
Construct a new Momentum optimizer.
##### Args:
* learning_rate: A `Tensor` or a floating point value. The learning rate.
* momentum: A `Tensor` or a floating point value. The momentum.
* use_locking: If `True` use locks for update operations.
* name: Optional name prefix for the operations created when applying
gradients. Defaults to "Momentum".
- - -
### class tf.train.AdamOptimizer {#AdamOptimizer}
Optimizer that implements the Adam algorithm.
- - -
#### tf.train.AdamOptimizer.__init__(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam') {#AdamOptimizer.__init__}
Construct a new Adam optimizer.
Implementation is based on: http://arxiv.org/pdf/1412.6980v7.pdf
Initialization:
```
m_0 <- 0 (Initialize initial 1st moment vector)
v_0 <- 0 (Initialize initial 2nd moment vector)
t <- 0 (Initialize timestep)
```
The update rule for `variable` with gradient `g` uses an optimization
described at the end of section2 of the paper:
```
t <- t + 1
lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g
variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
```
The default value of 1e-8 for epsilon might not be a good default in
general. For example, when training an Inception network on ImageNet a
current good choice is 1.0 or 0.1.
##### Args:
* learning_rate: A Tensor or a floating point value. The learning rate.
* beta1: A float value or a constant float tensor.
The exponential decay rate for the 1st moment estimates.
* beta2: A float value or a constant float tensor.
The exponential decay rate for the 2st moment estimates.
* epsilon: A small constant for numerical stability.
* use_locking: If True use locks for update operation.s
* name: Optional name for the operations created when applying gradients.
Defaults to "Adam".
- - -
### class tf.train.FtrlOptimizer {#FtrlOptimizer}
Optimizer that implements the FTRL algorithm.
- - -
#### tf.train.FtrlOptimizer.__init__(learning_rate, learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, use_locking=False, name='Ftrl') {#FtrlOptimizer.__init__}
Construct a new FTRL optimizer.
The Ftrl-proximal algorithm, abbreviated for Follow-the-regularized-leader,
is described in the paper [Ad Click Prediction: a View from the Trenches](
https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
It can give a good performance vs. sparsity tradeoff.
Ftrl-proximal uses its own global base learning rate and can behave like
Adagrad with `learning_rate_power=-0.5`, or like gradient descent with
`learning_rate_power=0.0`.
The effective learning rate is adjusted per parameter, relative to this
base learning rate as:
```
effective_learning_rate_i = (learning_rate /
pow(k + summed_squared_gradients_for_i, learning_rate_power));
```
where k is the small constant `initial_accumulator_value`.
Note that the real regularization coefficient of `|w|^2` for objective
function is `1 / lambda_2` if specifying `l2 = lambda_2` as argument when
using this function.
##### Args:
* learning_rate: A float value or a constant float `Tensor`.
* learning_rate_power: A float value, must be less or equal to zero.
* initial_accumulator_value: The starting value for accumulators.
Only positive values are allowed.
* l1_regularization_strength: A float value, must be greater than or
equal to zero.
* l2_regularization_strength: A float value, must be greater than or
equal to zero.
* use_locking: If `True` use locks for update operations.
* name: Optional name prefix for the operations created when applying
gradients. Defaults to "Ftrl".
##### Raises:
* ValueError: if one of the arguments is invalid.
- - -
### class tf.train.RMSPropOptimizer {#RMSPropOptimizer}
Optimizer that implements the RMSProp algorithm.
- - -
#### tf.train.RMSPropOptimizer.__init__(learning_rate, decay, momentum=0.0, epsilon=1e-10, use_locking=False, name='RMSProp') {#RMSPropOptimizer.__init__}
Construct a new RMSProp optimizer.
##### Args:
* learning_rate: A Tensor or a floating point value. The learning rate.
* decay: discounting factor for the history/coming gradient
* momentum: a scalar tensor.
* epsilon: small value to avoid zero denominator.
* use_locking: If True use locks for update operation.
* name: Optional name prefic for the operations created when applying
gradients. Defaults to "RMSProp".
## Gradient Computation. {#AUTOGENERATED-gradient-computation.}
TensorFlow provides functions to compute the derivatives for a given
TensorFlow computation graph, adding operations to the graph. The
optimizer classes automatically compute derivatives on your graph, but
creators of new Optimizers or expert users can call the lower-level
functions below.
- - -
### tf.gradients(ys, xs, grad_ys=None, name='gradients', colocate_gradients_with_ops=False, gate_gradients=False, aggregation_method=None) {#gradients}
Constructs symbolic partial derivatives of `ys` w.r.t. x in `xs`.
`ys` and `xs` are each a `Tensor` or a list of tensors. `grad_ys`
is a list of `Tensor`, holding the gradients received by the
`ys`. The list must be the same length as `ys`.
`gradients()` adds ops to the graph to output the partial
derivatives of `ys` with respect to `xs`. It returns a list of
`Tensor` of length `len(xs)` where each tensor is the `sum(dy/dx)`
for y in `ys`.
`grad_ys` is a list of tensors of the same length as `ys` that holds
the initial gradients for each y in `ys`. When `grad_ys` is None,
we fill in a tensor of '1's of the shape of y for each y in `ys`. A
user can provide their own initial 'grad_ys` to compute the
derivatives using a different initial gradient for each y (e.g., if
one wanted to weight the gradient differently for each value in
each y).
##### Args:
* ys: A `Tensor` or list of tensors to be differentiated.
* xs: A `Tensor` or list of tensors to be used for differentiation.
* grad_ys: Optional. A `Tensor` or list of tensors the same size as
`ys` and holding the gradients computed for each y in `ys`.
* name: Optional name to use for grouping all the gradient ops together.
defaults to 'gradients'.
* colocate_gradients_with_ops: If True, try colocating gradients with
the corresponding op.
* gate_gradients: If True, add a tuple around the gradients returned
for an operations. This avoids some race conditions.
* aggregation_method: Specifies the method used to combine gradient terms.
Accepted values are constants defined in the class `AggregationMethod`.
##### Returns:
A list of `sum(dy/dx)` for each x in `xs`.
##### Raises:
* LookupError: if one of the operations between `x` and `y` does not
have a registered gradient function.
* ValueError: if the arguments are invalid.
- - -
### class tf.AggregationMethod {#AggregationMethod}
A class listing aggregation methods used to combine gradients.
Computing partial derivatives can require aggregating gradient
contributions. This class lists the various methods that can
be used to combine gradients in the graph:
* `ADD_N`: All of the gradient terms are summed as part of one
operation using the "AddN" op. It has the property that all
gradients must be ready before any aggregation is performed.
* `DEFAULT`: The system-chosen default aggregation method.
- - -
### tf.stop_gradient(input, name=None) {#stop_gradient}
Stops gradient computation.
When executed in a graph, this op outputs its input tensor as-is.
When building ops to compute gradients, this op prevents the contribution of
its inputs to be taken into account. Normally, the gradient generator adds ops
to a graph to compute the derivatives of a specified 'loss' by recursively
finding out inputs that contributed to its computation. If you insert this op
in the graph it inputs are masked from the gradient generator. They are not
taken into account for computing gradients.
This is useful any time you want to compute a value with TensorFlow but need
to pretend that the value was a constant. Some examples include:
* The *EM* algorithm where the *M-step* should not involve backpropagation
through the output of the *E-step*.
* Contrastive divergence training of Boltzmann machines where, when
differentiating the energy function, the training must not backpropagate
through the graph that generated the samples from the model.
* Adversarial training, where no backprop should happen through the adversarial
example generation process.
##### Args:
* input: A `Tensor`.
* name: A name for the operation (optional).
##### Returns:
A `Tensor`. Has the same type as `input`.
## Gradient Clipping {#AUTOGENERATED-gradient-clipping}
TensorFlow provides several operations that you can use to add clipping
functions to your graph. You can use these functions to perform general data
clipping, but they're particularly useful for handling exploding or vanishing
gradients.
- - -
### tf.clip_by_value(t, clip_value_min, clip_value_max, name=None) {#clip_by_value}
Clips tensor values to a specified min and max.
Given a tensor `t`, this operation returns a tensor of the same type and
shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`.
Any values less than `clip_value_min` are set to `clip_value_min`. Any values
greater than `clip_value_max` are set to `clip_value_max`.
##### Args:
* t: A `Tensor`.
* clip_value_min: A 0-D (scalar) `Tensor`. The minimum value to clip by.
* clip_value_max: A 0-D (scalar) `Tensor`. The maximum value to clip by.
* name: A name for the operation (optional).
##### Returns:
A clipped `Tensor`.
- - -
### tf.clip_by_norm(t, clip_norm, name=None) {#clip_by_norm}
Clips tensor values to a maximum L2-norm.
Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
normalizes `t` so that its L2-norm is less than or equal to `clip_norm'.
Specifically, if the L2-norm is already less than or equal to `clip_norm`,
then `t` is not modified. If the L2-norm is greater than `clip_norm`, then
this operation returns a tensor of the same type and shape as `t` with its
values set to:
`t * clip_norm / l2norm(t)`
In this case, the L2-norm of the output tensor is `clip_norm`.
This operation is typically used to clip gradients before applying them with
an optimizer.
##### Args:
* t: A `Tensor`.
* clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
* name: A name for the operation (optional).
##### Returns:
A clipped `Tensor`.
- - -
### tf.clip_by_average_norm(t, clip_norm, name=None) {#clip_by_average_norm}
Clips tensor values to a maximum average L2-norm.
Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
normalizes `t` so that its average L2-norm is less than or equal to
`clip_norm'. Specifically, if the average L2-norm is already less than or
equal to `clip_norm`, then `t` is not modified. If the average L2-norm is
greater than `clip_norm`, then this operation returns a tensor of the same
type and shape as `t` with its values set to:
`t * clip_norm / l2norm_avg(t)`
In this case, the average L2-norm of the output tensor is `clip_norm`.
This operation is typically used to clip gradients before applying them with
an optimizer.
##### Args:
* t: A `Tensor`.
* clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
* name: A name for the operation (optional).
##### Returns:
A clipped `Tensor`.
- - -
### tf.clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None) {#clip_by_global_norm}
Clips values of multiple tensors by the ratio of the sum of their norms.
Given a tuple or list of tensors `t_list`, and a clipping ratio `clip_norm`,
this operation returns a list of clipped tensors `list_clipped`
and the global norm (`global_norm`) of all tensors in `t_list`. Optionally,
if you've already computed the global norm for `t_list`, you can specify
the global norm with `use_norm`.
To perform the clipping, the values t_list[i] are set to:
`t_list[i] * clip_norm / max(global_norm, clip_norm)`
where:
`global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))`
If `clip_norm > global_norm` then the entries in `t_list` remain as they are,
otherwise they're all shrunk by the global ratio.
Any of the entries of `t_list` that are of type None are ignored.
This is the correct way to perform gradient clipping (for example, see
R. Pascanu, T. Mikolov, and Y. Bengio, "On the difficulty of training
Recurrent Neural Networks". http://arxiv.org/abs/1211.5063)
However, it is slower than `clip_by_norm()` because all the parameters must be
ready before the clipping operation can be performed.
##### Args:
* t_list: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
* clip_norm: A 0-D (scalar) `Tensor` > 0. The clipping ratio.
* use_norm: A 0-D (scalar) `Tensor` of type `float` (optional). The global
norm to use. If not provided, `global_norm()` is used to compute the norm.
* name: A name for the operation (optional).
##### Returns:
* list_clipped: A list of `Tensors` of the same type as `list_t`.
* global_norm: A 0-D (scalar) `Tensor` representing the global norm.
##### Raises:
* TypeError: If `t_list` is not a sequence.
- - -
### tf.global_norm(t_list, name=None) {#global_norm}
Computes the global norm of multiple tensors.
Given a tuple or list of tensors `t_list`, this operation returns the
global norm of the elements in all tensors in `t_list`. The global norm is
computed as:
`global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))`
Any entries in `t_list` that are of type None are ignored.
##### Args:
* t_list: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
* name: A name for the operation (optional).
##### Returns:
A 0-D (scalar) `Tensor` of type `float`.
##### Raises:
* TypeError: If `t_list` is not a sequence.
## Decaying the learning rate. {#AUTOGENERATED-decaying-the-learning-rate.}
- - -
### tf.train.exponential_decay(learning_rate, global_step, decay_steps, decay_rate, staircase=False, name=None) {#exponential_decay}
Applies exponential decay to the learning rate.
When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies an exponential decay function
to a provided initial learning rate. It requires a `global_step` value to
compute the decayed learning rate. You can just pass a TensorFlow variable
that you increment at each training step.
The function returns the decayed learning rate. It is computed as:
```python
decayed_learning_rate = learning_rate *
decay_rate ^ (global_step / decay_steps)
```
If the argument `staircase` is `True`, then `global_step /decay_steps` is an
integer division and the decayed learning rate follows a staircase function.
Example: decay every 100000 steps with a base of 0.96:
```python
...
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
learning_rate = tf.exponential_decay(starter_learning_rate, global_step,
100000, 0.96, staircase=True)
optimizer = tf.GradientDescent(learning_rate)
# Passing global_step to minimize() will increment it at each step.
optimizer.minimize(...my loss..., global_step=global_step)
```
##### Args:
* learning_rate: A scalar `float32` or `float64` `Tensor` or a
Python number. The initial learning rate.
* global_step: A scalar `int32` or `int64` `Tensor` or a Python number.
Global step to use for the decay computation. Must not be negative.
* decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
Must be positive. See the decay computation above.
* decay_rate: A scalar `float32` or `float64` `Tensor` or a
Python number. The decay rate.
* staircase: Boolean. It `True` decay the learning rate at discrete intervals.
* name: string. Optional name of the operation. Defaults to 'ExponentialDecay'
##### Returns:
A scalar `Tensor` of the same type as `learning_rate`. The decayed
learning rate.
## Moving Averages. {#AUTOGENERATED-moving-averages.}
Some training algorithms, such as GradientDescent and Momentum often benefit
from maintaining a moving average of variables during optimization. Using the
moving averages for evaluations often improve results significantly.
- - -
### class tf.train.ExponentialMovingAverage {#ExponentialMovingAverage}
Maintains moving averages of variables by employing and exponential decay.
When training a model, it is often beneficial to maintain moving averages of
the trained parameters. Evaluations that use averaged parameters sometimes
produce significantly better results than the final trained values.
The `apply()` method adds shadow copies of trained variables and add ops that
maintain a moving average of the trained variables in their shadow copies.
It is used when building the training model. The ops that maintain moving
averages are typically run after each training step.
The `average()` and `average_name()` methods give access to the shadow
variables and their names. They are useful when building an evaluation
model, or when restoring a model from a checkpoint file. They help use the
moving averages in place of the last trained values for evaluations.
The moving averages are computed using exponential decay. You specify the
decay value when creating the `ExponentialMovingAverage` object. The shadow
variables are initialized with the same initial values as the trained
variables. When you run the ops to maintain the moving averages, each
shadow variable is updated with the formula:
`shadow_variable -= (1 - decay) * (shadow_variable - variable)`
This is mathematically equivalent to the classic formula below, but the use
of an `assign_sub` op (the `"-="` in the formula) allows concurrent lockless
updates to the variables:
`shadow_variable = decay * shadow_variable + (1 - decay) * variable`
Reasonable values for `decay` are close to 1.0, typically in the
multiple-nines range: 0.999, 0.9999, etc.
Example usage when creating a training model:
```python
# Create variables.
var0 = tf.Variable(...)
var1 = tf.Variable(...)
# ... use the variables to build a training model...
...
# Create an op that applies the optimizer. This is what we usually
# would use as a training op.
opt_op = opt.minimize(my_loss, [var0, var1])
# Create an ExponentialMovingAverage object
ema = tf.train.ExponentialMovingAverage(decay=0.9999)
# Create the shadow variables, and add ops to maintain moving averages
# of var0 and var1.
maintain_averages_op = ema.apply([var0, var1])
# Create an op that will update the moving averages after each training
# step. This is what we will use in place of the usuall trainig op.
with tf.control_dependencies([opt_op]):
training_op = tf.group(maintain_averages_op)
...train the model by running training_op...
```
There are two ways to use the moving averages for evaluations:
* Build a model that uses the shadow variables instead of the variables.
For this, use the `average()` method which returns the shadow variable
for a given variable.
* Build a model normally but load the checkpoint files to evaluate by using
the shadow variable names. For this use the `average_name()` method. See
the [Saver class](train.md#Saver) for more information on restoring saved
variables.
Example of restoring the shadow variable values:
```python
# Create a Saver that loads variables from their saved shadow values.
shadow_var0_name = ema.average_name(var0)
shadow_var1_name = ema.average_name(var1)
saver = tf.train.Saver({shadow_var0_name: var0, shadow_var1_name: var1})
saver.restore(...checkpoint filename...)
# var0 and var1 now hold the moving average values
```
- - -
#### tf.train.ExponentialMovingAverage.__init__(decay, num_updates=None, name='ExponentialMovingAverage') {#ExponentialMovingAverage.__init__}
Creates a new ExponentialMovingAverage object.
The `Apply()` method has to be called to create shadow variables and add
ops to maintain moving averages.
The optional `num_updates` parameter allows one to tweak the decay rate
dynamically. . It is typical to pass the count of training steps, usually
kept in a variable that is incremented at each step, in which case the
decay rate is lower at the start of training. This makes moving averages
move faster. If passed, the actual decay rate used is:
`min(decay, (1 + num_updates) / (10 + num_updates))`
##### Args:
* decay: Float. The decay to use.
* num_updates: Optional count of number of updates applied to variables.
* name: String. Optional prefix name to use for the name of ops added in
`Apply()`.
- - -
#### tf.train.ExponentialMovingAverage.apply(var_list=None) {#ExponentialMovingAverage.apply}
Maintains moving averages of variables.
`var_list` must be a list of `Variable` or `Tensor` objects. This method
creates shadow variables for all elements of `var_list`. Shadow variables
for `Variable` objects are initialized to the variable's initial value.
For `Tensor` objects, the shadow variables are initialized to 0.
shadow variables are created with `trainable=False` and added to the
`GraphKeys.ALL_VARIABLES` collection. They will be returned by calls to
`tf.all_variables()`.
Returns an op that updates all shadow variables as described above.
Note that `apply()` can be called multiple times with different lists of
variables.
##### Args:
* var_list: A list of Variable or Tensor objects. The variables
and Tensors must be of types float32 or float64.
##### Returns:
An Operation that updates the moving averages.
##### Raises:
* TypeError: If the arguments are not all float32 or float64.
* ValueError: If the moving average of one of the variables is already
being computed.
- - -
#### tf.train.ExponentialMovingAverage.average_name(var) {#ExponentialMovingAverage.average_name}
Returns the name of the `Variable` holding the average for `var`.
The typical scenario for `ExponentialMovingAverage` is to compute moving
averages of variables during training, and restore the variables from the
computed moving averages during evaluations.
To restore variables, you have to know the name of the shadow variables.
That name and the original variable can then be passed to a `Saver()` object
to restore the variable from the moving average value with:
`saver = tf.train.Saver({ema.average_name(var): var})`
`average_name()` can be called whether or not `apply()` has been called.
##### Args:
* var: A `Variable` object.
##### Returns:
A string: the name of the variable that will be used or was used
by the `ExponentialMovingAverage class` to hold the moving average of
`var`.
- - -
#### tf.train.ExponentialMovingAverage.average(var) {#ExponentialMovingAverage.average}
Returns the `Variable` holding the average of `var`.
##### Args:
* var: A `Variable` object.
##### Returns:
A `Variable` object or `None` if the moving average of `var`
is not maintained..
## Coordinator and QueueRunner. {#AUTOGENERATED-coordinator-and-queuerunner.}
See [Threading and Queues](../../how_tos/threading_and_queues/index.md)
for how to use threads and queues. For documentation on the Queue API,
see [Queues](../../api_docs/python/io_ops.md#queues).
- - -
### class tf.train.Coordinator {#Coordinator}
A coordinator for threads.
This class implements a simple mechanism to coordinate the termination of a
set of threads.
#### Usage:
```python
# Create a coordinator.
coord = Coordinator()
# Start a number of threads, passing the coordinator to each of them.
...start thread 1...(coord, ...)
...start thread N...(coord, ...)
# Wait for all the threads to terminate.
coord.join(threads)
```
Any of the threads can call `coord.request_stop()` to ask for all the threads
to stop. To cooperate with the requests, each thread must check for
`coord.should_stop()` on a regular basis. `coord.should_stop()` returns
`True` as soon as `coord.request_stop()` has been called.
A typical thread running with a Coordinator will do something like:
```python
while not coord.should_stop():
...do some work...
```
#### Exception handling:
A thread can report an exception to the Coordinator as part of the
`should_stop()` call. The exception will be re-raised from the
`coord.join()` call.
Thread code:
```python
try:
while not coord.should_stop():
...do some work...
except Exception, e:
coord.request_stop(e)
```
Main code:
```python
try:
...
coord = Coordinator()
# Start a number of threads, passing the coordinator to each of them.
...start thread 1...(coord, ...)
...start thread N...(coord, ...)
# Wait for all the threads to terminate.
coord.join(threads)
except Exception, e:
...exception that was passed to coord.request_stop()
```
#### Grace period for stopping:
After a thread has called `coord.request_stop()` the other threads have a
fixed time to stop, this is called the 'stop grace period' and defaults to 2
minutes. If any of the threads is still alive after the grace period expires
`coord.join()` raises a RuntimeException reporting the laggards.
```
try:
...
coord = Coordinator()
# Start a number of threads, passing the coordinator to each of them.
...start thread 1...(coord, ...)
...start thread N...(coord, ...)
# Wait for all the threads to terminate, give them 10s grace period
coord.join(threads, stop_grace_period_secs=10)
except RuntimeException:
...one of the threads took more than 10s to stop after request_stop()
...was called.
except Exception:
...exception that was passed to coord.request_stop()
```
- - -
#### tf.train.Coordinator.__init__() {#Coordinator.__init__}
Create a new Coordinator.
- - -
#### tf.train.Coordinator.join(threads, stop_grace_period_secs=120) {#Coordinator.join}
Wait for threads to terminate.
Blocks until all 'threads' have terminated or request_stop() is called.
After the threads stop, if an 'exc_info' was passed to request_stop, that
exception is re-reaised.
Grace period handling: When request_stop() is called, threads are given
'stop_grace_period_secs' seconds to terminate. If any of them is still
alive after that period expires, a RuntimeError is raised. Note that if
an 'exc_info' was passed to request_stop() then it is raised instead of
that RuntimeError.
##### Args:
* threads: List threading.Threads. The started threads to join.
* stop_grace_period_secs: Number of seconds given to threads to stop after
request_stop() has been called.
##### Raises:
* RuntimeError: If any thread is still alive after request_stop()
is called and the grace period expires.
- - -
#### tf.train.Coordinator.request_stop(ex=None) {#Coordinator.request_stop}
Request that the threads stop.
After this is called, calls to should_stop() will return True.
##### Args:
* ex: Optional Exception, or Python 'exc_info' tuple as returned by
sys.exc_info(). If this is the first call to request_stop() the
corresponding exception is recorded and re-raised from join().
- - -
#### tf.train.Coordinator.should_stop() {#Coordinator.should_stop}
Check if stop was requested.
##### Returns:
True if a stop was requested.
- - -
#### tf.train.Coordinator.wait_for_stop(timeout=None) {#Coordinator.wait_for_stop}
Wait till the Coordinator is told to stop.
##### Args:
* timeout: float. Sleep for up to that many seconds waiting for
should_stop() to become True.
##### Returns:
True if the Coordinator is told stop, False if the timeout expired.
- - -
### class tf.train.QueueRunner {#QueueRunner}
Holds a list of enqueue operations for a queue, each to be run in a thread.
Queues are a convenient TensorFlow mechanism to compute tensors
asynchronously using multiple threads. For example in the canonical 'Input
Reader' setup one set of threads generates filenames in a queue; a second set
of threads read records from the files, processes them, and enqueues tensors
on a second queue; a third set of threads dequeues these input records to
construct batches and runs them through training operations.
There are several delicate issues when running multiple threads that way:
closing the queues in sequence as the input is exhausted, correctly catching
and reporting exceptions, etc.
The `QueueRunner`, combined with the `Coordinator`, helps handle these issues.
- - -
#### tf.train.QueueRunner.__init__(queue, enqueue_ops) {#QueueRunner.__init__}
Create a QueueRunner.
On construction the `QueueRunner` adds an op to close the queue. That op
will be run if the enqueue ops raise exceptions.
When you later call the `create_threads()` method, the `QueueRunner` will
create one thread for each op in `enqueue_ops`. Each thread will run its
enqueue op in parallel with the other threads. The enqueue ops do not have
to all be the same op, but it is expected that they all enqueue tensors in
`queue`.
##### Args:
* queue: A `Queue`.
* enqueue_ops: List of enqueue ops to run in threads later.
- - -
#### tf.train.QueueRunner.create_threads(sess, coord=None, daemon=False, start=False) {#QueueRunner.create_threads}
Create threads to run the enqueue ops.
This method requires a session in which the graph was launched. It creates
a list of threads, optionally starting them. There is one thread for each
op passed in `enqueue_ops`.
The `coord` argument is an optional coordinator, that the threads will use
to terminate together and report exceptions. If a coordinator is given,
this method starts an additional thread to close the queue when the
coordinator requests a stop.
This method may be called again as long as all threads from a previous call
have stopped.
##### Args:
* sess: A `Session`.
* coord: Optional `Coordinator` object for reporting errors and checking
stop conditions.
* daemon: Boolean. If `True` make the threads daemon threads.
* start: Boolean. If `True` starts the threads. If `False` the
caller must call the `start()` method of the returned threads.
##### Returns:
A list of threads.
##### Raises:
* RuntimeError: If threads from a previous call to `create_threads()` are
still running.
- - -
#### tf.train.QueueRunner.exceptions_raised {#QueueRunner.exceptions_raised}
Exceptions raised but not handled by the `QueueRunner` threads.
Exceptions raised in queue runner threads are handled in one of two ways
depending on whether or not a `Coordinator` was passed to
`create_threads()`:
* With a `Coordinator`, exceptions are reported to the coordinator and
forgotten by the `QueueRunner`.
* Without a `Coordinator`, exceptions are captured by the `QueueRunner` and
made available in this `exceptions_raised` property.
##### Returns:
A list of Python `Exception` objects. The list is empty if no exception
was captured. (No exceptions are captured when using a Coordinator.)
- - -
### tf.train.add_queue_runner(qr, collection='queue_runners') {#add_queue_runner}
Adds a `QueueRunner` to a collection in the graph.
When building a complex model that uses many queues it is often difficult to
gather all the queue runners that need to be run. This convenience function
allows you to add a queue runner to a well known collection in the graph.
The companion method `start_queue_runners()` can be used to start threads for
all the collected queue runners.
##### Args:
* qr: A `QueueRunner`.
* collection: A `GraphKey` specifying the graph collection to add
the queue runner to. Defaults to `GraphKeys.QUEUE_RUNNERS`.
- - -
### tf.train.start_queue_runners(sess=None, coord=None, daemon=True, start=True, collection='queue_runners') {#start_queue_runners}
Starts all queue runners collected in the graph.
This is a companion method to `add_queue_runner()`. It just starts
threads for all queue runners collected in the graph. It returns
the list of all threads.
##### Args:
* sess: `Session` used to run the queue ops. Defaults to the
default session.
* coord: Optional `Coordinator` for coordinating the started threads.
* daemon: Whether the threads should be marked as `daemons`, meaning
they don't block program exit.
* start: Set to `False` to only create the threads, not start them.
* collection: A `GraphKey` specifying the graph collection to
get the queue runners from. Defaults to `GraphKeys.QUEUE_RUNNERS`.
##### Returns:
A list of threads.
## Summary Operations. {#AUTOGENERATED-summary-operations.}
The following ops output
[`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
protocol buffers as serialized string tensors.
You can fetch the output of a summary op in a session, and pass it to a
[SummaryWriter](train.md#SummaryWriter) to append it to an event file. You can
then use TensorBoard to visualize the contents of the event files. See
[TensorBoard and Summaries](../../how_tos/summaries_and_tensorboard/index.md)
for more details.
- - -
### tf.scalar_summary(tags, values, collections=None, name=None) {#scalar_summary}
Outputs a `Summary` protocol buffer with scalar values.
The input `tags` and `values` must have the same shape. The generated
summary has a summary value for each tag-value pair in `tags` and `values`.
##### Args:
* tags: A 1-D `string` `Tensor`. Tags for the summaries.
* values: A 1-D `float32` or `float64` Tensor. Values for the summaries.
* collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
* name: A name for the operation (optional).
##### Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
- - -
### tf.image_summary(tag, tensor, max_images=None, collections=None, name=None) {#image_summary}
Outputs a `Summary` protocol buffer with images.
The summary has up to `max_images` summary values containing images. The
images are built from `tensor` which must be 4-D with shape `[batch_size,
height, width, channels]` and where `channels` can be:
* 1: `tensor` is interpreted as Grayscale.
* 3: `tensor` is interpreted as RGB.
* 4: `tensor` is interpreted as RGBA.
The images have the same number of channels as the input tensor. Their values
are normalized, one image at a time, to fit in the range `[0, 255]`. The
op uses two different normalization algorithms:
* If the input values are all positive, they are rescaled so the largest one
is 255.
* If any input value is negative, the values are shifted so input value 0.0
is at 127. They are then rescaled so that either the smallest value is 0,
or the largest one is 255.
The `tag` argument is a scalar `Tensor` of type `string`. It is used to
build the `tag` of the summary values:
* If `max_images` is 1, the summary value tag is '*tag*/image'.
* If `max_images` is greater than 1, the summary value tags are
generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
##### Args:
* tag: A scalar `Tensor` of type `string`. Used to build the `tag`
of the summary values.
* tensor: A 4-D `float32` `Tensor` of shape `[batch_size, height, width,
channels]` where `channels` is 1, 3, or 4.
* max_images: Max number of batch elements to generate images for.
* collections: Optional list of ops.GraphKeys. The collections to add the
summary to. Defaults to [ops.GraphKeys.SUMMARIES]
* name: A name for the operation (optional).
##### Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
- - -
### tf.histogram_summary(tag, values, collections=None, name=None) {#histogram_summary}
Outputs a `Summary` protocol buffer with a histogram.
The generated
[`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`.
This op reports an `OutOfRange` error if any value is not finite.
##### Args:
* tag: A `string` `Tensor`. 0-D. Tag to use for the summary value.
* values: A `float32` `Tensor`. Any shape. Values to use to build the
histogram.
* collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
* name: A name for the operation (optional).
##### Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
- - -
### tf.nn.zero_fraction(value, name=None) {#zero_fraction}
Returns the fraction of zeros in `value`.
If `value` is empty, the result is `nan`.
This is useful in summaries to measure and report sparsity. For example,
z = tf.Relu(...)
summ = tf.scalar_summary('sparsity', tf.zero_fraction(z))
##### Args:
* value: A tensor of numeric type.
* name: A name for the operation (optional).
##### Returns:
The fraction of zeros in `value`, with type `float32`.
- - -
### tf.merge_summary(inputs, collections=None, name=None) {#merge_summary}
Merges summaries.
This op creates a
[`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
protocol buffer that contains the union of all the values in the input
summaries.
When the Op is run, it reports an `InvalidArgument` error if multiple values
in the summaries to merge use the same tag.
##### Args:
* inputs: A list of `string` `Tensor` objects containing serialized `Summary`
protocol buffers.
* collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
* name: A name for the operation (optional).
##### Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer resulting from the merging.
- - -
### tf.merge_all_summaries(key='summaries') {#merge_all_summaries}
Merges all summaries collected in the default graph.
##### Args:
* key: `GraphKey` used to collect the summaries. Defaults to
`GraphKeys.SUMMARIES`.
##### Returns:
If no summaries were collected, returns None. Otherwise returns a scalar
`Tensor` of type`string` containing the serialized `Summary` protocol
buffer resulting from the merging.
## Adding Summaries to Event Files. {#AUTOGENERATED-adding-summaries-to-event-files.}
See [Summaries and
TensorBoard](../../how_tos/summaries_and_tensorboard/index.md) for an
overview of summaries, event files, and visualization in TensorBoard.
- - -
### class tf.train.SummaryWriter {#SummaryWriter}
Writes `Summary` protocol buffers to event files.
The `SummaryWriter` class provides a mechanism to create an event file in a
given directory and add summaries and events to it. The class updates the
file contents asynchronously. This allows a training program to call methods
to add data to the file directly from the training loop, without slowing down
training.
- - -
#### tf.train.SummaryWriter.__init__(logdir, graph_def=None, max_queue=10, flush_secs=120) {#SummaryWriter.__init__}
Creates a `SummaryWriter` and an event file.
On construction the summary writer creates a new event file in `logdir`.
This event file will contain `Event` protocol buffers constructed when you
call one of the following functions: `add_summary()`, `add_event()`, or
`add_graph()`.
If you pass a `graph_def` protocol buffer to the constructor it is added to
the event file. (This is equivalent to calling `add_graph()` later).
TensorBoard will pick the graph from the file and display it graphically so
you can interactively explore the graph you built. You will usually pass
the graph from the session in which you launched it:
```python
...create a graph...
# Launch the graph in a session.
sess = tf.Session()
# Create a summary writer, add the 'graph_def' to the event file.
writer = tf.train.SummaryWriter(, sess.graph_def)
```
The other arguments to the constructor control the asynchronous writes to
the event file:
* `flush_secs`: How often, in seconds, to flush the added summaries
and events to disk.
* `max_queue`: Maximum number of summaries or events pending to be
written to disk before one of the 'add' calls block.
##### Args:
* logdir: A string. Directory where event file will be written.
* graph_def: A `GraphDef` protocol buffer.
* max_queue: Integer. Size of the queue for pending events and summaries.
* flush_secs: Number. How often, in seconds, to flush the
pending events and summaries to disk.
- - -
#### tf.train.SummaryWriter.add_summary(summary, global_step=None) {#SummaryWriter.add_summary}
Adds a `Summary` protocol buffer to the event file.
This method wraps the provided summary in an `Event` procotol buffer
and adds it to the event file.
You can pass the output of any summary op, as-is, to this function. You
can also pass a `Summary` procotol buffer that you manufacture with your
own data. This is commonly done to report evaluation results in event
files.
##### Args:
* summary: A `Summary` protocol buffer, optionally serialized as a string.
* global_step: Number. Optional global step value to record with the
summary.
- - -
#### tf.train.SummaryWriter.add_event(event) {#SummaryWriter.add_event}
Adds an event to the event file.
##### Args:
* event: An `Event` protocol buffer.
- - -
#### tf.train.SummaryWriter.add_graph(graph_def, global_step=None) {#SummaryWriter.add_graph}
Adds a `GraphDef` protocol buffer to the event file.
The graph described by the protocol buffer will be displayed by
TensorBoard. Most users pass a graph in the constructor instead.
##### Args:
* graph_def: A `GraphDef` protocol buffer.
* global_step: Number. Optional global step counter to record with the
graph.
- - -
#### tf.train.SummaryWriter.flush() {#SummaryWriter.flush}
Flushes the event file to disk.
Call this method to make sure that all pending events have been written to
disk.
- - -
#### tf.train.SummaryWriter.close() {#SummaryWriter.close}
Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore.
- - -
### tf.train.summary_iterator(path) {#summary_iterator}
An iterator for reading `Event` protocol buffers from an event file.
You can use this function to read events written to an event file. It returns
a Python iterator that yields `Event` protocol buffers.
Example: Print the contents of an events file.
```python
for e in tf.summary_iterator(path to events file):
print e
```
Example: Print selected summary values.
```python
# This example supposes that the events file contains summaries with a
# summary value tag 'loss'. These could have been added by calling
# `add_summary()`, passing the output of a scalar summary op created with
# with: `tf.scalar_summary(['loss'], loss_tensor)`.
for e in tf.summary_iterator(path to events file):
for v in e.summary.value:
if v.tag == 'loss':
print v.simple_value
```
See the protocol buffer definitions of
[Event](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/util/event.proto)
and
[Summary](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
for more information about their attributes.
##### Args:
* path: The path to an event file created by a `SummaryWriter`.
##### Yields:
`Event` protocol buffers.
## Training utilities. {#AUTOGENERATED-training-utilities.}
- - -
### tf.train.global_step(sess, global_step_tensor) {#global_step}
Small helper to get the global step.
```python
# Creates a variable to hold the global_step.
global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
# Creates a session.
sess = tf.Session()
# Initializes the variable.
sess.run(global_step_tensor.initializer)
print 'global_step:', tf.train.global_step(sess, global_step_tensor)
global_step: 10
```
##### Args:
* sess: A brain `Session` object.
* global_step_tensor: `Tensor` or the `name` of the operation that contains
the global step.
##### Returns:
The global step value.
- - -
### tf.train.write_graph(graph_def, logdir, name, as_text=True) {#write_graph}
Writes a graph proto on disk.
The graph is written as a binary proto unless as_text is `True`.
```python
v = tf.Variable(0, name='my_variable')
sess = tf.Session()
tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
```
##### Args:
* graph_def: A `GraphDef` protocol buffer.
* logdir: Directory where to write the graph.
* name: Filename for the graph.
* as_text: If `True`, writes the graph as an ASCII proto.