Type Optimizer
Namespace tensorflow.train
Parent PythonObjectContainer
Interfaces Trackable, IOptimizer
Base class for optimizers. This class defines the API to add Ops to train a model. You never use this
class directly, but instead instantiate one of its subclasses such as
`GradientDescentOptimizer`, `AdagradOptimizer`, or `MomentumOptimizer`. ### Usage
In the training program you will just have to run the returned Op.
### Processing gradients before applying them. Calling `minimize()` takes care of both computing the gradients and
applying them to the variables. If you want to process the gradients
before applying them you can instead use the optimizer in three steps: 1. Compute the gradients with `compute_gradients()`.
2. Process the gradients as you wish.
3. Apply the processed gradients with `apply_gradients()`. Example:
### Gating Gradients Both `minimize()` and `compute_gradients()` accept a `gate_gradients`
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 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.
Show Example
# Create an optimizer with the desired parameters.
opt = GradientDescentOptimizer(learning_rate=0.1)
# Add Ops to the graph to minimize a cost by updating a list of variables.
# "cost" is a Tensor, and the list of variables contains tf.Variable
# objects.
opt_op = opt.minimize(cost, var_list=)
Properties
Fields
Public properties
object GATE_GRAPH_dyn get; set;
object GATE_NONE_dyn get; set;
object GATE_OP_dyn get; set;
object PythonObject get;
Public fields
int GATE_NONE
return int
|
int GATE_OP
return int
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int GATE_GRAPH
return int
|