Training and Inference

Parameters

class paddle.v2.parameters.Parameters

Parameters is a dictionary contains Paddle’s parameter. The key of Parameters is the name of parameter. The value of Parameters is a plain numpy.ndarry .

Basically usage is

data = paddle.layers.data(...)
...
out = paddle.layers.fc(...)

parameters = paddle.parameters.create(out)

parameter_names = parameters.names()
fc_mat = parameters.get('fc')
print fc_mat
keys()

keys are the names of each parameter.

Returns:list of parameter name
Return type:list
names()

names of each parameter.

Returns:list of parameter name
Return type:list
has_key(key)

has_key return true if there are such parameter name == key

Parameters:key (basestring) – Parameter name
Returns:True if contains such key
get_shape(key)

get shape of the parameter.

Parameters:key (basestring) – parameter name
Returns:parameter’s shape
Return type:tuple
get(parameter_name)

Get parameter by parameter name.

Note:It will always copy the parameter from C++ side.
Parameters:parameter_name (basestring) – parameter name
Returns:The parameter matrix.
Return type:np.ndarray
set(parameter_name, value)

Set parameter by parameter name & matrix.

Parameters:
  • parameter_name (basestring) – parameter name
  • value (np.ndarray) – parameter matrix
Returns:

Nothing.
append_gradient_machine(gradient_machine)

append gradient machine to parameters. This method is used internally in Trainer.train.

Parameters:gradient_machine (api.GradientMachine) – Paddle C++ GradientMachine object.
Returns:
serialize(name, f)
Parameters:
  • name –
  • f (file) –
Returns:
deserialize(name, f)
Parameters:
  • name –
  • f (file) –
Returns:

Trainer

Module Trainer

class paddle.v2.trainer.SGD(cost, parameters, update_equation, extra_layers=None, is_local=True)

Simple SGD Trainer. SGD Trainer combines data reader, network topolopy and update_equation together to train/test a neural network.

Parameters:
  • update_equation (paddle.v2.optimizer.Optimizer) – The optimizer object.
  • cost (paddle.v2.config_base.Layer) – Target cost that neural network should be optimized.
  • parameters (paddle.v2.parameters.Parameters) – The parameters dictionary.
  • extra_layers (paddle.v2.config_base.Layer) – Some layers in the neural network graph are not in the path of cost layer.
train(reader, num_passes=1, event_handler=None, feeding=None)

Training method. Will train num_passes of input data.

Parameters:
  • reader (collections.Iterable) – A reader that reads and yeilds data items. Usually we use a batched reader to do mini-batch training.
  • num_passes – The total train passes.
  • event_handler ((BaseEvent) => None) – Event handler. A method will be invoked when event occurred.
  • feeding (dict|list) – Feeding is a map of neural network input name and array index that reader returns.
Returns:
test(reader, feeding=None)

Testing method. Will test input data.

Parameters:
  • reader (collections.Iterable) – A reader that reads and yeilds data items.
  • feeding (dict) – Feeding is a map of neural network input name and array index that reader returns.
Returns:

Event

Testing and training events.

There are:

  • TestResult
  • BeginIteration
  • EndIteration
  • BeginPass
  • EndPass
class paddle.v2.event.TestResult(evaluator, cost)

Result that trainer.test return.

class paddle.v2.event.BeginPass(pass_id)

Event On One Pass Training Start.

class paddle.v2.event.EndPass(pass_id, evaluator)

Event On One Pass Training Complete.

class paddle.v2.event.BeginIteration(pass_id, batch_id)

Event On One Batch Training Start.

class paddle.v2.event.EndIteration(pass_id, batch_id, cost, evaluator)

Event On One Batch Training Complete.

Inference

paddle.v2.infer(output_layer, parameters, input, feeding=None, field='value')

Infer a neural network by given neural network output and parameters. The user should pass either a batch of input data or reader method.

Example usage for sinlge output_layer:

result = paddle.infer(output_layer=prediction,
                      parameters=parameters,
                      input=SomeData)
print result

Example usage for multiple outout_layers and fields:

result = paddle.infer(output_layer=[prediction1, prediction2],
                      parameters=parameters,
                      input=SomeData,
                      field=[id, value]])
print result
Parameters:
  • output_layer (paddle.v2.config_base.Layer or a list of paddle.v2.config_base.Layer) – output of the neural network that would be inferred
  • parameters (paddle.v2.parameters.Parameters) – parameters of the neural network.
  • input (collections.Iterable) – input data batch. Should be a python iterable object, and each element is the data batch.
  • feeding – Reader dictionary. Default could generate from input value.
  • field (str) – The prediction field. It should in [value, id, prob]. value and prob mean return the prediction probabilities, id means return the prediction labels. Default is value. Note that prob only used when output_layer is beam_search or max_id.
Returns:

The prediction result. If there are multiple outout_layers and fields, the return order is outout_layer1.field1, outout_layer2.field1, ..., outout_layer1.field2, outout_layer2.field2 ...
Return type:

numpy.ndarray