Trainer API¶

Parameters¶

paddle.v2.parameters.create(layers)¶

Create parameter pool by topology.

Parameters:	layers –
Returns:

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¶

class paddle.v2.trainer.SGD(cost, parameters, update_equation)¶

Simple SGD Trainer. TODO(yuyang18): Complete comments

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.

train(reader, num_passes=1, event_handler=None, feeding=None)¶

Training method. Will train num_passes of input data.

Parameters:	reader – num_passes – The total train passes. event_handler ((BaseEvent) => None) – Event handler. A method will be invoked when event occurred. feeding (dict) – Feeding is a map of neural network input name and array index that reader returns.
Returns:

Event¶

All training events.

There are:

BeginIteration
EndIteration
BeginPass
EndPass

TODO(yuyang18): Complete it!

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 usages:

result = paddle.infer(prediction, parameters, input=SomeData,
                      batch_size=32)
print result

Parameters:	output_layer (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, ids]. value means return the prediction probabilities, ids means return the prediction labels. Default is value
Returns:	a numpy array
Return type:	numpy.ndarray