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comparison gradient_learner.py @ 26:672fe4b23032

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Fixed dataset errors so that _test_dataset.py works again.
author bengioy@grenat.iro.umontreal.ca
date Fri, 11 Apr 2008 11:14:54 -0400
parents 526e192b0699
children 46c5c90019c2
comparison
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23:526e192b0699 26:672fe4b23032
24 The learned function can map a subset of inputs to a subset of outputs (as long as the inputs subset 24 The learned function can map a subset of inputs to a subset of outputs (as long as the inputs subset
25 includes all the inputs required in the Theano expression for the selected outputs). 25 includes all the inputs required in the Theano expression for the selected outputs).
26 It is assumed that all the inputs are provided in the training set (as dataset fields 26 It is assumed that all the inputs are provided in the training set (as dataset fields
27 with the corresponding name), but not necessarily when using the learned function. 27 with the corresponding name), but not necessarily when using the learned function.
28 """ 28 """
29 def __init__(self, inputs, parameters, outputs, example_wise_cost, regularization_term, 29 def __init__(self, inputs, parameters, outputs, example_wise_cost, regularization_term=astensor(0.0),
30 regularization_coefficient = astensor(1.0)): 30 regularization_coefficient = astensor(1.0)):
31 self.inputs = inputs 31 self.inputs = inputs
32 self.outputs = outputs 32 self.outputs = outputs
33 self.parameters = parameters 33 self.parameters = parameters
34 self.example_wise_cost = example_wise_cost 34 self.example_wise_cost = example_wise_cost
46 : Function(inputs, outputs)} 46 : Function(inputs, outputs)}
47 47
48 def use(self,input_dataset,output_fields=None,copy_inputs=True): 48 def use(self,input_dataset,output_fields=None,copy_inputs=True):
49 # obtain the function that maps the desired inputs to desired outputs 49 # obtain the function that maps the desired inputs to desired outputs
50 input_fields = input_dataset.fieldNames() 50 input_fields = input_dataset.fieldNames()
51 # map names of input fields to Theano tensors in self.inputs
52 input_variables = ???
51 if output_fields is None: output_fields = [output.name for output in outputs] 53 if output_fields is None: output_fields = [output.name for output in outputs]
52 # handle special case of inputs that are directly copied into outputs 54 # handle special case of inputs that are directly copied into outputs
53 55 # map names of output fields to Theano tensors in self.outputs
56 output_variables = ???
54 use_function_key = input_fields+output_fields 57 use_function_key = input_fields+output_fields
55 if not self.use_functions.has_key(use_function_key): 58 if not self.use_functions.has_key(use_function_key):
56 self.use_function[use_function_key]=Function(input_fields,output_fields) 59 self.use_function[use_function_key]=Function(input_variables,output_variables)
57 use_function = self.use_functions[use_function_key] 60 use_function = self.use_functions[use_function_key]
58 # return a dataset that computes the outputs 61 # return a dataset that computes the outputs
59 return input_dataset.applyFunction(use_function,input_fields,output_fields,copy_inputs,compute_now=True) 62 return input_dataset.applyFunction(use_function,input_fields,output_fields,copy_inputs,compute_now=True)
60 63
64
65 class StochasticGradientDescent(object):
66 def update_parameters(self):
67
68 class StochasticGradientLearner(GradientLearner,StochasticGradientDescent):
69 def __init__(self,inputs, parameters, outputs, example_wise_cost, regularization_term=astensor(0.0),
70 regularization_coefficient = astensor(1.0),)
71 def update()