NeuralLayer.activation() - Code Metrics - Inspection of "improved layers" - zomux/deepy - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 662c2c...dccd0d )

by Raphael

created 2016-06-29 01:45 UTC

NeuralLayer.activation() A

↳ Parent: NeuralLayer

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cc	1
dl	0
loc	2
rs	10
c	1
b	0
f	0

#!/usr/bin/env python
# -*- coding: utf-8 -*-


import logging as loggers

import numpy as np
import theano

from deepy.utils import FLOATX, UniformInitializer, neural_computation, neural_computation_prefer_tensor
from deepy.utils import convert_to_neural_var, convert_to_theano_var, build_activation

logging = loggers.getLogger(__name__)

class NeuralLayer(object):

    def __init__(self, name=None):
        """
        Create a neural layer.
        """
        self.name = name if name else self.__class__.__name__
        self.input_dim = 0
        self.input_dims = [0]
        self.output_dim = 0
        self.output_dims= [0]

        self._linked_block = None

        self.initialized = False
        self.updates = []
        self.training_updates = []
        self.free_parameters = []
        self.parameters = []
        self.training_monitors = []
        self.testing_monitors = []
        self._registered_monitors = set()
        self._registered_updates = set()
        self._registered_training_updates = set()
        self.external_inputs = []
        self.external_targets = []
        self.parameter_count = 0
        self.epoch_callbacks = []
        self.training_callbacks = []
        self.testing_callbacks = []

    def initialize(self, input_dim=0, input_dims=None, no_prepare=False):
        """
        Initialize the layer.
        :param no_prepare: avoid calling preparation function
        """
        if self.initialized:
            return
        # configure input dimensions
        if input_dims:
            self.input_dims = input_dims
            self.input_dim = input_dims[0]
        else:
            self.input_dim = input_dim
            self.input_dims = [input_dims]
        # set default output dimension
        if self.output_dim == 0:
            self.output_dim = self.input_dim
        self.initialized = True
        # call prepare
        if not no_prepare:
            self.prepare()
        return self

    def compute(self, *inputs, **kwargs):
        """
        Compute based on NeuralVariable.
        :type inputs:  list of NeuralVariable
        :return: NeuralVariable
        """
        from var import NeuralVariable
        if type(inputs[0]) != NeuralVariable:
            raise SystemError("The input of `compute` must be NeuralVar")

        dims = [t.dim() for t in inputs]
        if len(inputs) == 1:
            self.initialize(input_dim=dims[0])
        else:
            self.initialize(input_dims=dims)
        # convert kwargs
        train_kwargs, test_kwargs, _, _ = convert_to_theano_var(kwargs)

        output = self.compute_tensor(*[t.tensor for t in inputs], **train_kwargs)
        test_output = self.compute_test_tesnor(*[t.test_tensor for t in inputs], **test_kwargs)

        if type(output) != list:
            return NeuralVariable(output, test_output, self.output_dim)
        else:
            return [NeuralVariable(*item) for item in zip(self.output_dims, output, test_output)]

    def prepare(self):
        """
        Prepare function will be called after connected.
        """
        return self.setup()

    def setup(self):
        """
        !!! DEPRECATED !!!
        Setup function will be called after connected.
        """
        pass

    @neural_computation_prefer_tensor
    def compute_tensor(self, *args, **kwargs):
        """
        Compute with tensors in Theano.
        """
        raise NotImplementedError("output function of '%s' is not implemented" % self.name)

    @neural_computation_prefer_tensor
    def compute_test_tesnor(self, *args, **kwargs):
        """
        Compute with tensors in Theano in test time.
        """
        return self.compute_tensor(*args, **kwargs)

    def compute_flexible_tensor(self, x, test=False):
        """
        Deprecated.
        Compute with tensors in Theano, with a parameter to switch test or not.
        """
        if test:
            return self.compute_test_tesnor(x)
        else:
            return self.compute_tensor(x)

    def belongs_to(self, block):
        """
        Let the given block or network manage the parameters of this layer.
        :param block: Block or NeuralNetwork
        :return: NeuralLayer
        """
        if self._linked_block:
            raise SystemError("One layer can not belong to two blocks")
        self._linked_block = block
        block.register_layer(self)
        return self

    def register(self, *layers):
        """
        Register inner layers.
        """
        self.register_inner_layers(*layers)

    def register_inner_layers(self, *layers):
        for layer in layers:
            self.register_parameters(*layer.parameters)
            self.register_updates(*layer.updates)
            self.register_training_updates(*layer.training_updates)

    def register_parameters(self, *parameters):
        """
        Register parameters.
        """
        for param in parameters:
            self.parameter_count += np.prod(param.get_value().shape)
        self.parameters.extend(parameters)

    def register_free_parameters(self, *free_parameters):
        """
        Register free parameters, which means their value will not be learned by trainer.
        """
        return self.free_parameters.extend(free_parameters)

    def register_updates(self, *updates):
        """
        Register updates that will be executed in each iteration.
        """
        for key, node in updates:
            if key not in self._registered_updates:
                self.updates.append((key, node))
                self._registered_updates.add(key)

    def register_training_updates(self, *updates):
        """
        Register updates that will only be executed in training phase.
        """
        for key, node in updates:
            if key not in self._registered_training_updates:
                self.training_updates.append((key, node))
                self._registered_training_updates.add(key)

    def register_monitors(self, *monitors):
        """
        Register monitors they should be tuple of name and Theano variable.
        """
        for key, node in monitors:
            if key not in self._registered_monitors:
                self.training_monitors.append((key, node))
                self.testing_monitors.append((key, node))
                self._registered_monitors.add(key)

    def register_external_inputs(self, *variables):
        """
        Register external input variables.
        """
        self.external_inputs.extend(variables)

    def register_external_targets(self, *variables):
        """
        Register extenal target variables.
        """
        self.external_targets.extend(variables)

    def register_training_callbacks(self, *callbacks):
        """
        Register callback for each iteration in the training.
        """
        self.training_callbacks.extend(callbacks)

    def register_testing_callbacks(self, *callbacks):
        """
        Register callback for each iteration in the testing.
        """
        self.testing_callbacks.extend(callbacks)

    def register_epoch_callbacks(self, *callbacks):
        """
        Register callback which will be called after epoch finished.
        """
        self.epoch_callbacks.extend(callbacks)

    def create_weight(self, input_n=1, output_n=1, suffix="", initializer=None, shape=None):
        if not shape:
            shape = (input_n, output_n)

        if not initializer:
            initializer = UniformInitializer()

        weight = theano.shared(initializer.sample(shape).astype(FLOATX), name='W_{}'.format(suffix))

        logging.info('create weight W_%s: %s', suffix, str(shape))
        return weight

    def create_bias(self, output_n=1, suffix="", value=0., shape=None):
        if not shape:
            shape = (output_n, )
        bs =  np.ones(shape)
        bs *= value
        bias = theano.shared(bs.astype(FLOATX), name='B_{}'.format(suffix))
        logging.info('create bias B_%s: %s', suffix, str(shape))
        return bias

    def create_vector(self, n, name, dtype=FLOATX):
        bs =  np.zeros(n)
        v = theano.shared(bs.astype(dtype), name='{}'.format(name))

        logging.info('create vector %s: %d', name, n)
        return v

    def create_matrix(self, m, n, name):

        matrix = theano.shared(np.zeros((m, n)).astype(FLOATX), name=name)

        logging.info('create matrix %s: %d x %d', name, m, n)
        return matrix

    def activation(self, name):
        return build_activation(name)

    def callback_forward_propagation(self):
        pass



1			#!/usr/bin/env python
2			# -- coding: utf-8 --
3
4
5			import logging as loggers
6
7			import numpy as np
8			import theano
9
10			from deepy.utils import FLOATX, UniformInitializer, neural_computation, neural_computation_prefer_tensor
11			from deepy.utils import convert_to_neural_var, convert_to_theano_var, build_activation
12
13			logging = loggers.getLogger(__name__)
14
15			class NeuralLayer(object):
16
17			def __init__(self, name=None):
18			"""
19			Create a neural layer.
20			"""
21			self.name = name if name else self.__class__.__name__
22			self.input_dim = 0
23			self.input_dims = [0]
24			self.output_dim = 0
25			self.output_dims= [0]
26
27			self._linked_block = None
28
29			self.initialized = False
30			self.updates = []
31			self.training_updates = []
32			self.free_parameters = []
33			self.parameters = []
34			self.training_monitors = []
35			self.testing_monitors = []
36			self._registered_monitors = set()
37			self._registered_updates = set()
38			self._registered_training_updates = set()
39			self.external_inputs = []
40			self.external_targets = []
41			self.parameter_count = 0
42			self.epoch_callbacks = []
43			self.training_callbacks = []
44			self.testing_callbacks = []
45
46			def initialize(self, input_dim=0, input_dims=None, no_prepare=False):
47			"""
48			Initialize the layer.
49			:param no_prepare: avoid calling preparation function
50			"""
51			if self.initialized:
52			return
53			# configure input dimensions
54			if input_dims:
55			self.input_dims = input_dims
56			self.input_dim = input_dims[0]
57			else:
58			self.input_dim = input_dim
59			self.input_dims = [input_dims]
60			# set default output dimension
61			if self.output_dim == 0:
62			self.output_dim = self.input_dim
63			self.initialized = True
64			# call prepare
65			if not no_prepare:
66			self.prepare()
67			return self
68
69			def compute(self, inputs, *kwargs):
70			"""
71			Compute based on NeuralVariable.
72			:type inputs: list of NeuralVariable
73			:return: NeuralVariable
74			"""
75			from var import NeuralVariable
76			if type(inputs[0]) != NeuralVariable:
77			raise SystemError("The input of `compute` must be NeuralVar")
78
79			dims = [t.dim() for t in inputs]
80			if len(inputs) == 1:
81			self.initialize(input_dim=dims[0])
82			else:
83			self.initialize(input_dims=dims)
84			# convert kwargs
85			train_kwargs, test_kwargs, _, _ = convert_to_theano_var(kwargs)
86
87			output = self.compute_tensor([t.tensor for t in inputs], *train_kwargs)
88			test_output = self.compute_test_tesnor([t.test_tensor for t in inputs], *test_kwargs)
89
90			if type(output) != list:
91			return NeuralVariable(output, test_output, self.output_dim)
92			else:
93			return [NeuralVariable(*item) for item in zip(self.output_dims, output, test_output)]
94
95			def prepare(self):
96			"""
97			Prepare function will be called after connected.
98			"""
99			return self.setup()
100
101			def setup(self):
102			"""
103			!!! DEPRECATED !!!
104			Setup function will be called after connected.
105			"""
106			pass
107
108			@neural_computation_prefer_tensor
109			def compute_tensor(self, args, *kwargs):
110			"""
111			Compute with tensors in Theano.
112			"""
113			raise NotImplementedError("output function of '%s' is not implemented" % self.name)
114
115			@neural_computation_prefer_tensor
116			def compute_test_tesnor(self, args, *kwargs):
117			"""
118			Compute with tensors in Theano in test time.
119			"""
120			return self.compute_tensor(args, *kwargs)
121
122			def compute_flexible_tensor(self, x, test=False):
123			"""
124			Deprecated.
125			Compute with tensors in Theano, with a parameter to switch test or not.
126			"""
127			if test:
128			return self.compute_test_tesnor(x)
129			else:
130			return self.compute_tensor(x)
131
132			def belongs_to(self, block):
133			"""
134			Let the given block or network manage the parameters of this layer.
135			:param block: Block or NeuralNetwork
136			:return: NeuralLayer
137			"""
138			if self._linked_block:
139			raise SystemError("One layer can not belong to two blocks")
140			self._linked_block = block
141			block.register_layer(self)
142			return self
143
144			def register(self, *layers):
145			"""
146			Register inner layers.
147			"""
148			self.register_inner_layers(*layers)
149
150			def register_inner_layers(self, *layers):
151			for layer in layers:
152			self.register_parameters(*layer.parameters)
153			self.register_updates(*layer.updates)
154			self.register_training_updates(*layer.training_updates)
155
156			def register_parameters(self, *parameters):
157			"""
158			Register parameters.
159			"""
160			for param in parameters:
161			self.parameter_count += np.prod(param.get_value().shape)
162			self.parameters.extend(parameters)
163
164			def register_free_parameters(self, *free_parameters):
165			"""
166			Register free parameters, which means their value will not be learned by trainer.
167			"""
168			return self.free_parameters.extend(free_parameters)
169
170			def register_updates(self, *updates):
171			"""
172			Register updates that will be executed in each iteration.
173			"""
174			for key, node in updates:
175			if key not in self._registered_updates:
176			self.updates.append((key, node))
177			self._registered_updates.add(key)
178
179			def register_training_updates(self, *updates):
180			"""
181			Register updates that will only be executed in training phase.
182			"""
183			for key, node in updates:
184			if key not in self._registered_training_updates:
185			self.training_updates.append((key, node))
186			self._registered_training_updates.add(key)
187
188			def register_monitors(self, *monitors):
189			"""
190			Register monitors they should be tuple of name and Theano variable.
191			"""
192			for key, node in monitors:
193			if key not in self._registered_monitors:
194			self.training_monitors.append((key, node))
195			self.testing_monitors.append((key, node))
196			self._registered_monitors.add(key)
197
198			def register_external_inputs(self, *variables):
199			"""
200			Register external input variables.
201			"""
202			self.external_inputs.extend(variables)
203
204			def register_external_targets(self, *variables):
205			"""
206			Register extenal target variables.
207			"""
208			self.external_targets.extend(variables)
209
210			def register_training_callbacks(self, *callbacks):
211			"""
212			Register callback for each iteration in the training.
213			"""
214			self.training_callbacks.extend(callbacks)
215
216			def register_testing_callbacks(self, *callbacks):
217			"""
218			Register callback for each iteration in the testing.
219			"""
220			self.testing_callbacks.extend(callbacks)
221
222			def register_epoch_callbacks(self, *callbacks):
223			"""
224			Register callback which will be called after epoch finished.
225			"""
226			self.epoch_callbacks.extend(callbacks)
227
228			def create_weight(self, input_n=1, output_n=1, suffix="", initializer=None, shape=None):
229			if not shape:
230			shape = (input_n, output_n)
231
232			if not initializer:
233			initializer = UniformInitializer()
234
235			weight = theano.shared(initializer.sample(shape).astype(FLOATX), name='W_{}'.format(suffix))
236
237			logging.info('create weight W_%s: %s', suffix, str(shape))
238			return weight
239
240			def create_bias(self, output_n=1, suffix="", value=0., shape=None):
241			if not shape:
242			shape = (output_n, )
243			bs = np.ones(shape)
244			bs *= value
245			bias = theano.shared(bs.astype(FLOATX), name='B_{}'.format(suffix))
246			logging.info('create bias B_%s: %s', suffix, str(shape))
247			return bias
248
249			def create_vector(self, n, name, dtype=FLOATX):
250			bs = np.zeros(n)
251			v = theano.shared(bs.astype(dtype), name='{}'.format(name))
252
253			logging.info('create vector %s: %d', name, n)
254			return v
255
256			def create_matrix(self, m, n, name):
257
258			matrix = theano.shared(np.zeros((m, n)).astype(FLOATX), name=name)
259
260			logging.info('create matrix %s: %d x %d', name, m, n)
261			return matrix
262
263			def activation(self, name):
264			return build_activation(name)
265
266			def callback_forward_propagation(self):
267			pass
268
269

zomux / deepy

Push — master ( 662c2c...dccd0d )

NeuralLayer.activation() A

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