deepy.layers.NeuralLayer.register_parameters() - Code Metrics - Inspection of "big refactoring for building abitrary computation..." - uaca/deepy - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( c8682f...5bbe2a )

by Raphael

created 2015-12-14 06:17 UTC

deepy.layers.NeuralLayer.register_parameters() A

↳ Parent: deepy.layers.NeuralLayer

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	2
dl	0
loc	7
rs	9.4286

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


import logging as loggers

import numpy as np
import theano

from deepy.utils import FLOATX, global_rand, UniformInitializer

logging = loggers.getLogger(__name__)

class NeuralLayer(object):

    def __init__(self, name="unknown"):
        """
        Create a neural layer.
        """
        self.name = name
        self.input_dim = 0
        self.input_dims = [0]
        self.output_dim = 0

        self._linked_block = None
        self._linked = False

        self.connected = 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 connect(self, input_dim=0, input_dims=None, previous_layer=None, network_config=None, no_prepare=False):
        """
        Connect to a previous layer.
        :param no_prepare: if avoid calling setup
        """
        # 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.previous_layer = previous_layer
        self.network_config = network_config
        self.connected = True
        # call prepare
        if not no_prepare:
            self.prepare()
        return self

    def compute_raw(self, inputs, dims):
        """
        Compute on raw Theano tensors.
        :type inputs: list of TensorLayer
        :type dims: list of int
        """
        from variable import NeuralVar
        tensors = [NeuralVar(d, t) for d, t in zip(dims, inputs)]
        return self.compute(*tensors)

    def compute(self, *inputs):
        """
        Take a TensorLayer or tensor as input, compute the result.
        Dimension must be given is the input is a tensor.
        :type inputs:  list of TensorLayer
        :return: TensorLayer
        """
        from variable import NeuralVar
        if type(inputs[0]) != NeuralVar:
            raise SystemError("The input of `compute` must be TensorLayer")

        dims = [t.dim() for t in inputs]
        if len(inputs) == 1:
            self.connect(input_dim=dims[0])
        else:
            self.connect(input_dims=dims)
        if self._linked_block and not self._linked:
            self._linked = True
            self._linked_block.register_layer(self)
        return NeuralVar(self.output_dim, self.output(*[t.tensor for t in inputs]), self.test_output(*[t.test_tensor for t in inputs]))

    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

    def output(self, x):
        """
        Output function.
        """
        raise NotImplementedError("output function of '%s' is not implemented" % self.name)

    def test_output(self, x):
        """
        Output function in test time.
        """
        return self.output(x)

    def call(self, x, test=False):
        """
        Call this layer, with a parameter to switch test or not.
        """
        if test:
            return self.test_output(x)
        else:
            return self.output(x)

    def link(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 be linked twice")
        self._linked_block = block
        if self.connected:
            self._linked = True
            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)

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

uaca / deepy

Push — master ( c8682f...5bbe2a )

deepy.layers.NeuralLayer.register_parameters() A

Complexity

Size

Duplication

Duplication Side-by-Side

Filter issues like