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from deepy.utils import build_activation, FLOATX |
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import theano.tensor as T |
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from . import NeuralLayer |
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class Dense(NeuralLayer): |
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""" |
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Fully connected layer. |
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""" |
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def __init__(self, size, activation='linear', init=None, disable_bias=False, random_bias=False): |
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super(Dense, self).__init__("dense") |
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self.activation = activation |
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self.output_dim = size |
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self.disable_bias = disable_bias |
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self.random_bias = random_bias |
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self.initializer = init |
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def prepare(self): |
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self._setup_params() |
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self._setup_functions() |
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def output(self, x): |
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return self._activation(T.dot(x, self.W) + self.B) |
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def _setup_functions(self): |
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self._activation = build_activation(self.activation) |
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def _setup_params(self): |
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self.W = self.create_weight(self.input_dim, self.output_dim, self.name, initializer=self.initializer) |
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self.register_parameters(self.W) |
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if self.disable_bias: |
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self.B = T.constant(0, dtype=FLOATX) |
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elif self.random_bias: |
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self.B = self.create_weight(suffix=self.name + "_bias", |
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initializer=self.initializer, |
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shape=(self.output_dim, )) |
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self.register_parameters(self.B) |
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else: |
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self.B = self.create_bias(self.output_dim, self.name) |
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self.register_parameters(self.B) |
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uaca /
deepy
