forked from TensorLayer/tensorlayer3
78 lines
1.9 KiB
Python
78 lines
1.9 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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import tensorlayer as tl
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from tensorlayer import logging
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from tensorlayer.layers.core import Module
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__all__ = ['Input', '_InputLayer']
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class _InputLayer(Module):
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"""
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The :class:`Input` class is the starting layer of a neural network.
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Parameters
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----------
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shape : tuple (int)
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Including batch size.
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dtype: dtype
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The type of input values. By default, tf.float32.
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name : None or str
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A unique layer name.
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"""
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def __init__(self, shape, dtype=tl.float32, name=None, init=None):
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super(_InputLayer, self).__init__(name)
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logging.info("Input %s: %s" % (self.name, str(shape)))
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self.shape = shape
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self.dtype = dtype
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self.shape_without_none = [_ if _ is not None else 1 for _ in shape]
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if init is None:
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self.outputs = tl.initializers.ones()(self.shape_without_none, dtype=self.dtype)
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else:
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self.outputs = init(self.shape_without_none, dtype=self.dtype)
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self._built = True
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def __repr__(self):
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s = 'Input(shape=%s' % str(self.shape)
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if self.name is not None:
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s += (', name=\'%s\'' % self.name)
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s += ')'
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return s
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def __call__(self, *args, **kwargs):
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return self.outputs
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def build(self, inputs_shape):
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pass
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def forward(self):
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return self.outputs
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def Input(shape, init=tl.initializers.ones(), dtype=tl.float32, name=None):
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"""
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The :class:`Input` class is the starting layer of a neural network.
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Parameters
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----------
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shape : tuple (int)
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Including batch size.
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name : None or str
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A unique layer name.
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Examples
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---------
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With TensorLayer
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>>> ni = tl.layers.Input([10, 50, 50, 32], name='input')
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>>> output shape : [10, 50, 50, 32]
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"""
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input_layer = _InputLayer(shape, dtype=dtype, name=name, init=init)
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outputs = input_layer()
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return outputs
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