# -*- coding: utf-8 -*-
from .core import *
[docs]class FlattenLayer(Layer):
"""A layer that reshapes high-dimension input into a vector.
Then we often apply DenseLayer, RNNLayer, ConcatLayer and etc on the top of a flatten layer.
[batch_size, mask_row, mask_col, n_mask] ---> [batch_size, mask_row * mask_col * n_mask]
Parameters
----------
layer : :class:`Layer`
Previous layer.
name : str
A unique layer name.
Examples
--------
>>> x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
>>> net = tl.layers.InputLayer(x, name='input')
>>> net = tl.layers.FlattenLayer(net, name='flatten')
... [?, 784]
"""
def __init__(
self,
prev_layer,
name='flatten_layer',
):
Layer.__init__(self, prev_layer=prev_layer, name=name)
self.inputs = prev_layer.outputs
self.outputs = flatten_reshape(self.inputs, name=name)
self.n_units = int(self.outputs.get_shape()[-1])
logging.info("FlattenLayer %s: %d" % (self.name, self.n_units))
# self.all_layers = list(layer.all_layers)
# self.all_params = list(layer.all_params)
# self.all_drop = dict(layer.all_drop)
self.all_layers.append(self.outputs)
[docs]class ReshapeLayer(Layer):
"""A layer that reshapes a given tensor.
Parameters
----------
layer : :class:`Layer`
Previous layer
shape : tuple of int
The output shape, see ``tf.reshape``.
name : str
A unique layer name.
Examples
--------
>>> x = tf.placeholder(tf.float32, shape=(None, 784))
>>> net = tl.layers.InputLayer(x, name='input')
>>> net = tl.layers.ReshapeLayer(net, [-1, 28, 28, 1], name='reshape')
>>> print(net.outputs)
... (?, 28, 28, 1)
"""
def __init__(
self,
prev_layer,
shape,
name='reshape_layer',
):
Layer.__init__(self, prev_layer=prev_layer, name=name)
self.inputs = prev_layer.outputs
self.outputs = tf.reshape(self.inputs, shape=shape, name=name)
logging.info("ReshapeLayer %s: %s" % (self.name, self.outputs.get_shape()))
# self.all_layers = list(layer.all_layers)
# self.all_params = list(layer.all_params)
# self.all_drop = dict(layer.all_drop)
self.all_layers.append(self.outputs)
[docs]class TransposeLayer(Layer):
"""A layer that transposes the dimension of a tensor.
See `tf.transpose() <https://www.tensorflow.org/api_docs/python/tf/transpose>`__ .
Parameters
----------
layer : :class:`Layer`
Previous layer
perm: list of int
The permutation of the dimensions, similar with ``numpy.transpose``.
name : str
A unique layer name.
Examples
----------
>>> x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
>>> net = tl.layers.InputLayer(x, name='input')
>>> net = tl.layers.TransposeLayer(net, perm=[0, 1, 3, 2], name='trans')
... [None, 28, 1, 28]
"""
def __init__(
self,
prev_layer,
perm,
name='transpose',
):
Layer.__init__(self, prev_layer=prev_layer, name=name)
self.inputs = prev_layer.outputs
assert perm is not None
logging.info("TransposeLayer %s: perm:%s" % (self.name, perm))
# with tf.variable_scope(name) as vs:
self.outputs = tf.transpose(self.inputs, perm=perm, name=name)
# self.all_layers = list(layer.all_layers)
# self.all_params = list(layer.all_params)
# self.all_drop = dict(layer.all_drop)
self.all_layers.append(self.outputs)
# self.all_params.extend( variables )