以“单元”的方式使用标准的tensorflow层
以“单元”的方式使用标准的tensorflow层
提问于 2018-07-17 08:14:59
我的问题与这个问题*有关。
是否有可能将标准的tensorflow层转换为“cell”，与RNN细胞一起组成递归神经网络？
因此，新的“单元”应该存储参数(权重，.)，并且能够在可变输入上调用。就像这样：
from tf.nn import batch_normalization, conv2d
from tf.contrib.rnn import MultiRNNCell, LSTMCell
bn_cell = cell_creation_fun(batch_normalization, otherparams) # batch norm cell
conv_cell = cell_creation_fun(conv2d, otherparams ) # non-rnn conv cell
# or `conv_cell = cell_creation_fun(tf.layers.Conv2D, otherparams )` # using tf.layers 这样他们就可以这样使用了：
multi_cell = MultiRNNCell([LSTMCell(...), conv_cell, bn_cell])或者像这样：
h = ...
conv_h, _ = conv_cell(h, state=None)
normed_h, _ = bn_cell(h, state=None)我唯一能想到的就是手动为我想要使用的每一层编写这样的“单元格”，并对RNNCell进行子类化。但是，如果在创建过程中不能够传递“input”参数，那么使用Conv2D这样的现有函数似乎就不那么简单了。(将在我管理时发布代码。)
*也许以一种更有针对性的方式提出问题有可能得到答案。
回答 1
Stack Overflow用户
发布于 2018-07-20 09:39:15
好吧，到目前为止，我的情况如下：
class LayerCell(rnn_cell_impl.RNNCell):
def __init__(self, tf_layer, **kwargs):
''' :param tf_layer: a tensorflow layer, e.g. tf.layers.Conv2D or
tf.keras.layers.Conv2D. NOT tf.layers.conv2d !'''
self.layer_fn = tf_layer(**kwargs)
def __call__(self, inputs, state, scope=None):
''' Every `RNNCell` must implement `call` with
the signature `(output, next_state) = call(input, state)`. The optional
third input argument, `scope`, is allowed for backwards compatibility
purposes; but should be left off for new subclasses.'''
return (self.layer_fn(inputs), state)
def __str__(self):
return "Cell wrapper of " + str(self.layer_fn)
def __getattr__(self, attr):
'''credits to https://stackoverflow.com/questions/1382871/dynamically-attaching-a-method-to-an-existing-python-object-generated-with-swig/1383646#1383646'''
return getattr(self.layer_fn, attr)
@property
def state_size(self):
"""size(s) of state(s) used by this cell.
It can be represented by an Integer, a TensorShape or a tuple of Integers
or TensorShapes.
"""
return (0,)
@property
def output_size(self):
"""Integer or TensorShape: size of outputs produced by this cell."""
# use with caution; could be uninitialized
return self.layer_fn.output_shape(当然，不要使用循环层，因为状态保持会被破坏。)
似乎与: tf.layers.Conv2D，tf.keras.layers.Conv2D，tf.keras.layers.Activation，tf.layers.BatchNormalization
不适用于: tf.keras.layers.BatchNormalization。至少当我在tf.while循环中使用它时，它失败了；它抱怨将来自不同帧的变量组合在一起，类似于这里。也许克拉斯用变量() .？
使用情况：
cell0 = tf.contrib.rnn.ConvLSTMCell(conv_ndims=2, input_shape=[40, 40, 3], output_channels=16, kernel_shape=[5, 5])
cell1 = LayerCell(tf.keras.layers.Conv2D, filters=8, kernel_size=[5, 5], strides=(1, 1), padding='same')
cell2 = LayerCell(tf.layers.BatchNormalization, axis=-1)
inputs = np.random.rand(10, 40, 40, 3).astype(np.float32)
multicell = tf.contrib.rnn.MultiRNNCell([cell0, cell1, cell2])
state = multicell.zero_state(batch_size=10, dtype=tf.float32)
output = multicell(inputs, state)
print("Yippee!")页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/51376396
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