本文整理汇总了Python中entropy_coder.lib.blocks.Conv2DLSTM方法的典型用法代码示例。如果您正苦于以下问题：Python blocks.Conv2DLSTM方法的具体用法？Python blocks.Conv2DLSTM怎么用？Python blocks.Conv2DLSTM使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类entropy_coder.lib.blocks的用法示例。
在下文中一共展示了blocks.Conv2DLSTM方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from entropy_coder.lib import blocks [as 别名]
# 或者: from entropy_coder.lib.blocks import Conv2DLSTM [as 别名]
def __init__(self, layer_count, code_depth, name=None):
super(LayerPrediction, self).__init__(name)
self._layer_count = layer_count
# No previous layer.
self._layer_state = None
self._current_layer = 0
with self._BlockScope():
# Layers used to do the conditional code prediction.
self._brnn_predictors = []
for _ in xrange(layer_count):
self._brnn_predictors.append(BrnnPredictor(code_depth))
# Layers used to generate the input of the LSTM operating on the
# iteration/depth domain.
hidden_depth = 2 * code_depth
self._state_blocks = []
for _ in xrange(layer_count):
self._state_blocks.append(blocks.CompositionOperator([
blocks.Conv2D(
hidden_depth, [3, 3], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh),
blocks.Conv2D(
code_depth, [3, 3], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh)
]))
# Memory of the RNN is equivalent to the size of 2 layers of binary
# codes.
hidden_depth = 2 * code_depth
self._layer_rnn = blocks.CompositionOperator([
blocks.Conv2DLSTM(
depth=hidden_depth,
filter_size=[1, 1],
hidden_filter_size=[1, 1],
strides=[1, 1],
padding='SAME'),
blocks.Conv2D(hidden_depth, [1, 1], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh),
blocks.Conv2D(code_depth, [1, 1], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh)
])