我引用了here的Tensorflow图表(完整代码text classification using CNN)中的以下内容(convolution-relu-maxpool
):
...
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.name_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, embedding_size, 1, num_filters]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
conv = tf.nn.conv2d(
self.embedded_chars_expanded,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
# Maxpooling over the outputs
pooled = tf.nn.max_pool(
h,
ksize=[1, sequence_length - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool")
pooled_outputs.append(pooled)
...
(修订版)
现在,请考虑以下示例代码:
import tensorflow as tf
shape = [1, 1, 1, 1]
for idx in [1, 2]:
with tf.name_scope("test%s" % idx):
W = tf.Variable(tf.truncated_normal(shape, stddev=0.1), name="W")
如何使用标识符W
访问这些权重变量/对象中的每一个,这些变量/对象都在各自的name_scope
之外(我需要执行手动更新)。或者是否有另一种方法来共享这些对象而不会丢失范围?
我尝试将共享变量与variable_scope
一起使用,如下所示:
shape = [1, 1, 1, 1]
for idx in [1, 2]:
with tf.name_scope("test%s" % idx):
with tf.variable_scope("in_scope%s" % idx, reuse=None):
W = tf.get_variable("W%s" % idx, shape, dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1))
但是,在W.name
内打印with tf.variable_scope("in_scope1", reuse=True)
会按预期和要求返回in_scope2/W2:0
而不是in_scope1/W1:0
。
我不确定我明白这里发生了什么。请有人澄清一下吗?
答案 0 :(得分:-1)
也许这可以帮到你。在name_scope
之外:
for v in tf.trainable_variables():
# print v.name
if 'W' in v.name:
# operate