[英]Using weights initializer with tf.nn.conv2d
使用tf.layers.conv2d
,设置初始化程序很简单,可以通过其参数完成。 但是如果我使用tf.nn.conv2d
怎么tf.nn.conv2d
? 我用这个代码。 这相当于在tf.layers.conv2d
设置kernel_initializer
参数吗? 虽然程序运行没有错误,但我不知道如何验证它是否按预期执行。
with tf.name_scope('conv1_2') as scope:
kernel = tf.get_variable(initializer=tf.contrib.layers.xavier_initializer(),
shape=[3, 3, 32, 32], name='weights')
conv = tf.nn.conv2d(conv1_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[32], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
下面的操作是相同的(见这里 )。
至于内核和初始化,我把代码中的惊鸿一瞥,它看起来都一样......在layers.conv2d
调用tf.get_variable
在这一天结束。
但是我想通过经验来看,所以这里有一个测试代码,它使用每个方法声明一个conv2d( tf.layers.conv2d
和tf.nn.conv2d
),评估初始化的内核并比较它们。
我随意设置了不应该干扰比较的东西,比如输入张量和步幅。
import tensorflow as tf
import numpy as np
# the way you described in your question
def _nn(input_tensor, initializer, filters, size):
kernel = tf.get_variable(
initializer=initializer,
shape=[size, size, 32, filters],
name='kernel')
conv = tf.nn.conv2d(
input=input_tensor,
filter=kernel,
strides=[1, 1, 1, 1],
padding='SAME')
return kernel
# the other way
def _layer(input_tensor, initializer, filters, size):
tf.layers.conv2d(
inputs=input_tensor,
filters=filters,
kernel_size=size,
kernel_initializer=initializer)
# 'conv2d/kernel:0' is the name of the generated kernel
return tf.get_default_graph().get_tensor_by_name('conv2d/kernel:0')
def _get_kernel(method):
# an isolated context for each conv2d
graph = tf.Graph()
sess = tf.Session(graph=graph)
with graph.as_default(), sess.as_default():
# important so that same randomness doesnt play a role
tf.set_random_seed(42)
# arbitrary input tensor with compatible shape
input_tensor = tf.constant(1.0, shape=[1, 64, 64, 32])
initializer = tf.contrib.layers.xavier_initializer()
kernel = method(
input_tensor=input_tensor,
initializer=initializer,
filters=32,
size=3)
sess.run(tf.global_variables_initializer())
return sess.run(kernel)
if __name__ == '__main__':
kernel_nn = _get_kernel(_nn)
kernel_layer = _get_kernel(_layer)
print('kernels are ', end='')
# compares shape and values
if np.array_equal(kernel_layer, kernel_nn):
print('exactly the same')
else:
print('not the same!')
输出是...... 内核完全相同 。
docs,btw: tf.nn.conv2d和tf.layers.conv2d 。
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