使用tf.layers.conv2d
时,设置初始化器很简单,可以通过它的参数来完成。但是如果我使用 tf.nn.conv2d
呢?我用这个代码。这是否等同于在 tf.layers.conv2d
中设置 kernel_initializer
参数?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]
最佳答案
下面的操作是一样的(见here)。
至于内核及其初始化,我瞥了一眼代码,它看起来是一样的……layers.conv2d
调用tf .get_variable
在一天结束时。
但我想从经验上看它,所以这里是一个测试代码,它使用每种方法(tf.layers.conv2d
和 tf.nn.conv2d
声明一个 conv2d ), 评估初始化内核并比较它们。
我任意设置了不应该干扰比较的东西,例如输入张量和步幅。
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。
文档,顺便说一句:tf.nn.conv2d和 tf.layers.conv2d .
关于python - 在 tf.nn.conv2d 中使用权重初始化器,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/47167409/