1
我已经写其由相同的卷积核卷积图像块num_unrollings
倍成一排,并随后尝试最小化平均平方所得的值和目标输出之间的差值小Tensorflow程序。但是,当我使用大于1的num_unrollings
运行模型时,我的损失(tf_loss
)项相对于卷积内核(tf_kernel
)的梯度为零,因此不会发生学习。Tensorflow梯度始终为零
这里是最小的代码(蟒蛇3)我可以想出一种再现问题,对长度遗憾:
import tensorflow as tf
import numpy as np
batch_size = 1
kernel_size = 3
num_unrollings = 2
input_image_size = (kernel_size//2 * num_unrollings)*2 + 1
graph = tf.Graph()
with graph.as_default():
# Input data
tf_input_images = tf.random_normal(
[batch_size, input_image_size, input_image_size, 1]
)
tf_outputs = tf.random_normal(
[batch_size]
)
# Convolution kernel
tf_kernel = tf.Variable(
tf.zeros([kernel_size, kernel_size, 1, 1])
)
# Perform convolution(s)
_convolved_input = tf_input_images
for _ in range(num_unrollings):
_convolved_input = tf.nn.conv2d(
_convolved_input,
tf_kernel,
[1, 1, 1, 1],
padding="VALID"
)
tf_prediction = tf.reshape(_convolved_input, shape=[batch_size])
tf_loss = tf.reduce_mean(
tf.squared_difference(
tf_prediction,
tf_outputs
)
)
# FIXME: why is this gradient zero when num_unrollings > 1??
tf_gradient = tf.concat(0, tf.gradients(tf_loss, tf_kernel))
# Calculate and report gradient
with tf.Session(graph=graph) as session:
tf.initialize_all_variables().run()
gradient = session.run(tf_gradient)
print(gradient.reshape(kernel_size**2))
#prints [ 0. 0. 0. 0. 0. 0. 0. 0. 0.]
谢谢您的帮助!
初始化内核采用全零是不是一个好主意,并会在这种情况下导致的0梯度。 – etarion