我正在使用TensorFlow,我修改了教程示例以获取我的RGB图像.

该算法在新图像集上完美无缺地工作,直到突然(仍然会聚,通常精度约为92%),它与ReluGrad接收到的非有限值的错误一起崩溃.调试显示数字没有异常发生,直到非常突然,由于未知原因,错误被抛出.添加

print "max W vales: %g %g %g %g"%(tf.reduce_max(tf.abs(W_conv1)).eval(),tf.reduce_max(tf.abs(W_conv2)).eval(),tf.reduce_max(tf.abs(W_fc1)).eval(),tf.reduce_max(tf.abs(W_fc2)).eval())
print "max b vales: %g %g %g %g"%(tf.reduce_max(tf.abs(b_conv1)).eval(),tf.reduce_max(tf.abs(b_conv2)).eval(),tf.reduce_max(tf.abs(b_fc1)).eval(),tf.reduce_max(tf.abs(b_fc2)).eval())

作为每个循环的调试代码,产生以下输出:

Step 8600
max W vales: 0.759422 0.295087 0.344725 0.583884
max b vales: 0.110509 0.111748 0.115327 0.124324
Step 8601
max W vales: 0.75947 0.295084 0.344723 0.583893
max b vales: 0.110516 0.111753 0.115322 0.124332
Step 8602
max W vales: 0.759521 0.295101 0.34472 0.5839
max b vales: 0.110521 0.111747 0.115312 0.124365
Step 8603
max W vales: -3.40282e+38 -3.40282e+38 -3.40282e+38 -3.40282e+38
max b …

我想重用Tensorflow"MNIST for Pros"CNN示例中的代码.我的图像是388px X 191px,只有2个输出类.原始代码可以在这里找到.我试图通过仅更改输入和输出层来重用此代码,如下所示:

输入层

x = tf.placeholder("float", shape=[None, 74108])

y_ = tf.placeholder("float", shape=[None, 2])

x_image = tf.reshape(x, [-1,388,191,1])

输出层

W_fc2 = weight_variable([1024, 2])

b_fc2 = bias_variable([2])

运行修改后的代码会产生模糊的堆栈跟踪:

W tensorflow/core/common_runtime/executor.cc:1027] 0x2136510 Compute status: Invalid argument: Input has 14005248 values, which isn't divisible by 3136
     [[Node: Reshape_4 = Reshape[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](MaxPool_5, Reshape_4/shape)]]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1267, in run
    _run_using_default_session(self, feed_dict, self.graph, session)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", …

我一直试图从Tensorflow教程创建卷积网络,但我遇到了麻烦.出于某种原因,我遇到的错误是y_conv的大小比y_的大小大4倍,我不明白为什么.我发现了这个问题,但它似乎与我的问题不同,尽管它看起来很相似.

要清楚,下面代码中的批处理大小是50,但它出现的错误是

tensorflow.python.framework.errors.InvalidArgumentError:不兼容的形状:[200]与[50]

当我将批量大小更改为10时,我得到了

tensorflow.python.framework.errors.InvalidArgumentError:不兼容的形状:[40]与[10]

所以它与批量大小有某种关系,但我无法弄明白.谁能告诉我这段代码有什么问题？它与上面链接的教程非常相似.

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

import tensorflow as tf
sess = tf.InteractiveSession()

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides = [1, 2, 2, 1], padding='SAME')

x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])

w_conv1 …