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个输出类别。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：
x = tf.placeholder("float", shape=[None, 74108])
y_ = tf.placeholder("float", shape=[None, 2])
x_image = tf.reshape(x, [-1,388,191,1])
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", line 2763, in _run_using_default_session
session.run(operation, feed_dict)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 345, in run
results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 419, in _do_run
e.code)
tensorflow.python.framework.errors.InvalidArgumentError: 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)]]
Caused by op u'Reshape_4', defined at:
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 554, in reshape
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 633, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1710, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 988, in __init__
self._traceback = _extract_stack()
tensorflow.python.framework.errors.InvalidArgumentError: 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)]]
但是您执行它的方式使您无法看到导致问题的实际行。将其保存到文件中并进行python<file>处理。
File "<stdin>", line 1, in <module>
但是答案是您没有更改卷积和池化图层的大小，因此当您过去运行28x28图像时，它们最终会缩小为7x7x（convolutional_depth）层。现在您正在运行巨大的图像，因此在第一个卷积层和2x2 maxpool之后，您将尝试引入非常大的东西，但是您将重塑为：
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
使用较大的图像，h_pool2的输出会大得多。您需要将它们缩小得更多-可能需要更多的卷积和maxpooling层。您也可以尝试增加W_fc1的大小以匹配到达那里的输入大小。它通过两个2x2 maxpool运行-每个x和y尺寸将尺寸缩小2。28x28x1-> 14x14x32-> 7x7x64。因此您的图像尺寸从388 x 191-> 194 x 95-> 97 x 47
作为警告，输入为97 * 47 = 4559的完全连接的层在冰川上会很慢。
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