Question

我试图添加非常简单的MNIST示例（https://github.com/tensorflow/tensorflow/blob/r1.8/tensorflow/examples/tutorials/layers/cnn_mnist.py）以适合我遇到的问题。问题似乎出在我输入的维度上（我相信）

原始MNIST数据具有以下属性（在第125行插入）：

print(train_data.shape) #    (55000, 784)
print(type(train_data)) #    <class 'numpy.ndarray'>
print(train_labels.shape) #  (55000, )
print(type(train_labels)) #  <class 'numpy.ndarray'>

我的数据具有以下形状

Train Data:  (10681, 9216)
Train Data:  <class 'numpy.ndarray'>
Train Labe:  (10681,)
Train Labe:  <class 'numpy.ndarray'>

尤其是784 = 28 * 28和9216 = 96 * 96。因此，原始行（31）读为

 input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

我将其替换为

 input_layer = tf.reshape(features["x"], [-1, 96, 96, 1])

但是，当我运行此命令时，出现的错误是：

Traceback (most recent call last):
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py", line686, in _call_cpp_shape_fn_impl
    input_tensors_as_shapes, status)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension size must be evenly divisible by 3136 but is 3686400 for 'Reshape_1' (op: 'Reshape') with input shapes: [100,24,24,64], [2] and with input tensors computed as partial shapes: input[1] = [?,3136].

During handling of the above exception, another exception occurred:
Traceback (most recent call last):
  File "tensorfuck/learn.py", line 252, in <module>
    tf.app.run()
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 124, in run
    _sys.exit(main(argv))
  File "tensorfuck/learn.py", line 238, in main
    hooks=[logging_hook])
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 314, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 743, in _train_model
    features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 725, in _call_model_fn
    model_fn_results = self._model_fn(features=features, **kwargs)
  File "tensorfuck/learn.py", line 88, in cnn_model_fn
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 3997,in reshape
    "Reshape", tensor=tensor, shape=shape, name=name)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3162, in create_op
    compute_device=compute_device)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3208, in _create_op_helper
    set_shapes_for_outputs(op)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2427, in set_shapes_for_outputs
    return _set_shapes_for_outputs(op)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2400, in _set_shapes_for_outputs
    shapes = shape_func(op)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2330, in call_with_requiring
    return call_cpp_shape_fn(op, require_shape_fn=True)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py", line627, in call_cpp_shape_fn
    require_shape_fn)
  File "/home/j-pc/.local/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py", line691, in _call_cpp_shape_fn_impl
    raise ValueError(err.message)
ValueError: Dimension size must be evenly divisible by 3136 but is 3686400 for 'Reshape_1' (op: 'Reshape') with input shapes: [100,24,24,64], [2] and with input tensors computed as partial shapes: input[1] = [?,3136].

出什么问题了？附言我在Ubuntu16.04上使用Python3.5.2和Tensorflow1.5.0 运行原始的MNIST示例不会出错。

Answer 1

问题

实际上，这是一个尺寸问题。它与您的一般网络体系结构有关，后者与您的新输入大小不匹配。您采用了cnn_mnist.py中的体系结构。这是第72行引起的问题：

pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64]) // 7*7*64=3136

该体系结构采用[-1, 7, 7, 64]的形状，但是您正在传递[-1,24,24,64]。

如何修复

更改pool2_flat以匹配您的实际输出形状：

pool2_flat = tf.reshape(pool2, [-1, 24 * 24 * 64])

重塑时的Tensorflow尺寸错误

1 个答案: