根据这里的教程，我正在尝试实现一个用于使用Keras和Tensorflow进行图像分类的神经网络。
我添加了以下代码：
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(3, 150, 150)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
但是，问题是我得到：
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 671, in _call_cpp_shape_fn_impl
16.4s
3
input_tensors_as_shapes, status)
File "/opt/conda/lib/python3.6/contextlib.py", line 89, in __exit__
next(self.gen)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_1/MaxPool' (op: 'MaxPool') with input shapes: [?,1,148,32].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "../src/script.py", line 49, in <module>
model.add(MaxPooling2D(pool_size=(2, 2)))
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/models.py", line 469, in add
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/engine/topology.py", line 596, in __call__
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/layers/pooling.py", line 154, in call
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/layers/pooling.py", line 217, in _pooling_function
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/backend/tensorflow_backend.py", line 3378, in pool2d
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 1769, in max_pool
16.4s
4
name=name)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 1605, in _max_pool
data_format=data_format, name=name)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2508, in create_op
set_shapes_for_outputs(ret)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1873, in set_shapes_for_outputs
shapes = shape_func(op)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1823, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 610, in call_cpp_shape_fn
debug_python_shape_fn, require_shape_fn)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 676, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_1/MaxPool' (op: 'MaxPool') with input shapes: [?,1,148,32].
之后，我查看了一个可能的答案，并将最后一行更改为：
model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="tf"))
但是在此更改之后，我遇到了相同的错误。
知道可能出了什么问题吗？
您提供的代码是假设您的后端Theano编写的。如果是Tensorflow，您应该将输入更改为具有形状(width, height, channels)因此您应该更改此行：
model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3)))
您的问题来自卷积(带有valid填充(后输出的形状(1, 148, 32)因此无法应用具有步幅MaxPooling2D(2, 2)这是默认值。