ValueError: Negative dimension size caused by subtracting 2 from 1 for 'pool_2/MaxPool' (op: 'MaxPool') with input shapes: [?,1,1,32].
tf.layers.max_pooling1d( inputs, pool_size, strides, padding='valid', data_format='channels_last', name=None )
用于1维输入的MaxPooling层
pool_size:表示pooling window的大小strides:指定pooling操作的步长padding:一个字符串。padding的方法：string，valid或same，大小写不敏感。data_format:一个字符串，channels_last（默认）或channels_first中的一个，输入中维度的排序，channels_last对应于具有形状(batch, length, channels)的输入，而channels_first对应于具有形状(batch, channels, length)的输入。name:一个字符串，表示层的名称。是图片通道的问题，也就是”channels_last”和”channels_first”数据格式的问题。
input_shape=(3,28,28)是theano的写法，而tensorflow需要写出：(28,28,3)
查了很多方法我的问题都没有解决：
法一：配置.keras下的keras.json文件，将channels_last修改为channels_first
{
"image_data_format" : "channels_first",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "tensorflow"
}法二：在运行代码前面加两行代码：
from keras import backend as K
K.set_image_dim_ordering('tf') 直接在出现错误的代码中补充一个参数，加上data_format='channels_first'就可以啦，，
pool_4 = MaxPooling1D(pool_size=2, name='pool_4',data_format='channels_first')(conv_4)
注：此方法适用MaxPooling2D
import tensorflow as tf
from tensorflow import keras
input_shape = (2, 3, 4)
x = tf.random.normal(input_shape)
print(x)
y=keras.layers.GlobalMaxPool1D()(x)
print("*"*20)
print(y)
'''
"""Global average pooling operation for temporal data.
Examples:
>>> input_shape = (2, 3, 4)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.GlobalAveragePooling1D()(x)
>>> print(y.shape)
(2, 4)
Arguments:
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, steps, features)` while `channels_first`
corresponds to inputs with shape
`(batch, features, steps)`.
Call arguments:
inputs: A 3D tensor.
mask: Binary tensor of shape `(batch_size, steps)` indicating whether
a given step should be masked (excluded from the average).
Input shape:
- If `data_format='channels_last'`:
3D tensor with shape:
`(batch_size, steps, features)`
- If `data_format='channels_first'`:
3D tensor with shape:
`(batch_size, features, steps)`
Output shape:
2D tensor with shape `(batch_size, features)`.
"""
'''
print("--"*20)
input_shape = (2, 3, 4)
x = tf.random.normal(input_shape)
print(x)
y=keras.layers.MaxPool1D(pool_size=2,strides=1)(x) # strides 不指定 默认等于 pool_size
print("*"*20)
print(y)
输出如下图 上图GlobalMaxPool1D 相当于给每一个样本每列的最大值
而MaxPool1D就是普通的对每一个样本进行一个窗口（1D是一维列窗口）滑动取最大值。
以上为个人经验，希望能给大家一个参考，也希望大家多多支持。
