### MaxPooling1D

```
keras.layers.pooling.MaxPooling1D(pool_length=2, stride=None, border_mode='valid')
```

Max pooling operation for temporal data.

**Input shape**

3D tensor with shape: `(samples, steps, features)`

.

**Output shape**

3D tensor with shape: `(samples, downsampled_steps, features)`

.

**Arguments**

**pool_length**: size of the region to which max pooling is applied**stride**: integer, or None. factor by which to downscale. 2 will halve the input. If None, it will default to`pool_length`

.**border_mode**: 'valid' or 'same'.

### MaxPooling2D

```
keras.layers.pooling.MaxPooling2D(pool_size=(2, 2), strides=None, border_mode='valid', dim_ordering='default')
```

Max pooling operation for spatial data.

**Arguments**

**pool_size**: tuple of 2 integers, factors by which to downscale (vertical, horizontal). (2, 2) will halve the image in each dimension.**strides**: tuple of 2 integers, or None. Strides values. If None, it will default to`pool_size`

.**border_mode**: 'valid' or 'same'.**dim_ordering**: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 3. It defaults to the`image_dim_ordering`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "tf".

**Input shape**

4D tensor with shape:
`(samples, channels, rows, cols)`

if dim_ordering='th'
or 4D tensor with shape:
`(samples, rows, cols, channels)`

if dim_ordering='tf'.

**Output shape**

4D tensor with shape:
`(nb_samples, channels, pooled_rows, pooled_cols)`

if dim_ordering='th'
or 4D tensor with shape:
`(samples, pooled_rows, pooled_cols, channels)`

if dim_ordering='tf'.

### MaxPooling3D

```
keras.layers.pooling.MaxPooling3D(pool_size=(2, 2, 2), strides=None, border_mode='valid', dim_ordering='default')
```

Max pooling operation for 3D data (spatial or spatio-temporal).

**Arguments**

**pool_size**: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). (2, 2, 2) will halve the size of the 3D input in each dimension.**strides**: tuple of 3 integers, or None. Strides values.**border_mode**: 'valid' or 'same'.**dim_ordering**: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 4. It defaults to the`image_dim_ordering`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "tf".

**Input shape**

5D tensor with shape:
`(samples, channels, len_pool_dim1, len_pool_dim2, len_pool_dim3)`

if dim_ordering='th'
or 5D tensor with shape:
`(samples, len_pool_dim1, len_pool_dim2, len_pool_dim3, channels)`

if dim_ordering='tf'.

**Output shape**

5D tensor with shape:
`(nb_samples, channels, pooled_dim1, pooled_dim2, pooled_dim3)`

if dim_ordering='th'
or 5D tensor with shape:
`(samples, pooled_dim1, pooled_dim2, pooled_dim3, channels)`

if dim_ordering='tf'.

### AveragePooling1D

```
keras.layers.pooling.AveragePooling1D(pool_length=2, stride=None, border_mode='valid')
```

Average pooling for temporal data.

**Arguments**

**pool_length**: factor by which to downscale. 2 will halve the input.**stride**: integer, or None. Stride value. If None, it will default to`pool_length`

.**border_mode**: 'valid' or 'same'.

**Input shape**

3D tensor with shape: `(samples, steps, features)`

.

**Output shape**

3D tensor with shape: `(samples, downsampled_steps, features)`

.

### AveragePooling2D

```
keras.layers.pooling.AveragePooling2D(pool_size=(2, 2), strides=None, border_mode='valid', dim_ordering='default')
```

Average pooling operation for spatial data.

**Arguments**

**pool_size**: tuple of 2 integers, factors by which to downscale (vertical, horizontal). (2, 2) will halve the image in each dimension.**strides**: tuple of 2 integers, or None. Strides values. If None, it will default to`pool_size`

.**border_mode**: 'valid' or 'same'.**dim_ordering**: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 3. It defaults to the`image_dim_ordering`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "tf".

**Input shape**

4D tensor with shape:
`(samples, channels, rows, cols)`

if dim_ordering='th'
or 4D tensor with shape:
`(samples, rows, cols, channels)`

if dim_ordering='tf'.

**Output shape**

4D tensor with shape:
`(nb_samples, channels, pooled_rows, pooled_cols)`

if dim_ordering='th'
or 4D tensor with shape:
`(samples, pooled_rows, pooled_cols, channels)`

if dim_ordering='tf'.

### AveragePooling3D

```
keras.layers.pooling.AveragePooling3D(pool_size=(2, 2, 2), strides=None, border_mode='valid', dim_ordering='default')
```

Average pooling operation for 3D data (spatial or spatio-temporal).

**Arguments**

**pool_size**: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). (2, 2, 2) will halve the size of the 3D input in each dimension.**strides**: tuple of 3 integers, or None. Strides values.**border_mode**: 'valid' or 'same'.**dim_ordering**: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 4. It defaults to the`image_dim_ordering`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "tf".

**Input shape**

5D tensor with shape:
`(samples, channels, len_pool_dim1, len_pool_dim2, len_pool_dim3)`

if dim_ordering='th'
or 5D tensor with shape:
`(samples, len_pool_dim1, len_pool_dim2, len_pool_dim3, channels)`

if dim_ordering='tf'.

**Output shape**

5D tensor with shape:
`(nb_samples, channels, pooled_dim1, pooled_dim2, pooled_dim3)`

if dim_ordering='th'
or 5D tensor with shape:
`(samples, pooled_dim1, pooled_dim2, pooled_dim3, channels)`

if dim_ordering='tf'.

### GlobalMaxPooling1D

```
keras.layers.pooling.GlobalMaxPooling1D()
```

Global max pooling operation for temporal data.

**Input shape**

3D tensor with shape: `(samples, steps, features)`

.

**Output shape**

2D tensor with shape: `(samples, features)`

.

### GlobalAveragePooling1D

```
keras.layers.pooling.GlobalAveragePooling1D()
```

Global average pooling operation for temporal data.

**Input shape**

3D tensor with shape: `(samples, steps, features)`

.

**Output shape**

2D tensor with shape: `(samples, features)`

.

### GlobalMaxPooling2D

```
keras.layers.pooling.GlobalMaxPooling2D(dim_ordering='default')
```

Global max pooling operation for spatial data.

**Arguments**

**dim_ordering**: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 3. It defaults to the`image_dim_ordering`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "tf".

**Input shape**

4D tensor with shape:
`(samples, channels, rows, cols)`

if dim_ordering='th'
or 4D tensor with shape:
`(samples, rows, cols, channels)`

if dim_ordering='tf'.

**Output shape**

2D tensor with shape:
`(nb_samples, channels)`

### GlobalAveragePooling2D

```
keras.layers.pooling.GlobalAveragePooling2D(dim_ordering='default')
```

Global average pooling operation for spatial data.

**Arguments**

**dim_ordering**: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 3. It defaults to the`image_dim_ordering`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "tf".

**Input shape**

`(samples, channels, rows, cols)`

if dim_ordering='th'
or 4D tensor with shape:
`(samples, rows, cols, channels)`

if dim_ordering='tf'.

**Output shape**

2D tensor with shape:
`(nb_samples, channels)`