layer_max_pooling_2d: Max pooling operation for 2D spatial data.
In rstudio/keras: R Interface to 'Keras'
layer_max_pooling_2d R Documentation
Max pooling operation for 2D spatial data.
Description
Downsamples the input along its spatial dimensions (height and width)
by taking the maximum value over an input window
(of size defined by pool_size) for each channel of the input.
The window is shifted by strides along each dimension.
The resulting output when using the "valid" padding option has a spatial
shape (number of rows or columns) of:
output_shape = floor((input_shape - pool_size) / strides) + 1
(when input_shape >= pool_size)
The resulting output shape when using the "same" padding option is:
output_shape = floor((input_shape - 1) / strides) + 1
Usage
layer_max_pooling_2d(
object,
pool_size = list(2L, 2L),
strides = NULL,
padding = "valid",
data_format = NULL,
name = NULL,
...
)
Arguments
object
Object to compose the layer with. A tensor, array, or sequential model.
pool_size
int or list of 2 integers, factors by which to downscale
(dim1, dim2). If only one integer is specified, the same
window length will be used for all dimensions.
strides
int or list of 2 integers, or NULL. Strides values. If NULL,
it will default to pool_size. If only one int is specified, the
same stride size will be used for all dimensions.
padding
string, either "valid" or "same" (case-insensitive).
"valid" means no padding. "same" results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
data_format
string, either "channels_last" or "channels_first".
The ordering of the dimensions in the inputs. "channels_last"
corresponds to inputs with shape (batch, height, width, channels)
while "channels_first" corresponds to inputs with shape
(batch, channels, height, width). It defaults to the
image_data_format value found in your Keras config file at
~/.keras/keras.json. If you never set it, then it will be
"channels_last".
name
String, name for the object
...
For forward/backward compatability.
Value
The return value depends on the value provided for the first argument.
If object is:
a keras_model_sequential(), then the layer is added to the sequential model
(which is modified in place). To enable piping, the sequential model is also
returned, invisibly.
a keras_input(), then the output tensor from calling layer(input) is returned.
-
NULL or missing, then a Layer instance is returned.
Input Shape
If data_format="channels_last":
4D tensor with shape (batch_size, height, width, channels).
If data_format="channels_first":
4D tensor with shape (batch_size, channels, height, width).
Output Shape
If data_format="channels_last":
4D tensor with shape
(batch_size, pooled_height, pooled_width, channels).
If data_format="channels_first":
4D tensor with shape
(batch_size, channels, pooled_height, pooled_width).
Examples
strides = (1, 1) and padding = "valid":
x <- rbind(c(1., 2., 3.),
c(4., 5., 6.),
c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1))
max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),
strides = c(1, 1),
padding = "valid")
max_pool_2d(x)
## tf.Tensor(
## [[[[5.]
## [6.]]
##
## [[8.]
## [9.]]]], shape=(1, 2, 2, 1), dtype=float32)
strides = c(2, 2) and padding = "valid":
x <- rbind(c(1., 2., 3., 4.),
c(5., 6., 7., 8.),
c(9., 10., 11., 12.)) |> op_reshape(c(1, 3, 4, 1))
max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),
strides = c(2, 2),
padding = "valid")
max_pool_2d(x)
## tf.Tensor(
## [[[[6.]
## [8.]]]], shape=(1, 1, 2, 1), dtype=float32)
stride = (1, 1) and padding = "same":
x <- rbind(c(1., 2., 3.),
c(4., 5., 6.),
c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1))
max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),
strides = c(1, 1),
padding = "same")
max_pool_2d(x)
## tf.Tensor(
## [[[[5.]
## [6.]
## [6.]]
##
## [[8.]
## [9.]
## [9.]]
##
## [[8.]
## [9.]
## [9.]]]], shape=(1, 3, 3, 1), dtype=float32)
See Also
Other pooling layers:
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_max_pooling_1d()
layer_max_pooling_3d()
Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()
rstudio/keras documentation built on May 8, 2024, 7:36 a.m.
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| layer_max_pooling_2d | R Documentation |
Max pooling operation for 2D spatial data.
Description
Downsamples the input along its spatial dimensions (height and width)
by taking the maximum value over an input window
(of size defined by pool_size) for each channel of the input.
The window is shifted by strides along each dimension.
The resulting output when using the "valid" padding option has a spatial
shape (number of rows or columns) of:
output_shape = floor((input_shape - pool_size) / strides) + 1
(when input_shape >= pool_size)
The resulting output shape when using the "same" padding option is:
output_shape = floor((input_shape - 1) / strides) + 1
Usage
layer_max_pooling_2d(
object,
pool_size = list(2L, 2L),
strides = NULL,
padding = "valid",
data_format = NULL,
name = NULL,
...
)
Arguments
object |
Object to compose the layer with. A tensor, array, or sequential model. |
pool_size |
int or list of 2 integers, factors by which to downscale (dim1, dim2). If only one integer is specified, the same window length will be used for all dimensions. |
strides |
int or list of 2 integers, or |
padding |
string, either |
data_format |
string, either |
name |
String, name for the object |
... |
For forward/backward compatability. |
Value
The return value depends on the value provided for the first argument.
If object is:
a
keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.a
keras_input(), then the output tensor from callinglayer(input)is returned.-
NULLor missing, then aLayerinstance is returned.
Input Shape
If
data_format="channels_last": 4D tensor with shape(batch_size, height, width, channels).If
data_format="channels_first": 4D tensor with shape(batch_size, channels, height, width).
Output Shape
If
data_format="channels_last": 4D tensor with shape(batch_size, pooled_height, pooled_width, channels).If
data_format="channels_first": 4D tensor with shape(batch_size, channels, pooled_height, pooled_width).
Examples
strides = (1, 1) and padding = "valid":
x <- rbind(c(1., 2., 3.),
c(4., 5., 6.),
c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1))
max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),
strides = c(1, 1),
padding = "valid")
max_pool_2d(x)
## tf.Tensor( ## [[[[5.] ## [6.]] ## ## [[8.] ## [9.]]]], shape=(1, 2, 2, 1), dtype=float32)
strides = c(2, 2) and padding = "valid":
x <- rbind(c(1., 2., 3., 4.),
c(5., 6., 7., 8.),
c(9., 10., 11., 12.)) |> op_reshape(c(1, 3, 4, 1))
max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),
strides = c(2, 2),
padding = "valid")
max_pool_2d(x)
## tf.Tensor( ## [[[[6.] ## [8.]]]], shape=(1, 1, 2, 1), dtype=float32)
stride = (1, 1) and padding = "same":
x <- rbind(c(1., 2., 3.),
c(4., 5., 6.),
c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1))
max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),
strides = c(1, 1),
padding = "same")
max_pool_2d(x)
## tf.Tensor( ## [[[[5.] ## [6.] ## [6.]] ## ## [[8.] ## [9.] ## [9.]] ## ## [[8.] ## [9.] ## [9.]]]], shape=(1, 3, 3, 1), dtype=float32)
See Also
Other pooling layers:
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_max_pooling_1d()
layer_max_pooling_3d()
Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()
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