a Sic~@sTdZddlmZddlmZddlmZddlZddlmZddl m Z ddl m Z dd l m Z e d gd e d gd Gd ddeje jZe dgd e dgd d=ddZe dgd e dgd Gdddeje jZe dgd e dgd d>ddZe dgd e dgd Gdd d eje jZe d!gd e d"gd d?d#d$Ze d%gd e d&gd Gd'd(d(eje jZe d)gd e d*gd d@d+d,Ze d-gd e d.gd Gd/d0d0eje jZe d1gd e d2gd dAd3d4Ze d5gd e d6gd Gd7d8d8eje jZe d9gd e d:gd dBd;d<ZeZeZeZeZdS)Cz@Contains the pooling layer classes and their functional aliases.)absolute_import)division)print_functionN)layers)base) keras_export) tf_exportz1keras.__internal__.legacy.layers.AveragePooling1D)v1zlayers.AveragePooling1Dcs"eZdZdZdfdd ZZS)AveragePooling1Da4Average Pooling layer for 1D inputs. Args: pool_size: An integer or tuple/list of a single integer, representing the size of the pooling window. strides: An integer or tuple/list of a single integer, specifying the strides of the pooling operation. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. data_format: A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, length, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, length)`. name: A string, the name of the layer. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.AveragePooling1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python pooling = tf.compat.v1.layers.AveragePooling1D(pool_size=2, strides=2) ``` After: ```python pooling = tf.keras.layers.AveragePooling1D(pool_size=2, strides=2) ``` @end_compatibility valid channels_lastNc s2|durtdtjf|||||d|dSNz$Argument `strides` must not be None. pool_sizestridespadding data_formatname ValueErrorsuper__init__selfrrrrrkwargs __class__Z/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/legacy_tf_layers/pooling.pyrRs zAveragePooling1D.__init__)r r N__name__ __module__ __qualname____doc__r __classcell__rrrrr s 4r z2keras.__internal__.legacy.layers.average_pooling1dzlayers.average_pooling1dr r cCs(tjdddt|||||d}||S)aAverage Pooling layer for 1D inputs. Args: inputs: The tensor over which to pool. Must have rank 3. pool_size: An integer or tuple/list of a single integer, representing the size of the pooling window. strides: An integer or tuple/list of a single integer, specifying the strides of the pooling operation. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. 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, length, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, length)`. name: A string, the name of the layer. Returns: The output tensor, of rank 3. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.AveragePooling1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.average_pooling1d(x, pool_size=2, strides=2) ``` After: To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional): ```python x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.AveragePooling1D(pool_size=2, strides=2)(x) model = tf.keras.Model(x, y) ``` @end_compatibility z`tf.layers.average_pooling1d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.AveragePooling1D` instead. stacklevelr)warningswarnr inputsrrrrrlayerrrraverage_pooling1dgsEr-z-keras.__internal__.legacy.layers.MaxPooling1Dzlayers.MaxPooling1Dcs"eZdZdZdfdd ZZS) MaxPooling1Da$Max Pooling layer for 1D inputs. Args: pool_size: An integer or tuple/list of a single integer, representing the size of the pooling window. strides: An integer or tuple/list of a single integer, specifying the strides of the pooling operation. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. 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, length, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, length)`. name: A string, the name of the layer. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.MaxPooling1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python pooling = tf.compat.v1.layers.MaxPooling1D(pool_size=2, strides=2) ``` After: ```python pooling = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2) ``` @end_compatibility r r Nc s2|durtdtjf|||||d|dSr rrrrrrs zMaxPooling1D.__init__)r r Nrrrrrr.s 4r.z.keras.__internal__.legacy.layers.max_pooling1dzlayers.max_pooling1dcCs(tjdddt|||||d}||S)aMax Pooling layer for 1D inputs. Args: inputs: The tensor over which to pool. Must have rank 3. pool_size: An integer or tuple/list of a single integer, representing the size of the pooling window. strides: An integer or tuple/list of a single integer, specifying the strides of the pooling operation. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. 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, length, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, length)`. name: A string, the name of the layer. Returns: The output tensor, of rank 3. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.MaxPooling1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.max_pooling1d(x, pool_size=2, strides=2) ``` After: To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional): ```python x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2)(x) model = tf.keras.Model(x, y) ``` @end_compatibility z`tf.layers.max_pooling1d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling1D` instead.r%r&r)r(r)r.r*rrr max_pooling1dsEr/z1keras.__internal__.legacy.layers.AveragePooling2Dzlayers.AveragePooling2Dcs"eZdZdZdfdd ZZS)AveragePooling2Da Average pooling layer for 2D inputs (e.g. images). Args: pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. data_format: A string. The ordering of the dimensions in the inputs. `channels_last` (default) and `channels_first` are supported. `channels_last` corresponds to inputs with shape `(batch, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. name: A string, the name of the layer. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.AveragePooling2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python pooling = tf.compat.v1.layers.AveragePooling2D(pool_size=2, strides=2) ``` After: ```python pooling = tf.keras.layers.AveragePooling2D(pool_size=2, strides=2) ``` @end_compatibility r r Nc s2|durtdtjf|||||d|dSr rrrrrrs zAveragePooling2D.__init__)r r Nrrrrrr0Ys 8r0z2keras.__internal__.legacy.layers.average_pooling2dzlayers.average_pooling2dcCs(tjdddt|||||d}||S)aAverage pooling layer for 2D inputs (e.g. images). Args: inputs: The tensor over which to pool. Must have rank 4. pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. data_format: A string. The ordering of the dimensions in the inputs. `channels_last` (default) and `channels_first` are supported. `channels_last` corresponds to inputs with shape `(batch, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. name: A string, the name of the layer. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.AveragePooling2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.average_pooling2d(x, pool_size=2, strides=2) ``` After: To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional): ```python x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.AveragePooling2D(pool_size=2, strides=2)(x) model = tf.keras.Model(x, y) ``` @end_compatibility z`tf.layers.average_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.AveragePooling2D` instead.r%r&r)r(r)r0r*rrraverage_pooling2dsIr1z-keras.__internal__.legacy.layers.MaxPooling2Dzlayers.MaxPooling2Dcs"eZdZdZdfdd ZZS) MaxPooling2DaMax pooling layer for 2D inputs (e.g. images). Args: pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. data_format: A string. The ordering of the dimensions in the inputs. `channels_last` (default) and `channels_first` are supported. `channels_last` corresponds to inputs with shape `(batch, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. name: A string, the name of the layer. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.MaxPooling2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python pooling = tf.compat.v1.layers.MaxPooling2D(pool_size=2, strides=2) ``` After: ```python pooling = tf.keras.layers.MaxPooling2D(pool_size=2, strides=2) ``` @end_compatibility r r Nc s2|durtdtjf|||||d|dSr rrrrrr5s zMaxPooling2D.__init__)r r Nrrrrrr2s 8r2z.keras.__internal__.legacy.layers.max_pooling2dzlayers.max_pooling2dcCs(tjdddt|||||d}||S)a{Max pooling layer for 2D inputs (e.g. images). Args: inputs: The tensor over which to pool. Must have rank 4. pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. data_format: A string. The ordering of the dimensions in the inputs. `channels_last` (default) and `channels_first` are supported. `channels_last` corresponds to inputs with shape `(batch, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. name: A string, the name of the layer. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.MaxPooling2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.max_pooling2d(x, pool_size=2, strides=2) ``` After: To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional): ```python x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.MaxPooling2D(pool_size=2, strides=2)(x) model = tf.keras.Model(x, y) ``` @end_compatibility z`tf.layers.max_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling2D` instead.r%r&r)r(r)r2r*rrr max_pooling2dJsIr3z1keras.__internal__.legacy.layers.AveragePooling3Dzlayers.AveragePooling3Dcs"eZdZdZdfdd ZZS)AveragePooling3DaKAverage pooling layer for 3D inputs (e.g. volumes). Args: pool_size: An integer or tuple/list of 3 integers: (pool_depth, pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 3 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. data_format: A string. The ordering of the dimensions in the inputs. `channels_last` (default) and `channels_first` are supported. `channels_last` corresponds to inputs with shape `(batch, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. name: A string, the name of the layer. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.AveragePooling3D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python pooling = tf.compat.v1.layers.AveragePooling3D(pool_size=2, strides=2) ``` After: ```python pooling = tf.keras.layers.AveragePooling3D(pool_size=2, strides=2) ``` @end_compatibility r r Nc s2|durtdtjf|||||d|dSr rrrrrrs zAveragePooling3D.__init__)r r Nrrrrrr4s :r4z2keras.__internal__.legacy.layers.average_pooling3dzlayers.average_pooling3dcCs(tjdddt|||||d}||S)aAverage pooling layer for 3D inputs (e.g. volumes). Args: inputs: The tensor over which to pool. Must have rank 5. pool_size: An integer or tuple/list of 3 integers: (pool_depth, pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 3 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. data_format: A string. The ordering of the dimensions in the inputs. `channels_last` (default) and `channels_first` are supported. `channels_last` corresponds to inputs with shape `(batch, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. name: A string, the name of the layer. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.AveragePooling3D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.average_pooling3d(x, pool_size=2, strides=2) ``` After: To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional): ```python x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.AveragePooling3D(pool_size=2, strides=2)(x) model = tf.keras.Model(x, y) ``` @end_compatibility z`tf.layers.average_pooling3d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.AveragePooling3D` instead.r%r&r)r(r)r4r*rrraverage_pooling3dsKr5z-keras.__internal__.legacy.layers.MaxPooling3Dzlayers.MaxPooling3Dcs"eZdZdZdfdd ZZS) MaxPooling3Da;Max pooling layer for 3D inputs (e.g. volumes). Args: pool_size: An integer or tuple/list of 3 integers: (pool_depth, pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 3 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. data_format: A string. The ordering of the dimensions in the inputs. `channels_last` (default) and `channels_first` are supported. `channels_last` corresponds to inputs with shape `(batch, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. name: A string, the name of the layer. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.MaxPooling3D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python pooling = tf.compat.v1.layers.MaxPooling3D(pool_size=2, strides=2) ``` After: ```python pooling = tf.keras.layers.MaxPooling3D(pool_size=2, strides=2) ``` @end_compatibility r r Nc s2|durtdtjf|||||d|dSr rrrrrrs zMaxPooling3D.__init__)r r Nrrrrrr6Ls :r6z.keras.__internal__.legacy.layers.max_pooling3dzlayers.max_pooling3dcCs(tjdddt|||||d}||S)aMax pooling layer for 3D inputs (e.g. volumes). Args: inputs: The tensor over which to pool. Must have rank 5. pool_size: An integer or tuple/list of 3 integers: (pool_depth, pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 3 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions. padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive. data_format: A string. The ordering of the dimensions in the inputs. `channels_last` (default) and `channels_first` are supported. `channels_last` corresponds to inputs with shape `(batch, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. name: A string, the name of the layer. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is a legacy api that is only compatible with eager execution and `tf.function` if you combine it with `tf.compat.v1.keras.utils.track_tf1_style_variables` Please refer to [tf.layers model mapping section of the migration guide] (https://www.tensorflow.org/guide/migrate/model_mapping) to learn how to use your TensorFlow v1 model in TF2 with Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.MaxPooling3D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.max_pooling3d(x, pool_size=2, strides=2) ``` After: To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional): ```python x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.MaxPooling3D(pool_size=2, strides=2)(x) model = tf.keras.Model(x, y) ``` @end_compatibility z`tf.layers.max_pooling3d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling3D` instead.r%r&r)r(r)r6r*rrr max_pooling3dsJr7)r r N)r r N)r r N)r r N)r r N)r r N)r# __future__rrrr(kerasr keras_layersZkeras.legacy_tf_layersr tensorflow.python.util.tf_exportrrr Layerr-r.r/r0r1r2r3r4r5r6r7 AvgPool2D MaxPool2D max_pool2d avg_pool2drrrrs         F  S  F  S  J  W  J  W  L  Y  L  Z