a Sic!@szdZddlmZddlmZddlmZddlmZddlmZddlm Z ddl m Z e d d Gd d d e Z e Z d S)zKeras 2D convolution layer.) activations) constraints) initializers) regularizers)utils)Conv) keras_exportzkeras.layers.Conv2Dzkeras.layers.Convolution2Dcs(eZdZdZejd fd d ZZS) Conv2Da2D convolution layer (e.g. spatial convolution over images). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If `use_bias` is True, a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. When using this layer as the first layer in a model, provide the keyword argument `input_shape` (tuple of integers or `None`, does not include the sample axis), e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures in `data_format="channels_last"`. You can use `None` when a dimension has variable size. Examples: >>> # The inputs are 28x28 RGB images with `channels_last` and the batch >>> # size is 4. >>> input_shape = (4, 28, 28, 3) >>> x = tf.random.normal(input_shape) >>> y = tf.keras.layers.Conv2D( ... 2, 3, activation='relu', input_shape=input_shape[1:])(x) >>> print(y.shape) (4, 26, 26, 2) >>> # With `dilation_rate` as 2. >>> input_shape = (4, 28, 28, 3) >>> x = tf.random.normal(input_shape) >>> y = tf.keras.layers.Conv2D( ... 2, 3, ... activation='relu', ... dilation_rate=2, ... input_shape=input_shape[1:])(x) >>> print(y.shape) (4, 24, 24, 2) >>> # With `padding` as "same". >>> input_shape = (4, 28, 28, 3) >>> x = tf.random.normal(input_shape) >>> y = tf.keras.layers.Conv2D( ... 2, 3, activation='relu', padding="same", input_shape=input_shape[1:])(x) >>> print(y.shape) (4, 28, 28, 2) >>> # With extended batch shape [4, 7]: >>> input_shape = (4, 7, 28, 28, 3) >>> x = tf.random.normal(input_shape) >>> y = tf.keras.layers.Conv2D( ... 2, 3, activation='relu', input_shape=input_shape[2:])(x) >>> print(y.shape) (4, 7, 26, 26, 2) Args: filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution 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 convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding with zeros evenly to the left/right or up/down of the input. When `padding="same"` and `strides=1`, the output has the same size as the input. 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_size, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch_size, 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`. dilation_rate: an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. groups: A positive integer specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with `filters / groups` filters. The output is the concatenation of all the `groups` results along the channel axis. Input channels and `filters` must both be divisible by `groups`. activation: Activation function to use. If you don't specify anything, no activation is applied (see `keras.activations`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix (see `keras.initializers`). Defaults to 'glorot_uniform'. bias_initializer: Initializer for the bias vector (see `keras.initializers`). Defaults to 'zeros'. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see `keras.regularizers`). bias_regularizer: Regularizer function applied to the bias vector (see `keras.regularizers`). activity_regularizer: Regularizer function applied to the output of the layer (its "activation") (see `keras.regularizers`). kernel_constraint: Constraint function applied to the kernel matrix (see `keras.constraints`). bias_constraint: Constraint function applied to the bias vector (see `keras.constraints`). Input shape: 4+D tensor with shape: `batch_shape + (channels, rows, cols)` if `data_format='channels_first'` or 4+D tensor with shape: `batch_shape + (rows, cols, channels)` if `data_format='channels_last'`. Output shape: 4+D tensor with shape: `batch_shape + (filters, new_rows, new_cols)` if `data_format='channels_first'` or 4+D tensor with shape: `batch_shape + (new_rows, new_cols, filters)` if `data_format='channels_last'`. `rows` and `cols` values might have changed due to padding. Returns: A tensor of rank 4+ representing `activation(conv2d(inputs, kernel) + bias)`. Raises: ValueError: if `padding` is `"causal"`. ValueError: when both `strides > 1` and `dilation_rate > 1`. r validNr Tglorot_uniformzerosc sjtjfd|||||||t|| t| t| t| t| t|t|t|d|dS)N)rankfilters kernel_sizestridespadding data_format dilation_rategroups activationuse_biaskernel_initializerbias_initializerkernel_regularizerbias_regularizeractivity_regularizerkernel_constraintbias_constraint)super__init__rgetrrr)selfrrrrrrrrrrrrrrrr kwargs __class__]/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/layers/convolutional/conv2d.pyr"s*zConv2D.__init__)r r Nr r NTr rNNNNN)__name__ __module__ __qualname____doc__rallow_initializer_layoutr" __classcell__r(r(r&r)r s"~r N)r-kerasrrrr keras.dtensorr$keras.layers.convolutional.base_convr tensorflow.python.util.tf_exportrr Convolution2Dr(r(r(r)s       -