o Me ã@szdZddlmZddlmZddlmZddlmZddlmZddlm Z ddl m Z e d d ƒGd d „d e ƒƒZ e Z d S)zKeras 3D convolution layer.é)Ú activations)Ú constraints)Ú initializers)Ú regularizers)Úutils)ÚConv)Ú keras_exportzkeras.layers.Conv3Dzkeras.layers.Convolution3DcsDeZdZdZej              d ‡fd d „ ƒZ‡ZS) ÚConv3Da#3D convolution layer (e.g. spatial convolution over volumes). 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, 128, 1)` for 128x128x128 volumes with a single channel, in `data_format="channels_last"`. Examples: >>> # The inputs are 28x28x28 volumes with a single channel, and the >>> # batch size is 4 >>> input_shape =(4, 28, 28, 28, 1) >>> x = tf.random.normal(input_shape) >>> y = tf.keras.layers.Conv3D( ... 2, 3, activation='relu', input_shape=input_shape[1:])(x) >>> print(y.shape) (4, 26, 26, 26, 2) >>> # With extended batch shape [4, 7], e.g. a batch of 4 videos of >>> # 3D frames, with 7 frames per video. >>> input_shape = (4, 7, 28, 28, 28, 1) >>> x = tf.random.normal(input_shape) >>> y = tf.keras.layers.Conv3D( ... 2, 3, activation='relu', input_shape=input_shape[2:])(x) >>> print(y.shape) (4, 7, 26, 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 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along each spatial dimension. 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 such that output has the same height/width dimension 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_shape + (spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `batch_shape + (channels, spatial_dim1, spatial_dim2, spatial_dim3)`. 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". Note that the `channels_first` format is currently not supported by TensorFlow on CPU. dilation_rate: an integer or tuple/list of 3 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: 5+D tensor with shape: `batch_shape + (channels, conv_dim1, conv_dim2, conv_dim3)` if data_format='channels_first' or 5+D tensor with shape: `batch_shape + (conv_dim1, conv_dim2, conv_dim3, channels)` if data_format='channels_last'. Output shape: 5+D tensor with shape: `batch_shape + (filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)` if data_format='channels_first' or 5+D tensor with shape: `batch_shape + (new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)` if data_format='channels_last'. `new_conv_dim1`, `new_conv_dim2` and `new_conv_dim3` values might have changed due to padding. Returns: A tensor of rank 5+ representing `activation(conv3d(inputs, kernel) + bias)`. Raises: ValueError: if `padding` is "causal". ValueError: when both `strides > 1` and `dilation_rate > 1`. ©ér r ÚvalidNr TÚglorot_uniformÚzerosc s¬tƒjdidd“d|“d|“d|“d|“d|“d|“d |“d t |¡“d | “d t | ¡“d t | ¡“dt | ¡“dt | ¡“dt |¡“dt |¡“dt |¡“|¤ŽdS)NZrankéÚfiltersÚ kernel_sizeÚstridesÚpaddingÚ data_formatÚ dilation_rateÚgroupsÚ activationÚuse_biasÚkernel_initializerÚbias_initializerÚkernel_regularizerÚbias_regularizerÚactivity_regularizerÚkernel_constraintÚbias_constraint©)ÚsuperÚ__init__rÚgetrrr)ÚselfrrrrrrrrrrrrrrrrÚkwargs©Ú __class__r úQD:\Projects\ConvertPro\env\Lib\site-packages\keras/layers/convolutional/conv3d.pyr"ŽsH ÿþýüûúùø ÷ ö õ ô ó ò ñ ð  îzConv3D.__init__)r r Nr r NTr rNNNNN)Ú__name__Ú __module__Ú __qualname__Ú__doc__rZallow_initializer_layoutr"Ú __classcell__r r r&r(r s$oïr N)r,ZkerasrrrrZ keras.dtensorrZ$keras.layers.convolutional.base_convrZ tensorflow.python.util.tf_exportrr Z Convolution3Dr r r r(Ús