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3D convolution layer (e.g. spatial convolution over volumes).
tf.keras.layers.Conv3D( filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format=None, dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs )
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, 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"
.
Arguments | |
---|---|
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). |
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, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds to inputs with shape (batch, 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". |
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. |
activation | Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x ). |
use_bias | Boolean, whether the layer uses a bias vector. |
kernel_initializer | Initializer for the kernel weights matrix. |
bias_initializer | Initializer for the bias vector. |
kernel_regularizer | Regularizer function applied to the kernel weights matrix. |
bias_regularizer | Regularizer function applied to the bias vector. |
activity_regularizer | Regularizer function applied to the output of the layer (its "activation").. |
kernel_constraint | Constraint function applied to the kernel matrix. |
bias_constraint | Constraint function applied to the bias vector. |
5D tensor with shape: (samples, channels, conv_dim1, conv_dim2, conv_dim3)
if data_format='channels_first' or 5D tensor with shape: (samples, conv_dim1, conv_dim2, conv_dim3, channels)
if data_format='channels_last'.
5D tensor with shape: (samples, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)
if data_format='channels_first' or 5D tensor with shape: (samples, 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.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/layers/Conv3D