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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), groups=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"`

.

# 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)

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_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". |

`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` ). |

`bias_initializer` | Initializer for the bias vector (see `keras.initializers` ). |

`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` ). |

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'.

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` . |

© 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/r2.3/api_docs/python/tf/keras/layers/Conv3D