tfmri.layers.Conv1D

class Conv1D(*args, **kwargs)[source]

Bases: keras.layers.convolutional.conv1d.Conv1D

1D convolution layer (e.g. temporal convolution).

Note

This layer can be used as a drop-in replacement for tf.keras.layers.Conv1D. However, this one also supports complex-valued convolutions. Simply pass dtype='complex64' or dtype='complex128' to the layer constructor.

This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension 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 an input_shape argument (tuple of integers or None, e.g. (10, 128) for sequences of 10 vectors of 128-dimensional vectors, or (None, 128) for variable-length sequences of 128-dimensional vectors.

Examples:

>>> # The inputs are 128-length vectors with 10 timesteps, and the
>>> # batch size is 4.
>>> input_shape = (4, 10, 128)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv1D(
... 32, 3, activation='relu',input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 8, 32)

>>> # With extended batch shape [4, 7] (e.g. weather data where batch
>>> # dimensions correspond to spatial location and the third dimension
>>> # corresponds to time.)
>>> input_shape = (4, 7, 10, 128)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv1D(
... 32, 3, activation='relu', input_shape=input_shape[2:])(x)
>>> print(y.shape)
(4, 7, 8, 32)
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 a single integer,

specifying the length of the 1D convolution window.

strides: An integer or tuple/list of a single integer,

specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.

padding: One of "valid", "same" or "causal" (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. "causal" results in causal (dilated) convolutions, e.g. output[t] does not depend on input[t+1:]. Useful when modeling temporal data where the model should not violate the temporal order. See [WaveNet: A Generative Model for Raw Audio, section

data_format: A string,

one of channels_last (default) or channels_first.

dilation_rate: an integer or tuple/list of a single integer, specifying

the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides 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:

3+D tensor with shape: batch_shape + (steps, input_dim)

Output shape:
3+D tensor with shape: batch_shape + (new_steps, filters)

steps value might have changed due to padding or strides.

Returns:

A tensor of rank 3 representing activation(conv1d(inputs, kernel) + bias).

Raises:

ValueError: when both strides > 1 and dilation_rate > 1.