1D convolution layer (e.g. temporal convolution).
Compat aliases for migration
See Migration guide for more details.
tf.layers.Conv1D( filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs )
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias is True (and a
bias_initializer is provided), a bias vector is created and added to the outputs. Finally, if
activation is not
None, it is applied to the outputs as well.
| ||Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).|
| ||An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.|
| || 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 |
| || One of |
| || A string, one of |
| || An integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any |
| ||Activation function. Set it to None to maintain a linear activation.|
| ||Boolean, whether the layer uses a bias.|
| ||An initializer for the convolution kernel.|
| ||An initializer for the bias vector. If None, the default initializer will be used.|
| ||Optional regularizer for the convolution kernel.|
| ||Optional regularizer for the bias vector.|
| ||Optional regularizer function for the output.|
| || Optional projection function to be applied to the kernel after being updated by an |
| || Optional projection function to be applied to the bias after being updated by an |
| || Boolean, if |
| ||A string, the name of the layer.|
| ||DEPRECATED FUNCTION|
© 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.