tf.compat.v1.layers.conv3d

Densely-connected layer class.

Inherits From: Dense, Layer, Layer, Module

tf.compat.v1.layers.Dense(
    units, 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 implements the operation: outputs = activation(inputs * kernel + bias) Where activation is the activation function passed as the activation argument (if not None), kernel is a weights matrix created by the layer, and bias is a bias vector created by the layer (only if use_bias is True).

Arguments
`units`	Integer or Long, dimensionality of the output space.
`activation`	Activation function (callable). Set it to None to maintain a linear activation.
`use_bias`	Boolean, whether the layer uses a bias.
`kernel_initializer`	Initializer function for the weight matrix. If `None` (default), weights are initialized using the default initializer used by `tf.compat.v1.get_variable`.
`bias_initializer`	Initializer function for the bias.
`kernel_regularizer`	Regularizer function for the weight matrix.
`bias_regularizer`	Regularizer function for the bias.
`activity_regularizer`	Regularizer function for the output.
`kernel_constraint`	An optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.
`bias_constraint`	An optional projection function to be applied to the bias after being updated by an `Optimizer`.
`trainable`	Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
`name`	String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
`_reuse`	Boolean, whether to reuse the weights of a previous layer by the same name.

Properties:

units: Python integer, dimensionality of the output space.
activation: Activation function (callable).
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer instance (or name) for the kernel matrix.
bias_initializer: Initializer instance (or name) for the bias.
kernel_regularizer: Regularizer instance for the kernel matrix (callable)
bias_regularizer: Regularizer instance for the bias (callable).
activity_regularizer: Regularizer instance for the output (callable)
kernel_constraint: Constraint function for the kernel matrix.
bias_constraint: Constraint function for the bias.
kernel: Weight matrix (TensorFlow variable or tensor).
bias: Bias vector, if applicable (TensorFlow variable or tensor).

Attributes
`graph`
`scope_name`

TensorFlow

tf

tf.audio

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tf.config

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tf.compat / v1 / v1.layers.conv3d_transpose

Arguments

Properties:

Attributes