Dense
classkeras.layers.Dense(
units,
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,
lora_rank=None,
**kwargs
)
Just your regular densely-connected NN layer.
Dense
implements the operation:
output = activation(dot(input, kernel) + bias)
where activation
is the element-wise activation function
passed as the activation
argument, kernel
is a weights matrix
created by the layer, and bias
is a bias vector created by the layer
(only applicable if use_bias
is True
).
Note: If the input to the layer has a rank greater than 2, Dense
computes the dot product between the inputs
and the kernel
along the
last axis of the inputs
and axis 0 of the kernel
(using tf.tensordot
).
For example, if input has dimensions (batch_size, d0, d1)
, then we create
a kernel
with shape (d1, units)
, and the kernel
operates along axis 2
of the input
, on every sub-tensor of shape (1, 1, d1)
(there are
batch_size * d0
such sub-tensors). The output in this case will have
shape (batch_size, d0, units)
.
Arguments
a(x) = x
).kernel
weights matrix.kernel
weights matrix.kernel
weights matrix.Dense
layer by calling layer.enable_lora(rank)
.Input shape
N-D tensor with shape: (batch_size, ..., input_dim)
.
The most common situation would be
a 2D input with shape (batch_size, input_dim)
.
Output shape
N-D tensor with shape: (batch_size, ..., units)
.
For instance, for a 2D input with shape (batch_size, input_dim)
,
the output would have shape (batch_size, units)
.