TensorFlow 1 version | View source on GitHub |
Just your regular densely-connected NN layer.
Inherits From: Layer
tf.keras.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,
**kwargs
)
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
).
Example:
# as first layer in a sequential model:
model = Sequential()
model.add(Dense(32, input_shape=(16,)))
# now the model will take as input arrays of shape (*, 16)
# and output arrays of shape (*, 32)
# after the first layer, you don't need to specify
# the size of the input anymore:
model.add(Dense(32))
Arguments | |
---|---|
units
|
Positive integer, dimensionality of the output space. |
activation
|
Activation function to use.
If you don't specify anything, no activation is applied
(ie. "linear" activation: a(x) = x ).
|
use_bias
|
Boolean, whether the layer uses a bias vector. |
kernel_initializer
|
Initializer for the kernel weights matrix.
|
bias_initializer
|
Initializer for the bias vector. |
kernel_regularizer
|
Regularizer function applied to
the kernel weights matrix.
|
bias_regularizer
|
Regularizer function applied to the bias vector. |
activity_regularizer
|
Regularizer function applied to the output of the layer (its "activation").. |
kernel_constraint
|
Constraint function applied to
the kernel weights matrix.
|
bias_constraint
|
Constraint function applied to the bias vector. |
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)
.