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). These are all attributes of
Dense.
Besides, layer attributes cannot be modified after the layer has been called
once (except the trainable attribute).
When a popular kwarg input_shape is passed, then keras will create
an input layer to insert before the current layer. This can be treated
equivalent to explicitly defining an InputLayer.
Example:
# Create a `Sequential` model and add a Dense layer as the first layer.model = tf.keras.models.Sequential()model.add(tf.keras.Input(shape=(16,)))model.add(tf.keras.layers.Dense(32, activation='relu'))# Now the model will take as input arrays of shape (None, 16)# and output arrays of shape (None, 32).# Note that after the first layer, you don't need to specify# the size of the input anymore:model.add(tf.keras.layers.Dense(32))model.output_shape(None, 32)
Args
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).