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tf.keras.layers.Dense

Just your regular densely-connected NN layer.

Inherits From: 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).

Example:

# as first layer in a sequential model:
model = Sequential()
# 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:

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).

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