- TensorFlow
- API
- TensorFlow Core v2.8.0
- Python
tf.keras.layers.Dense
Just your regular densely-connected NN layer.
Inherits From: Layer, Module
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tf.compat.v1.keras.layers.Dense
Used in the notebooks
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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]| 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].