Google I/O is a wrap! Catch up on TensorFlow sessions

# 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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