参考：Keras API reference / Layers API / Core layers / Dense layer
语法如下：
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
)
Just your regular densely-connected NN 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).
Note: If the input to the layer has a rank greater than 2, then Dense computes the dot product between the inputs and the kernel along the last axis of the inputs and axis 1 of the kernel (using tf.tensordot). For example, if input has dimensions (batch_size, d0, d1), then we create a kernel with shape (d1, units), and the kernel operates along axis 2 of the input, on every sub-tensor of shape (1, 1, d1) (there are batch_size * d0 such sub-tensors). The output in this case will have shape (batch_size, d0, units).
Besides, layer attributes cannot be modified after the layer has been called once (except the trainable attribute).
主要是针对高亮的部分进行解读。
当 inputs 的数据的秩超过2（这里粗浅的认为是维度）时，Dense 沿着 inputs 的最后一个维度与 kernel 做叉乘。
举例：
inputs 的维度为 $X=(batch\_size, d_0, d_1)$, kernel 的维度为 $W=(d_1, units)$，因此输出层可以按照如下计算：
$$Y=X \times W$$
由此可得，输出维度为 $Y=(batch\_size, d_0, units)$。这个实际上是不难理解的，但是应用到神经网络上就不一样了。
相当于最后一个维度 $d_1$ 对 $units$ 做了 $d_0$ 个全连接，同时它们公用一个 kernel，这也就是 Attention 实现的方法，只要对三维的输入做了一个 Dense，就相当于都变成了一个数，也就是 $\alpha$。
