Exercise

# Simple network using Keras

By now you have an intuitive understanding of how the gradient values become lesser and lesser as we back-propagate. In this exercise, you'll work on an example to demonstrate this vanishing gradient problem. You'll create a simple network of Dense layers using Keras and checkout the gradient values of the weights for one iteration of back-propagation.

The `Sequential`

model and the `Dense`

and `Activation`

layers are already imported from Keras. The Keras module `backend`

is also imported. This has a method `.gradients()`

that can be used to get the gradient values of the weights.

Instructions 1/2

**undefined XP**

- Create a
`Sequential`

model. - Add a
`Dense`

layer of 12 units with`'relu'`

activation,`'uniform'`

initialization and input dimension of 8 to the model - Add a
`Dense`

layer of 8 units with`'relu'`

activation,`'uniform'`

initialization to the model.