This chapter deals with the autograd and initializer API in MXNet.

mxnet.autograd

This is MXNet’ autograd API for NDArray. It has the following class −

Class: Function()

It is used for customised differentiation in autograd. It can be written as mxnet.autograd.Function. If, for any reason, the user do not want to use the gradients that are computed by the default chain-rule, then he/she can use Function class of mxnet.autograd to customize differentiation for computation. It has two methods namely Forward() and Backward().

Let us understand the working of this class with the help of following points −

• First, we need to define our computation in the forward method.

• Then, we need to provide the customized differentiation in the backward method.

• Now during gradient computation, instead of user-defined backward function, mxnet.autograd will use the backward function defined by the user. We can also cast to numpy array and back for some operations in forward as well as backward.

Example

Before using the mxnet.autograd.function class, let’s define a stable sigmoid function with backward as well as forward methods as follows −

class sigmoid(mx.autograd.Function):
   def forward(self, x):
      y = 1 / (1 + mx.nd.exp(-x))
      self.save_for_backward(y)
      return y
   
   def backward(self, dy):
      y, = self.saved_tensors
      return dy * y * (1-y)

Now, the function class can be used as follows −

func = sigmoid()
x = mx.nd.random.uniform(shape=(10,))
x.attach_grad()
with mx.autograd.record():
m = func(x)
m.backward()
dx_grad = x.grad.asnumpy()
dx_grad

Output

When you run the code, you will see the following output −

array([0.21458015, 0.21291625, 0.23330082, 0.2361367 , 0.23086983,
0.24060014, 0.20326573, 0.21093895, 0.24968489, 0.24301809],
dtype=float32)

Methods and their parameters

Following are the methods and their parameters of mxnet.autogard.function class −

Implementation Example

In the below example, we will be using mxnet.autograd.grad() method to compute the gradient of head with respect to variables −

x = mx.nd.ones((2,))
x.attach_grad()
with mx.autograd.record():
z = mx.nd.elemwise_add(mx.nd.exp(x), x)
dx_grad = mx.autograd.grad(z, [x], create_graph=True)
dx_grad

Output

The output is mentioned below −

[
[3.7182817 3.7182817]
<NDArray 2 @cpu(0)>]

We can use mxnet.autograd.predict_mode() method to return a scope to be used in ‘with’ statement −

with mx.autograd.record():
y = model(x)
with mx.autograd.predict_mode():
y = sampling(y)
backward([y])

mxnet.intializer

This is MXNet’ API for weigh initializer. It has the following classes −

Classes and their parameters

Following are the methods and their parameters of mxnet.autogard.function class:

Implementation Example

In the below example, we will be using mxnet.init.Normal() class create an initializer and retrieve its parameters −

init = mx.init.Normal(0.8)
init.dumps()

Output

The output is given below −

''["normal", {"sigma": 0.8}]''

Example

init = mx.init.Xavier(factor_type="in", magnitude=2.45)
init.dumps()

Output

The output is shown below −

''["xavier", {"rnd_type": "uniform", "factor_type": "in", "magnitude": 2.45}]''

In the below example, we will be using mxnet.initializer.Mixed() class to initialize parameters using multiple initializers −

init = mx.initializer.Mixed([''bias'', ''.*''], [mx.init.Zero(),
mx.init.Uniform(0.1)])
module.init_params(init)

for dictionary in module.get_params():
for key in dictionary:
print(key)
print(dictionary[key].asnumpy())

Output

The output is shown below −

fullyconnected1_weight
[[ 0.0097627 0.01856892 0.04303787]]
fullyconnected1_bias
[ 0.]