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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 ObJAX CIFAR10 example
This example is based on cifar10_simple.py with few minor changes
Step1:  Parameters
Step3:  Setup dataset and model
Step4:  Model parameters
Step5:  Training loop
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Python Code:
%pip --quiet install objax
import math
import random
import numpy as np
import tensorflow_datasets as tfds
import objax
from objax.zoo.wide_resnet import WideResNet
Explanation: ObJAX CIFAR10 example
This example is based on cifar10_simple.py with few minor changes:
it demonstrates how to do weight decay,
it uses Momentum optimizer with learning rate schedule,
it uses tensorflow_datasets instead of Keras dataset.
It's recommended to run this notebook on GPU. In Google Colab this could be set through Runtime -&gt; Change runtime type menu.
Installation and Imports
End of explanation
base_learning_rate = 0.1 # Learning rate
lr_decay_epochs = 30     # How often to decay learning rate
lr_decay_factor = 0.2    # By how much to decay learning rate
weight_decay =  0.0005   # Weight decay
batch_size = 128         # Batch size
num_train_epochs = 100   # Number of training epochs
wrn_width = 2            # Width of WideResNet
wrn_depth = 28           # Depth of WideResNet
Explanation: Parameters
End of explanation
# Augmentation function for input data
def augment(x):  # x is NCHW
  Random flip and random shift augmentation of image batch.
  if random.random() < .5:
    x = x[:, :, :, ::-1]  # Flip the batch images about the horizontal axis
  # Pixel-shift all images in the batch by up to 4 pixels in any direction.
  x_pad = np.pad(x, [[0, 0], [0, 0], [4, 4], [4, 4]], 'reflect')
  rx, ry = np.random.randint(0, 4), np.random.randint(0, 4)
  x = x_pad[:, :, rx:rx + 32, ry:ry + 32]
  return x
# Data
data = tfds.as_numpy(tfds.load(name='cifar10', batch_size=-1))
x_train = data['train']['image'].transpose(0, 3, 1, 2) / 255.0
y_train = data['train']['label']
x_test = data['test']['image'].transpose(0, 3, 1, 2) / 255.0
y_test = data['test']['label']
del data
# Model
model = WideResNet(nin=3, nclass=10, depth=wrn_depth, width=wrn_width)
weight_decay_vars = [v for k, v in model.vars().items() if k.endswith('.w')]
# Optimizer
opt = objax.optimizer.Momentum(model.vars(), nesterov=True)
# Prediction operation
predict_op = objax.nn.Sequential([objax.ForceArgs(model, training=False), objax.functional.softmax])
predict_op = objax.Jit(predict_op)
# Loss and training op
@objax.Function.with_vars(model.vars())
def loss_fn(x, label):
  logit = model(x, training=True)
  xe_loss = objax.functional.loss.cross_entropy_logits_sparse(logit, label).mean()
  wd_loss = sum((v ** 2).sum() for v in weight_decay_vars)
  return xe_loss + weight_decay * wd_loss
loss_gv = objax.GradValues(loss_fn, model.vars())
@objax.Function.with_vars(model.vars() + loss_gv.vars() + opt.vars())
def train_op(x, y, learning_rate):
    grads, loss = loss_gv(x, y)
    opt(learning_rate, grads)
    return loss
train_op = objax.Jit(train_op)
Explanation: Setup dataset and model
End of explanation
print(model.vars())
Explanation: Model parameters
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def lr_schedule(epoch):
  return base_learning_rate * math.pow(lr_decay_factor, epoch // lr_decay_epochs)
num_train_examples = x_train.shape[0]
num_test_examples = x_test.shape[0]
for epoch in range(num_train_epochs):
  # Training
  example_indices = np.arange(num_train_examples)
  np.random.shuffle(example_indices)
  for idx in range(0, num_train_examples, batch_size):
    x = x_train[example_indices[idx:idx + batch_size]]
    y = y_train[example_indices[idx:idx + batch_size]]
    loss = train_op(augment(x), y, lr_schedule(epoch))[0]
  # Eval
  accuracy = 0
  for idx in range(0, num_test_examples, batch_size):
    x = x_test[idx:idx + batch_size]
    y = y_test[idx:idx + batch_size]
    p = predict_op(x)
    accuracy += (np.argmax(p, axis=1) == y).sum()
  accuracy /= num_test_examples
  print(f'Epoch {epoch+1:3} -- train loss {loss:.3f}   test accuracy {accuracy*100:.1f}', flush=True)
Explanation: Training loop
End of explanation
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