convert this code from sequential to parallel code

and

provide an explenation.

import numpy as np

import mnist

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten

from tensorflow.keras.utils import to_categorical

train_images = mnist.train_images()

train_labels = mnist.train_labels()

test_images = mnist.test_images()

test_labels = mnist.test_labels()

Normalize the images.

train_images = (train_images / 255) - 0.5

test_images = (test_images / 255) - 0.5

# Reshape the images.

train_images = np.expand_dims(train_images, axis=3)

test_images = np.expand_dims(test_images, axis=3)

num_filters = 8

filter_size = 3

pool_size = 2

# Build the model.

model = Sequential([

Conv2D(num_filters, filter_size, input_shape=(28, 28, 1)),

MaxPooling2D(pool_size=pool_size),

Flatten(),

Dense(10, activation='softmax'),

])

# Compile the model.

model.compile(

'adam',

loss='categorical_crossentropy',

metrics=['accuracy'],

)

# Train the model.

model.fit(

train_images,

to_categorical(train_labels),

epochs=3,

validation_data=(test_images, to_categorical(test_labels)),

)

# Predict on the first 5 test images.

predictions = model.predict(test_images[:5])

# Print our model's predictions.

print(np.argmax(predictions, axis=1)) # [7, 2, 1, 0, 4]

# Check our predictions against the ground truths.

print(test_labels[:5]) # [7, 2, 1, 0, 4]