fabclmnt

def scale(image, label):
  image = tf.cast(image, tf.float32)
  image /= 255

  return image, label

train_dataset = beans_train.map(scale).cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
eval_dataset = beans_test.map(scale).batch(BATCH_SIZE)

fabclmnt

#Make CNN using the distributed learning algorithm
with strategy.scope():
  model = tf.keras.Sequential([
      tf.keras.layers.Conv2D(16, 3, activation='relu', input_shape=(500, 500, 3)),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Conv2D(32, 3, activation='relu'),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Conv2D(64, 3, activation='relu'),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Flatten(),

fabclmnt

# Define the checkpoint directory to store the checkpoints
checkpoint_dir = './training_checkpoints'
# Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")

# Function for decaying the learning rate.
def decay(epoch):
  if epoch < 3:
    return 1e-3
  elif epoch >= 3 and epoch < 7:

fabclmnt

#Initializing the distributed learning algorithm
strategy = tf.distribute.MirroredStrategy()

#Defining some necessary hyperparamters
num_train_examples = info.splits['train'].num_examples
num_test_examples = info.splits['test'].num_examples

BUFFER_SIZE = 10000

BATCH_SIZE_PER_REPLICA = 32

fabclmnt

# Import TensorFlow and TensorFlow Datasets
import tensorflow_datasets as tfds
import tensorflow as tf

#Download the dataset and divide it into train and test
datasets, info = tfds.load('beans', with_info=True, as_supervised=True)
beans_train, beans_test = datasets['train'], datasets['test']

fabclmnt

import time

start = time.time()
model.fit(train_dataset, epochs=7, callbacks=callbacks)
end = time.time()
print("Time elapsed: {}".format(end-start))

fabclmnt

import tensorflow as tf
# Declaring constants
x = tf.constants(4)
y = tf.constants(7)
# Performing operations
z = x + y

# Printing the result
with tf.Session() as sess:
  print(sess.run(z))

fabclmnt

import tensorflow as tf
# Declaring constants
x = tf.constants(4)
y = tf.constants(7)
# Performing operations
z = x + y

# Printing the result
print(z)

fabclmnt

import tensorflow as tf

# Creating tensorflow constants
a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[1, 2], [3, 4]])
# Summing the two tensors
c = a + b
# Printing the results
print(c)
# Converting tensor to numpy

fabclmnt

import tensorflow as tf
# Adding the decorator
@tf.function
def matprod(x, y):
    return tf.matmul(x, y)