import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.Variable(0)

b = tf.constant(1)

c = tf.add(a, b)

c

<tf.Tensor 'Add_2:0' shape=() dtype=int32>

# assign the new value to a
update = tf.assign(a, c)

a

<tf.Variable 'Variable_4:0' shape=() dtype=int32_ref>

# You can't change the constant by using assign. 

# Initialize global variables

init_op = tf.global_variables_initializer()
session = tf.Session()
session.run(init_op)

print(session.run(a))

0

session.run(update)

1

session.run(a)

1

for _ in range(4):
    session.run(update)
    print(session.run(a))

2
3
4
5

session.close()

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import numpy as np
import pandas as pd

census = pd.read_csv("/Users/rajacsp/datasets …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

tf.enable_eager_execution()

a = tf.constant([1.0, 2.0])

a

<tf.Tensor: id=3, shape …

import tensorflow as tf
import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
c = tf.multiply(a, b)

with tf …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.constant(2)
b = tf.constant(3)

c = tf.add(a, b)
d = tf.multiply(a, b …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

graph = tf.get_default_graph()

graph.get_operations()

[]

a = tf.constant(2, name = "one")

operations = graph.get_operations()

b = tf.constant …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

init_op = tf.global_variables_initializer()

session = tf.Session()

# initialize variables
session.run(init_op)

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# to clear the graph before new nodes
tf.reset_default_graph()

with tf.name_scope("march"):
    a = tf.constant(1 …

import tensorflow as tf
import numpy as np
import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = np.zeros((3, 3))
ta = tf.convert_to_tensor(a)

with tf.Session …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

matrix1 = tf.constant([[3., 3.0]])
matrix2 = tf.constant([[2.0],[2.0]])
product = tf …