What does TensorFlow do?

TensorFlow is a way of representing computation without actually performing it until asked. In this sense, it is a form of lazy computing, and it allows for some great improvements to the running of code:

Faster computation of complex variables
Distributed computation across multiple systems, including GPUs.
Reduced redundency in some computations

Add two numbers



In [1]:

    
import tensorflow as tf

x = tf.constant(35, name='x')
y = tf.Variable(x + 5, name='y')

print(y)









    



<tensorflow.python.ops.variables.Variable object at 0x7f149009fa58>



In [2]:

    
x = tf.constant(35, name='x')
y = tf.Variable(x + 5, name='y')

model = tf.initialize_all_variables()

with tf.Session() as session:
    session.run(model)
    print(session.run(y))

Exercise

1.



In [3]:

    
import tensorflow as tf
x = tf.constant([35, 40, 45], name='x')
y = tf.Variable(x + 5, name='y')


model = tf.initialize_all_variables()

with tf.Session() as session:
	session.run(model)
	print(session.run(y))

2.



In [16]:

    
import numpy as np
x=np.random.rand(10)
y=tf.Variable(5*x**2,name='y')
model = tf.initialize_all_variables()

with tf.Session() as session:
    session.run(model)
    print(session.run(y))









    



[ 3.35509394  1.70215193  1.05872909  1.76074012  4.60089264  2.84141962
  0.40380513  3.7792622   2.61410542  0.08275855]

tensorboard



In [26]:

    
import tensorflow as tf

x = tf.constant(35, name='x')
print(x)
y = tf.Variable(x + 5, name='y')

with tf.Session() as session:
    merged = tf.merge_all_summaries()
    writer = tf.train.SummaryWriter("", session.graph)
    model = tf.initialize_all_variables()
    session.run(model)
    print(session.run(y))









    



Tensor("x_5:0", shape=(), dtype=int32)
40



In [20]:

    
import matplotlib.image as mpimg

# First, load the image
filename = "MarshOrchid.jpg"
image = mpimg.imread(filename)

# Print out its shape
print(image.shape)









    



(5528, 3685, 3)



In [21]:

    
import matplotlib.pyplot as plt
plt.imshow(image)
plt.show()



In [25]:

    
import tensorflow as tf
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

# First, load the image again
filename = "MarshOrchid.jpg"
image = mpimg.imread(filename)

# Create a TensorFlow Variable
x = tf.Variable(image, name='x')

model = tf.initialize_all_variables()

with tf.Session() as session:
    x = tf.transpose(x, perm=[1, 0, 2])
    session.run(model)
    result = session.run(x)


plt.imshow(result)
plt.show()



In [ ]: