Variables and Placeholders

In [1]:

    

import tensorflow as tf


    

In [2]:

    

sess = tf.InteractiveSession()


    

In [3]:

    

# Create a tensor of shape 4 by 4 with uniformly distributed random values
my_tensor = tf.random_uniform(shape = (4,4), 
                              minval = 0,
                              maxval = 1)


    

In [4]:

    

my_tensor


    

    
Out[4]:





<tf.Tensor 'random_uniform:0' shape=(4, 4) dtype=float32>

In [5]:

    

# Initialize the tensor as a Variable
my_var = tf.Variable(initial_value = my_tensor)


    

In [6]:

    

my_var


    

    
Out[6]:





<tf.Variable 'Variable:0' shape=(4, 4) dtype=float32_ref>

In [7]:

    

# OBLIGATORY!
# Initializing all variables
init = tf.global_variables_initializer()


    

In [8]:

    

init.run()


    

In [9]:

    

my_var.eval()


    

    
Out[9]:





array([[0.10739136, 0.8106226 , 0.00213122, 0.88670003],
       [0.3259567 , 0.7325934 , 0.97831714, 0.0457505 ],
       [0.71009386, 0.12011993, 0.11913311, 0.2890252 ],
       [0.9749534 , 0.15022421, 0.09399927, 0.5066788 ]], dtype=float32)

In [10]:

    

sess.run(my_var)


    

    
Out[10]:





array([[0.10739136, 0.8106226 , 0.00213122, 0.88670003],
       [0.3259567 , 0.7325934 , 0.97831714, 0.0457505 ],
       [0.71009386, 0.12011993, 0.11913311, 0.2890252 ],
       [0.9749534 , 0.15022421, 0.09399927, 0.5066788 ]], dtype=float32)

In [11]:

    

# Defining a Plaholder of a specific type
ph = tf.placeholder(tf.float64)


    

In [12]:

    

ph = tf.placeholder(tf.int32)


    

In [13]:

    

# For shape its common to use (None, Numbers of Features) 
# because None can be filled by number of samples in data
ph = tf.placeholder(shape = (None, 5),
                    dtype = tf.float32 )