My **previous** post.** .** All my code can be found on **github **(**5_Variables.ipynb)**

#### Variables

We use variables to store data during computation.

#### Types

There are many different ways to create Variables. Here are some examples.

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#Different Tensor Variables # Basic y = tf.Variable(10, name='y') #array array = tf.Variable([1,2,3,4], name='array') # Zeros zeros=tf.zeros([3,2]) # Ones ones = tf.ones([3,2]) # Random uniform values rand_uniform = tf.random_uniform([3,2], minval = -10, maxval=10) # Normally distributed numbers rand_normal = tf.random_normal([3,2],mean=0.0,stddev=3.0) |

We need to allocate memory for the variables. We do that by calling

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# Add an op to initialize the variables. init_op = tf.global_variables_initializer() #execute with examples session = tf.Session() session.run(init_op) |

Then when the above commands are run we get:

#### Changing values

To change the value of a variable we use the **assign** operation.

We can also save an operation on a variable and run it later.

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my_variable = tf.Variable(0) increment_my_variable = my_variable.assign(my_variable + 1) |

Here i have created a new variable **my_variable** and initialized it to 0.

I have also created an operation called **increment_my_variable** that adds one to its current vale.

I can run both the operation or assign the variable directly.

Above each time **increment_my_variable** runs it increments the value. But we can also directly assign a value to **my_variable** as well.

#### Saving

If you want to save the values of variables use the **tf.train.Saver()** operation.

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# Use this operation to save the session variables saver = tf.train.Saver() # Run the save operation save_path = saver.save(session, "./model.ckpt") print("Saved: %s" % save_path) |

This will create the files in the current directory.

You will use this later to save the weights and biases of trained models.

#### Restoring

Then later call

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saver.restore(session, "./model.ckpt") |

To restore the values from the file. Example: