🏃‍♀️ Introduction to Tensorflow

Brief Introduction to Tensorflow

🚩 Main flow of programs in Tensorflow

  1. 1.
    Create Tensors (variables) that are not yet executed/evaluated.
  2. 2.
    Write operations between those Tensors.
  3. 3.
    Initialize your Tensors.
  4. 4.
    Create a Session.
  5. 5.
    Run the Session. This will run the operations you'd written above.
To summarize, remember to initialize your variables, create a session and run the operations inside the session. 👩‍🏫

👩‍💻 Code Example

To calculate the following formula:
# Creating tensors and writing operations between them
y_hat = tf.constant(36, name='y_hat')
y = tf.constant(39, name='y')
loss = tf.Variable((y - y_hat)**2, name='loss')
# Initializing tensors
init = tf.global_variables_initializer()
# Creating session
with tf.Session() as session:
# Running the operations
# printing results
When we created a variable for the loss, we simply defined the loss as a function of other quantities, but did not evaluate its value. To evaluate it, we had to use the initializer.

❗ Değişken Başlatma (initalization) Hakkında Not

For the following code:
a = tf.constant(2)
b = tf.constant(10)
c = tf.multiply(a,b)
🤸‍♀️ The output is
Tensor("Mul:0", shape=(), dtype=int32)
As expected, we will not see 20 🤓! We got a tensor saying that the result is a tensor that does not have the shape attribute, and is of type "int32". All we did was put in the 'computation graph', but we have not run this computation yet.

📦 Placeholders in TF

  • A placeholder is an object whose value you can specify only later. To specify values for a placeholder, we can pass in values by using a feed dictionary.
  • Below, a placeholder has been created for x. This allows us to pass in a number later when we run the session.
x = tf.placeholder(tf.int64, name = 'x')
print( * x, feed_dict = {x: 3}))

🎀 More examples

Computing sigmoid function with TF
def sigmoid(z):
Computes the sigmoid of z
z -- input value, scalar or vector
results -- the sigmoid of z
# Creating a placeholder for x. Naming it 'x'.
x = tf.placeholder(tf.float32, name = 'x')
# computing sigmoid(x)
sigmoid = tf.sigmoid(x)
# Creating a session, and running it.
with tf.Session() as sess:
# Running session and call the output "result"
result =, feed_dict = {x: z})
return result
Computing cost function with TF
def cost(logits, labels):
Computes the cost using the sigmoid cross entropy
logits -- vector containing z, output of the last linear unit (before the final sigmoid activation)
labels -- vector of labels y (1 or 0)
cost -- runs the session of the cost function
# Creating the placeholders for "logits" (z) and "labels" (y)
z = tf.placeholder(tf.float32, name = 'z')
y = tf.placeholder(tf.float32, name = 'y')
# Using the loss function
cost = tf.nn.sigmoid_cross_entropy_with_logits(logits = z, labels = y)
# Creating a session
sess = tf.Session()
# Running the session
cost =, feed_dict = {z: logits, y: labels})
# Closing the session
return cost