# πββοΈ Introduction to Tensorflow

Brief Introduction to Tensorflow

## π© Main flow of programs in Tensorflow

1. Create Tensors (variables) that are not yet executed/evaluated.

2. Write operations between those Tensors.

3. Initialize your Tensors.

4. Create a Session.

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. π©βπ«

## π©βπ» Code Example

To calculate the following formula:

$loss=L(\hat{y},y)=(\hat{y}^{(i)}-y^{(i)})^2$

# 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
session.run(init)

# printing results
print(session.run(loss))

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.

## β DeΔiΕken BaΕlatma (initalization) HakkΔ±nda Not

For the following code:

a = tf.constant(2)
b = tf.constant(10)
c = tf.multiply(a,b)
print(c)

π€ΈββοΈ The output is

Tensor("Mul:0", shape=(), dtype=int32)

As expected, we will not see 20 π€! 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.

## π¦ 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(sess.run(2 * x, feed_dict = {x: 3}))
sess.close()

## π More examples

Computing sigmoid function with TF

def sigmoid(z):
"""
Computes the sigmoid of z

Arguments:
z -- input value, scalar or vector

Returns:
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 = sess.run(sigmoid, feed_dict = {x: z})

return result

Computing cost function with TF

def cost(logits, labels):
"""
Computes the cost using the sigmoid cross entropy

Arguments:
logits -- vector containing z, output of the last linear unit (before the final sigmoid activation)
labels -- vector of labels y (1 or 0)

Returns:
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 = sess.run(cost, feed_dict = {z: logits, y: labels})

# Closing the session
sess.close()

return cost

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