πŸƒβ€β™€οΈ Introduction to Tensorflow

Brief Introduction to Tensorflow

🚩 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. πŸ‘©β€πŸ«

πŸ‘©β€πŸ’» Code Example

To calculate the following formula:

​loss=L(y^,y)=(y^(i)βˆ’y(i))2loss=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.

❗ Değişken Başlatma (initalization) HakkΔ±nda Not

For the following code:

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

πŸ€Έβ€β™€οΈ The output is

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

As expected, we will not see 20 πŸ€“! 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.

πŸ“¦ 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()

πŸŽ€ 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