Lets start this programming exercise by computing the following equation:  Y=WX+b , where  W  and  X  are random matrices and b is a random vector.

Exercise: Compute  WX+bW  where  W,X , and  b  are drawn from a random normal distribution. W is of shape (4, 3), X is (3,1) and b is (4,1). As an example, here is how you would define a constant X that has shape (3,1):

X = tf.constant(np.random.randn(3,1), name = "X")
You might find the following functions helpful:

tf.matmul(..., ...) to do a matrix multiplication
tf.add(..., ...) to do an addition
np.random.randn(...) to initialize randomly

def linear_function():
    """
    Implements a linear function:
            Initializes W to be a random tensor of shape (4,3)
            Initializes X to be a random tensor of shape (3,1)
            Initializes b to be a random tensor of shape (4,1)
    Returns:
    result -- runs the session for Y = WX + b
    """

    np.random.seed(1)

    ### START CODE HERE ### (4 lines of code)
    X = tf.constant(np.random.randn(3,1), name = X)
    W = tf.constant(np.random.randn(4,3), name = W)
    b = tf.constant(np.random.randn(4,1), name = b)
    Y = tf.constant(tf.add(tf.matmul(W, X), b), name = Y)
    ### END CODE HERE ###

    # Create the session using tf.Session() and run it with sess.run(...) on the variable you want to calculate

    ### START CODE HERE ###
    init = tf.global_variables_initializer()
    sess = tf.Session()
    session.run(init)
    result = session.run(Y)

    ### END CODE HERE ###

    # close the session
    sess.close()

    return result