import tensorflow as tf
import numpy as np

print(tf.__version__)

"""
Explanation: <h1> Getting started with TensorFlow </h1>

In this notebook, you play around with the TensorFlow Python API.
End of explanation
"""


a = np.array([5, 3, 8])
b = np.array([3, -1, 2])
c = np.add(a, b)
print(c)

"""
Explanation: <h2> Adding two tensors </h2>

First, let's try doing this using numpy, the Python numeric package. numpy code is immediately evaluated.
End of explanation
"""


a = tf.constant([5, 3, 8])
b = tf.constant([3, -1, 2])
c = tf.add(a, b)
print(c)

"""
Explanation: The equivalent code in TensorFlow consists of two steps:
<p>
<h3> Step 1: Build the graph </h3>
End of explanation
"""


with tf.Session() as sess:
  result = sess.run(c)
  print(result)

"""
Explanation: c is an Op ("Add") that returns a tensor of shape (3,) and holds int32. The shape is inferred from the computation graph.
Try the following in the cell above:
<ol>
<li> Change the 5 to 5.0, and similarly the other five numbers. What happens when you run this cell? </li>
<li> Add an extra number to a, but leave b at the original (3,) shape. What happens when you run this cell? </li>
<li> Change the code back to a version that works </li>
</ol>

<p/>
<h3> Step 2: Run the graph
End of explanation
"""


a = tf.placeholder(dtype=tf.int32, shape=(None,))  # batchsize x scalar
b = tf.placeholder(dtype=tf.int32, shape=(None,))
c = tf.add(a, b)
with tf.Session() as sess:
  result = sess.run(c, feed_dict={
      a: [3, 4, 5],
      b: [-1, 2, 3]
    })
  print(result)

"""
Explanation: <h2> Using a feed_dict </h2>

Same graph, but without hardcoding inputs at build stage
End of explanation
"""


def compute_area(sides):
  # slice the input to get the sides
  a = sides[:,0]  # 5.0, 2.3
  b = sides[:,1]  # 3.0, 4.1
  c = sides[:,2]  # 7.1, 4.8
  
  # Heron's formula
  s = (a + b + c) * 0.5   # (a + b) is a short-cut to tf.add(a, b)
  areasq = s * (s - a) * (s - b) * (s - c) # (a * b) is a short-cut to tf.multiply(a, b), not tf.matmul(a, b)
  return tf.sqrt(areasq)

with tf.Session() as sess:
  # pass in two triangles
  area = compute_area(tf.constant([
      [5.0, 3.0, 7.1],
      [2.3, 4.1, 4.8]
    ]))
  result = sess.run(area)
  print(result)

"""
Explanation: <h2> Heron's Formula in TensorFlow </h2>

The area of triangle whose three sides are $(a, b, c)$ is $\sqrt{s(s-a)(s-b)(s-c)}$ where $s=\frac{a+b+c}{2}$ 
Look up the available operations at https://www.tensorflow.org/api_docs/python/tf
End of explanation
"""


with tf.Session() as sess:
  sides = tf.placeholder(tf.float32, shape=(None, 3))  # batchsize number of triangles, 3 sides
  area = compute_area(sides)
  result = sess.run(area, feed_dict = {
      sides: [
        [5.0, 3.0, 7.1],
        [2.3, 4.1, 4.8]
      ]
    })
  print(result)

"""
Explanation: <h2> Placeholder and feed_dict </h2>

More common is to define the input to a program as a placeholder and then to feed in the inputs. The difference between the code below and the code above is whether the "area" graph is coded up with the input values or whether the "area" graph is coded up with a placeholder through which inputs will be passed in at run-time.
End of explanation
"""


import tensorflow as tf
from tensorflow.contrib.eager.python import tfe

tfe.enable_eager_execution()

def compute_area(sides):
  # slice the input to get the sides
  a = sides[:,0]  # 5.0, 2.3
  b = sides[:,1]  # 3.0, 4.1
  c = sides[:,2]  # 7.1, 4.8
  
  # Heron's formula
  s = (a + b + c) * 0.5   # (a + b) is a short-cut to tf.add(a, b)
  areasq = s * (s - a) * (s - b) * (s - c) # (a * b) is a short-cut to tf.multiply(a, b), not tf.matmul(a, b)
  return tf.sqrt(areasq)

area = compute_area(tf.constant([
      [5.0, 3.0, 7.1],
      [2.3, 4.1, 4.8]
    ]))

print(area)

"""
Explanation: tf.eager
tf.eager allows you to avoid the build-then-run stages. However, most production code will follow the lazy evaluation paradigm because the lazy evaluation paradigm is what allows for multi-device support and distribution. 
<p>
One thing you could do is to develop using tf.eager and then comment out the eager execution and add in the session management code.

<b> To run this block, you must first reset the notebook using Reset on the menu bar, then run this block only. </b>
End of explanation
"""


