hackintoshrao

def power(x, n):
  """
  Compute the value x n for integer n

  """
  if n == 0:
    return 1 
  else:
    partial = power(x, n // 2) 
  

hackintoshrao

import tensorflow as tf
x = tf.constant(10)
y = tf.constant(2)
z = tf.subtract(tf.divide(x,y),tf.cast(tf.constant(1), tf.float64))
with tf.Session() as sess:
    output = sess.run(z)
    print(output)

hackintoshrao

import tensorflow as tf
x = tf.placeholder(tf.string)
y = tf.placeholder(tf.int32)
z = tf.placeholder(tf.float32)
with tf.Session() as sess:
    output = sess.run(x, feed_dict={x: 'Test String', y: 123, z: 45.67})

hackintoshrao

import tensorflow as tf
x = tf.placeholder(tf.string)

with tf.Session() as sess:
    output = sess.run(x, feed_dict={x: 'Hello World'})

hackintoshrao

import tensorflow as tf
# You can create constants in TF to hold specific values
a = tf.constant(1)
b = tf.constant(2)
A = tf.constant(1234) 
# B is a 1-dimensional int32 tensor
B = tf.constant([123,456,789]) 
# C is a 2-dimensional int32 tensor
C = tf.constant([ [123,456,789], [222,333,444] ])
# Adding and multiplying the constants

hackintoshrao

with tf.Session() as sess:
    sess.run(init)

    display_step = 20
    # Fit all training data
    for iteration in range(training_iteration):
        # Run the gradient descent optimizer 
        sess.run(optimizer,  feed_dict={X: X_train, Y: Y_train})

hackintoshrao

def get_cost_optimizer_tensor(Y, model, size, learning_rate):
  """
  Tensor for calculating the cost function and the the optimizer for 
  minimizing the cost.
  """
  # Cost function tensor.
  cost_function = tf.reduce_sum(tf.pow(model - Y, 2))/(2 * size) 
  # gradient descent tensor.
  gradient_descent = tf.train.GradientDescentOptimizer(learning_rate) 
  # optimization tensor.

hackintoshrao

def get_model_tensors():
  """
  function obtain model tensor
  """
  # X is the placeholder for size of the house from the dataset.
  # Y is the placeholder for size of the house from the dataset.
  X = tf.placeholder("float")
  Y = tf.placeholder("float")
  
  # The parameters theta0 and theta1 

hackintoshrao

import pandas as pd
# import library to split the data
from sklearn.model_selection import train_test_split

%matplotlib inline
import matplotlib.pyplot as plt


def read_data():
  """

hackintoshrao

FROM python:2.7
MAINTAINER Karthic Rao "[email protected]"
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
ENTRYPOINT ["python"]
CMD ["app.py"]