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TensorFlow basics

Python

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  • main.py
main.py
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import tensorflow as tf 

# hello_constant = tf.constant('Hello World!')

# with tf.Session() as sess:
#   output = sess.run(hello_constant)
#   print(output)


################################################

# x = tf.Variable(5)

# init = tf.global_variables_initializer()
# with tf.Session() as sess:
#   sess.run(init)
  
# tf.truncated_normal() # generate random numbers from normal dist

# import numpy as np 


# def softmax(x):
#   """
#   Compute softmax values for each sets of scores in x.
#   """
  
  
# logits = [3., 1., 0.2]
# print(softmax(logits))


# tf.reduce_sum([1,2,3,4]) # 10
# tf.log()  # natural log


def normalize_grayscale(image_data):
    """
    Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]
    :param image_data: The image data to be normalized
    :return: Normalized image data
    """
    # TODO: Implement Min-Max scaling for grayscale image data
    a = 0.1
    b = 0.9
    grayscale_min = 0
    grayscale_max = 255
    
    return a + (( (image_data - grayscale_min) * (b - a)) / (grayscale_max - grayscale_min) )
    
    
##### HOW TO START A REPO USING jupyter ######
# Clone the LeNet Lab repo: git clone https://github.com/udacity/CarND-LeNet-Lab.git
# Enter the repo directory: cd CarND-LeNet-Lab
# Activate the new environment: source activate carnd-term1
# Run the notebook: jupyter notebook LeNet-Lab-Solution.ipynb


##### HOW TO SAVE VARIABLES ######

import tensorflow as tf

save_file = "./model.ckpt"

weights = tf.Variable(tf.truncated_normal([2, 3]))
bias = tf.Variable(tf.truncated_normal([3]))

# save your TF model with:
saver = tf.train.Saver()

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  
  print('Weights:')
  print(sess.run(weights))
  print('Bias:')
  print(sess.run(bias))
  
  saver.save(sess, save_file)
  
  
tf.Variable(tf.truncated_normal([6,1]))