q2_initialization.py

import numpy as np
import tensorflow as tf
import math

def xavier_weight_init():
  """
  Returns function that creates random tensor. 

  The specified function will take in a shape (tuple or 1-d array) and must
  return a random tensor of the specified shape and must be drawn from the
  Xavier initialization distribution.

  Hint: You might find tf.random_uniform useful.
  """
  def _xavier_initializer(shape, **kwargs):
    """Defines an initializer for the Xavier distribution.

    This function will be used as a variable scope initializer.

    https://www.tensorflow.org/versions/r0.7/how_tos/variable_scope/index.html#initializers-in-variable-scope

    Args:
      shape: Tuple or 1-d array that specifies dimensions of requested tensor.
    Returns:
      out: tf.Tensor of specified shape sampled from Xavier distribution.
    """
    ### YOUR CODE HERE
    #raise NotImplementedError
    xavier_range = np.sqrt(6.0 / (np.sum(shape) ))
    out          = tf.random_uniform(shape, -xavier_range, xavier_range)
    #print "output shape xavier ", out.shape
    ### END YOUR CODE
    return out
  # Returns defined initializer function.
  return _xavier_initializer

def test_initialization_basic():
  """
  Some simple tests for the initialization.
  """
  print "Running basic tests..."
  xavier_initializer = xavier_weight_init()
  shape = (1,)
  xavier_mat = xavier_initializer(shape)
  assert xavier_mat.get_shape() == shape

  shape = (1, 2, 3)
  xavier_mat = xavier_initializer(shape)
  assert xavier_mat.get_shape() == shape
  print "Basic (non-exhaustive) Xavier initialization tests pass\n"

def test_initialization():
  """ 
  Use this space to test your Xavier initialization code by running:
      python q1_initialization.py 
  This function will not be called by the autograder, nor will
  your tests be graded.
  """
  print "Running your tests..."
  ### YOUR CODE HERE
  #raise NotImplementedError
  test_dims = (100, 64)
  xavier_initializer = xavier_weight_init()
  out = xavier_initializer(test_dims)
  assert out.get_shape() == test_dims

  s   = tf.Session()
  ref = np.sqrt(6.0/np.sum(test_dims))
  y   = np.max(s.run(out))

  ### END YOUR CODE  

if __name__ == "__main__":
    test_initialization()
    test_initialization_basic()