pdfcoffee

• To create random values from a given gamma distribution of shape
[M,N] in the range [minval (default=0), maxval] and using seed:
t_random = tf.random_uniform([2,3], maxval=4, seed=12) ==> [[
2.54461002 3.69636583 2.70510912], [ 2.00850058 3.84459829
3.54268885]]
• To randomly crop a given tensor to a specified size:
tf.random_crop(t_random, [2,5],seed=12) where t_random is an
already defined tensor. This will result in a [2,5] Tensor
randomly cropped from Tensor t_random.
Chapter 2
• Every time we need to present the training sample in a random order, we
can use tf.random_shuffle() to randomly shuffle a tensor along its first
dimension. If t_random is the tensor we want to shuffle then we use:
tf.random_shuffle(t_random)
• Randomly generated tensors are affected by the value of an initial seed.
To obtain the same random numbers in multiple runs or sessions, the seed
should be set to a constant value. When there is a large number of random
tensors in use, we can set the seed for all randomly generated tensors by
using tf.set_random_seed(). The following command sets the seed for
random tensors for all sessions as 54:
tf.set_random_seed(54) //Seed can be any integer value.
Variables
Variables are created using the tf.Variable class. The definition of variables
also includes the constant or random values from which they should be initialized.
In the following code, we create two different tensor variables t_a and t_b. Both
will be initialized to random uniform distributions of shape [50, 50], minval=0,
and maxval=10:
rand_t = tf.random_uniform([50,50], 0, 10, seed=0)
t_a = tf.Variable(rand_t)
t_b = tf.Variable(rand_t)
Variables are often used to represent weights and biases in a neural network:
weights = tf.Variable(tf.random_normal([100,100],stddev=2))
bias = tf.Variable(tf.zeros[100], name = 'biases')
[ 57 ]