Constants, Sequences, and Random Values
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Constant Value Tensors
TensorFlow provides several operations that you can use to generate constants.
Sequences
Random Tensors
TensorFlow has several ops that create random tensors with different
distributions. The random ops are stateful, and create new random values each
time they are evaluated.
Examples:
# Create a tensor of shape [2, 3] consisting of random normal values, with mean
# -1 and standard deviation 4.
norm = tf.random_normal([2, 3], mean=-1, stddev=4)
# Shuffle the first dimension of a tensor
c = tf.constant([[1, 2], [3, 4], [5, 6]])
shuff = tf.random_shuffle(c)
# Each time we run these ops, different results are generated
sess = tf.Session()
print(sess.run(norm))
print(sess.run(norm))
# Set an op-level seed to generate repeatable sequences across sessions.
norm = tf.random_normal([2, 3], seed=1234)
sess = tf.Session()
print(sess.run(norm))
print(sess.run(norm))
sess = tf.Session()
print(sess.run(norm))
print(sess.run(norm))
# Use random uniform values in [0, 1) as the initializer for a variable of shape
# [2, 3]. The default type is float32.
var = tf.Variable(tf.random_uniform([2, 3]), name="var")
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
print(sess.run(var))
