def random_uniform(shape,
minval=0,
maxval=None,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
For floats, the default range is `[0, 1)`. For ints, at least `maxval` must
be specified explicitly.
In the integer case, the random integers are slightly biased unless
`maxval - minval` is an exact power of two. The bias is small for values of
`maxval - minval` significantly smaller than the range of the output (either
`2**32` or `2**64`).
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
the range of random values to generate. Defaults to 1 if `dtype` is
floating point.
dtype: The type of the output: 'float16`, `float32`, `float64`, `int32`,
or `int64`.
seed: A Python integer. Used to create a random seed for the distribution.
See @{tf.set_random_seed}
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and `maxval` is not specified.
"""
dtype = dtypes.as_dtype(dtype)
if dtype not in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
dtypes.int64):
raise ValueError("Invalid dtype %r" % dtype)
if maxval is None:
if dtype.is_integer:
raise ValueError("Must specify maxval for integer dtype %r" % dtype)
maxval = 1
with ops.name_scope(name, "random_uniform", [shape, minval, maxval]) as name:
shape = _ShapeTensor(shape)
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
seed1, seed2 = random_seed.get_seed(seed)
if dtype.is_integer:
return gen_random_ops._random_uniform_int(
shape, minval, maxval, seed=seed1, seed2=seed2, name=name)
else:
rnd = gen_random_ops._random_uniform(
shape, dtype, seed=seed1, seed2=seed2)
return math_ops.add(rnd * (maxval - minval), minval, name=name)
def make_chain_minmax(length, node_mbs=1):
"""Creates chain of nodes alternating minimum/maximum."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = node_mbs * 250000
dtype = tf.float32
upper = gen_random_ops._random_uniform((n, ), dtype, name="u")
y = gen_random_ops._random_uniform((n, ), dtype, name="x")
min_nodes = []
max_nodes = []
nodes = [y]
for i in range(length):
y = tf.maximum(upper, y, name="cl")
max_nodes.append(y)
nodes.append(y)
y = tf.minimum(upper, y, name="cu")
min_nodes.append(y)
nodes.append(y)
return nodes
def make_chain_minmax(length, node_mbs=1):
"""Creates chain of nodes alternating minimum/maximum."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = node_mbs * 250000
dtype = tf.float32
upper = gen_random_ops._random_uniform((n,), dtype, name="u")
y = gen_random_ops._random_uniform((n,), dtype, name="x")
min_nodes = []
max_nodes = []
nodes = [y]
for i in range(length):
y = tf.maximum(upper, y, name="cl")
max_nodes.append(y)
nodes.append(y)
y = tf.minimum(upper, y, name="cu")
min_nodes.append(y)
nodes.append(y)
return nodes
def random_uniform(shape,
minval=0.0,
maxval=1.0,
dtype=types.float32,
seed=None,
name=None):
"""Outputs random values from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
the range of random values to generate.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
"""
with ops.op_scope([shape, minval, maxval], name, "random_uniform") as name:
shape_tensor = _ShapeTensor(shape)
min_tensor = ops.convert_to_tensor(minval, dtype=dtype, name="min")
range_tensor = ops.convert_to_tensor(maxval - minval,
dtype=dtype,
name="range")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops._random_uniform(shape_tensor,
dtype,
seed=seed1,
seed2=seed2)
mul = rnd * range_tensor
value = math_ops.add(mul, min_tensor, name=name)
return value
def random_uniform(shape, minval=0.0, maxval=1.0,
dtype=types.float32, seed=None,
name=None):
"""Outputs random values from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
the range of random values to generate.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
"""
with ops.op_scope([shape, minval, maxval], name, "random_uniform") as name:
shape_tensor = _ShapeTensor(shape)
min_tensor = ops.convert_to_tensor(minval, dtype=dtype, name="min")
range_tensor = ops.convert_to_tensor(
maxval - minval, dtype=dtype, name="range")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops._random_uniform(shape_tensor, dtype,
seed=seed1,
seed2=seed2)
mul = rnd * range_tensor
value = math_ops.add(mul, min_tensor, name=name)
return value
def make_leaf(i): name = "leaf"+str(i) val = gen_random_ops._random_uniform((n2, n2), dtype, name=name) return val
def make_leaf(i):
name = "leaf" + str(i)
val = gen_random_ops._random_uniform((n2, n2), dtype, name=name)
return val
