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.compat.v1.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.bfloat16, 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 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.bfloat16, 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 old_uniform_int(dtype, seed1, seed2): minval2 = constant_op.constant(minval, dtype=dtype) maxval2 = constant_op.constant(maxval, dtype=dtype) return gen_random_ops.random_uniform_int(shape, minval=minval2, maxval=maxval2, seed=seed1, seed2=seed2)
def rtt_random_uniform(shape, minval=0, maxval=None, dtype=dtypes.float32, seed=None, name=None): """Outputs random values from a uniform distribution.""" dtype = dtypes.as_dtype(dtype) if dtype not in (dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64, dtypes.string): raise ValueError("Invalid dtype %r" % dtype) bk_dtype = dtype if (dtype == dtypes.string): dtype = dtypes.float32 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 = random_ops._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: rv = gen_random_ops.random_uniform_int(shape, minval, maxval, seed=seed1, seed2=seed2, name=name) else: rnd = gen_random_ops.random_uniform(shape, dtypes.float32, seed=seed1, seed2=seed2) rv = math_ops.add(rnd * (maxval - minval), minval, name=name) if (bk_dtype == dtypes.string): return tf.as_string(rv) else: return rv
def old_uniform_int(dtype, seed1, seed2): minval2 = constant_op.constant(minval, dtype=dtype) maxval2 = constant_op.constant(maxval, dtype=dtype) return gen_random_ops.random_uniform_int( shape, minval=minval2, maxval=maxval2, seed=seed1, seed2=seed2)
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`). Examples: >>> tf.random.uniform(shape=[2]) <tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)> >>> tf.random.uniform(shape=[], minval=-1., maxval=0.) <tf.Tensor: shape=(), dtype=float32, numpy=-...> >>> tf.random.uniform(shape=[], minval=5, maxval=10, dtype=tf.int64) <tf.Tensor: shape=(), dtype=int64, numpy=...> The `seed` argument produces a deterministic sequence of tensors across multiple calls. To repeat that sequence, use `tf.random.set_seed`: >>> tf.random.set_seed(5) >>> tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10) <tf.Tensor: shape=(), dtype=int32, numpy=2> >>> tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10) <tf.Tensor: shape=(), dtype=int32, numpy=0> >>> tf.random.set_seed(5) >>> tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10) <tf.Tensor: shape=(), dtype=int32, numpy=2> >>> tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10) <tf.Tensor: shape=(), dtype=int32, numpy=0> Without `tf.random.set_seed` but with a `seed` argument is specified, small changes to function graphs or previously executed operations will change the returned value. See `tf.random.set_seed` for details. Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. minval: A Tensor or Python value of type `dtype`, broadcastable with `maxval`. The lower bound on the range of random values to generate (inclusive). Defaults to 0. maxval: A Tensor or Python value of type `dtype`, broadcastable with `minval`. The upper bound on the range of random values to generate (exclusive). 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 in combination with `tf.random.set_seed` to create a reproducible sequence of tensors across multiple calls. 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.bfloat16, 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 = tensor_util.shape_tensor(shape) # TODO(b/143079601): Remove this once the compatible window is passed. if compat.forward_compatible(2019, 12, 3): # In case of [0,1) floating results, minval and maxval is unused. We do an # `is` comparison here since this is cheaper than isinstance or __eq__. minval_is_zero = minval is 0 # pylint: disable=literal-comparison maxval_is_one = maxval is 1 # pylint: disable=literal-comparison if not minval_is_zero or not maxval_is_one or dtype.is_integer: 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: result = gen_random_ops.random_uniform_int(shape, minval, maxval, seed=seed1, seed2=seed2, name=name) else: result = gen_random_ops.random_uniform(shape, dtype, seed=seed1, seed2=seed2) if minval_is_zero: if not maxval_is_one: result = result * maxval else: result = math_ops.add(result * (maxval - minval), minval, name=name) else: 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: result = 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) result = math_ops.add(rnd * (maxval - minval), minval, name=name) # TODO(b/132092188): C++ shape inference inside functional ops does not # cross FuncGraph boundaries since that information is only available in # python. So we manually get the static shape using # `constant_value_as_shape` which *does* cross function boundaries. tensor_util.maybe_set_static_shape(result, shape) return result