def _typecheck_input(x1_type, x2_type):
"""
Check input tensor types to be valid and confirm they are the same type.
"""
const_utils.check_valid_type(x1_type, [mstype.float32, mstype.float16],
'x1')
const_utils.check_valid_type(x2_type, [mstype.float32, mstype.float16],
'x2')
if x1_type != x2_type:
raise TypeError(
f'Both Inputs must be the same Type. x1 is \'{x1_type}\' and x2 is \'{x2_type}\' '
)
def repeat_elements(x, rep, axis=0):
"""
Repeat elements of a tensor along an axis, like np.repeat.
Args:
- **x** (Tensor) - The tensor to repeat values for. Must be of type: float16,
float32, int8, uint8, int16, int32, or int64.
- **rep** (int) - The number of times to repeat, must be positive, required.
- **axis** (int) - The axis along which to repeat, default 0.
Outputs:
One tensor with values repeated along the specified axis. If x has shape
(s1, s2, ..., sn) and axis is i, the output will have shape (s1, s2, ...,
si * rep, ..., sn). The output type will be the same as the type of `x`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
>>> output = C.repeat_elements(x, rep = 2, axis = 0)
>>> print(output)
[[0 1 2]
[0 1 2]
[3 4 5]
[3 4 5]]
"""
const_utils.check_valid_type(F.dtype(x), mstype.number_type, 'input x')
rep = _check_positive_int(rep, "rep", "repeat_elements")
axis = _check_is_int(axis, "axis", "repeat_elements")
shape_op = P.Shape()
rank_op = P.Rank()
tile_op = P.Tile()
expand_dims_op = P.ExpandDims()
reshape_op = P.Reshape()
x_rank = rank_op(x)
axis = _check_axis_range(axis, x_rank, "axis", "repeat_elements")
expand_axis = axis + 1
x_expand = expand_dims_op(x, expand_axis)
rep_dims = _cal_repeat_dims(x_rank, rep, expand_axis)
x_expand = tile_op(x_expand, rep_dims)
x_shape = shape_op(x)
x_reshape = _cal_reshape(x_shape, rep, axis)
x_rep = reshape_op(x_expand, x_reshape)
return x_rep
def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
"""
Count number of nonzero elements across axis of input tensor
Args:
x (Tensor): Input data is used to count non-zero numbers.
axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Only constant value is allowed.
Default: (), reduce all dimensions.
keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions. Default: False.
dtype (Union[Number, mstype.bool_]): The data type of the output tensor. Only constant value is allowed.
Default: mstype.int32
Returns:
Tensor, number of nonzero element. The data type is dtype.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> input_x = Tensor(np.array([[0, 1, 0], [1, 1, 0]]).astype(np.float32))
>>> nonzero_num = count_nonzero(x=input_x, axis=[0, 1], keep_dims=True, dtype=mstype.int32)
>>> print(nonzero_num)
[[3]]
"""
const_utils.check_valid_type(F.dtype(x), mstype.number_type, 'input x')
axis = _check_validate_axis(axis, "count_nonzero")
keep_dims = _check_validate_keepdims(keep_dims, "count_nonzero")
const_utils.check_valid_type(dtype, mstype.number_type + (mstype.bool_, ),
'dtype')
not_equal = P.NotEqual()
cast = P.Cast()
reduce_sum = P.ReduceSum(keep_dims)
nonzero_bool = not_equal(x, 0)
# ReduceSum only support float16 or float32 tensor.
nonzero_val = cast(nonzero_bool, mstype.float16)
nonzero_num = cast(reduce_sum(nonzero_val, axis), dtype)
return nonzero_num
def construct(self, weights):
const_utils.check_valid_type(weights.dtype, mstype.number_type, 'weights')
l1_regularization = self.scale * self.reduce_sum(self.abs(weights))
return l1_regularization
