未验证 提交 222a5137 编写于 作者: D danleifeng 提交者: GitHub

Add new tensor in API2.0: max,min,t,eye,log1p (#23228)

add new tensor: max,min,t,eye,log1p; test=develop
上级 146bed76
......@@ -279,6 +279,15 @@ Natural logarithm of x.
)DOC";
UNUSED constexpr char Log1pDoc[] = R"DOC(
Log Activation Operator.
$out = \ln(x+1)$
Natural logarithm of x.
)DOC";
UNUSED constexpr char SquareDoc[] = R"DOC(
The OP square each elements of the inputs.
......@@ -634,6 +643,7 @@ REGISTER_ACTIVATION_OP_MAKER(Sin, SinDoc);
REGISTER_ACTIVATION_OP_MAKER(Round, RoundDoc);
REGISTER_ACTIVATION_OP_MAKER(Reciprocal, ReciprocalDoc);
REGISTER_ACTIVATION_OP_MAKER(Log, LogDoc);
REGISTER_ACTIVATION_OP_MAKER(Log1p, Log1pDoc);
REGISTER_ACTIVATION_OP_MAKER(Square, SquareDoc);
REGISTER_ACTIVATION_OP_MAKER(Softplus, SoftplusDoc);
REGISTER_ACTIVATION_OP_MAKER(Softsign, SoftsignDoc);
......
......@@ -737,6 +737,26 @@ struct LogGradFunctor : public BaseActivationFunctor<T> {
static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
};
// log1p(x) = natural logarithm of x+1
template <typename T>
struct Log1pFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = (static_cast<T>(1) + x).log();
}
};
template <typename T>
struct Log1pGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * (static_cast<T>(1) / (x + static_cast<T>(1)));
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
};
// square(x) = x^2
template <typename T>
struct SquareFunctor : public BaseActivationFunctor<T> {
......@@ -1718,6 +1738,7 @@ class PowGradKernel
__macro(round, Round, RoundFunctor, ZeroGradFunctor); \
__macro(reciprocal, Reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \
__macro(log, Log, LogFunctor, LogGradFunctor); \
__macro(log1p, Log1p, Log1pFunctor, Log1pGradFunctor); \
__macro(brelu, BRelu, BReluFunctor, BReluGradFunctor); \
__macro(soft_relu, SoftRelu, SoftReluFunctor, SoftReluGradFunctor); \
__macro(stanh, STanh, STanhFunctor, STanhGradFunctor); \
......
......@@ -43,7 +43,7 @@ import paddle.nn
# from .tensor.creation import create_random_int_lod.tensor #DEFINE_ALIAS
# from .tensor.creation import crop_.tensor #DEFINE_ALIAS
# from .tensor.creation import diag #DEFINE_ALIAS
# from .tensor.creation import eye #DEFINE_ALIAS
from .tensor.creation import eye #DEFINE_ALIAS
from .tensor.creation import fill_constant #DEFINE_ALIAS
# from .tensor.creation import get_.tensor_from_selected_rows #DEFINE_ALIAS
from .tensor.creation import linspace #DEFINE_ALIAS
......@@ -131,15 +131,15 @@ from .tensor.math import sum #DEFINE_ALIAS
# from .tensor.math import sums #DEFINE_ALIAS
from .tensor.math import tanh #DEFINE_ALIAS
from .tensor.math import elementwise_sum #DEFINE_ALIAS
# from .tensor.math import max #DEFINE_ALIAS
# from .tensor.math import min #DEFINE_ALIAS
from .tensor.math import max #DEFINE_ALIAS
from .tensor.math import min #DEFINE_ALIAS
from .tensor.math import mm #DEFINE_ALIAS
from .tensor.math import div #DEFINE_ALIAS
from .tensor.math import add #DEFINE_ALIAS
# from .tensor.math import atan #DEFINE_ALIAS
from .tensor.math import logsumexp #DEFINE_ALIAS
# from .tensor.math import inverse #DEFINE_ALIAS
# from .tensor.math import log1p #DEFINE_ALIAS
from .tensor.math import log1p #DEFINE_ALIAS
# from .tensor.math import erf #DEFINE_ALIAS
# from .tensor.math import addcmul #DEFINE_ALIAS
from .tensor.math import addmm #DEFINE_ALIAS
......@@ -153,7 +153,7 @@ from .tensor.linalg import dot #DEFINE_ALIAS
from .tensor.linalg import norm #DEFINE_ALIAS
# from .tensor.linalg import transpose #DEFINE_ALIAS
from .tensor.linalg import dist #DEFINE_ALIAS
# from .tensor.linalg import t #DEFINE_ALIAS
from .tensor.linalg import t #DEFINE_ALIAS
# from .tensor.linalg import cross #DEFINE_ALIAS
# from .tensor.linalg import cholesky #DEFINE_ALIAS
# from .tensor.linalg import .tensordot #DEFINE_ALIAS
......
......@@ -775,6 +775,57 @@ class TestLog(TestActivation):
self.assertRaises(TypeError, fluid.layers.log, in2)
class TestLog1p(TestActivation):
def setUp(self):
self.op_type = "log1p"
self.init_dtype()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.log1p(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
def test_api(self):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input_x = np.random.uniform(0.1, 1, [11, 17]).astype("float64")
data_x = fluid.layers.data(
name="data_x",
shape=[11, 17],
append_batch_size=False,
dtype="float64")
res_log1p = fluid.layers.data(
name="res_log1p",
shape=[11, 17],
append_batch_size=False,
dtype="float64")
out1 = paddle.log1p(data_x)
out2 = paddle.log1p(data_x, out=res_log1p)
exe = fluid.Executor(place=fluid.CPUPlace())
exe.run(fluid.default_startup_program())
res1, res_in = exe.run(fluid.default_main_program(),
feed={"data_x": input_x},
fetch_list=[out1, res_log1p])
expected_res = np.log1p(input_x)
np.testing.assert_allclose(res1, expected_res)
np.testing.assert_allclose(res_in, expected_res)
# dygraph
with fluid.dygraph.guard():
np_x = np.random.uniform(0.1, 1, [11, 17]).astype("float64")
data_x = fluid.dygraph.to_variable(np_x)
z = paddle.log1p(data_x)
np_z = z.numpy()
z_expected = np.array(np.log1p(np_x))
np.testing.assert_allclose(np_z, z_expected)
class TestSquare(TestActivation):
def setUp(self):
self.op_type = "square"
......@@ -1173,6 +1224,7 @@ create_test_act_fp16_class(TestSoftRelu)
create_test_act_fp16_class(TestELU)
create_test_act_fp16_class(TestReciprocal)
create_test_act_fp16_class(TestLog)
create_test_act_fp16_class(TestLog1p, grad_atol=0.9)
create_test_act_fp16_class(TestSquare)
create_test_act_fp16_class(TestPow, atol=5e-2)
create_test_act_fp16_class(TestPow_factor_tensor, atol=5e-2)
......
......@@ -18,6 +18,8 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.framework as framework
......@@ -70,5 +72,45 @@ class TestEyeOp2(OpTest):
self.check_output()
class API_TestTensorEye(unittest.TestCase):
def test_out(self):
with fluid.program_guard(fluid.Program()):
data = paddle.eye(10)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
result, = exe.run(fetch_list=[data])
expected_result = np.eye(10, dtype="float32")
self.assertEqual((result == expected_result).all(), True)
with fluid.program_guard(fluid.Program()):
data = paddle.eye(10, num_columns=7, dtype="float64")
place = fluid.CPUPlace()
exe = fluid.Executor(place)
result, = exe.run(fetch_list=[data])
expected_result = np.eye(10, 7, dtype="float64")
self.assertEqual((result == expected_result).all(), True)
with fluid.program_guard(fluid.Program()):
data = paddle.eye(10, dtype="int64")
place = fluid.CPUPlace()
exe = fluid.Executor(place)
result, = exe.run(fetch_list=[data])
expected_result = np.eye(10, dtype="int64")
self.assertEqual((result == expected_result).all(), True)
def test_errors(self):
with fluid.program_guard(fluid.Program()):
def test_num_rows_type_check():
paddle.eye(-1, dtype="int64")
self.assertRaises(TypeError, test_num_rows_type_check)
def test_num_columns_type_check():
paddle.eye(10, num_columns=5.2, dtype="int64")
self.assertRaises(TypeError, test_num_columns_type_check)
if __name__ == "__main__":
unittest.main()
......@@ -574,5 +574,69 @@ class API_TestSumOp(unittest.TestCase):
self.assertEqual((np_z == z_expected).all(), True)
class API_TestMaxOp(unittest.TestCase):
def test_1(self):
# type: float
with fluid.program_guard(fluid.Program(), fluid.Program()):
data = fluid.data("data", shape=[10, 10], dtype="float32")
result_max = paddle.max(input=data, dim=1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
input_data = np.random.rand(10, 10).astype(np.float32)
res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
self.assertEqual((res == np.max(input_data, axis=1)).all(), True)
# type: int
with fluid.program_guard(fluid.Program(), fluid.Program()):
data = fluid.data("data", shape=[10, 10], dtype="int64")
result_max = paddle.max(input=data, dim=1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
self.assertEqual((res == np.max(input_data, axis=1)).all(), True)
# dygraph
with fluid.dygraph.guard():
np_x = np.array([10, 10]).astype('float64')
x = fluid.dygraph.to_variable(np_x)
z = paddle.max(x, dim=0)
np_z = z.numpy()
z_expected = np.array(np.max(np_x, axis=0))
self.assertEqual((np_z == z_expected).all(), True)
class API_TestMinOp(unittest.TestCase):
def test_1(self):
# type: float
with fluid.program_guard(fluid.Program(), fluid.Program()):
data = fluid.data("data", shape=[10, 10], dtype="float32")
result_min = paddle.min(input=data, dim=1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
input_data = np.random.rand(10, 10).astype(np.float32)
res, = exe.run(feed={"data": input_data}, fetch_list=[result_min])
self.assertEqual((res == np.min(input_data, axis=1)).all(), True)
# type: int
with fluid.program_guard(fluid.Program(), fluid.Program()):
data = fluid.data("data", shape=[10, 10], dtype="int64")
result_min = paddle.min(input=data, dim=1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
res, = exe.run(feed={"data": input_data}, fetch_list=[result_min])
self.assertEqual((res == np.min(input_data, axis=1)).all(), True)
# dygraph
with fluid.dygraph.guard():
np_x = np.array([10, 10]).astype('float64')
x = fluid.dygraph.to_variable(np_x)
z = paddle.min(x, dim=0)
np_z = z.numpy()
z_expected = np.array(np.min(np_x, axis=0))
self.assertEqual((np_z == z_expected).all(), True)
if __name__ == '__main__':
unittest.main()
......@@ -17,6 +17,7 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
......@@ -137,5 +138,71 @@ class TestTransposeOpError(unittest.TestCase):
self.assertRaises(ValueError, test_each_elem_value_check)
class TestTAPI(unittest.TestCase):
def test_out(self):
with fluid.program_guard(fluid.Program()):
data = fluid.data(shape=[10], dtype="float64", name="data")
data_t = paddle.t(data)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
data_np = np.random.random([10]).astype("float64")
result, = exe.run(feed={"data": data_np}, fetch_list=[data_t])
expected_result = np.transpose(data_np)
self.assertEqual((result == expected_result).all(), True)
with fluid.program_guard(fluid.Program()):
data = fluid.data(shape=[10, 5], dtype="float64", name="data")
data_t = paddle.t(data)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
data_np = np.random.random([10, 5]).astype("float64")
result, = exe.run(feed={"data": data_np}, fetch_list=[data_t])
expected_result = np.transpose(data_np)
self.assertEqual((result == expected_result).all(), True)
with fluid.program_guard(fluid.Program()):
data = fluid.data(shape=[1, 5], dtype="float64", name="data")
data_t = paddle.t(data)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
data_np = np.random.random([1, 5]).astype("float64")
result, = exe.run(feed={"data": data_np}, fetch_list=[data_t])
expected_result = np.transpose(data_np)
self.assertEqual((result == expected_result).all(), True)
with fluid.dygraph.guard():
np_x = np.random.random([10]).astype("float64")
data = fluid.dygraph.to_variable(np_x)
z = paddle.t(data)
np_z = z.numpy()
z_expected = np.array(np.transpose(np_x))
self.assertEqual((np_z == z_expected).all(), True)
with fluid.dygraph.guard():
np_x = np.random.random([10, 5]).astype("float64")
data = fluid.dygraph.to_variable(np_x)
z = paddle.t(data)
np_z = z.numpy()
z_expected = np.array(np.transpose(np_x))
self.assertEqual((np_z == z_expected).all(), True)
with fluid.dygraph.guard():
np_x = np.random.random([1, 5]).astype("float64")
data = fluid.dygraph.to_variable(np_x)
z = paddle.t(data)
np_z = z.numpy()
z_expected = np.array(np.transpose(np_x))
self.assertEqual((np_z == z_expected).all(), True)
def test_errors(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data(name='x', shape=[10, 5, 3], dtype='float64')
def test_x_dimension_check():
paddle.t(x)
self.assertRaises(ValueError, test_x_dimension_check)
if __name__ == '__main__':
unittest.main()
......@@ -11,6 +11,11 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
#from .math import *
#from .creation import *
#from .linalg import *
# TODO: define alias in tensor and framework directory
# from .creation import create_tensor #DEFINE_ALIAS
......@@ -18,7 +23,7 @@
# from .creation import create_random_int_lod #DEFINE_ALIAS
# from .creation import crop_tensor #DEFINE_ALIAS
# from .creation import diag #DEFINE_ALIAS
# from .creation import eye #DEFINE_ALIAS
from .creation import eye #DEFINE_ALIAS
# from .creation import fill_constant #DEFINE_ALIAS
# from .creation import get__from_selected_rows #DEFINE_ALIAS
from .creation import linspace #DEFINE_ALIAS
......@@ -106,15 +111,15 @@ from .math import sum #DEFINE_ALIAS
# from .math import sums #DEFINE_ALIAS
from .math import tanh #DEFINE_ALIAS
from .math import elementwise_sum #DEFINE_ALIAS
# from .math import max #DEFINE_ALIAS
# from .math import min #DEFINE_ALIAS
from .math import max #DEFINE_ALIAS
from .math import min #DEFINE_ALIAS
from .math import mm #DEFINE_ALIAS
from .math import div #DEFINE_ALIAS
from .math import add #DEFINE_ALIAS
# from .math import atan #DEFINE_ALIAS
from .math import logsumexp #DEFINE_ALIAS
# from .math import inverse #DEFINE_ALIAS
# from .math import log1p #DEFINE_ALIAS
from .math import log1p #DEFINE_ALIAS
# from .math import erf #DEFINE_ALIAS
# from .math import addcmul #DEFINE_ALIAS
from .math import addmm #DEFINE_ALIAS
......@@ -128,7 +133,7 @@ from .linalg import dot #DEFINE_ALIAS
from .linalg import norm #DEFINE_ALIAS
# from .linalg import transpose #DEFINE_ALIAS
from .linalg import dist #DEFINE_ALIAS
# from .linalg import t #DEFINE_ALIAS
from .linalg import t #DEFINE_ALIAS
# from .linalg import cross #DEFINE_ALIAS
# from .linalg import cholesky #DEFINE_ALIAS
# from .manipulation import cast #DEFINE_ALIAS
......
......@@ -38,7 +38,7 @@ __all__ = [
'zeros',
'zeros_like',
# 'arrange',
# 'eye',
'eye',
'full',
'full_like',
'triu',
......@@ -396,6 +396,66 @@ def zeros_like(input, dtype=None, device=None, name=None):
return out
def eye(num_rows,
num_columns=None,
out=None,
dtype='float32',
stop_gradient=True,
name=None):
"""
**eye**
This function constructs an identity tensor, or a batch of tensor.
Args:
num_rows(int): the number of rows in each batch tensor.
num_columns(int, optional): the number of columns in each batch tensor.
If None, default: num_rows.
out(Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of operation.
if out is None, a new Varibale will be create to store the result.
dtype(string, optional): The data type of the returned tensor.
It should be int32, int64, float16, float32, float64.
stop_gradient(bool, optional): Whether stop calculating gradients. Default:True.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: An identity Tensor or LoDTensor of shape [num_rows, num_columns].
Examples:
.. code-block:: python
import paddle
data = paddle.eye(3, dtype='int32')
# [[1, 0, 0]
# [0, 1, 0]
# [0, 0, 1]]
data = paddle.eye(2, 3, dtype='int32')
# [[1, 0, 0]
# [0, 1, 0]]
"""
helper = LayerHelper("eye", **locals())
if not isinstance(num_rows, int) or num_rows < 0:
raise TypeError("num_rows should be a non-negative int")
if num_columns is not None:
if not isinstance(num_columns, int) or num_columns < 0:
raise TypeError("num_columns should be a non-negative int")
else:
num_columns = num_rows
if out is None:
out = helper.create_variable_for_type_inference(dtype=dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='eye',
inputs={},
outputs={'Out': [out]},
attrs={
'num_rows': num_rows,
'num_columns': num_columns,
'dtype': c_dtype
},
stop_gradient=True)
out.stop_gradient = stop_gradient
return out
def full(shape,
fill_value,
out=None,
......
......@@ -23,7 +23,7 @@ __all__ = [
'norm',
# 'transpose',
'dist',
# 't',
't',
# 'cross',
# 'cholesky',
# 'tensordot'
......@@ -458,3 +458,74 @@ def dot(x, y, name=None):
type="dot", inputs={'X': x,
'Y': y}, attrs={}, outputs={"Out": out})
return out
def t(input, name=None):
"""
Transpose <=2-D tensor.
0-D and 1-D tensors are returned as it is and 2-D tensor is equal to
the fluid.layers.transpose function which perm dimensions set 0 and 1.
Args:
input (Variable): The input Tensor. It is a N-D (N<=2) Tensor of data types float32, float64, int32.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: A transposed n-D Tensor, with data type being float32, float64, int32, int64.
For Example:
.. code-block:: text
# Example 1 (0-D tensor)
x = tensor([0.79])
paddle.t(x) = tensor([0.79])
# Example 2 (1-D tensor)
x = tensor([0.79, 0.84, 0.32])
paddle.t(x) = tensor([0.79, 0.84, 0.32])
# Example 3 (2-D tensor)
x = tensor([0.79, 0.84, 0.32],
[0.64, 0.14, 0.57])
paddle.t(x) = tensor([0.79, 0.64],
[0.84, 0.14],
[0.32, 0.57])
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
x = fluid.data(name='x', shape=[2, 3],
dtype='float32')
x_transposed = paddle.t(x)
print x_transposed.shape
#(3L, 2L)
"""
if len(input.shape) > 2:
raise ValueError(
"Input(input) only support N-D (N<=2) tensor, but received "
"length of Input(input) is %s. Perhaps you can use paddle."
"tensor.transpose() instead." % len(input.shape))
if in_dygraph_mode():
if len(input.shape) == 1:
return input
# 2-D tensor
perm = [1, 0]
out, _ = core.ops.transpose2(input, 'axis', perm)
return out
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64', 'int32', 'int64'],
'transpose')
helper = LayerHelper('t', **locals())
out = helper.create_variable_for_type_inference(input.dtype)
input_shape = helper.create_variable_for_type_inference(input.dtype)
if len(input.shape) == 1:
out = input
else:
helper.append_op(
type='transpose2',
inputs={'X': [input]},
outputs={'Out': [out],
'XShape': [input_shape]},
attrs={'axis': [1, 0]})
return out
......@@ -65,15 +65,15 @@ __all__ = [
# 'sums',
'tanh',
'elementwise_sum',
# 'max',
# 'min',
'max',
'min',
'mm',
'div',
'add',
# 'atan',
'logsumexp',
# 'inverse',
# 'log1p',
'log1p',
# 'erf',
# 'addcmul',
'addmm'
......@@ -1062,3 +1062,196 @@ Examples:
return out
return layers.log(sum_out, name)
def max(input, dim=None, keep_dim=False, out=None, name=None):
"""
Computes the maximum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor, the data type is float32,
float64, int32, int64.
dim (list|int, optional): The dimension along which the maximum is computed.
If :attr:`None`, compute the maximum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
keep_dim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true, default
value is False.
out(Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of operation.
if out is None, a new Varibale will be create to store the result.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: Tensor, results of maximum on the specified dim of input tensor,
it's data type is the same as input's Tensor.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the corresponding output tensor.
x = fluid.data(name='x', shape=[2, 4], dtype='float32')
paddle.max(x) # [0.9]
paddle.max(x, dim=0) # [0.2, 0.3, 0.6, 0.9]
paddle.max(x, dim=-1) # [0.9, 0.7]
paddle.max(x, dim=1, keep_dim=True) # [[0.9], [0.7]]
# y is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1.0, 2.0], [3.0, 4.0]],
# [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the corresponding output tensor.
y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
paddle.max(y, dim=[1, 2]) # [4.0, 8.0]
paddle.max(y, dim=[0, 1]) # [7.0, 8.0]
"""
helper = LayerHelper('max', **locals())
if out is None:
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
check_variable_and_dtype(
input, 'input', ['float32', 'float64', 'int32', 'int64'], 'max')
reduce_all = True if dim == None or dim == [] else False
dim = dim if dim != None and dim != [] else [0]
if in_dygraph_mode():
return core.ops.reduce_max(input, 'dim', dim, 'keep_dim', keep_dim,
'reduce_all', reduce_all)
helper.append_op(
type='reduce_max',
inputs={'X': input},
outputs={'Out': out},
attrs={
'dim': dim,
'keep_dim': keep_dim,
'reduce_all': reduce_all
})
return out
def min(input, dim=None, keep_dim=False, out=None, name=None):
"""
Computes the minimum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor, the data type is float32,
float64, int32, int64.
dim (list|int, optional): The dimensions along which the minimum is computed.
If :attr:`None`, compute the minimum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
keep_dim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true, default
value is False.
out(Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of operation.
if out is None, a new Varibale will be create to store the result.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: Tensor, result of minimum on the specified dim of input tensor,
it's data type is the same as input's Tensor.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the corresponding output tensor.
x = fluid.data(name='x', shape=[2, 4], dtype='float32')
paddle.min(x) # [0.1]
paddle.min(x, dim=0) # [0.1, 0.2, 0.5, 0.7]
paddle.min(x, dim=-1) # [0.2, 0.1]
paddle.min(x, dim=1, keep_dim=True) # [[0.2], [0.1]]
# y is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1.0, 2.0], [3.0, 4.0]],
# [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the corresponding output tensor.
y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
paddle.min(y, dim=[1, 2]) # [1.0, 5.0]
paddle.min(y, dim=[0, 1]) # [1.0, 2.0]
"""
helper = LayerHelper('min', **locals())
if out is None:
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
check_variable_and_dtype(
input, 'input', ['float32', 'float64', 'int32', 'int64'], 'max')
reduce_all = True if dim == None or dim == [] else False
dim = dim if dim != None and dim != [] else [0]
if in_dygraph_mode():
return core.ops.reduce_min(input, 'dim', dim, 'keep_dim', keep_dim,
'reduce_all', reduce_all)
helper.append_op(
type='reduce_min',
inputs={'X': input},
outputs={'Out': out},
attrs={
'dim': dim,
'keep_dim': keep_dim,
'reduce_all': reduce_all
})
return out
def log1p(x, out=None, name=None):
"""
Calculates the natural log of the given input tensor, element-wise.
.. math::
Out = \\ln(x+1)
Args:
x (Variable): Input LoDTensor or Tensor. Must be one of the following types: float32, float64.
out(Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of operation.
if out is None, a new Varibale will be create to store the result.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: The natural log of the input LoDTensor or Tensor computed element-wise.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
# Graph Organizing
x = fluid.data(name="x", shape=[2,1], dtype="float32")
res = paddle.log1p(x)
# Create an executor using CPU as an example
exe = fluid.Executor(fluid.CPUPlace())
# Execute
x_i = np.array([[0], [1]]).astype(np.float32)
res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res])
print(res_val) # [[0.], [0.6931472]]
"""
if in_dygraph_mode():
return core.ops.log1p(x)
check_variable_and_dtype(x, 'x', ['float32', 'float64'], "log1p")
inputs = {'X': [x]}
helper = LayerHelper('log1p', **locals())
dtype = helper.input_dtype(input_param_name='x')
if out is None:
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(type="log1p", inputs={"X": x}, outputs={"Out": out})
return out
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