Created by: still-wait

test=develop

Add a new public nn API，paddle.nn.loss.CrossEntropyLoss(reduction='sum'|'mean'|'none').

Usage example:

# declarative mode
import paddle
import paddle.fluid as fluid
import numpy as np

input = fluid.layers.data(name='input', shape=[3,5], dtype='float32')
label = fluid.layers.data(name='label', shape=[3,1], dtype='int64')
weight = fluid.layers.data(name='weight', shape=[5], dtype='float32')
ce_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight, reduction='mean')
output = ce_loss(input,label)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
input_data = np.random.random([3,5]).astype("float32")
label_data = np.array([[1], [3], [4]]).astype("int64")
weight_data = np.random.random([5]).astype("float32")
output = exe.run(fluid.default_main_program(),
              feed={"input":input_data, "label":label_data,"weight":weight_data},
              fetch_list=[output],
              return_numpy=True)
 print(output)

# imperative mode
import paddle.fluid.dygraph as dg
with dg.guard(place) as g:
    input = dg.to_variable(input_data)
    label = dg.to_variable(label_data)
    weight = dg.to_variable(weight_data)
    ce_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight, reduction='mean')
    output = ce_loss(input,label)
    print(output.numpy())