Created by: still-wait

Fix bug in paddle.nn.loss.CrossEntropyLoss()

Problem Description:

When with weight parameter, the calculation result is wrong. Now This OP combines softmax, log, and NLLLoss together.

Instructions

import paddle
import paddle.fluid as fluid
import numpy as np

input = fluid.data(name='input', shape=[5, 100], dtype='float64')
label = fluid.data(name='label', shape=[5], dtype='int64')
weight = fluid.data(name='weight', shape=[100], dtype='float64')
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([5, 100]).astype("float64")
label_data = np.random.randint(0, 100, size=(5)).astype(np.int64)
weight_data = np.random.random([100]).astype("float64")
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())

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