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Expose sigmoid_cross_entropy_with_logits (#6147)

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Also, change the `labels` to `label` for api consistency
release/0.11.0
Yu Yang 7 years ago committed by GitHub
parent 44e391449c
commit 1fe5acb25a
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 41 additions and 29 deletions
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24
paddle/operators/sigmoid_cross_entropy_with_logits_op.cc
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@ -25,20 +25,19 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input(Labels) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto labels_dims = ctx->GetInputDim("Labels");
auto labels_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
"Input(Labels)'s rank should be 2.");
"Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
"The 1st dimension of Input(X) and Input(Labels) should "
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
"The 2nd dimension of Input(X) and Input(Labels) should "
"The 2nd dimension of Input(X) and Input(Label) should "
"be equal.");
ctx->SetOutputDim("Out", x_dims);
@ -53,26 +52,25 @@ class SigmoidCrossEntropyWithLogitsGradOp
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input(Labels) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shoudl be not null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Output(X@GRAD) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto labels_dims = ctx->GetInputDim("Labels");
auto labels_dims = ctx->GetInputDim("Label");
auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
"Input(Labels)'s rank should be 2.");
"Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(dout_dims.size(), 2,
"Input(Out@Grad)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
"The 1st dimension of Input(X) and Input(Labels) should "
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
"The 2nd dimension of Input(X) and Input(Labels) should "
"The 2nd dimension of Input(X) and Input(Label) should "
"be equal.");
PADDLE_ENFORCE_EQ(x_dims[0], dout_dims[0],
"The 1st dimension of Input(X) and Input(Out@Grad) "
@ -97,7 +95,7 @@ class SigmoidCrossEntropyWithLogitsOpMaker
"This input is a tensor of logits computed by the previous "
" operator. Logits are unscaled log probabilities given as "
"log(p/(1-p)).");
AddInput("Labels",
AddInput("Label",
"(Tensor, default Tensor<float>), a 2-D tensor of the same type "
"and shape as X. This input is a tensor of probabalistic labels "
"for each logit");

6
paddle/operators/sigmoid_cross_entropy_with_logits_op.h
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@ -25,8 +25,7 @@ class SigmoidCrossEntropyWithLogitsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const framework::Tensor *X = context.Input<framework::Tensor>("X");
const framework::Tensor *Labels =
context.Input<framework::Tensor>("Labels");
const framework::Tensor *Labels = context.Input<framework::Tensor>("Label");
framework::Tensor *Out = context.Output<framework::Tensor>("Out");
Out->mutable_data<T>(context.GetPlace());
@ -52,8 +51,7 @@ class SigmoidCrossEntropyWithLogitsGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const framework::Tensor *X = context.Input<framework::Tensor>("X");
const framework::Tensor *Labels =
context.Input<framework::Tensor>("Labels");
const framework::Tensor *Labels = context.Input<framework::Tensor>("Label");
const framework::Tensor *dOut =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
framework::Tensor *dX =

1
python/paddle/v2/fluid/layers.py
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@ -403,6 +403,7 @@ _create_op_func_('sigmoid')
_create_op_func_('scale')
_create_op_func_('reshape')
_create_op_func_('transpose')
_create_op_func_('sigmoid_cross_entropy_with_logits')
def cast(x, dtype, main_program=None):

10
python/paddle/v2/fluid/tests/test_layers.py
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@ -137,6 +137,16 @@ class TestBook(unittest.TestCase):
print(str(program))
def test_sigmoid_cross_entropy(self):
program = Program()
with program_guard(program):
dat = layers.data(name='data', shape=[10], dtype='float32')
lbl = layers.data(name='label', shape=[10], dtype='float32')
self.assertIsNotNone(
layers.sigmoid_cross_entropy_with_logits(
x=dat, label=lbl))
print(str(program))
if __name__ == '__main__':
unittest.main()

29
python/paddle/v2/fluid/tests/test_sigmoid_cross_entropy_with_logits_op.py
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@ -2,11 +2,12 @@ import numpy as np
from op_test import OpTest
from scipy.special import logit
from scipy.special import expit
import unittest
class TestSigmoidCrossEntropyWithLogitsOp1(OpTest):
'''Test sigmoid_cross_entropy_with_logit_op with binary labels
'''
"""Test sigmoid_cross_entropy_with_logit_op with binary label
"""
def setUp(self):
self.op_type = "sigmoid_cross_entropy_with_logits"
@ -16,16 +17,16 @@ class TestSigmoidCrossEntropyWithLogitsOp1(OpTest):
'X': logit(
np.random.uniform(0, 1, (batch_size, num_classes))
.astype("float32")),
'Labels': np.random.randint(0, 2, (batch_size, num_classes))
'Label': np.random.randint(0, 2, (batch_size, num_classes))
.astype("float32")
}
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X))
# Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
sigmoid_X = expit(self.inputs['X'])
term1 = self.inputs['Labels'] * np.log(sigmoid_X)
term2 = (1 - self.inputs['Labels']) * np.log(1 - sigmoid_X)
term1 = self.inputs['Label'] * np.log(sigmoid_X)
term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
self.outputs = {'Out': -term1 - term2}
def test_check_output(self):
@ -36,8 +37,8 @@ class TestSigmoidCrossEntropyWithLogitsOp1(OpTest):
class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
'''Test sigmoid_cross_entropy_with_logit_op with probabalistic labels
'''
"""Test sigmoid_cross_entropy_with_logit_op with probabalistic label
"""
def setUp(self):
self.op_type = "sigmoid_cross_entropy_with_logits"
@ -47,16 +48,16 @@ class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
'X': logit(
np.random.uniform(0, 1, (batch_size, num_classes))
.astype("float32")),
'Labels': np.random.uniform(0, 1, (batch_size, num_classes))
'Label': np.random.uniform(0, 1, (batch_size, num_classes))
.astype("float32")
}
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X))
# Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
sigmoid_X = expit(self.inputs['X'])
term1 = self.inputs['Labels'] * np.log(sigmoid_X)
term2 = (1 - self.inputs['Labels']) * np.log(1 - sigmoid_X)
term1 = self.inputs['Label'] * np.log(sigmoid_X)
term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
self.outputs = {'Out': -term1 - term2}
def test_check_output(self):
@ -64,3 +65,7 @@ class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == '__main__':
unittest.main()

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