Add accuracy layer (#4958)

* Complete accuray layer

* Fix error

* Fix error

* Add 'accuracy' to __all__

* update

* Fix Type error

* Fix error

* Refine unit tests

* Fix an unit test error

paddle/operators/accuracy_op.cc

@@ -32,7 +32,8 @@ class AccuracyOp : public framework::OperatorWithKernel {
     auto inference_dim = ctx->GetInputDim("Inference");
     auto label_dim = ctx->GetInputDim("Label");
 
-    PADDLE_ENFORCE_EQ(label_dim.size(), 1, "label must be a vector");
+    PADDLE_ENFORCE_EQ(label_dim.size(), 2, "label's rank must be 2.");
+    PADDLE_ENFORCE_EQ(label_dim[1], 1, "label's second dimension must be 1");
     PADDLE_ENFORCE_EQ(inference_dim[0], label_dim[0],
                       "inference size must be the same as label size");
 
@@ -68,7 +69,8 @@ information, or not. But the output only shares the LoD with input `Inference`.
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(accuracy, ops::AccuracyOp, ops::AccuracyOpMaker);
+REGISTER_OPERATOR(accuracy, ops::AccuracyOp, ops::AccuracyOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
 REGISTER_OP_CPU_KERNEL(
     accuracy, ops::AccuracyKernel<paddle::platform::CPUPlace, int>,
     ops::AccuracyKernel<paddle::platform::CPUPlace, int64_t>);

paddle/operators/top_k_op.cc

@@ -52,7 +52,11 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("Out", "The output tensor of Topk op");
     AddOutput("Indices", "The indices of Topk elements of input");
     AddComment(
-        R"DOC(If the input is a vector (1d tensor), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j].
+        R"DOC(If the input is a vector (1d tensor), 
+        finds the k largest entries in the vector 
+        and outputs their values and indices as vectors. 
+        Thus values[j] is the j-th largest entry in input, 
+        and its index is indices[j].
 
     For matrices, computes the top k entries in each row. )DOC");
     AddAttr<int>("k",
@@ -66,6 +70,7 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(top_k, ops::TopkOp, ops::TopkOpMaker);
+REGISTER_OPERATOR(top_k, ops::TopkOp, ops::TopkOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
 REGISTER_OP_CPU_KERNEL(top_k,
                        ops::TopkKernel<paddle::platform::CPUPlace, float>);

python/paddle/v2/framework/layers.py

@@ -5,7 +5,7 @@ import re
 
 __all__ = [
     'fc', 'data', 'cross_entropy', 'conv2d', 'pool2d', 'embedding', 'concat',
-    'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool'
+    'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool', 'accuracy'
 ]
 
 
@@ -229,6 +229,26 @@ def square_error_cost(input, label, **kwargs):
     return square_out
 
 
+def accuracy(input, label, k=1, **kwargs):
+    helper = LayerHelper("accuracy", **kwargs)
+    topk_out = helper.create_tmp_variable(dtype=input.data_type)
+    topk_indices = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="top_k",
+        inputs={"X": [input]},
+        outputs={"Out": [topk_out],
+                 "Indices": [topk_indices]},
+        attrs={"k": k})
+    acc_out_dtype = kwargs.get("out_dtype", "float32")
+    acc_out = helper.create_tmp_variable(dtype=acc_out_dtype)
+    helper.append_op(
+        type="accuracy",
+        inputs={"Inference": [topk_indices],
+                "Label": [label]},
+        outputs={"Accuracy": [acc_out]})
+    return acc_out
+
+
 def sequence_conv(input,
                   num_filters,
                   name=None,

python/paddle/v2/framework/tests/test_accuracy_op.py

@@ -8,12 +8,12 @@ class TestAccuracyOp(OpTest):
         self.op_type = "accuracy"
         n = 8192
         infer = np.random.randint(0, 2, (n, 1)).astype("int")
-        label = np.random.randint(0, 2, (n, )).astype("int")
+        label = np.random.randint(0, 2, (n, 1)).astype("int")
         self.inputs = {'Inference': infer, "Label": label}
         num_correct = 0
         for rowid in xrange(n):
             for ele in infer[rowid]:
-                if ele == label[rowid]:
+                if ele == label[rowid][0]:
                     num_correct += 1
                     break
         self.outputs = {

python/paddle/v2/framework/tests/test_recognize_digits_conv.py

@@ -51,12 +51,14 @@ predict = layers.fc(input=conv_pool_2,
 cost = layers.cross_entropy(
     input=predict, label=label, program=program, init_program=init_program)
 avg_cost = layers.mean(x=cost, program=program)
+accuracy = layers.accuracy(
+    input=predict, label=label, program=program, init_program=init_program)
 
 sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
 opts = sgd_optimizer.minimize(avg_cost)
 
 BATCH_SIZE = 50
-PASS_NUM = 1
+PASS_NUM = 3
 train_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.mnist.train(), buf_size=500),
@@ -83,10 +85,11 @@ for pass_id in range(PASS_NUM):
         outs = exe.run(program,
                        feed={"pixel": tensor_img,
                              "label": tensor_y},
-                       fetch_list=[avg_cost])
-
+                       fetch_list=[avg_cost, accuracy])
         loss = np.array(outs[0])
+        acc = np.array(outs[1])
 
-        if loss < 10.0:
-            exit(0)  # if avg cost less than 10.0, we think our code is good.
+        if loss < 10.0 and acc > 0.9:
+            # if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
+            exit(0)
 exit(1)