lstm

paddle/operators/lstm_unit_op.cc

@@ -64,7 +64,7 @@ class LstmUnitOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(Lstm-Unit Operator
 
 Equation: 
-  i, j, f, o = split(X)
+  i, f, o, j = split(X)
   C = C_prev * sigm(f + forget_bias) + sigm(i) * tanh(j)
   H = C * sigm(o)
    
@@ -99,3 +99,5 @@ REGISTER_OP(lstm_unit, ops::LstmUnitOp, ops::LstmUnitOpMaker<float>,
             lstm_unit_grad, ops::LstmUnitGradOp);
 REGISTER_OP_CPU_KERNEL(lstm_unit,
                        ops::LstmUnitKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    lstm_unit_grad, ops::LstmUnitGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/lstm_unit_op.h

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #pragma once
+#include "glog/logging.h"
 #include "paddle/framework/op_registry.h"
 
 namespace paddle {

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

@@ -0,0 +1,39 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+import glog as log
+
+
+def sigmoid_np(x):
+    return 1. / (1. + np.exp(-x))
+
+
+def tanh_np(x):
+    return 2 * sigmoid_np(2. * x) - 1.
+
+
+class LstmUnitTest(OpTest):
+    def setUp(self):
+        self.op_type = "lstm_unit"
+        x_np = np.random.normal(size=(5, 16)).astype("float32")
+        c_np = np.random.normal(size=(5, 4)).astype("float32")
+        i_np, f_np, o_np, j_np = np.split(x_np, 4, axis=1)
+        forget_bias_np = 0.
+        self.attrs = {'forget_bias': 0.}
+
+        new_c = c_np * sigmoid_np(f_np + forget_bias_np) + sigmoid_np(
+            i_np) * tanh_np(j_np)
+        new_h = tanh_np(new_c) * sigmoid_np(o_np)
+
+        self.inputs = {'X': x_np, 'C_prev': c_np}
+        self.outputs = {'C': new_c, 'H': new_h}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X', 'C_prev'], ['C', 'H'], max_relative_error=0.01)
+
+
+if __name__ == "__main__":
+    unittest.main()