diff --git a/python/paddle/fluid/tests/unittests/test_fc_op.py b/python/paddle/fluid/tests/unittests/test_fc_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bb920710a9b10f3a8159bad3b33dd15ffbada19
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_fc_op.py
@@ -0,0 +1,90 @@
+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def fc_refer(matrix, with_bias):
+    in_n, in_c, in_h, in_w = matrix.input.shape
+    w_i, w_o = matrix.weights.shape
+
+    x_data = np.reshape(matrix.input, [in_n, in_c * in_h * in_w])
+    w_data = np.reshape(matrix.weights, [w_i, w_o])
+    b_data = np.reshape(matrix.bias, [1, w_o])
+    result = None
+
+    if with_bias:
+        result = np.dot(x_data, w_data) + b_data
+    else:
+        result = np.dot(x_data, w_data)
+
+    return result
+
+
+class MatrixGenerate:
+    def __init__(self, mb, ic, oc, h, w):
+        self.input = np.random.random((mb, ic, h, w)).astype("float32")
+        self.weights = np.random.random((ic * h * w, oc)).astype("float32")
+        self.bias = np.random.random((1, oc)).astype("float32")
+
+
+class TestFCOp(OpTest):
+    def setUp(self):
+        self.op_type = "fc"
+        self.matrix = MatrixGenerate(1, 10, 15, 3, 3)
+
+        self.with_bias = True
+        if self.with_bias:
+            self.inputs = {
+                'Input': self.matrix.input,
+                'W': self.matrix.weights,
+                'Bias': self.matrix.bias
+            }
+        else:
+            self.inputs = {'Input': self.matrix.input, 'W': self.matrix.weights}
+
+        self.attrs = {'use_mkldnn': False}
+
+        self.outputs = {'Out': fc_refer(self.matrix, self.with_bias)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestFCOpBiasBoth(TestFCOp):
+    def init_shapes(self, mb, ic, oc, h, w):
+        for with_bias in {True, False}:
+            self.with_bias = with_bias
+            self.matrix = MatrixGenerate(mb, ic, oc, h, w)
+
+
+class TestFCOp1(TestFCOpBiasBoth):
+    def init_op_type(self):
+        self.init_shapes(2, 8, 10, 1, 1)
+
+
+class TestFCOp2(TestFCOpBiasBoth):
+    def init_op_type(self):
+        self.init_shapes(4, 5, 6, 2, 2)
+
+
+class TestFCOp4(TestFCOpBiasBoth):
+    def init_op_type(self):
+        self.init_shapes(1, 32, 64, 3, 3)
+
+
+if __name__ == "__main__":
+    unittest.main()