diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py
index 21f078b763..2b3f8a249c 100644
--- a/deepmd/train/trainer.py
+++ b/deepmd/train/trainer.py
@@ -372,7 +372,7 @@ def _build_training(self):
         if self.mixed_prec is not None:
             _TF_VERSION = Version(TF_VERSION)
             # check the TF_VERSION, when TF < 1.12, mixed precision is not allowed 
-            if _TF_VERSION < Version('1.12.0'):
+            if _TF_VERSION < Version('1.14.0'):
                 raise RuntimeError("TensorFlow version %s is not compatible with the mixed precision setting. Please consider upgrading your TF version!" % TF_VERSION)
             elif _TF_VERSION < Version('2.4.0'):
                 optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer)
diff --git a/deepmd/utils/network.py b/deepmd/utils/network.py
index 57dd90f893..befd571f24 100644
--- a/deepmd/utils/network.py
+++ b/deepmd/utils/network.py
@@ -79,15 +79,13 @@ def one_layer(inputs,
                 if use_timestep :
                     if mixed_prec is not None and not final_layer:
                        idt = tf.cast(idt, get_precision(mixed_prec['compute_prec']))
-                    return tf.reshape(activation_fn(hidden), [-1, outputs_size]) * idt
+                    hidden = tf.reshape(activation_fn(hidden), [-1, outputs_size]) * idt
                 else :
-                    return tf.reshape(activation_fn(hidden), [-1, outputs_size])                    
-        else:
-            if useBN:
-                None
-                # return self._batch_norm(hidden, name=name+'_normalization', reuse=reuse)
-            else:
-                return hidden
+                    hidden = tf.reshape(activation_fn(hidden), [-1, outputs_size])                    
+
+        if mixed_prec is not None:
+            hidden = tf.cast(hidden, get_precision(mixed_prec['output_prec']))
+        return hidden
 
 
 def embedding_net_rand_seed_shift(
@@ -237,6 +235,8 @@ def embedding_net(xx,
                 xx = tf.concat([xx,xx], 1) + hidden
         else:
             xx = hidden
+    if mixed_prec is not None:
+        xx = tf.cast(xx, get_precision(mixed_prec['output_prec']))
     return xx
 
 def variable_summaries(var: tf.Variable, name: str):
diff --git a/source/tests/test_mixed_prec_training.py b/source/tests/test_mixed_prec_training.py
new file mode 100644
index 0000000000..28a1d485f7
--- /dev/null
+++ b/source/tests/test_mixed_prec_training.py
@@ -0,0 +1,60 @@
+import os,json
+import numpy as np
+import unittest
+import subprocess as sp
+from packaging.version import Version
+
+from deepmd.infer import DeepPot
+# from deepmd.entrypoints.compress import compress
+from common import j_loader, tests_path
+from deepmd.env import TF_VERSION
+
+
+def _file_delete(file) :
+    if os.path.isdir(file):
+        os.rmdir(file)
+    elif os.path.isfile(file):
+        os.remove(file)
+
+def _subprocess_run(command):
+    popen = sp.Popen(command.split(), shell=False, stdout=sp.PIPE, stderr=sp.STDOUT)
+    for line in iter(popen.stdout.readline, b''):
+        if hasattr(line, 'decode'):
+            line = line.decode('utf-8')
+        line = line.rstrip()
+        print(line)
+    popen.wait()
+    return popen.returncode
+
+class TestMixedPrecTraining(unittest.TestCase):
+    def setUp(self):
+        data_file  = str(tests_path / os.path.join("model_compression", "data"))
+        self.INPUT = str(tests_path / "input.json")
+        jdata = j_loader(str(tests_path / os.path.join("model_compression", "input.json")))
+        jdata["training"]["training_data"]["systems"] = data_file
+        jdata["training"]["validation_data"]["systems"] = data_file
+        jdata["training"]["mixed_precision"] = {}
+        jdata["training"]["mixed_precision"]["compute_prec"] = "float16"
+        jdata["training"]["mixed_precision"]["output_prec"] = "float32"
+        with open(self.INPUT, "w") as fp:
+            json.dump(jdata, fp, indent=4)
+
+    def test_training(self):
+        _TF_VERSION = Version(TF_VERSION)
+        # check the TF_VERSION, when TF < 1.12, mixed precision is not allowed 
+        if _TF_VERSION >= Version('1.14.0'):
+            ret = _subprocess_run("dp train " + self.INPUT)
+            np.testing.assert_equal(ret, 0, 'DP train failed!')
+
+    def tearDown(self):
+        _file_delete(self.INPUT)
+        _file_delete("out.json")
+        _file_delete("checkpoint")
+        _file_delete("model.ckpt.meta")
+        _file_delete("model.ckpt.index")
+        _file_delete("model.ckpt.data-00000-of-00001")
+        _file_delete("model.ckpt-100.meta")
+        _file_delete("model.ckpt-100.index")
+        _file_delete("model.ckpt-100.data-00000-of-00001")
+        _file_delete("input_v2_compat.json")
+        _file_delete("lcurve.out")