Numpy ndarray should be serialized as Python list. (#10727)

* Numpy ndarray should be serialized as Python list.

* Add test case.

* Fix annotation

keras/engine/saving.py

@@ -79,8 +79,7 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True):
         # if obj is any numpy type
         if type(obj).__module__ == np.__name__:
             if isinstance(obj, np.ndarray):
-                return {'type': type(obj),
-                        'value': obj.tolist()}
+                return obj.tolist()
             else:
                 return obj.item()
 

tests/test_model_saving.py

@@ -12,6 +12,7 @@ from keras.models import Model, Sequential
 from keras.layers import Dense, Lambda, RepeatVector, TimeDistributed, Bidirectional, GRU, LSTM, CuDNNGRU, CuDNNLSTM
 from keras.layers import Conv2D, Flatten
 from keras.layers import Input, InputLayer
+from keras.initializers import Constant
 from keras import optimizers
 from keras import losses
 from keras import metrics
@@ -641,6 +642,21 @@ def test_saving_recurrent_layer_without_bias():
     os.remove(fname)
 
 
+@keras_test
+def test_saving_constant_initializer_with_numpy():
+    """Test saving and loading model of constant initializer with numpy ndarray as input.
+    """
+    model = Sequential()
+    model.add(Dense(2, input_shape=(3,), kernel_initializer=Constant(np.ones((3, 2)))))
+    model.add(Dense(3))
+    model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
+
+    _, fname = tempfile.mkstemp('.h5')
+    save_model(model, fname)
+    model = load_model(fname)
+    os.remove(fname)
+
+
 @keras_test
 @pytest.mark.parametrize('implementation', [1, 2], ids=['impl1', 'impl2'])
 @pytest.mark.parametrize('bidirectional', [False, True], ids=['single', 'bidirectional'])