Network.to_json should handle numpy.ndarray correctly. (#10754)

Author: yanboliang

keras/engine/network.py

@@ -1193,7 +1193,10 @@ def to_json(self, **kwargs):
         def get_json_type(obj):
             # If obj is any numpy type
             if type(obj).__module__ == np.__name__:
-                return obj.item()
+                if isinstance(obj, np.ndarray):
+                    return obj.tolist()
+                else:
+                    return obj.item()
 
             # If obj is a python 'type'
             if type(obj).__name__ == type.__name__:

tests/keras/engine/test_topology.py

@@ -9,6 +9,7 @@
 from keras import backend as K
 from keras.models import model_from_json, model_from_yaml
 from keras.utils.test_utils import keras_test
+from keras.initializers import Constant
 
 
 skipif_no_tf_gpu = pytest.mark.skipif(
@@ -797,5 +798,19 @@ def call(self, inputs, **kwargs):
     assert K.int_shape(z)[1:] == (16, 16, 3)
 
 
+@keras_test
+def test_constant_initializer_with_numpy():
+    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'])
+
+    json_str = model.to_json()
+    model_from_json(json_str).summary()
+
+    yaml_str = model.to_yaml()
+    model_from_yaml(yaml_str).summary()
+
+
 if __name__ == '__main__':
     pytest.main([__file__])