Tensor conversion requested dtype int32 for Tensor with dtype float32

When I try to load a model (keras.models.load_model()) that was saved by Keras using tensorflow 1.1 on a tensorflow 1.2 based keras I got following error:

/usr/local/lib/python3.5/dist-packages/keras/models.py in load_model(filepath, custom_objects, compile)
    231             raise ValueError('No model found in config file.')
    232         model_config = json.loads(model_config.decode('utf-8'))
--> 233         model = model_from_config(model_config, custom_objects=custom_objects)
    234 
    235         # set weights

/usr/local/lib/python3.5/dist-packages/keras/models.py in model_from_config(config, custom_objects)
    305                         'Maybe you meant to use '
    306                         '`Sequential.from_config(config)`?')
--> 307     return layer_module.deserialize(config, custom_objects=custom_objects)
    308 
    309 

/usr/local/lib/python3.5/dist-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
     52                                     module_objects=globs,
     53                                     custom_objects=custom_objects,
---> 54                                     printable_module_name='layer')

/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    137                 return cls.from_config(config['config'],
    138                                        custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 139                                                            list(custom_objects.items())))
    140             with CustomObjectScope(custom_objects):
    141                 return cls.from_config(config['config'])

/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py in from_config(cls, config, custom_objects)
   2448 
   2449         for layer_data in config['layers']:
-> 2450             process_layer(layer_data)
   2451 
   2452         name = config.get('name')

/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py in process_layer(layer_data)
   2443                 if input_tensors:
   2444                     if len(input_tensors) == 1:
-> 2445                         layer(input_tensors[0], **kwargs)
   2446                     else:
   2447                         layer(input_tensors, **kwargs)

/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py in __call__(self, inputs, **kwargs)
    567                                          '`layer.build(batch_input_shape)`')
    568                 if len(input_shapes) == 1:
--> 569                     self.build(input_shapes[0])
    570                 else:
    571                     self.build(input_shapes)

/usr/local/lib/python3.5/dist-packages/keras/layers/embeddings.py in build(self, input_shape)
     99             regularizer=self.embeddings_regularizer,
    100             constraint=self.embeddings_constraint,
--> 101             dtype=self.dtype)
    102         self.built = True
    103 

/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     85                 warnings.warn('Update your `' + object_name +
     86                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87             return func(*args, **kwargs)
     88         wrapper._original_function = func
     89         return wrapper

/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
    389         if dtype is None:
    390             dtype = K.floatx()
--> 391         weight = K.variable(initializer(shape), dtype=dtype, name=name)
    392         if regularizer is not None:
    393             self.add_loss(regularizer(weight))

/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py in variable(value, dtype, name)
    319         v._uses_learning_phase = False
    320         return v
--> 321     v = tf.Variable(value, dtype=_convert_string_dtype(dtype), name=name)
    322     if isinstance(value, np.ndarray):
    323         v._keras_shape = value.shape

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py in __init__(self, initial_value, trainable, collections, validate_shape, caching_device, name, variable_def, dtype, expected_shape, import_scope)
    198           name=name,
    199           dtype=dtype,
--> 200           expected_shape=expected_shape)
    201 
    202   def __repr__(self):

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py in _init_from_args(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, expected_shape)
    287         else:
    288           self._initial_value = ops.convert_to_tensor(
--> 289               initial_value, name="initial_value", dtype=dtype)
    290           shape = (self._initial_value.get_shape()
    291                    if validate_shape else tensor_shape.unknown_shape())

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, preferred_dtype)
    674       name=name,
    675       preferred_dtype=preferred_dtype,
--> 676       as_ref=False)
    677 
    678 

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
    739 
    740         if ret is None:
--> 741           ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    742 
    743         if ret is NotImplemented:

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in _TensorTensorConversionFunction(t, dtype, name, as_ref)
    612     raise ValueError(
    613         "Tensor conversion requested dtype %s for Tensor with dtype %s: %r"
--> 614         % (dtype.name, t.dtype.name, str(t)))
    615   return t
    616 

ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("embedding_1/random_uniform:0", shape=(5000, 60), dtype=float32)'

[Erechtheus]

Not sure if this is the general case, but I observed this error when training models using Keras 2.0.5 and loading in Keras 2.0.6. If I retrain the model in 2.0.6, I can successfully reload the model using keras.models.load_model().

[ft512835]

same problem