在 Keras Layer 的构建过程中，什么会导致类型错误？
What could cause a type error during the build process of Keras Layer?
我正在根据 NALU 论文创建一个自定义层，但是在 Google Colab（tensorflow 版本 1.10.0）中测试我的代码时出现类型错误。这在我本地的 Jupyter 笔记本（tensorflow cpu 版本 1.8.0）中没有发生。
在Layer的构建函数中添加权重时出现类型错误。添加权重时，当代码尝试将维度除以浮点数时会发生类型错误。我认为这可能是 Keras 处理参数的方式出错，所以我放弃了初始化程序。即使没有初始化器，也不可能向层添加权重。我从 tensorflow 的 keras 教程中获得了我的代码结构，我想知道是否需要做任何事情来更新我的代码以使用最新版本的 tensorflow。
TypeError Traceback (most recent call last)
<ipython-input-7-55fb94a8f3b1> in <module>()
82 y_test = x_test[:, 0] * x_test[:, 1]
83
---> 84 model = nalu_model()
85
86 model.compile(optimizer='RMSProp',
<ipython-input-7-55fb94a8f3b1> in nalu_model()
48 def nalu_model():
49 inp = tf.keras.layers.Input(shape=(2,))
---> 50 out = NALU(1)(inp)
51
52 model = tf.keras.models.Model(inputs=inp, outputs=out)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
726 if all(hasattr(x, 'shape') for x in input_list):
727 input_shapes = nest.map_structure(lambda x: x.shape, inputs)
--> 728 self.build(input_shapes)
729 self.built = True
730
<ipython-input-7-55fb94a8f3b1> in build(self, input_shape)
9 shape = tf.TensorShape((input_shape[1], self.num_outputs))
10 get = tf.keras.initializers.get
---> 11 self.W_ = self.add_variable("W_", shape=shape, initializer=get('glorot_uniform'))
12 self.M_ = self.add_variable("M_", shape=shape, initializer=get('glorot_uniform'))
13 self.GAM = self.add_variable("GAM", shape=shape, initializer=get('glorot_uniform')) # Gate add & multiply
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in add_variable(self, *args, **kwargs)
459 def add_variable(self, *args, **kwargs):
460 """Alias for `add_weight`."""
--> 461 return self.add_weight(*args, **kwargs)
462
463 def add_weight(self,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint, partitioner, use_resource, synchronization, aggregation, getter)
563 use_resource=use_resource,
564 synchronization=synchronization,
--> 565 aggregation=aggregation)
566
567 if regularizer is not None:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/checkpointable/base.py in _add_variable_with_custom_getter(self, name, shape, dtype, initializer, getter, overwrite, **kwargs_for_getter)
533 new_variable = getter(
534 name=name, shape=shape, dtype=dtype, initializer=initializer,
--> 535 **kwargs_for_getter)
536
537 # If we set an initializer and the variable processed it, tracking will not
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in make_variable(name, shape, dtype, initializer, partition_info, trainable, caching_device, validate_shape, constraint, use_resource, synchronization, aggregation, partitioner)
1916 use_resource=use_resource,
1917 synchronization=synchronization,
-> 1918 aggregation=aggregation)
1919 return v
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in variable(initial_value, trainable, collections, validate_shape, caching_device, name, dtype, constraint, use_resource, synchronization, aggregation)
2441 use_resource=use_resource,
2442 synchronization=synchronization,
-> 2443 aggregation=aggregation)
2444
2445
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in <lambda>(**kwargs)
2423 synchronization=VariableSynchronization.AUTO,
2424 aggregation=VariableAggregation.NONE):
-> 2425 previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
2426 for getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access
2427 previous_getter = _make_getter(getter, previous_getter)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in default_variable_creator(next_creator, **kwargs)
2393 collections=collections, validate_shape=validate_shape,
2394 caching_device=caching_device, name=name, dtype=dtype,
-> 2395 constraint=constraint)
2396 elif not use_resource and context.executing_eagerly():
2397 raise RuntimeError(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py in __init__(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, variable_def, import_scope, constraint)
310 name=name,
311 dtype=dtype,
--> 312 constraint=constraint)
313
314 # pylint: disable=unused-argument
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py in _init_from_args(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, constraint)
415 with ops.name_scope("Initializer"), ops.device(None):
416 initial_value = ops.convert_to_tensor(
--> 417 initial_value(), name="initial_value", dtype=dtype)
418 self._handle = _eager_safe_variable_handle(
419 shape=initial_value.get_shape(),
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in <lambda>()
1901 initializer = initializer(dtype=dtype)
1902 init_val = lambda: initializer( # pylint: disable=g-long-lambda
-> 1903 shape, dtype=dtype, partition_info=partition_info)
1904 variable_dtype = dtype.base_dtype
1905 if use_resource is None:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py in __call__(self, shape, dtype, partition_info)
474 scale /= max(1., fan_out)
475 else:
--> 476 scale /= max(1., (fan_in + fan_out) / 2.)
477 if self.distribution == "normal" or self.distribution == "truncated_normal":
478 # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
TypeError: unsupported operand type(s) for /: 'Dimension' and 'float'
代码：
https://gist.github.com/Carliceiv/0b68fe0d9f2a9fc9a1b901e8a722f6cd
堆栈跟踪表明在代码的某处给出了一个 Dimension 值作为 n 参数，而您通常期望一个 integer/float。
我认为这可能是这条线造成的
shape = tf.TensorShape((input_shape[1], self.num_outputs))
请尝试将其更改为
shape = (input_shape[1], self.num_outputs)
编辑：正如评论中所指出的，这不起作用 input_shape 是一个 Dimension 对象而不是 int。要解决此问题，请使用此修改：
shape = tf.TensorShape((input_shape[1], self.num_outputs)).as_list()
这将首先创建一个新的 TensorShape，它能够处理 Dimension 数据类型，然后将其转换为整数列表。
我正在根据 NALU 论文创建一个自定义层，但是在 Google Colab（tensorflow 版本 1.10.0）中测试我的代码时出现类型错误。这在我本地的 Jupyter 笔记本（tensorflow cpu 版本 1.8.0）中没有发生。
在Layer的构建函数中添加权重时出现类型错误。添加权重时，当代码尝试将维度除以浮点数时会发生类型错误。我认为这可能是 Keras 处理参数的方式出错，所以我放弃了初始化程序。即使没有初始化器，也不可能向层添加权重。我从 tensorflow 的 keras 教程中获得了我的代码结构，我想知道是否需要做任何事情来更新我的代码以使用最新版本的 tensorflow。
TypeError Traceback (most recent call last)
<ipython-input-7-55fb94a8f3b1> in <module>()
82 y_test = x_test[:, 0] * x_test[:, 1]
83
---> 84 model = nalu_model()
85
86 model.compile(optimizer='RMSProp',
<ipython-input-7-55fb94a8f3b1> in nalu_model()
48 def nalu_model():
49 inp = tf.keras.layers.Input(shape=(2,))
---> 50 out = NALU(1)(inp)
51
52 model = tf.keras.models.Model(inputs=inp, outputs=out)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
726 if all(hasattr(x, 'shape') for x in input_list):
727 input_shapes = nest.map_structure(lambda x: x.shape, inputs)
--> 728 self.build(input_shapes)
729 self.built = True
730
<ipython-input-7-55fb94a8f3b1> in build(self, input_shape)
9 shape = tf.TensorShape((input_shape[1], self.num_outputs))
10 get = tf.keras.initializers.get
---> 11 self.W_ = self.add_variable("W_", shape=shape, initializer=get('glorot_uniform'))
12 self.M_ = self.add_variable("M_", shape=shape, initializer=get('glorot_uniform'))
13 self.GAM = self.add_variable("GAM", shape=shape, initializer=get('glorot_uniform')) # Gate add & multiply
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in add_variable(self, *args, **kwargs)
459 def add_variable(self, *args, **kwargs):
460 """Alias for `add_weight`."""
--> 461 return self.add_weight(*args, **kwargs)
462
463 def add_weight(self,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint, partitioner, use_resource, synchronization, aggregation, getter)
563 use_resource=use_resource,
564 synchronization=synchronization,
--> 565 aggregation=aggregation)
566
567 if regularizer is not None:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/checkpointable/base.py in _add_variable_with_custom_getter(self, name, shape, dtype, initializer, getter, overwrite, **kwargs_for_getter)
533 new_variable = getter(
534 name=name, shape=shape, dtype=dtype, initializer=initializer,
--> 535 **kwargs_for_getter)
536
537 # If we set an initializer and the variable processed it, tracking will not
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in make_variable(name, shape, dtype, initializer, partition_info, trainable, caching_device, validate_shape, constraint, use_resource, synchronization, aggregation, partitioner)
1916 use_resource=use_resource,
1917 synchronization=synchronization,
-> 1918 aggregation=aggregation)
1919 return v
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in variable(initial_value, trainable, collections, validate_shape, caching_device, name, dtype, constraint, use_resource, synchronization, aggregation)
2441 use_resource=use_resource,
2442 synchronization=synchronization,
-> 2443 aggregation=aggregation)
2444
2445
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in <lambda>(**kwargs)
2423 synchronization=VariableSynchronization.AUTO,
2424 aggregation=VariableAggregation.NONE):
-> 2425 previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
2426 for getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access
2427 previous_getter = _make_getter(getter, previous_getter)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in default_variable_creator(next_creator, **kwargs)
2393 collections=collections, validate_shape=validate_shape,
2394 caching_device=caching_device, name=name, dtype=dtype,
-> 2395 constraint=constraint)
2396 elif not use_resource and context.executing_eagerly():
2397 raise RuntimeError(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py in __init__(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, variable_def, import_scope, constraint)
310 name=name,
311 dtype=dtype,
--> 312 constraint=constraint)
313
314 # pylint: disable=unused-argument
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py in _init_from_args(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, constraint)
415 with ops.name_scope("Initializer"), ops.device(None):
416 initial_value = ops.convert_to_tensor(
--> 417 initial_value(), name="initial_value", dtype=dtype)
418 self._handle = _eager_safe_variable_handle(
419 shape=initial_value.get_shape(),
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in <lambda>()
1901 initializer = initializer(dtype=dtype)
1902 init_val = lambda: initializer( # pylint: disable=g-long-lambda
-> 1903 shape, dtype=dtype, partition_info=partition_info)
1904 variable_dtype = dtype.base_dtype
1905 if use_resource is None:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py in __call__(self, shape, dtype, partition_info)
474 scale /= max(1., fan_out)
475 else:
--> 476 scale /= max(1., (fan_in + fan_out) / 2.)
477 if self.distribution == "normal" or self.distribution == "truncated_normal":
478 # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
TypeError: unsupported operand type(s) for /: 'Dimension' and 'float'
代码： https://gist.github.com/Carliceiv/0b68fe0d9f2a9fc9a1b901e8a722f6cd
堆栈跟踪表明在代码的某处给出了一个 Dimension 值作为 n 参数，而您通常期望一个 integer/float。
我认为这可能是这条线造成的
shape = tf.TensorShape((input_shape[1], self.num_outputs))
请尝试将其更改为
shape = (input_shape[1], self.num_outputs)
编辑：正如评论中所指出的，这不起作用 input_shape 是一个 Dimension 对象而不是 int。要解决此问题，请使用此修改：
shape = tf.TensorShape((input_shape[1], self.num_outputs)).as_list()
这将首先创建一个新的 TensorShape，它能够处理 Dimension 数据类型，然后将其转换为整数列表。