我想在Tensorflow 2中编写一个简单的，自己的 Exponentiation 层。它应该接受n个输入[x_1，...，x_n]并输出一些幂[x_1 ^ e_1，... ，x_n ^ e_n]，其中e_1，...，e_n是可训练的参数。
例如，该幂运算层与密集层（输出尺寸为1）一起可以学习a_1 x_1 ^ e_1 + ... + a_n x_n ^ e_n形式的任何函数线性回归的简单扩展。
但是，我没有运气去上班。到目前为止，我已经写了以下内容：
import tensorflow as tf
class Exponent(tf.keras.layers.Layer):
def __init__(self):
super().__init__()
def build(self, input_shape):
self.exp = self.add_weight(name='Exponent', shape=input_shape, initializer=tf.constant_initializer(value=1.), trainable=True)
super().build(input_shape)
def call(self, inputs, training=False):
return tf.math.pow(inputs, self.exp)
我可以实例化此类的对象，并且一切正常。
e = Exponent()
e.build(input_shape=(2,))
e([[1., 2.]]) # works fine
如果我尝试将其嵌入模型中，则在尝试预测（或拟合）时会引发错误：
model = tf.keras.Sequential([
Exponent(),
tf.keras.layers.Dense(1)
])
model.compile(
loss='mse',
optimizer='sgd'
)
model.predict([[1., 2., 3.]])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-401-2ea9d74ffa37> in <module>
9 )
10
---> 11 model.predict([[1., 2., 3.]])
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
128 raise ValueError('{} is not supported in multi-worker mode.'.format(
129 method.__name__))
--> 130 return method(self, *args, **kwargs)
131
132 return tf_decorator.make_decorator(
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1597 for step in data_handler.steps():
1598 callbacks.on_predict_batch_begin(step)
-> 1599 tmp_batch_outputs = predict_function(iterator)
1600 if data_handler.should_sync:
1601 context.async_wait()
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
821 # This is the first call of __call__, so we have to initialize.
822 initializers = []
--> 823 self._initialize(args, kwds, add_initializers_to=initializers)
824 finally:
825 # At this point we know that the initialization is complete (or less
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
695 self._concrete_stateful_fn = (
696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 697 *args, **kwds))
698
699 def invalid_creator_scope(*unused_args, **unused_kwds):
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853 args, kwargs = None, None
2854 with self._lock:
-> 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856 return graph_function
2857
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args, kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function, args, kwargs
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3073 arg_names=arg_names,
3074 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075 capture_by_value=self._capture_by_value),
3076 self._function_attributes,
3077 function_spec=self.function_spec,
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984 _, original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
987
988 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
598 # __wrapped__ allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().__wrapped__(*args, **kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1462 predict_function *
return step_function(self, iterator)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1452 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1445 run_step **
outputs = model.predict_step(data)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1418 predict_step
return self(x, training=False)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\base_layer.py:985 __call__
outputs = call_fn(inputs, *args, **kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\sequential.py:386 call
outputs = layer(inputs, **kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\base_layer.py:982 __call__
self._maybe_build(inputs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\base_layer.py:2643 _maybe_build
self.build(input_shapes) # pylint:disable=not-callable
<ipython-input-357-5a69bae7457e>:8 build
self.exp = self.add_weight(name='Exponent', shape=input_shape, initializer=tf.constant_initializer(value=2.), trainable=True)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\base_layer.py:614 add_weight
caching_device=caching_device)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\training\tracking\base.py:750 _add_variable_with_custom_getter
**kwargs_for_getter)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\base_layer_utils.py:145 make_variable
shape=variable_shape if variable_shape else None)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\variables.py:260 __call__
return cls._variable_v1_call(*args, **kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\variables.py:221 _variable_v1_call
shape=shape)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2857 creator
return next_creator(**kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2857 creator
return next_creator(**kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2857 creator
return next_creator(**kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py:685 variable_capturing_scope
lifted_initializer_graph=lifted_initializer_graph, **kwds)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\variables.py:264 __call__
return super(VariableMetaclass, cls).__call__(*args, **kwargs)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py:226 __init__
initial_value() if init_from_fn else initial_value,
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\init_ops_v2.py:263 __call__
self.value, dtype=dtype, shape=shape)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\constant_op.py:264 constant
allow_broadcast=True)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\constant_op.py:275 _constant_impl
return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\constant_op.py:321 _constant_eager_impl
return _eager_fill(shape.as_list(), t, ctx)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\constant_op.py:54 _eager_fill
dims = convert_to_eager_tensor(dims, ctx, dtypes.int32)
C:\Users\robkuble\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\constant_op.py:98 convert_to_eager_tensor
return ops.EagerTensor(value, ctx.device_name, dtype)
ValueError: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor.
您知道为什么在某处出现none类型吗？非常感谢！
最佳 罗伯特
答案 0 :(得分：1)
input_shape方法的build参数中的第一个维对应于批处理大小，通常为None（这意味着网络可以处理任何大小的批处理）。例如，在呼叫model.predict([[1., 2., 3.]])时，input_shape将是(None, 3)。
这意味着您需要稍微更改build方法的实现以使用input_shape[-1]而不是input_shape
...
def build(self, input_shape):
self.exp = self.add_weight(name='Exponent', shape=input_shape[-1], initializer=tf.constant_initializer(value=1.), trainable=True)
super().build(input_shape)