以下是我的代码片段，我想在其中使用 @tf.function 装饰器和 Keras API，但它给了我一个错误:
@tf.function
def convnet(filters, strides, size, norm_type='instancenorm', apply_norm=True, relu = 'relu', apply_relu=True):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(tf.keras.layers.Conv3D(filters, size, strides, padding='same',
kernel_initializer=initializer, use_bias=False, input_shape=(None, None, None, 3)))
if apply_norm:
if norm_type.lower() == 'batchnorm':
result.add(tf.keras.layers.BatchNormalization())
elif norm_type.lower() == 'instancenorm':
result.add(InstanceNormalization())
if apply_relu:
if relu == 'relu':
result.add(tf.keras.layers.ReLU())
elif relu == 'leakyrelu':
result.add(tf.keras.layers.LeakyReLU(alpha=0.2))
return result
执行时出现以下错误:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
540 try:
--> 541 str_values = [compat.as_bytes(x) for x in proto_values]
542 except TypeError:
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/tensor_util.py in <listcomp>(.0)
540 try:
--> 541 str_values = [compat.as_bytes(x) for x in proto_values]
542 except TypeError:
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/util/compat.py in as_bytes(bytes_or_text, encoding)
70 raise TypeError('Expected binary or unicode string, got %r' %
---> 71 (bytes_or_text,))
72
TypeError: Expected binary or unicode string, got <tensorflow.python.keras.engine.sequential.Sequential object at 0x7fa65de7e198>
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in convert(x)
875 try:
--> 876 x = ops.convert_to_tensor_or_composite(x)
877 except (ValueError, TypeError):
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in convert_to_tensor_or_composite(value, dtype, name)
1419 return internal_convert_to_tensor_or_composite(
-> 1420 value=value, dtype=dtype, name=name, as_ref=False)
1421
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in internal_convert_to_tensor_or_composite(value, dtype, name, as_ref)
1458 as_ref=as_ref,
-> 1459 accept_composite_tensors=True)
1460
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx, accept_composite_tensors)
1295 if ret is None:
-> 1296 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1297
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
285 _ = as_ref
--> 286 return constant(v, dtype=dtype, name=name)
287
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/constant_op.py in constant(value, dtype, shape, name)
226 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 227 allow_broadcast=True)
228
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
264 value, dtype=dtype, shape=shape, verify_shape=verify_shape,
--> 265 allow_broadcast=allow_broadcast))
266 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
544 "Contents: %s. Consider casting elements to a "
--> 545 "supported type." % (type(values), values))
546 tensor_proto.string_val.extend(str_values)
TypeError: Failed to convert object of type <class 'tensorflow.python.keras.engine.sequential.Sequential'> to Tensor. Contents: <tensorflow.python.keras.engine.sequential.Sequential object at 0x7fa65de7e198>. Consider casting elements to a supported type.
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-12-c3a2ba712dc3> in <module>
1 OUTPUT_CHANNELS = 3
2
----> 3 generator = pix2pix_new.generator(OUTPUT_CHANNELS, norm_type='instancenorm')
4
5 discriminator_seq = pix2pix_new.discriminator_seq(norm_type='instancenorm', target=False)
/gpfs-volume/GANs_Work/Scripts/pix2pix_new.py in generator(output_channels, norm_type)
186 """
187
--> 188 convnets = [first_convnet(128, (1, 1, 1), (7, 7, 4), norm_type, apply_norm=False, relu='relu', apply_relu=False), # (bs, 128, 128, 64)
189 convnet(128, 2, (3, 3, 2), norm_type), # (bs, 64, 64, 128)
190 convnet(256, 2, (3, 3, 1), norm_type), # (bs, 32, 32, 256)
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
501 # This is the first call of __call__, so we have to initialize.
502 initializer_map = object_identity.ObjectIdentityDictionary()
--> 503 self._initialize(args, kwds, add_initializers_to=initializer_map)
504 finally:
505 # At this point we know that the initialization is complete (or less
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
406 self._concrete_stateful_fn = (
407 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 408 *args, **kwds))
409
410 def invalid_creator_scope(*unused_args, **unused_kwds):
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 if self.input_signature:
1847 args, kwargs = None, None
-> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849 return graph_function
1850
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2148 graph_function = self._function_cache.primary.get(cache_key, None)
2149 if graph_function is None:
-> 2150 graph_function = self._create_graph_function(args, kwargs)
2151 self._function_cache.primary[cache_key] = graph_function
2152 return graph_function, args, kwargs
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039 arg_names=arg_names,
2040 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041 capture_by_value=self._capture_by_value),
2042 self._function_attributes,
2043 # Tell the ConcreteFunction to clean up its graph once it goes out of
/usr/local/lib/python3.7/site-packages/tensorflow_core/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)
918 # TensorArrays and `None`s.
919 func_outputs = nest.map_structure(convert, func_outputs,
--> 920 expand_composites=True)
921
922 check_mutation(func_args_before, func_args)
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/util/nest.py in map_structure(func, *structure, **kwargs)
533
534 return pack_sequence_as(
--> 535 structure[0], [func(*x) for x in entries],
536 expand_composites=expand_composites)
537
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/util/nest.py in <listcomp>(.0)
533
534 return pack_sequence_as(
--> 535 structure[0], [func(*x) for x in entries],
536 expand_composites=expand_composites)
537
/usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in convert(x)
880 "must return zero or more Tensors; in compilation of %s, found "
881 "return value of type %s, which is not a Tensor." %
--> 882 (str(python_func), type(x)))
883 if add_control_dependencies:
884 x = a.mark_as_return(x)
TypeError: To be compatible with tf.contrib.eager.defun, Python functions must return zero or more Tensors; in compilation of <function first_convnet at 0x7fa6ade9bd90>, found return value of type <class 'tensorflow.python.keras.engine.sequential.Sequential'>, which is not a Tensor.
错误:
TypeError:为了与 tf.contrib.eager.defun 兼容，Python 函数必须返回零个或多个张量；在 的编译中，发现 <class 'tensorflow.python.keras.engine.sequential.Sequential'> 类型的返回值不是 Tensor 。
当我尝试执行以下命令时出现类似的错误:
return tf.keras.Model(inputs=inputs, outputs=x)
解决方法是什么？因为我是 tf 2.0 的新手，所以我想用它来加快训练过程。
最佳答案
通常，您在外部构建模型，然后将其作为参数传递给 tf.function:
@tf.function
def use_model(model, ...):
...
outputs = model(...)
...
# Create the model
model = convnet(...)
# It's a good idea to initialize it too
model(<dummy input>) # or model.build(...)
use_model(model, ...)
关于python - 如何将 @tf.function 与 Keras 顺序 API 一起使用？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/61910143/