我试图通过使用权重修剪来优化我的机器学习模型。但是无论我做什么，我都无法摆脱错误AttributeError：
对象没有属性"assign"
下面是我的修剪代码
#pruning
import tensorflow_model_optimization as tfmot
import numpy as np
origModelFile = 'modeltest.h5'
model = tf.keras.models.load_model(origModelFile)
prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude
epochs = 15
batch_size = 2048
validation_split = 0.1
num_images = x_train.shape[0] * (1 - validation_split)
end_step = np.ceil(num_images / batch_size).astype(np.int32) * epochs
pruning_params = {
'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=.95,
final_sparsity=.8,
begin_step=0,
end_step=end_step)
}
model_for_pruning = prune_low_magnitude(model, **pruning_params) #this line gives the error
model_for_pruning.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
pruned_model = tfmot.sparsity.keras.strip_pruning(model_for_pruning)
pruned_model.summary()下面是错误的完整堆栈跟踪
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_33564\3560105584.py in <module>
13 end_step=end_step)
14 }
---> 15 model_for_pruning = prune_low_magnitude(model, **pruning_params)
16
17 model_for_pruning.compile(optimizer='adam',
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\keras\metrics.py in inner(*args, **kwargs)
72 except Exception as error:
73 self.bool_gauge.get_cell(MonitorBoolGauge._FAILURE_LABEL).set(True)
---> 74 raise error
75
76 if self.bool_gauge:
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\keras\metrics.py in inner(*args, **kwargs)
67 def inner(*args, **kwargs):
68 try:
---> 69 results = func(*args, **kwargs)
70 self.bool_gauge.get_cell(MonitorBoolGauge._SUCCESS_LABEL).set(True)
71 return results
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\prune.py in prune_low_magnitude(to_prune, pruning_schedule, block_size, block_pooling_type, pruning_policy, sparsity_m_by_n, **kwargs)
208 if pruning_policy:
209 pruning_policy.ensure_model_supports_pruning(to_prune)
--> 210 return _add_pruning_wrapper(to_prune)
211 elif is_keras_layer:
212 params.update(kwargs)
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\prune.py in _add_pruning_wrapper(layer)
179 raise ValueError('Subclassed models are not supported currently.')
180
--> 181 return keras.models.clone_model(
182 layer, input_tensors=None, clone_function=_add_pruning_wrapper)
183 if isinstance(layer, pruning_wrapper.PruneLowMagnitude):
A:\Anaconda\lib\site-packages\keras\models\cloning.py in clone_model(model, input_tensors, clone_function)
446 model, input_tensors=input_tensors, layer_fn=clone_function)
447 else:
--> 448 return _clone_functional_model(
449 model, input_tensors=input_tensors, layer_fn=clone_function)
450
A:\Anaconda\lib\site-packages\keras\models\cloning.py in _clone_functional_model(model, input_tensors, layer_fn)
187 # Reconstruct model from the config, using the cloned layers.
188 input_tensors, output_tensors, created_layers = (
--> 189 functional.reconstruct_from_config(model_configs,
190 created_layers=created_layers))
191 metrics_names = model.metrics_names
A:\Anaconda\lib\site-packages\keras\engine\functional.py in reconstruct_from_config(config, custom_objects, created_layers)
1310 while layer_nodes:
1311 node_data = layer_nodes[0]
-> 1312 if process_node(layer, node_data):
1313 layer_nodes.pop(0)
1314 else:
A:\Anaconda\lib\site-packages\keras\engine\functional.py in process_node(layer, node_data)
1254 input_tensors = (
1255 base_layer_utils.unnest_if_single_tensor(input_tensors))
-> 1256 output_tensors = layer(input_tensors, **kwargs)
1257
1258 # Update node index map.
A:\Anaconda\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_wrapper.py in tf__call(self, inputs, training, **kwargs)
71 update_mask = ag__.converted_call(ag__.ld(utils).smart_cond, (ag__.ld(training), ag__.ld(add_update), ag__.ld(no_op)), None, fscope)
72 ag__.converted_call(ag__.ld(self).add_update, (ag__.ld(update_mask),), None, fscope)
---> 73 ag__.converted_call(ag__.ld(self).add_update, (ag__.converted_call(ag__.ld(self).pruning_obj.weight_mask_op, (), None, fscope),), None, fscope)
74
75 def get_state_1():
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_impl.py in tf__weight_mask_op(self)
11 try:
12 do_return = True
---> 13 retval_ = ag__.converted_call(ag__.ld(tf).group, (ag__.converted_call(ag__.ld(self)._weight_assign_objs, (), None, fscope),), None, fscope)
14 except:
15 do_return = False
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_impl.py in tf___weight_assign_objs(self)
122 _ = ag__.Undefined('_')
123 masked_weight = ag__.Undefined('masked_weight')
--> 124 ag__.if_stmt(ag__.converted_call(ag__.ld(tf).distribute.get_replica_context, (), None, fscope), if_body_1, else_body_1, get_state_4, set_state_4, (), 0)
125 try:
126 do_return = True
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_impl.py in if_body_1()
98 def else_body():
99 pass
--> 100 ag__.if_stmt(ag__.ld(values_and_vars), if_body, else_body, get_state_2, set_state_2, (), 0)
101
102 def else_body_1():
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_impl.py in if_body()
94
95 def if_body():
---> 96 ag__.converted_call(ag__.ld(assign_objs).append, (ag__.converted_call(ag__.converted_call(ag__.ld(tf).distribute.get_replica_context, (), None, fscope).merge_call, (ag__.ld(update_fn),), dict(args=(ag__.ld(values_and_vars),)), fscope),), None, fscope)
97
98 def else_body():
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_impl.py in update_fn(distribution, values_and_vars)
52 var = ag__.Undefined('var')
53 value = ag__.Undefined('value')
---> 54 ag__.for_stmt(ag__.ld(values_and_vars), None, loop_body, get_state, set_state, (), {'iterate_names': '(value, var)'})
55 try:
56 do_return_1 = True
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_impl.py in loop_body(itr)
49 def loop_body(itr):
50 (value, var) = itr
---> 51 ag__.converted_call(ag__.ld(update_objs).append, (ag__.converted_call(ag__.ld(distribution).extended.update, (ag__.ld(var), ag__.ld(update_var)), dict(args=(ag__.ld(value),)), fscope_1),), None, fscope_1)
52 var = ag__.Undefined('var')
53 value = ag__.Undefined('value')
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_impl.py in update_var(variable, reduced_value)
34 try:
35 do_return_2 = True
---> 36 retval__2 = ag__.converted_call(ag__.ld(tf_compat).assign, (ag__.ld(variable), ag__.ld(reduced_value)), None, fscope_2)
37 except:
38 do_return_2 = False
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\keras\compat.py in tf__assign(ref, value, name)
34 do_return = False
35 raise
---> 36 ag__.if_stmt(ag__.converted_call(ag__.ld(hasattr), (ag__.ld(tf), 'assign'), None, fscope), if_body, else_body, get_state, set_state, ('do_return', 'retval_'), 2)
37 return fscope.ret(retval_, do_return)
38 return tf__assign
A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\keras\compat.py in else_body()
30 try:
31 do_return = True
---> 32 retval_ = ag__.converted_call(ag__.ld(ref).assign, (ag__.ld(value),), dict(name=ag__.ld(name)), fscope)
33 except:
34 do_return = False
AttributeError: Exception encountered when calling layer "prune_low_magnitude_conv2d" (type PruneLowMagnitude).
in user code:
File "A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_wrapper.py", line 288, in call *
self.add_update(self.pruning_obj.weight_mask_op())
File "A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_impl.py", line 254, in weight_mask_op *
return tf.group(self._weight_assign_objs())
File "A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\sparsity\keras\pruning_impl.py", line 225, in update_var *
return tf_compat.assign(variable, reduced_value)
File "A:\Anaconda\lib\site-packages\tensorflow_model_optimization\python\core\keras\compat.py", line 28, in assign *
return ref.assign(value, name=name)
AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'assign'
Call arguments received by layer "prune_low_magnitude_conv2d" (type PruneLowMagnitude):
• inputs=tf.Tensor(shape=(None, 14, 8, 8), dtype=float32)
• training=False
• kwargs=<class 'inspect._empty'>我尝试使用here示例，但使用的是我自己的模型
1条答案
按热度按时间gudnpqoy1#
只是需要用不同的方式来处理事情