问题描述
这段代码有个问题，我删除SeBlock类，只运行CNN类，然后一切正常。如果我将 SeBlock 插入 CNN 类，则会发生错误，并显示 NotImplementedError。我不知道导致这个问题的原因，我试图解决这个问题，但是我搜索的所有方法都不起作用。谁能帮帮我，非常感谢！
import tensorflow as tf
class SeBlock(tf.keras.Model):
def __init__(self,ratio,channel):
super(SeBlock,self).__init__()
self.kernel_initializer = tf.keras.initializers.VarianceScaling()
self.bias_initializer = tf.constant_initializer(value=0.0)
self.ratio = ratio
self.ReduceMean = tf.keras.layers.GlobalAveragePooling2D()
self.DenseCut = tf.keras.Sequential([
tf.keras.layers.Dense(units=channel,activation=tf.nn.relu,kernel_initializer=self.kernel_initializer,bias_constraint=self.bias_initializer),tf.keras.layers.Dense(units=channel,activation=tf.nn.sigmoid,bias_constraint=self.bias_initializer)
])
self.flatten = tf.keras.layers.Reshape(target_shape=(1,1,channel,))
def call(self,inputs,training=True):
if training:print("training network")
x = self.ReduceMean(inputs)
x = self.DenseCut(x,training)
scale = self.flatten(x)
scale = tf.keras.layers.multiply([inputs,scale])
# scale *= inputs
return scale
class CNN(tf.keras.Model):
def __init__(self,se_block):
super(CNN,self).__init__()
self.conv1 = tf.keras.layers.Conv2D(
filters=32,#
kernel_size=[5,5],#
padding='same',#
activation=tf.nn.relu #
)
self.seblock1 = self._make_layer(se_block= se_block,ratio=1,input_channel=32)
self.pool1 = tf.keras.layers.MaxPool2D(pool_size=[2,2],strides=2)
self.conv2 = tf.keras.layers.Conv2D(
filters=64,kernel_size=[5,padding='same',activation=tf.nn.relu
)
self.pool2 = tf.keras.layers.MaxPool2D(pool_size=[2,strides=2)
self.flatten = tf.keras.layers.Reshape(target_shape=(112 * 112 * 64,))
self.dense2 = tf.keras.layers.Dense(units=10)
def _make_layer(self,se_block,input_channel):
return tf.keras.Sequential([se_block(ratio=ratio,channel=input_channel)])
def call(self,training=True):
print("1",inputs.get_shape().as_list())
x = self.conv1(inputs) # [batch_size,28,32]
# print("start se-block")
x = self.seblock1(x,training)
# print("end se-block")
x = self.pool1(x) # [batch_size,14,32]
x = self.conv2(x) # [batch_size,64]
x = self.pool2(x) # [batch_size,7,64]
x = self.flatten(x) # [batch_size,7 * 7 * 64]
x = self.dense2(x) # [batch_size,10]
return tf.nn.softmax(x)
def CNNDense():
return CNN(SeBlock)
主要代码如下。
import tensorflow as tf
import LoadImage as readimage
import DenseBSE
tf.keras.backend.clear_session()
train_path = r"E:\BaiduNetdiskDownload\板角\boardtrain"
test_path = r"E:\BaiduNetdiskDownload\板角\boardtest"
BatchSize = 4
Epoch = 60
lr = 0.001
ds_train,train_count = readimage.load_tensor_img(train_path,batch_size=BatchSize,epoch=Epoch)
ds_test,test_count = readimage.load_tensor_img(test_path,epoch=Epoch)
model = DenseBSE.CNNDense()
model.build(input_shape=(BatchSize,448,3))
model.summary()
model.compile(optimizer=tf.keras.optimizers.Adam(),loss='sparse_categorical_crossentropy',metrics=['accuracy'])
epoch_steps = train_count // BatchSize
val_steps = test_count // BatchSize
model.fit(ds_train,epochs=Epoch,steps_per_epoch = epoch_steps,validation_data=ds_test,validation_steps = val_steps)
Traceback (most recent call last):
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py",line 3343,in run_code
exec(code_obj,self.user_global_ns,self.user_ns)
File "<ipython-input-2-41c34ae2b3b4>",line 1,in <module>
runfile('E:/PythonProject/CNN_training.py',wdir='E:/PythonProject')
File "C:\Program Files\JetBrains\PyCharm 2020.3.5\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py",line 197,in runfile
pydev_imports.execfile(filename,global_vars,local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.5\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py",line 18,in execfile
exec(compile(contents+"\n",file,'exec'),glob,loc)
File "E:/PythonProject/CNN_training.py",line 35,in <module>
model.fit(ds_train,File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py",line 1100,in fit
tmp_logs = self.train_function(iterator)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py",line 828,in __call__
result = self._call(*args,**kwds)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py",line 871,in _call
self._initialize(args,kwds,add_initializers_to=initializers)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py",line 725,in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py",line 2969,in _get_concrete_function_internal_garbage_collected
graph_function,_ = self._maybe_define_function(args,kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py",line 3361,in _maybe_define_function
graph_function = self._create_graph_function(args,line 3196,in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py",line 990,in func_graph_from_py_func
func_outputs = python_func(*func_args,**func_kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py",line 634,in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args,**kwds)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py",line 977,in wrapper
raise e.ag_error_Metadata.to_exception(e)
NotImplementedError: in user code:
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function *
return step_function(self,iterator)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step,args=(data,))
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn,args,kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
return fn(*args,**kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step **
outputs = model.train_step(data)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:757 train_step
self.optimizer.minimize(loss,self.trainable_variables,tape=tape)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py:498 minimize
return self.apply_gradients(grads_and_vars,name=name)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py:631 apply_gradients
return distribute_ctx.get_replica_context().merge_call(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2941 merge_call
return self._merge_call(merge_fn,kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2948 _merge_call
return merge_fn(self._strategy,*args,**kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py:682 _distributed_apply **
update_ops.extend(distribution.extended.update(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2494 update
return self._update(var,fn,kwargs,group)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3431 _update
return self._update_non_slot(var,(var,) + tuple(args),group)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3437 _update_non_slot
result = fn(*args,**kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py:661 apply_grad_to_update_var **
return var.assign(var.constraint(var))
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\init_ops_v2.py:290 __call__
return constant_op.constant(self.value,dtype=dtype,shape=shape)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py:264 constant
return _constant_impl(value,dtype,shape,name,verify_shape=False,C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py:281 _constant_impl
tensor_util.make_tensor_proto(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_util.py:454 make_tensor_proto
if shape is not None and np.prod(shape,dtype=np.int64) == 0:
<__array_function__ internals>:5 prod
C:\ProgramData\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:2961 prod
return _wrapreduction(a,np.multiply,'prod',axis,out,C:\ProgramData\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:90 _wrapreduction
return ufunc.reduce(obj,**passkwargs)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:483 __array__
return np.asarray(self.numpy())
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:619 numpy
raise NotImplementedError(
NotImplementedError: numpy() is only available when eager execution is enabled.
解决方法
此 notebook 应该有助于升级、检查和启用。祝你好运！