AttributeError: 'Tensor' object has no attribute 'is_initialized'
AttributeError: 'Tensor' object has no attribute 'is_initialized'
我尝试拟合模型时出现此错误。
我尝试使用单个 GPU 版本，但它仍然存在。如果我升级到 TensorFlow 2，它将得到解决，但我需要在此版本的 TensorFlow 中保留它。
这是我使用过的模型的代码。该模型由不同的层组成。
def hybrid_LSTM(depth=2,conv_size=16,dense_size=512,input_dim=(100,5,9,),dropoutRate=0.2):
"""
Autoencoder model builder composes of CNNs and a LSTM
Args:
depth (int): number of CNN blocks, each has 3 CNN layers with BN and a dropout
conv_size (int): initial CNN filter size, doubled in each depth level
dense_size (int): size of latent vector and a number of filters of ConvLSTM2D
input_dim (tuple): input dimention, should be in (y_spatial,x_spatial,temporal)
dropoutRate (float): dropout rate used in all nodes
Return:
keras model
"""
"""Setup"""
temp_filter = conv_size
X = Input(shape=input_dim, name = 'input')
model_input = X
# X = Permute((3,1,2))(X) #move temporal axes to be first dim
X = Reshape((100,5,9,1))(X) #reshape (,1) to be feature of each spatial
"""Encoder"""
for i in range(depth):
for j in range(3):
if j == 0: #j==0 is first layer(j) of the CNN block(i); apply stride with double filter size
X = TimeDistributed(Conv2D(2*temp_filter,(3,3),padding='same' ,strides=(2,2),data_format="channels_last"),name = 'encoder_'+str(i)+str(j)+'_timeConv2D')(X)
else:
X = TimeDistributed(Conv2D(temp_filter,(3,3), padding='same', data_format="channels_last"),name = 'encoder_'+str(i)+str(j)+'_timeConv2D')(X)
X = BatchNormalization(name = 'encoder_'+str(i)+str(j)+'_BN')(X)
X = LeakyReLU(alpha=0.1,name = 'encoder_'+str(i)+str(j)+'_relu')(X)
X = Dropout(dropoutRate,name = 'encoder_'+str(i)+str(j)+'_drop')(X)
temp_filter = int(temp_filter * 2)
X = TimeDistributed(Flatten())(X)
X = LSTM(dense_size, recurrent_dropout=dropoutRate ,return_sequences=False, implementation=2)(X)
"""Latent"""
latent = X
"""Setup for decoder"""
X = RepeatVector(100)(X)
temp_filter = int(temp_filter/2)
"""Decoder"""
X = LSTM(temp_filter*2*3, recurrent_dropout=dropoutRate ,return_sequences=True, implementation=2)(X)
X = Reshape((100,2,3,temp_filter))(X)
for i in range(depth):
for j in range(3):
if j == 0:
X = TimeDistributed(UpSampling2D((2,2)),name = 'decoder_'+str(i)+str(j)+'_upsampling')(X)
X = TimeDistributed(ZeroPadding2D(((1,0),(1,0))),name = 'decoder_'+str(i)+str(j)+'_padding')(X)
X = TimeDistributed(Conv2D(temp_filter,(3,3),data_format="channels_last"),name = 'decoder_'+str(i)+str(j)+'_timeConv2D')(X)
else:
X = TimeDistributed(Conv2D(temp_filter,(3,3), padding='same', data_format="channels_last"),name = 'decoder_'+str(i)+str(j)+'_timeConv2D')(X)
X = BatchNormalization(name = 'decoder_'+str(i)+str(j)+'_BN')(X)
X = LeakyReLU(alpha=0.1,name = 'decoder_'+str(i)+str(j)+'_relu')(X)
X = Dropout(dropoutRate,name = 'decoder_'+str(i)+str(j)+'_drop')(X)
temp_filter = int(temp_filter / 2)
X = TimeDistributed(Conv2D(1,(1,1), padding='same', data_format="channels_last"),name = 'decoder__timeConv2D')(X)
X = Reshape((100,5,9))(X)
# X = Permute((2,3,1))(X)
decoded = X
X = latent
X = Dense(1,name = 'Dense10',activation='sigmoid')(X)
return Model(inputs = model_input, outputs = [decoded,X])
File "/Midgard/home/projects/Pre-trained-EEG-for-Deep-Learning-master/trainSafe_version1.py", line 167, in train_subtasks_all_tasks_keras
parallel_model = multi_gpu_model(model, gpus=2)
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/utils/multi_gpu_utils.py", line 172, in multi_gpu_model
available_devices = _get_available_devices()
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/utils/multi_gpu_utils.py", line 28, in _get_available_devices
return [x.name for x in K.get_session().list_devices()]
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session)
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars])
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 879, in <listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars])
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/util/tf_should_use.py", line 193, in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/ops/variables.py", line 3083, in is_variable_initialized
return state_ops.is_variable_initialized(variable)
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/ops/state_ops.py", line 133, in is_variable_initialized
return ref.is_initialized(name=name)
AttributeError: 'Tensor' object has no attribute 'is_initialized'
model = hybrid_LSTM(depth=2, conv_size=8, dense_size=512, input_dim=(100, 5, 9), dropoutRate=0.2)
model.compile(optimizer=SGD(learning_rate=lr, decay=1E-5),
loss=[mean_squared_error_ignore_0, 'binary_crossentropy'],
# metrics=['AUC','Recall', 'Precision','binary_accuracy','accuracy'],
metrics={'Dense10': ['AUC', 'Recall',
tf.keras.metrics.SensitivityAtSpecificity(specificity=0.02),
tf.keras.metrics.SpecificityAtSensitivity(sensitivity=0.02),
'accuracy']},
loss_weights=[0.4, 0.6])
parallel_model = multi_gpu_model(model, gpus=2)
parallel_model.__setattr__('callback_model', model)
parallel_model.compile(optimizer=SGD(learning_rate=lr, decay=1E-5),
loss=[mean_squared_error_ignore_0, 'binary_crossentropy'],
# metrics=['AUC','Recall', 'Precision','binary_accuracy','accuracy'],
metrics={'Dense10': ['AUC', 'Recall',
tf.keras.metrics.SensitivityAtSpecificity(specificity=0.02),
tf.keras.metrics.SpecificityAtSensitivity(sensitivity=0.02),
'accuracy']},
loss_weights=[0.4, 0.6])
tensorflow-GPU 1.14.0,
cudatoolkit 10.1.243，
cudnn 7.6.5，
这可能是您的 TF 版本与 Keras 不兼容。 Daniel Möller 让您走上了正确的道路，但 tf.keras 是 TF2 的东西，而您使用的是 TF1，因此您的解决方案会有所不同。
您需要做的是安装与 TF 1.14 兼容的 Keras 版本。根据 pypi，TF 1.14 于 2019 年 6 月 18 日发布。
https://pypi.org/project/tensorflow/#history
您应该对该日期前后的 Keras 版本进行网格搜索。
https://pypi.org/project/keras/#history
我会选择这些 Keras 版本。
2.2.4
2.2.5
2.3.1
2.4.1
使用例如
安装这些版本
pip3 install --upgrade keras==2.2.4
我最近在 TF2.7/2.8 和 Keras 2.7/2.8 之间的不匹配中遇到了类似的问题运行。
我尝试拟合模型时出现此错误。 我尝试使用单个 GPU 版本，但它仍然存在。如果我升级到 TensorFlow 2，它将得到解决，但我需要在此版本的 TensorFlow 中保留它。
这是我使用过的模型的代码。该模型由不同的层组成。
def hybrid_LSTM(depth=2,conv_size=16,dense_size=512,input_dim=(100,5,9,),dropoutRate=0.2):
"""
Autoencoder model builder composes of CNNs and a LSTM
Args:
depth (int): number of CNN blocks, each has 3 CNN layers with BN and a dropout
conv_size (int): initial CNN filter size, doubled in each depth level
dense_size (int): size of latent vector and a number of filters of ConvLSTM2D
input_dim (tuple): input dimention, should be in (y_spatial,x_spatial,temporal)
dropoutRate (float): dropout rate used in all nodes
Return:
keras model
"""
"""Setup"""
temp_filter = conv_size
X = Input(shape=input_dim, name = 'input')
model_input = X
# X = Permute((3,1,2))(X) #move temporal axes to be first dim
X = Reshape((100,5,9,1))(X) #reshape (,1) to be feature of each spatial
"""Encoder"""
for i in range(depth):
for j in range(3):
if j == 0: #j==0 is first layer(j) of the CNN block(i); apply stride with double filter size
X = TimeDistributed(Conv2D(2*temp_filter,(3,3),padding='same' ,strides=(2,2),data_format="channels_last"),name = 'encoder_'+str(i)+str(j)+'_timeConv2D')(X)
else:
X = TimeDistributed(Conv2D(temp_filter,(3,3), padding='same', data_format="channels_last"),name = 'encoder_'+str(i)+str(j)+'_timeConv2D')(X)
X = BatchNormalization(name = 'encoder_'+str(i)+str(j)+'_BN')(X)
X = LeakyReLU(alpha=0.1,name = 'encoder_'+str(i)+str(j)+'_relu')(X)
X = Dropout(dropoutRate,name = 'encoder_'+str(i)+str(j)+'_drop')(X)
temp_filter = int(temp_filter * 2)
X = TimeDistributed(Flatten())(X)
X = LSTM(dense_size, recurrent_dropout=dropoutRate ,return_sequences=False, implementation=2)(X)
"""Latent"""
latent = X
"""Setup for decoder"""
X = RepeatVector(100)(X)
temp_filter = int(temp_filter/2)
"""Decoder"""
X = LSTM(temp_filter*2*3, recurrent_dropout=dropoutRate ,return_sequences=True, implementation=2)(X)
X = Reshape((100,2,3,temp_filter))(X)
for i in range(depth):
for j in range(3):
if j == 0:
X = TimeDistributed(UpSampling2D((2,2)),name = 'decoder_'+str(i)+str(j)+'_upsampling')(X)
X = TimeDistributed(ZeroPadding2D(((1,0),(1,0))),name = 'decoder_'+str(i)+str(j)+'_padding')(X)
X = TimeDistributed(Conv2D(temp_filter,(3,3),data_format="channels_last"),name = 'decoder_'+str(i)+str(j)+'_timeConv2D')(X)
else:
X = TimeDistributed(Conv2D(temp_filter,(3,3), padding='same', data_format="channels_last"),name = 'decoder_'+str(i)+str(j)+'_timeConv2D')(X)
X = BatchNormalization(name = 'decoder_'+str(i)+str(j)+'_BN')(X)
X = LeakyReLU(alpha=0.1,name = 'decoder_'+str(i)+str(j)+'_relu')(X)
X = Dropout(dropoutRate,name = 'decoder_'+str(i)+str(j)+'_drop')(X)
temp_filter = int(temp_filter / 2)
X = TimeDistributed(Conv2D(1,(1,1), padding='same', data_format="channels_last"),name = 'decoder__timeConv2D')(X)
X = Reshape((100,5,9))(X)
# X = Permute((2,3,1))(X)
decoded = X
X = latent
X = Dense(1,name = 'Dense10',activation='sigmoid')(X)
return Model(inputs = model_input, outputs = [decoded,X])
File "/Midgard/home/projects/Pre-trained-EEG-for-Deep-Learning-master/trainSafe_version1.py", line 167, in train_subtasks_all_tasks_keras
parallel_model = multi_gpu_model(model, gpus=2)
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/utils/multi_gpu_utils.py", line 172, in multi_gpu_model
available_devices = _get_available_devices()
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/utils/multi_gpu_utils.py", line 28, in _get_available_devices
return [x.name for x in K.get_session().list_devices()]
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session)
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars])
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 879, in <listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars])
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/util/tf_should_use.py", line 193, in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/ops/variables.py", line 3083, in is_variable_initialized
return state_ops.is_variable_initialized(variable)
File "/Midgard/home/miniconda3/envs/erpenet5/lib/python3.7/site-packages/tensorflow/python/ops/state_ops.py", line 133, in is_variable_initialized
return ref.is_initialized(name=name)
AttributeError: 'Tensor' object has no attribute 'is_initialized'
model = hybrid_LSTM(depth=2, conv_size=8, dense_size=512, input_dim=(100, 5, 9), dropoutRate=0.2)
model.compile(optimizer=SGD(learning_rate=lr, decay=1E-5),
loss=[mean_squared_error_ignore_0, 'binary_crossentropy'],
# metrics=['AUC','Recall', 'Precision','binary_accuracy','accuracy'],
metrics={'Dense10': ['AUC', 'Recall',
tf.keras.metrics.SensitivityAtSpecificity(specificity=0.02),
tf.keras.metrics.SpecificityAtSensitivity(sensitivity=0.02),
'accuracy']},
loss_weights=[0.4, 0.6])
parallel_model = multi_gpu_model(model, gpus=2)
parallel_model.__setattr__('callback_model', model)
parallel_model.compile(optimizer=SGD(learning_rate=lr, decay=1E-5),
loss=[mean_squared_error_ignore_0, 'binary_crossentropy'],
# metrics=['AUC','Recall', 'Precision','binary_accuracy','accuracy'],
metrics={'Dense10': ['AUC', 'Recall',
tf.keras.metrics.SensitivityAtSpecificity(specificity=0.02),
tf.keras.metrics.SpecificityAtSensitivity(sensitivity=0.02),
'accuracy']},
loss_weights=[0.4, 0.6])
tensorflow-GPU 1.14.0, cudatoolkit 10.1.243， cudnn 7.6.5，
这可能是您的 TF 版本与 Keras 不兼容。 Daniel Möller 让您走上了正确的道路，但 tf.keras 是 TF2 的东西，而您使用的是 TF1，因此您的解决方案会有所不同。
您需要做的是安装与 TF 1.14 兼容的 Keras 版本。根据 pypi，TF 1.14 于 2019 年 6 月 18 日发布。
https://pypi.org/project/tensorflow/#history
您应该对该日期前后的 Keras 版本进行网格搜索。
https://pypi.org/project/keras/#history
我会选择这些 Keras 版本。
2.2.4 2.2.5 2.3.1 2.4.1
使用例如
安装这些版本pip3 install --upgrade keras==2.2.4
我最近在 TF2.7/2.8 和 Keras 2.7/2.8 之间的不匹配中遇到了类似的问题运行。