InvalidArgumentError: Cannot update variable with shape [] using a Tensor with shape [32]
InvalidArgumentError: Cannot update variable with shape [] using a Tensor with shape [32]
我正在尝试开始使用神经结构化学习，但是当我 运行 页面上给出的示例进行测试时，出现以下错误
我尝试压缩维度，我尝试了不同版本的 tensorflow --- 我对 tensorflow 还是很陌生，所以在这一点上我真的是在猜测。
# Create a base model -- sequential, functional, or subclass.
model = tf.keras.Sequential([
tf.keras.Input((28, 28), name='feature'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
# Wrap the model with adversarial regularization.
adv_config = nsl.configs.make_adv_reg_config(multiplier=0.2, adv_step_size=0.05)
adv_model = nsl.keras.AdversarialRegularization(model, adv_config=adv_config)
# Compile, train, and evaluate.
adv_model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
#let us now fit the model
adv_model.fit({'feature': x_train, 'label': y_train}, batch_size=32, epochs=5)
W0906 13:48:30.427690 140388427564928 training_utils.py:1101] Output output_1 missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to output_1.
Epoch 1/5
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-21-a5b951c24c49> in <module>()
----> 1 adv_model.fit({'feature': x_train, 'label': y_train}, batch_size=32, epochs=5)
3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
1456 ret = tf_session.TF_SessionRunCallable(self._session._session,
1457 self._handle, args,
-> 1458 run_metadata_ptr)
1459 if run_metadata:
1460 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
InvalidArgumentError: Cannot update variable with shape [] using a Tensor with shape [32], shapes must be equal.
[[{{node AdversarialRegularization_1/AssignAddVariableOp_2}}]]
该模型应该进行训练，我从中获得了一些准确性。我不明白问题出在我的代码中。
我运行在 Google Colab with Tensorflow v1.14.0
我正在尝试开始使用神经结构化学习，但是当我 运行 页面上给出的示例进行测试时，出现以下错误
我尝试压缩维度，我尝试了不同版本的 tensorflow --- 我对 tensorflow 还是很陌生，所以在这一点上我真的是在猜测。
# Create a base model -- sequential, functional, or subclass.
model = tf.keras.Sequential([
tf.keras.Input((28, 28), name='feature'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
# Wrap the model with adversarial regularization.
adv_config = nsl.configs.make_adv_reg_config(multiplier=0.2, adv_step_size=0.05)
adv_model = nsl.keras.AdversarialRegularization(model, adv_config=adv_config)
# Compile, train, and evaluate.
adv_model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
#let us now fit the model
adv_model.fit({'feature': x_train, 'label': y_train}, batch_size=32, epochs=5)
W0906 13:48:30.427690 140388427564928 training_utils.py:1101] Output output_1 missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to output_1.
Epoch 1/5
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-21-a5b951c24c49> in <module>()
----> 1 adv_model.fit({'feature': x_train, 'label': y_train}, batch_size=32, epochs=5)
3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
1456 ret = tf_session.TF_SessionRunCallable(self._session._session,
1457 self._handle, args,
-> 1458 run_metadata_ptr)
1459 if run_metadata:
1460 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
InvalidArgumentError: Cannot update variable with shape [] using a Tensor with shape [32], shapes must be equal.
[[{{node AdversarialRegularization_1/AssignAddVariableOp_2}}]]
该模型应该进行训练，我从中获得了一些准确性。我不明白问题出在我的代码中。
我运行在 Google Colab with Tensorflow v1.14.0