RuntimeError: The Session graph is empty. Add operations to the graph before calling run() about alibi HOT 6 OPEN

Hi @fraseralex96,

Try to add the following lines as in the example from here:

import tensorflow as tf
tf.get_logger().setLevel(40) # suppress deprecation messages
tf.compat.v1.disable_v2_behavior() # disable TF2 behaviour as alibi code still relies on TF1 constructs

from alibi.

Hi Robert,

Thank you for your response.

Unfortunately I now receive the following error message when I build my NN model because of the above code:

WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., tf.keras.optimizers.legacy.Adam`.

NotImplementedError Traceback (most recent call last)
Input In [12], in <cell line: 7>()
4 momentum = 0.5
5 initializer_seed = 42
----> 7 model = DeepIC50(X_train, learning_rate, momentum, initializer_seed)

File ~/ML_notebook/ML_models.py:354, in DeepIC50(X_train, learning_rate, momentum, seed)
350 output = layers.Dense(3, activation = 'softmax',activity_regularizer=keras.regularizers.l2())(x) # actiation layer is 3 neuron for each class
352 model = keras.Model(x_input, output)
--> 354 model.compile( loss='sparse_categorical_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
355 return model

File ~/.local/lib/python3.9/site-packages/tensorflow/python/trackable/base.py:204, in no_automatic_dependency_tracking.._method_wrapper(self, *args, **kwargs)
202 self._self_setattr_tracking = False # pylint: disable=protected-access
203 try:
--> 204 result = method(self, *args, **kwargs)
205 finally:
206 self._self_setattr_tracking = previous_value # pylint: disable=protected-access

File ~/.local/lib/python3.9/site-packages/keras/src/engine/training_v1.py:321, in Model.compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs)
314 if self.run_eagerly:
315 raise ValueError(
316 "Session keyword arguments are not supported "
317 "when run_eagerly=True. You passed the following "
318 "Session arguments: %s" % (self._function_kwargs,)
319 )
--> 321 self._set_optimizer(optimizer)
322 is_any_keras_optimizer_v1 = any(
323 (
324 isinstance(opt, optimizer_v1.Optimizer)
(...)
327 for opt in tf.nest.flatten(self.optimizer)
328 )
330 if (
331 is_any_keras_optimizer_v1
332 and tf.compat.v1.executing_eagerly_outside_functions()
333 ):

File ~/.local/lib/python3.9/site-packages/keras/src/engine/training_v1.py:1473, in Model._set_optimizer(self, optimizer)
1471 self.optimizer = [optimizers.get(opt) for opt in optimizer]
1472 else:
-> 1473 self.optimizer = optimizers.get(optimizer)
1475 if self._dtype_policy.name == "mixed_float16" and not isinstance(
1476 self.optimizer, loss_scale_optimizer.LossScaleOptimizer
1477 ):
1478 if isinstance(self.optimizer, list):

File ~/.local/lib/python3.9/site-packages/keras/src/optimizers/init.py:298, in get(identifier, **kwargs)
291 optimizer_name = identifier.class.name
292 logging.warning(
293 "There is a known slowdown when using v2.11+ Keras optimizers "
294 "on M1/M2 Macs. Falling back to the "
295 "legacy Keras optimizer, i.e., "
296 f"tf.keras.optimizers.legacy.{optimizer_name}."
297 )
--> 298 return convert_to_legacy_optimizer(identifier)
300 # Wrap legacy TF optimizer instances
301 elif isinstance(identifier, tf.compat.v1.train.Optimizer):

File ~/.local/lib/python3.9/site-packages/keras/src/optimizers/init.py:222, in convert_to_legacy_optimizer(optimizer)
216 raise ValueError(
217 "convert_to_legacy_optimizer should only be called "
218 "on instances of tf.keras.optimizers.Optimizer, but "
219 f"received {optimizer} of type {type(optimizer)}."
220 )
221 optimizer_name = optimizer.class.name.lower()
--> 222 config = optimizer.get_config()
223 # Remove fields that only exist in experimental optimizer.
224 keys_to_remove = [
225 "weight_decay",
226 "use_ema",
(...)
230 "is_legacy_optimizer",
231 ]

File ~/.local/lib/python3.9/site-packages/keras/src/optimizers/adam.py:211, in Adam.get_config(self)
206 def get_config(self):
207 config = super().get_config()
209 config.update(
210 {
--> 211 "learning_rate": self._serialize_hyperparameter(
212 self._learning_rate
213 ),
214 "beta_1": self.beta_1,
215 "beta_2": self.beta_2,
216 "epsilon": self.epsilon,
217 "amsgrad": self.amsgrad,
218 }
219 )
220 return config

File ~/.local/lib/python3.9/site-packages/keras/src/optimizers/optimizer.py:736, in _BaseOptimizer._serialize_hyperparameter(self, hyperparameter)
734 return learning_rate_schedule.serialize(hyperparameter)
735 if isinstance(hyperparameter, tf.Variable):
--> 736 return hyperparameter.numpy()
737 if callable(hyperparameter):
738 return hyperparameter()

File ~/.local/lib/python3.9/site-packages/tensorflow/python/ops/resource_variable_ops.py:689, in BaseResourceVariable.numpy(self)
687 if context.executing_eagerly():
688 return self.read_value().numpy()
--> 689 raise NotImplementedError(
690 "numpy() is only available when eager execution is enabled.")

NotImplementedError: numpy() is only available when eager execution is enabled.`

from alibi.

Try to use tensorflow v2.10:

pip install --upgrade tensorflow==2.10

from alibi.

Hi Robert,

I get the following error message now when I try to run CEM (and also when I individually run the model I am trying to explain):

`2023-08-03 11:04:01.763603: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled

ValueError Traceback (most recent call last)
Input In [22], in <cell line: 4>()
2 shape = X_train.shape
3 mode = 'PN'
----> 4 cem = CEM(model, mode, shape, kappa=0., beta=.1,
5 feature_range=(X_train.min(), X_train.max()),
6 gamma=100, max_iterations=1000,
7 c_init=1., c_steps=10, learning_rate_init=1e-2,
8 clip=(-1000.,1000.), no_info_val=-1.)

File ~/.local/lib/python3.9/site-packages/alibi/explainers/cem.py:235, in CEM.init(self, predict, mode, shape, kappa, beta, feature_range, gamma, ae_model, learning_rate_init, max_iterations, c_init, c_steps, eps, clip, update_num_grad, no_info_val, write_dir, sess)
233 self.pred_proba_s = self.predict(self.delta_s)
234 elif self.mode == "PN":
--> 235 self.pred_proba = self.predict(self.adv)
236 self.pred_proba_s = self.predict(self.adv_s)
238 # probability of target label prediction

File ~/.local/lib/python3.9/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.traceback)
68 # To get the full stack trace, call:
69 # tf.debugging.disable_traceback_filtering()
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb

File ~/.local/lib/python3.9/site-packages/keras/engine/input_spec.py:250, in assert_input_compatibility(input_spec, inputs, layer_name)
248 ndim = x.shape.rank
249 if ndim is not None and ndim < spec.min_ndim:
--> 250 raise ValueError(
251 f'Input {input_index} of layer "{layer_name}" '
252 "is incompatible with the layer: "
253 f"expected min_ndim={spec.min_ndim}, "
254 f"found ndim={ndim}. "
255 f"Full shape received: {tuple(shape)}"
256 )
257 # Check dtype.
258 if spec.dtype is not None:

ValueError: Input 0 of layer "conv1d_18" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (Dimension(10427), Dimension(1378))`

I tested it and it is directly because of this code:

import tensorflow as tf tf.get_logger().setLevel(40) # suppress deprecation messages tf.compat.v1.disable_v2_behavior() # disable TF2 behaviour as alibi code still relies on TF1 constructs

Do you have any suggestion?

Thanks!
Alex

from alibi.

My guess is that you input shape to the Input layer is not properly set. I see you are initialising it as:

x_input = layers.Input(shape=(X_train.shape[1],1))

I think the correct initialisation is:

x_input = layers.Input(shape=X_train.shape[1:])

You should be able to test if everything is ok by performing a forward pass through your model:

model(X_train[:1])

See the documentation here. Also, since you are using conv1d, your training data must have the following shape (B, L, D), where B is the batch size, L is the sequence length, and D is the channels size.

from alibi.

Related to a previous error you had when initialising the optimizer, you can probably try using the legacy optimizers with the newe vesions of tensorflow (see here). There has been a change to the optimizers from tensorflow 2.11 (see release notes here)

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