Error using class_weights parameter with Keras in Multi-Class Classification problem

Question

This problem has been asked in other forums and I have tried their variations with no avail: class_weight for imbalanced data - Keras

how to set class-weights for imbalanced classes in keras

However it seems stale as no one has answered the question. Does anyone know how to implement the class_weight parameter in Keras when using categorical_crossentropy? I have been trying to use the class_weight parameter in Keras but keep getting this error:

ValueError: Expected class_weight to be a dict with keys from 0 to one less than the number of classes, found {'prediction': {0: 1.217169570760731, 1: 5.323420074349443, 2: 0.5023680056130504}

Each sample will be classified either 0, 1, or 2 (softmax). The bias in this dataset is significant. My model using the Keras functional API.

The class_weights were calculated using Sklearn:

class_weights = class_weight.compute_class_weight('balanced', np.unique(np.array(y_trn_labels_HB_2_pd['labels'])), y_trn_labels_HB_2_pd['labels'])
class_weight_dict = dict(enumerate(class_weights))
class_weight_dict

Here is my last layer:

prediction = Dense(3, activation="softmax", name = 'prediction')(x)

Here is my model:

tf.__version__ = 2.3.0

model = Model(inputs = [sequence_input_head, sequence_input_body, semantic_feat,
             wordOL_feat, avg_subj_feat], outputs = [prediction])
model.compile(loss = 'categorical_crossentropy',
             optimizer='adam',
             metrics = ['accuracy'])
model.summary()

Here is my class_weight parameter:

class_weight= {'prediction': {0:1.217169570760731, 1:5.323420074349443, 2:0.5023680056130504} })

Here is the full error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-271-e3bb78b84171> in <module>()
     26                                        y_val_2_cat),
     27                     callbacks = [es],
---> 28                     class_weight= {'prediction': class_weights})
     29 
     30 modeled = model.save(os.path.join(save_path, path_model))

3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1061           use_multiprocessing=use_multiprocessing,
   1062           model=self,
-> 1063           steps_per_execution=self._steps_per_execution)
   1064 
   1065       # Container that configures and calls `tf.keras.Callback`s.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution)
   1120     dataset = self._adapter.get_dataset()
   1121     if class_weight:
-> 1122       dataset = dataset.map(_make_class_weight_map_fn(class_weight))
   1123     self._inferred_steps = self._infer_steps(steps_per_epoch, dataset)
   1124 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in _make_class_weight_map_fn(class_weight)
   1299         "Expected `class_weight` to be a dict with keys from 0 to one less "
   1300         "than the number of classes, found {}").format(class_weight)
-> 1301     raise ValueError(error_msg)
   1302 
   1303   class_weight_tensor = ops.convert_to_tensor_v2(

ValueError: Expected `class_weight` to be a dict with keys from 0 to one less than the number of classes, found {'prediction': {0: 1.217169570760731, 1: 5.323420074349443}}

EDIT1:

I tried your suggestion @Prateek Bhatt

history = model.fit({'headline': hl_pd_tr, 'articleBody':bd_pd_train, 'semantic': semantic_sim_180_train_x, 'wordOverlap': wrd_OvLp_train_x, 'avg_subjectivity': avg_subj_hb_train_x}, #@param ["model.fit({'headline': hl_pd_tr, 'articleBody':bd_pd_train},", "model.fit({'headline': hl_pd_tr, 'articleBody':bd_pd_train, 'semantic': semantic_x_tr},", "model.fit({'headline': hl_pd_tr, 'articleBody':bd_pd_train, 'semantic': semantic_x_tr, 'wordOverlap': wrd_OvLp_x_tr},", "model.fit({'headline': hl_pd_tr, 'articleBody':bd_pd_train, 'semantic': semantic_x_tr, 'wordOverlap': wrd_OvLp_x_tr, 'avgsubj': avg_subj_x_tr},"] {type:"raw", allow-input: true}
                    {'prediction':y_train_2_cat},
                    epochs=100,
                    batch_size= BATCH__SIZE,
                    shuffle= True,
                    validation_data = ([hl_pd_val, bd_pd_val, semantic_sim_180_val_x, wrd_OvLp_val_x, avg_subj_hb_val_x], y_val_2_cat),
                    callbacks = [es],
                    class_weight= {0:1.217169570760731, 1:5.323420074349443, 2:0.5023680056130504})

However, I get this error:

ValueError: `class_weight` is only supported for Models with a single output.

Full error:

ValueError                                Traceback (most recent call last)
<ipython-input-272-bfbab936a723> in <module>()
     26                                        y_val_2_cat),
     27                     callbacks = [es],
---> 28                     class_weight= {0:1.217169570760731, 1:5.323420074349443, 2:0.5023680056130504})
     29 
     30 modeled = model.save(os.path.join(save_path, path_model))

16 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1061           use_multiprocessing=use_multiprocessing,
   1062           model=self,
-> 1063           steps_per_execution=self._steps_per_execution)
   1064 
   1065       # Container that configures and calls `tf.keras.Callback`s.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution)
   1120     dataset = self._adapter.get_dataset()
   1121     if class_weight:
-> 1122       dataset = dataset.map(_make_class_weight_map_fn(class_weight))
   1123     self._inferred_steps = self._infer_steps(steps_per_epoch, dataset)
   1124 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py in map(self, map_func, num_parallel_calls, deterministic)
   1693     """
   1694     if num_parallel_calls is None:
-> 1695       return MapDataset(self, map_func, preserve_cardinality=True)
   1696     else:
   1697       return ParallelMapDataset(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py in __init__(self, input_dataset, map_func, use_inter_op_parallelism, preserve_cardinality, use_legacy_function)
   4043         self._transformation_name(),
   4044         dataset=input_dataset,
-> 4045         use_legacy_function=use_legacy_function)
   4046     variant_tensor = gen_dataset_ops.map_dataset(
   4047         input_dataset._variant_tensor,  # pylint: disable=protected-access

/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py in __init__(self, func, transformation_name, dataset, input_classes, input_shapes, input_types, input_structure, add_to_graph, use_legacy_function, defun_kwargs)
   3369       with tracking.resource_tracker_scope(resource_tracker):
   3370         # TODO(b/141462134): Switch to using garbage collection.
-> 3371         self._function = wrapper_fn.get_concrete_function()
   3372         if add_to_graph:
   3373           self._function.add_to_graph(ops.get_default_graph())

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in get_concrete_function(self, *args, **kwargs)
   2937     """
   2938     graph_function = self._get_concrete_function_garbage_collected(
-> 2939         *args, **kwargs)
   2940     graph_function._garbage_collector.release()  # pylint: disable=protected-access
   2941     return graph_function

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_garbage_collected(self, *args, **kwargs)
   2904       args, kwargs = None, None
   2905     with self._lock:
-> 2906       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
   2907       seen_names = set()
   2908       captured = object_identity.ObjectIdentitySet(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3211 
   3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
   3214       self._function_cache.primary[cache_key] = graph_function
   3215       return graph_function, args, kwargs

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3073             arg_names=arg_names,
   3074             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075             capture_by_value=self._capture_by_value),
   3076         self._function_attributes,
   3077         function_spec=self.function_spec,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    984         _, original_func = tf_decorator.unwrap(python_func)
    985 
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
    987 
    988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py in wrapper_fn(*args)
   3362           attributes=defun_kwargs)
   3363       def wrapper_fn(*args):  # pylint: disable=missing-docstring
-> 3364         ret = _wrapper_helper(*args)
   3365         ret = structure.to_tensor_list(self._output_structure, ret)
   3366         return [ops.convert_to_tensor(t) for t in ret]

/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py in _wrapper_helper(*args)
   3297         nested_args = (nested_args,)
   3298 
-> 3299       ret = autograph.tf_convert(func, ag_ctx)(*nested_args)
   3300       # If `func` returns a list of tensors, `nest.flatten()` and
   3301       # `ops.convert_to_tensor()` would conspire to attempt to stack

/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    253       try:
    254         with conversion_ctx:
--> 255           return converted_call(f, args, kwargs, options=options)
    256       except Exception as e:  # pylint:disable=broad-except
    257         if hasattr(e, 'ag_error_metadata'):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py in converted_call(f, args, kwargs, caller_fn_scope, options)
    530 
    531   if not options.user_requested and conversion.is_whitelisted(f):
--> 532     return _call_unconverted(f, args, kwargs, options)
    533 
    534   # internal_convert_user_code is for example turned off when issuing a dynamic

/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py in _call_unconverted(f, args, kwargs, options, update_cache)
    337 
    338   if kwargs is not None:
--> 339     return f(*args, **kwargs)
    340   return f(*args)
    341 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in _class_weights_map_fn(*data)
   1310     if nest.is_sequence(y):
   1311       raise ValueError(
-> 1312           "`class_weight` is only supported for Models with a single output.")
   1313 
   1314     if y.shape.rank > 2:

ValueError: `class_weight` is only supported for Models with a single output.

Answer 1

I had the same error and was able to solve it by removing the name in my output layer. In your case, change

prediction = Dense(3, activation="softmax", name = 'prediction')(x)

to

prediction = Dense(3, activation="softmax")(x)

Answer 2

I see you have 3 classes. In your training set you will have X training samples labelled as class 0, Y training samples labelled as class 1 and Z training samples labelled as class 2. Now choose from X,Y or Z the largest value. AS an example say X=100, Y=200 and Z=400 samples. So Z=400 is the largest. The weight dictionary can be determined as

weight_dict={0:400/100, 1:400/200, 2:400/400}

The idea here is that class 2 with 400 samples influences the loss function 4 times more than class 0 and 2 times more than class 1 if you do not use a weights_dict. The weights_dict tries to rebalance this in terms of net impact on the loss function

Answer 3

Just use class_weights as following:

class_weight= {0:1.217169570760731, 1:5.323420074349443, 2:0.5023680056130504}

This should be enough.

Update:

There is currently a bug in tensorflow, try using TF2.1.0

Answer 4

If you have a pandas dataframe you can first compute the class_weight parameter using compute_class_weight function and passing the target column like:

from sklearn.utils import class_weight 
class_weights = class_weight.compute_class_weight('balanced',np.unique(df['target']),df['target'])
class_weights = dict(enumerate(class_weights))

then when fitting you pass the class_weights:

history = model.fit(X, Y,epochs=n,class_weight=class_weights)

Then make sure you are using one-hot-encoded labels for loss calculation using categorical_crossentropy. In case you are using the index values use sparse_categorical_crossentropy instead.