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问题描述
我想在训练神经网络时使用以下一些指标:
I want to use some of these metrics when training my neural network:
METRICS = [
keras.metrics.TruePositives(name='tp'),
keras.metrics.FalsePositives(name='fp'),
keras.metrics.TrueNegatives(name='tn'),
keras.metrics.FalseNegatives(name='fn'),
keras.metrics.Precision(name='precision'),
keras.metrics.Recall(name='recall'),
keras.metrics.CategoricalAccuracy(name='acc'),
keras.metrics.AUC(name='auc'),
]
BATCH_SIZE = 1024
SHUFFLE_BUFFER_SIZE = 4000
train_dataset = tf.data.Dataset.from_tensor_slices((sent_vectors, labels))
train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embed_dim)))
for units in [256, 256]:
model.add(tf.keras.layers.Dense(units, activation='relu'))
model.add(tf.keras.layers.Dense(4, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=METRICS)
model.fit(
train_dataset,
epochs=100)
但是我得到了Shapes (None, 4) and (None, 1) are incompatible.我相信这是因为我正在对4类进行多类分类，但是度量是根据二进制分类计算的.如何为多类分类调整代码?
But I get Shapes (None, 4) and (None, 1) are incompatible. I believe this is because I am doing multiclass classification on 4 classes but the metrics are calculated based on binary classification. How do I adjust my code for multiclass classification?
更新:我有兴趣在学习过程中收集指标，就像 Tensorflow不平衡分类，而不仅仅是在拟合过程结束时.
Update: I am interested in gathering the metrics during the learning process like in Tensorflow Imbalanced Classification, not just at the end of the fitting process.
其他信息:我的输入数据是形状为sent_vectors.shape = (number_examples, 65, 300)和labels=(number_examples, 1)的numpy数组.我有4个标签:0-3.
Additional infos:My input data are numpy arrays with the shape sent_vectors.shape = (number_examples, 65, 300) and labels=(number_examples, 1). I have 4 labels: 0-3.
Stacktrace:
Stacktrace:
ValueErrorTraceback (most recent call last)
<ipython-input-46-2b73afaf7726> in <module>
1 model.fit(
2 train_dataset,
----> 3 epochs=10)
/opt/conda/lib/python3.7/site-packages/tensorflow_core/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_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
322 mode=ModeKeys.TRAIN,
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121 step=step, mode=mode, size=current_batch_size) as batch_logs:
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in execution_function(input_fn)
84 # `numpy` translates Tensors to values in Eager mode.
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
88 return execution_function
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
501 # This is the first call of __call__, so we have to initialize.
502 initializer_map = object_identity.ObjectIdentityDictionary()
--> 503 self._initialize(args, kwds, add_initializers_to=initializer_map)
504 finally:
505 # At this point we know that the initialization is complete (or less
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
406 self._concrete_stateful_fn = (
407 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 408 *args, **kwds))
409
410 def invalid_creator_scope(*unused_args, **unused_kwds):
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 if self.input_signature:
1847 args, kwargs = None, None
-> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849 return graph_function
1850
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2148 graph_function = self._function_cache.primary.get(cache_key, None)
2149 if graph_function is None:
-> 2150 graph_function = self._create_graph_function(args, kwargs)
2151 self._function_cache.primary[cache_key] = graph_function
2152 return graph_function, args, kwargs
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039 arg_names=arg_names,
2040 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041 capture_by_value=self._capture_by_value),
2042 self._function_attributes,
2043 # Tell the ConcreteFunction to clean up its graph once it goes out of
/opt/conda/lib/python3.7/site-packages/tensorflow_core/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)
913 converted_func)
914
--> 915 func_outputs = python_func(*func_args, **func_kwargs)
916
917 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
356 # __wrapped__ allows AutoGraph to swap in a converted function. We give
357 # the function a weak reference to itself to avoid a reference cycle.
--> 358 return weak_wrapped_fn().__wrapped__(*args, **kwds)
359 weak_wrapped_fn = weakref.ref(wrapped_fn)
360
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in distributed_function(input_iterator)
71 strategy = distribution_strategy_context.get_strategy()
72 outputs = strategy.experimental_run_v2(
---> 73 per_replica_function, args=(model, x, y, sample_weights))
74 # Out of PerReplica outputs reduce or pick values to return.
75 all_outputs = dist_utils.unwrap_output_dict(
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
758 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
759 convert_by_default=False)
--> 760 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
761
762 def reduce(self, reduce_op, value, axis):
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
1785 kwargs = {}
1786 with self._container_strategy().scope():
-> 1787 return self._call_for_each_replica(fn, args, kwargs)
1788
1789 def _call_for_each_replica(self, fn, args, kwargs):
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
2130 self._container_strategy(),
2131 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2132 return fn(*args, **kwargs)
2133
2134 def _reduce_to(self, reduce_op, value, destinations):
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics)
262 y,
263 sample_weights=sample_weights,
--> 264 output_loss_metrics=model._output_loss_metrics)
265
266 if reset_metrics:
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in train_on_batch(model, inputs, targets, sample_weights, output_loss_metrics)
313 outs = [outs]
314 metrics_results = _eager_metrics_fn(
--> 315 model, outs, targets, sample_weights=sample_weights, masks=masks)
316 total_loss = nest.flatten(total_loss)
317 return {'total_loss': total_loss,
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _eager_metrics_fn(model, outputs, targets, sample_weights, masks)
72 masks=masks,
73 return_weighted_and_unweighted_metrics=True,
---> 74 skip_target_masks=model._prepare_skip_target_masks())
75
76 # Add metric results from the `add_metric` metrics.
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in _handle_metrics(self, outputs, targets, skip_target_masks, sample_weights, masks, return_weighted_metrics, return_weighted_and_unweighted_metrics)
2061 metric_results.extend(
2062 self._handle_per_output_metrics(self._per_output_metrics[i],
-> 2063 target, output, output_mask))
2064 if return_weighted_and_unweighted_metrics or return_weighted_metrics:
2065 metric_results.extend(
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in _handle_per_output_metrics(self, metrics_dict, y_true, y_pred, mask, weights)
2012 with K.name_scope(metric_name):
2013 metric_result = training_utils.call_metric_function(
-> 2014 metric_fn, y_true, y_pred, weights=weights, mask=mask)
2015 metric_results.append(metric_result)
2016 return metric_results
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_utils.py in call_metric_function(metric_fn, y_true, y_pred, weights, mask)
1065
1066 if y_pred is not None:
-> 1067 return metric_fn(y_true, y_pred, sample_weight=weights)
1068 # `Mean` metric only takes a single value.
1069 return metric_fn(y_true, sample_weight=weights)
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py in __call__(self, *args, **kwargs)
191 from tensorflow.python.keras.distribute import distributed_training_utils # pylint:disable=g-import-not-at-top
192 return distributed_training_utils.call_replica_local_fn(
--> 193 replica_local_fn, *args, **kwargs)
194
195 @property
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py in call_replica_local_fn(fn, *args, **kwargs)
1133 with strategy.scope():
1134 return strategy.extended.call_for_each_replica(fn, args, kwargs)
-> 1135 return fn(*args, **kwargs)
1136
1137
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py in replica_local_fn(*args, **kwargs)
174 def replica_local_fn(*args, **kwargs):
175 """Updates the state of the metric in a replica-local context."""
--> 176 update_op = self.update_state(*args, **kwargs) # pylint: disable=not-callable
177 with ops.control_dependencies([update_op]):
178 result_t = self.result() # pylint: disable=not-callable
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/metrics_utils.py in decorated(metric_obj, *args, **kwargs)
73
74 with tf_utils.graph_context_for_symbolic_tensors(*args, **kwargs):
---> 75 update_op = update_state_fn(*args, **kwargs)
76 if update_op is not None: # update_op will be None in eager execution.
77 metric_obj.add_update(update_op)
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py in update_state(self, y_true, y_pred, sample_weight)
881 y_pred,
882 thresholds=self.thresholds,
--> 883 sample_weight=sample_weight)
884
885 def result(self):
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/metrics_utils.py in update_confusion_matrix_variables(variables_to_update, y_true, y_pred, thresholds, top_k, class_id, sample_weight)
276 y_true], _ = ragged_assert_compatible_and_get_flat_values([y_pred, y_true],
277 sample_weight)
--> 278 y_pred.shape.assert_is_compatible_with(y_true.shape)
279
280 if not any(
/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/framework/tensor_shape.py in assert_is_compatible_with(self, other)
1113 """
1114 if not self.is_compatible_with(other):
-> 1115 raise ValueError("Shapes %s and %s are incompatible" % (self, other))
1116
1117 def most_specific_compatible_shape(self, other):
ValueError: Shapes (None, 4) and (None, 1) are incompatible
推荐答案
更新:
OP编辑他的问题时，我决定编辑我的解决方案，以提供更紧凑的答案:
Update:
As OP edited his question, I decided to edit my solution either with the intention of providing a more compact answer:
导入并定义以后需要的所有内容:
import numpy as np
from numpy import random as random
import tensorflow as tf
import keras
import keras.backend as K
tf.config.experimental_run_functions_eagerly(False)
VERBOSE = 1
keras.backend.clear_session()
sess = tf.compat.v1.Session()
sess.as_default()
### Just for dummy data
sent_vectors = random.rand(100, 65, 300).astype(np.float32)
labels = random.randint(0, 4, (100, 1))
labels = np.squeeze(labels, 1)
NUM_CLASSES = np.max(labels) + 1
BATCH_SIZE = 10
SHUFFLE_BUFFER_SIZE = 200
embed_dim = 8
### Just for dummy data
创建自定义指标:
class CategoricalTruePositives(tf.keras.metrics.Metric):
def __init__(self, num_classes, batch_size,
name="categorical_true_positives", **kwargs):
super(CategoricalTruePositives, self).__init__(name=name, **kwargs)
self.batch_size = batch_size
self.num_classes = num_classes
self.cat_true_positives = self.add_weight(name="ctp", initializer="zeros")
def update_state(self, y_true, y_pred, sample_weight=None):
y_true = K.argmax(y_true, axis=-1)
y_pred = K.argmax(y_pred, axis=-1)
y_true = K.flatten(y_true)
true_poss = K.sum(K.cast((K.equal(y_true, y_pred)), dtype=tf.float32))
self.cat_true_positives.assign_add(true_poss)
def result(self):
return self.cat_true_positives
首先仅使用多标签评估指标(包括我们的自定义功能)来编译和拟合模型:
METRICS = [
tf.keras.metrics.CategoricalAccuracy(name='acc'),
CategoricalTruePositives(NUM_CLASSES, BATCH_SIZE),
]
# Transform labels to onehot encoding for metric CategoricalAccuracy
labels = tf.compat.v1.one_hot(labels, depth=NUM_CLASSES)
train_dataset = tf.data.Dataset.from_tensor_slices((sent_vectors, labels))
train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embed_dim)))
for units in [256, 256]:
model.add(tf.keras.layers.Dense(units, activation='relu'))
model.add(tf.keras.layers.Dense(4, activation='softmax'))
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=[METRICS])
model.fit(
train_dataset,
epochs=10,
verbose=VERBOSE,
shuffle=True)
预测结果并对其进行后处理:
result = model.predict(train_dataset)
pred_size = sent_vectors.shape[0]
preds = K.argmax(result, axis=-1)
preds = K.one_hot(preds, NUM_CLASSES)
print("\nTrue positives per classes:")
for i in range(4):
m = tf.keras.metrics.TruePositives(name='tp')
m.update_state(labels[:, i], preds[:, i])
print("Class {} true positives: {}".format(i, m.result()))
退出:
Epoch 1/10
10/10 [==============================] - 3s 328ms/step - loss: 1.4226 - acc: 0.2300 - categorical_true_positives: 23.0000
Epoch 2/10
10/10 [==============================] - 0s 21ms/step - loss: 1.3876 - acc: 0.2900 - categorical_true_positives: 29.0000
Epoch 3/10
10/10 [==============================] - 0s 20ms/step - loss: 1.3721 - acc: 0.2800 - categorical_true_positives: 28.0000
Epoch 4/10
10/10 [==============================] - 0s 20ms/step - loss: 1.3628 - acc: 0.2900 - categorical_true_positives: 29.0000
Epoch 5/10
10/10 [==============================] - 0s 22ms/step - loss: 1.3447 - acc: 0.3800 - categorical_true_positives: 38.0000
Epoch 6/10
10/10 [==============================] - 0s 22ms/step - loss: 1.3187 - acc: 0.3800 - categorical_true_positives: 38.0000
Epoch 7/10
10/10 [==============================] - 0s 22ms/step - loss: 1.2653 - acc: 0.4300 - categorical_true_positives: 43.0000
Epoch 8/10
10/10 [==============================] - 0s 21ms/step - loss: 1.1760 - acc: 0.6000 - categorical_true_positives: 60.0000
Epoch 9/10
10/10 [==============================] - 0s 22ms/step - loss: 1.1809 - acc: 0.4600 - categorical_true_positives: 46.0000
Epoch 10/10
10/10 [==============================] - 0s 22ms/step - loss: 1.2739 - acc: 0.3800 - categorical_true_positives: 38.0000
True positives per classes:
Class 0 true positives: 16.0
Class 1 true positives: 0.0
Class 2 true positives: 5.0
Class 3 true positives: 7.0
注意:
我们可以认识到，真实肯定的总和与我们的训练结果不相等，这是因为我们是根据来自train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)的不同数据对模型进行训练和预测的.
We can recognize, that the true positives' sum is not equal with our training result, that is because we trained and predicted our model against a different data get from train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE).
这篇关于Tensorflow:如何在多类分类中使用tf.keras.metrics?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！