[英]Why differ metrics calculated by model.evaluate() from tracked metrics during training in Keras?
[英]How to prevent Keras from computing metrics during training
我正在使用 Tensorflow/Keras 2.4.1,并且我有一个(无监督的)自定义指标,它将我的几个模型输入作为参数,例如:
model = build_model() # returns a tf.keras.Model object
my_metric = custom_metric(model.output, model.input[0], model.input[1])
model.add_metric(my_metric)
[...]
model.fit([...]) # training with fit
但是,恰好custom_metric
非常昂贵,因此我希望仅在验证期间对其进行计算。 我找到了这个答案,但我几乎不明白如何使解决方案适应使用多个模型输入作为参数的指标,因为update_state
方法似乎不灵活。
在我的上下文中,除了编写我自己的训练循环之外,有没有办法避免在训练期间计算我的指标? 此外,我很惊讶我们不能在本地指定 Tensorflow 某些指标只能在验证时计算,这有什么原因吗?
此外,由于模型是为了优化损失而训练的,而且训练数据集不应该用于评估模型,我什至不明白为什么默认情况下 Tensorflow 在训练期间计算指标。
我认为仅在验证时计算指标的最简单解决方案是使用自定义回调。
在这里我们定义我们的虚拟回调:
class MyCustomMetricCallback(tf.keras.callbacks.Callback):
def __init__(self, train=None, validation=None):
super(MyCustomMetricCallback, self).__init__()
self.train = train
self.validation = validation
def on_epoch_end(self, epoch, logs={}):
mse = tf.keras.losses.mean_squared_error
if self.train:
logs['my_metric_train'] = float('inf')
X_train, y_train = self.train[0], self.train[1]
y_pred = self.model.predict(X_train)
score = mse(y_train, y_pred)
logs['my_metric_train'] = np.round(score, 5)
if self.validation:
logs['my_metric_val'] = float('inf')
X_valid, y_valid = self.validation[0], self.validation[1]
y_pred = self.model.predict(X_valid)
val_score = mse(y_pred, y_valid)
logs['my_metric_val'] = np.round(val_score, 5)
鉴于这个虚拟模型:
def build_model():
inp1 = Input((5,))
inp2 = Input((5,))
out = Concatenate()([inp1, inp2])
out = Dense(1)(out)
model = Model([inp1, inp2], out)
model.compile(loss='mse', optimizer='adam')
return model
这个数据:
X_train1 = np.random.uniform(0,1, (100,5))
X_train2 = np.random.uniform(0,1, (100,5))
y_train = np.random.uniform(0,1, (100,1))
X_val1 = np.random.uniform(0,1, (100,5))
X_val2 = np.random.uniform(0,1, (100,5))
y_val = np.random.uniform(0,1, (100,1))
您可以使用自定义回调来计算训练和验证的指标:
model = build_model()
model.fit([X_train1, X_train2], y_train, epochs=10,
callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train), validation=([X_val1, X_val2],y_val))])
仅在验证时:
model = build_model()
model.fit([X_train1, X_train2], y_train, epochs=10,
callbacks=[MyCustomMetricCallback(validation=([X_val1, X_val2],y_val))])
仅在火车上:
model = build_model()
model.fit([X_train1, X_train2], y_train, epochs=10,
callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train))])
请记住,回调会一次性评估数据上的指标,就像 keras 在validation_data
上默认计算的任何指标/损失一样。
这是运行代码。
我能够使用learning_phase
但只能在符号张量模式(图形)模式下使用:
因此,首先我们需要禁用 Eager 模式(这必须在导入 tensorflow 后立即完成):
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
然后,您可以使用符号 if ( backend.switch
) 创建指标:
def metric_graph(in1, in2, out):
actual_metric = out * (in1 + in2)
return K.switch(K.learning_phase(), tf.zeros((1,)), actual_metric)
方法add_metric
将询问名称和聚合方法,您可以将其设置为"mean"
。
所以,这里有一个例子:
x1 = numpy.ones((5,3))
x2 = numpy.ones((5,3))
y = 3*numpy.ones((5,1))
vx1 = numpy.ones((5,3))
vx2 = numpy.ones((5,3))
vy = 3*numpy.ones((5,1))
def metric_eager(in1, in2, out):
if (K.learning_phase()):
return 0
else:
return out * (in1 + in2)
def metric_graph(in1, in2, out):
actual_metric = out * (in1 + in2)
return K.switch(K.learning_phase(), tf.zeros((1,)), actual_metric)
ins1 = Input((3,))
ins2 = Input((3,))
outs = Concatenate()([ins1, ins2])
outs = Dense(1)(outs)
model = Model([ins1, ins2],outs)
model.add_metric(metric_graph(ins1, ins2, outs), name='my_metric', aggregation='mean')
model.compile(loss='mse', optimizer='adam')
model.fit([x1, x2],y, validation_data=([vx1, vx2], vy), epochs=3)
由于指标是在keras.Model
的train_step
函数中运行的,因此在不更改 API 的情况下过滤掉训练禁用指标需要对keras.Model
进行子类化。
我们定义了一个简单的度量包装器:
class TrainDisabledMetric(Metric):
def __init__(self, metric: Metric):
super().__init__(name=metric.name)
self._metric = metric
def update_state(self, *args, **kwargs):
return self._metric.update_state(*args, **kwargs)
def reset_state(self):
return self._metric.reset_state()
def result(self):
return self._metric.result()
和子类keras.Model
在训练期间过滤掉这些指标:
class CustomModel(keras.Model):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def compile(self, optimizer='rmsprop', loss=None, metrics=None,
loss_weights=None, weighted_metrics=None, run_eagerly=None,
steps_per_execution=None, jit_compile=None, **kwargs):
from_serialized = kwargs.get('from_serialized', False)
super().compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights,
weighted_metrics=weighted_metrics, run_eagerly=run_eagerly,
steps_per_execution=steps_per_execution,
jit_compile=jit_compile, **kwargs)
self.on_train_compiled_metrics = self.compiled_metrics
if metrics is not None:
def get_on_train_traverse_tree(structure):
flat = tf.nest.flatten(structure)
on_train = [not isinstance(e, TrainDisabledMetric) for e in flat]
full_tree = tf.nest.pack_sequence_as(structure, on_train)
return get_traverse_shallow_structure(lambda s: any(tf.nest.flatten(s)),
full_tree)
on_train_sub_tree = get_on_train_traverse_tree(metrics)
flat_on_train = flatten_up_to(on_train_sub_tree, metrics)
def clean_tree(tree):
if isinstance(tree, list):
_list = []
for t in tree:
r = clean_tree(t)
if r:
_list.append(r)
return _list
elif isinstance(tree, dict):
_tree = {}
for k, v in tree.items():
r = clean_tree(v)
if r:
_tree[k] = r
return _tree
else:
return tree
pruned_on_train_sub_tree = clean_tree(on_train_sub_tree)
pruned_flat_on_train = [m for keep, m in
zip(tf.nest.flatten(on_train_sub_tree),
flat_on_train) if keep]
on_train_metrics = tf.nest.pack_sequence_as(pruned_on_train_sub_tree,
pruned_flat_on_train)
self.on_train_compiled_metrics = compile_utils.MetricsContainer(
on_train_metrics, weighted_metrics=None, output_names=self.output_names,
from_serialized=from_serialized)
def train_step(self, data):
x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)
# Run forward pass.
with tf.GradientTape() as tape:
y_pred = self(x, training=True)
loss = self.compute_loss(x, y, y_pred, sample_weight)
self._validate_target_and_loss(y, loss)
# Run backwards pass.
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
return self.compute_metrics(x, y, y_pred, sample_weight, training=True)
def compute_metrics(self, x, y, y_pred, sample_weight, training=False):
del x # The default implementation does not use `x`.
if training:
self.on_train_compiled_metrics.update_state(y, y_pred, sample_weight)
metrics = self.on_train_metrics
else:
self.compiled_metrics.update_state(y, y_pred, sample_weight)
metrics = self.metrics
# Collect metrics to return
return_metrics = {}
for metric in metrics:
result = metric.result()
if isinstance(result, dict):
return_metrics.update(result)
else:
return_metrics[metric.name] = result
return return_metrics
@property
def on_train_metrics(self):
metrics = []
if self._is_compiled:
# TODO(omalleyt): Track `LossesContainer` and `MetricsContainer` objects
# so that attr names are not load-bearing.
if self.compiled_loss is not None:
metrics += self.compiled_loss.metrics
if self.on_train_compiled_metrics is not None:
metrics += self.on_train_compiled_metrics.metrics
for l in self._flatten_layers():
metrics.extend(l._metrics) # pylint: disable=protected-access
return metrics
现在给定一个 keras 模型,我们可以包装它并使用禁用训练的指标对其进行编译:
model: keras.Model = ...
custom_model = CustomModel(inputs=model.input, outputs=model.output)
train_enabled_metrics = [tf.keras.metrics.SparseCategoricalAccuracy()]
# wrap train disabled metrics with `TrainDisabledMetric`:
train_disabled_metrics = [
TrainDisabledMetric(tf.keras.metrics.SparseCategoricalCrossentropy())]
metrics = train_enabled_metrics + train_disabled_metrics
custom_model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True), metrics=metrics, )
custom_model.fit(ds_train, epochs=6, validation_data=ds_test, )
度量SparseCategoricalCrossentropy
仅在验证期间计算:
Epoch 1/6
469/469 [==============================] - 2s 2ms/step - loss: 0.3522 - sparse_categorical_accuracy: 0.8366 - val_loss: 0.1978 - val_sparse_categorical_accuracy: 0.9086 - val_sparse_categorical_crossentropy: 1.3197
Epoch 2/6
469/469 [==============================] - 1s 1ms/step - loss: 0.1631 - sparse_categorical_accuracy: 0.9526 - val_loss: 0.1429 - val_sparse_categorical_accuracy: 0.9587 - val_sparse_categorical_crossentropy: 1.1910
Epoch 3/6
469/469 [==============================] - 1s 1ms/step - loss: 0.1178 - sparse_categorical_accuracy: 0.9654 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9661 - val_sparse_categorical_crossentropy: 1.1369
Epoch 4/6
469/469 [==============================] - 1s 1ms/step - loss: 0.0909 - sparse_categorical_accuracy: 0.9735 - val_loss: 0.0981 - val_sparse_categorical_accuracy: 0.9715 - val_sparse_categorical_crossentropy: 1.0434
Epoch 5/6
469/469 [==============================] - 1s 1ms/step - loss: 0.0735 - sparse_categorical_accuracy: 0.9784 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9721 - val_sparse_categorical_crossentropy: 0.9862
Epoch 6/6
469/469 [==============================] - 1s 1ms/step - loss: 0.0606 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0824 - val_sparse_categorical_accuracy: 0.9761 - val_sparse_categorical_crossentropy: 1.0024
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.