我正在拟合一个train_generator,并通过自定义回调函数来计算validation_generator上的自定义指标。
如何在自定义回调中访问params validation_steps
和validation_data
?
它们不在self.params
中,在self.model
中也找不到。这是我想要做的事情,欢迎其他不同的方法。
model.fit_generator(generator=train_generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_steps,
callbacks=[CustomMetrics()])
class CustomMetrics(keras.callbacks.Callback):
def on_epoch_end(self, batch, logs={}):
for i in validation_steps:
# features, labels = next(validation_data)
# compute custom metric: f(features, labels)
return
keras: 2.1.1
更新
我成功将验证数据传递给自定义回调函数的构造函数。然而,这会导致一个令人讨厌的 "内核似乎已经死亡。它将自动重新启动 "的消息。我怀疑这是否是正确的方法。有什么建议吗?
class CustomMetrics(keras.callbacks.Callback):
def __init__(self, validation_generator, validation_steps):
self.validation_generator = validation_generator
self.validation_steps = validation_steps
def on_epoch_end(self, batch, logs={}):
self.scores = {
'recall_score': [],
'precision_score': [],
'f1_score': []
}
for batch_index in range(self.validation_steps):
features, y_true = next(self.validation_generator)
y_pred = np.asarray(self.model.predict(features))
y_pred = y_pred.round().astype(int)
self.scores['recall_score'].append(recall_score(y_true[:,0], y_pred[:,0]))
self.scores['precision_score'].append(precision_score(y_true[:,0], y_pred[:,0]))
self.scores['f1_score'].append(f1_score(y_true[:,0], y_pred[:,0]))
return
metrics = CustomMetrics(validation_generator, validation_steps)
model.fit_generator(generator=train_generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_steps,
shuffle=True,
callbacks=[metrics],
verbose=1)