当使用tensorflow keras训练模型时，如何每10个epoch打印一条日志行?

Question

当我拟合模型时:

model.fit(X, y, epochs=40, batch_size=32, validation_split=0.2, verbose=2)，它为每个epoch打印一条日志行:

Epoch 1/100
0s - loss: 0.2506 - acc: 0.5750 - val_loss: 0.2501 - val_acc: 0.3750
Epoch 2/100
0s - loss: 0.2487 - acc: 0.6250 - val_loss: 0.2498 - val_acc: 0.6250
Epoch 3/100
0s - loss: 0.2495 - acc: 0.5750 - val_loss: 0.2496 - val_acc: 0.6250
.....

如何按如下方式每隔10个epoch打印日志行?

Epoch 10/100
0s - loss: 0.2506 - acc: 0.5750 - val_loss: 0.2501 - val_acc: 0.3750
Epoch 20/100
0s - loss: 0.2487 - acc: 0.6250 - val_loss: 0.2498 - val_acc: 0.6250
Epoch 30/100
0s - loss: 0.2495 - acc: 0.5750 - val_loss: 0.2496 - val_acc: 0.6250
.....

Answer 1

这个回调将创建并写入您想要的日志文本文件:

log_path = "text_file_name.txt"  # it will be created automatically

class print_training_on_text_every_10_epochs_Callback(Callback):
def __init__(self, logpath):
self.logpath = logpath
def on_epoch_end(self, epoch, logs=None):
with open(self.logpath, "a") as writefile:  # put log_path here
with redirect_stdout(writefile):
if (int(epoch) % 10) == 0:
print(
f"Epoch: {epoch:>3}"
+ f" | Loss: {logs['loss']:.4e}"
+ f" | Accuracy: {logs['accuracy']:.4e}"
+ f" | Validation loss: {logs['val_loss']:.4e}"
+ f" | Validation accuracy: {logs['val_accuracy']:.4e}"
)
writefile.write("n")

my_callbacks = [
print_training_on_text_every_10_epochs_Callback(logpath=log_path),
]

你想这样称呼它

model.fit(
training_dataset,
epochs=60,
verbose=0, # Must be zero, else it would still print log per epoch.
validation_data=validation_dataset, 
callbacks=my_callbacks,
)

文本文件只有在经过10个epoch后才会更新

这是我得到的文本文件

Epoch:   0 | Loss: 5.3454e+00 | Valid loss: 4.2420e-01
Epoch:  10 | Loss: 3.1342e-02 | Valid loss: 3.4554e-02
Epoch:  20 | Loss: 1.6330e-02 | Valid loss: 2.2512e-02

第一个纪元编号为0，第二个纪元编号为1，依此类推。