使用 tensorflow keras 訓練模型時，如何每 10 個 epoch 打印一個日志行？

Question

當我擬合模型時：

model.fit(X, y, epochs=40, batch_size=32, validation_split=0.2, verbose=2)它為每個 epoch 打印一個日志行：

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
.....

如何每 10 個 epoch 打印日志行，如下所示？

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

此回調將創建並在日志文本文件中寫入您想要的內容：

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("Epoch: {:>3} | Loss: ".format(epoch) + f"{logs['loss']:.4e}" + " | Valid loss: " + f"{logs['val_loss']:.4e}")
          writefile.write("\n")

my_callbacks = [
  print_training_on_text_every_10_epochs_Callback(logpath=log_path),
  ]

你想這樣稱呼它。

model.fit(training_dataset, epochs=60, validation_data=validation_dataset, callbacks=my_callbacks)

文本文件將僅在 10 個 epoch 過去后更新

這就是我在文本文件中得到的

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

第一個時期編號為 0，第二個時期編號為 1，依此類推。