CSV Logger for Keras in Python

If you want to keep track of the performance of your Keras models during training, you might want to save the metrics in a CSV file for further analysis. The CSVLogger callback in Keras allows you to do that easily.

Here's how you can create a CSVLogger and use it in your Keras model:

from tensorflow.keras.callbacks import CSVLogger

csv_logger = CSVLogger('training.log')

model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val), callbacks=[csv_logger])

This will save the metrics of the training and validation steps to a file called training.log. You can then load the file into a pandas DataFrame to analyze it:

import pandas as pd

data = pd.read_csv('training.log')
print(data.head())

This will print the first 5 rows of the CSV file.

You can also customize the format of the CSV file and the frequency of the updates using the CSVLogger parameters:

csv_logger = CSVLogger('training.log', separator=',', append=False)

model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val), callbacks=[csv_logger])

This will save the metrics to a new file, overwrite existing metrics and use a comma as separator.

In summary, the CSVLogger in Keras is a useful tool to keep track of your model's performance during training and perform further analysis.

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