Have you tried using tools such as TensorBoard, which is a visualization tool for TensorFlow that can be used to track the performance of your Keras models?
To use TensorBoard with Keras, you will need to install TensorFlow and modify your Keras code to write log files that TensorBoard can read. This can be done by using the TensorBoard callback provided by Keras, which writes log files to a specified directory that TensorBoard can use to visualize the results of your training runs.
Here is an example of how you might use the TensorBoard callback in your Keras code:

from tensorflow.keras.callbacks import TensorBoard
# Create a TensorBoard callback
tensorboard_callback = TensorBoard(log_dir='/path/to/logs')
# Compile and fit your Keras model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, callbacks=[tensorboard_callback])

After you have trained your model, you can start TensorBoard by running the tensorboard command in a terminal, specifying the directory where the log files are stored. TensorBoard will then start a web server that you can access in a web browser to visualize the results of your training runs.