How to visualize accuracy in Deep Leaning Model in Keras
Visualizing the accuracy of a deep learning model in Keras can provide insights into how well the model is performing during training and help identify overfitting or underfitting. The accuracy is a measure of how many predictions made by the model are correct and it increases as the model becomes better at making predictions.
To visualize the accuracy of a deep learning model in Keras, you can use the history object returned by the fit() function. The history object contains a record of the accuracy and other metrics at each training epoch. You can then use a Python visualization library such as matplotlib to plot the accuracy values over time.
When you’re training a model in Keras, you can pass it the parameter “verbose=1” to see the accuracy at each epoch, it will be printed on the console. This can give you an idea of how the accuracy is changing over time and help you determine if the model is overfitting or underfitting.
In addition to monitoring the accuracy, it’s also important to monitor other metrics such as loss or F1 score, to better understand the model’s performance. A combination of accuracy and other metrics can provide a more complete picture of how well the model is performing.
In summary, to visualize the accuracy of a deep learning model in Keras, you can use the history object returned by the fit() function, which contains a record of the accuracy and other metrics at each training epoch. You can then use a Python visualization library such as matplotlib to plot the accuracy values over time and monitor the accuracy during the training process, in addition to other metrics such as loss or F1 score.
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