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I'm training an RNN using keras and would like to see how the validation accuracy changes with the data set size. Keras has a list called val_acc in its history object which gets appended after every epoch with the respective validation set accuracy (link to the post in google group). I want to get the average of val_acc for the number of epochs run and plot that against the respective data set size.
Question: How can I retrieve the elements in the val_acc list and perform an operation like numpy.mean(val_acc)?
EDIT: As @runDOSrun said, getting the mean of the val_accs doesn't make sense. Let me focus on getting the final val_acc.
I tried what's been suggested by @nemo but no luck. Here's what I got when I print
model.fit(X_train, y_train, batch_size = 512, nb_epoch = 5, validation_split = 0.05).__dict__
output:
{'model': <keras.models.Sequential object at 0x000000001F752A90>, 'params': {'verbose': 1, 'nb_epoch': 5, 'batch_size': 512, 'metrics': ['loss', 'val_loss'], 'nb_sample': 1710, 'do_validation': True}, 'epoch': [0, 1, 2, 3, 4], 'history': {'loss': [0.96936064512408959, 0.66933631673890948, 0.63404161288724303, 0.62268789783555867, 0.60833334699708819], 'val_loss': [0.84040999412536621, 0.75676006078720093, 0.73714292049407959, 0.71032363176345825, 0.71341043710708618]}}
It turns out there's no list as val_acc in my history dictionary.
Question: How to include val_acc in to the history dictionary?
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Last Updated on October 3, 2019 You can learn a lot about neural networks and deep learning models by observing their performance over time during training. Keras is a powerful library in Python that provides a clean interface for creating deep learning models and wraps the more technical TensorFlow and Theano backends.
So this can be done by learning curve. 1. Imports Digit dataset and necessary libraries 2. Imports Learning curve function for visualization 3. Splits dataset into train and test 4. Plots graphs using matplotlib to analyze the learning curve So this recipe is a short example of how we can plot a learning Curve in Python.
Access Model Training History in Keras. Keras provides the capability to register callbacks when training a deep learning model. One of the default callbacks that is registered when training all deep learning models is the History callback. It records training metrics for each epoch.
Sometimes calculating accuracy does not make sense, so it is not enabled by default in Keras. However, it is a built-in metric, and easy to add. To add the metric, use metrics= ['accuracy'] parameter to model.compile.
To get accuracy values, you need to request that they are calculated during fit, because accuracy is not an objective function, but a (common) metric. Sometimes calculating accuracy does not make sense, so it is not enabled by default in Keras. However, it is a built-in metric, and easy to add.
To add the metric, use metrics=['accuracy'] parameter to model.compile.
In your example:
history = model.fit(X_train, y_train, batch_size = 512,
nb_epoch = 5, validation_split = 0.05)
You can then access validation accuracy as history.history['val_acc']
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