Created
August 19, 2020 09:14
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Using tensorflow keras callbacks
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| def train_model(): | |
| # ... Define the model and training data etc here | |
| def my_callback(epoch, logs): | |
| # Optionally clear the file if it exists here? or use on_train_begin cb | |
| # Focus on the point here | |
| with open('train_logs.json', 'a') as l: | |
| data =json.dumps({'epoch': epoch, **logs}) | |
| l.write(data + '\n') | |
| my_callbacks = [ | |
| tf.keras.callbacks.LambdaCallback( | |
| on_epoch_end=my_callback | |
| ) | |
| ] | |
| batch_size = 512 | |
| epochs = 10 | |
| model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) | |
| model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1, callbacks=my_callbacks) | |
| return model |
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