keras.fit() and keras.fit_generator() methods in Python

Keras deep learning library provides three different methods to train Deep Learning models. Each model has its own specialized property to train a deep neural network. Here we will discuss keras.fit() and keras.

keras.fit() and keras.fit_generator()

Both methods do the same work, but the method they use is different. So, let’s discuss both methods.

keras.fit() method:

The model is trained for a number of epochs i.e. iterations in a dataset.

  • Syntax

fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False, **kwargs)

  • Returns the `History` item. `History.history` records the training loss rates, metric values, the guaranteed loss rates and the validation metric values ​​per epoch.
  • How to use:
model.fit(xtrain, ytrain, batch_size=32, epochs=100)
  • keras.fit properties where while training a model, all of our training data will be equal to RAM and not allow for real-time data addition to images.

 

keras.fit_generator() method:

The model is trained on batch-by-batch data generated by the Python constructor.

  • Syntax

fit_generator(self, generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0)

  • Returns the `History` item. `History.history` records the training loss rates, metric values, the guaranteed loss rates and the validation metric values ​​per epoch.
  • How to use:
model.fit_generator(generate_arrays_from_file('/my_file.txt'),steps_per_epoch=10000, epochs=10)
  • Model training process with fit_geneartor: :
  1. Generator function is called associated with .fit_generator.
  2. The generator function produces a group with the given size in the .fit_generator function.
  3. The fit_generator function performs backpropagation in the data batch and updates the bits.
  4. Repeat the above steps until we reach the desired number of epochs.

We have seen that keras.fit () is used where all learning information can be entered into memory and data can be illuminated while keras.fit_generator () is used when either we have big data to enter into memory or when data addition needs to be used.

Also read:How to Configure Image Data Augmentation in Keras TensorFlow

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