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Introduction to Keras fit

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What is Keras’s fit?

Keras fit is one of the APIs used to train the model. In contrast, the model is being trained repetitively for the specific number of iterations or epochs specified for the mentioned dataset.

How to Use Keras fit?

We can use Keras fit function by following the below syntax of a function and passing the necessary values or parameters according to our necessity. For example, the below statement – value, y value, epochs = 100, batch_size = 32)

When the call to the fit function of Keras is made, certain assumptions are made prior, which are specified below –

The complete set of training data can be accommodated in the provided RAM of the system, Random Access Memory, as the fit function is only used when there is no data augmentation.

The already trained weights will not be reinitialized when given the call to fit function a second time or again. We can even carry out consecutive calls for the Keras fit function but manage the same on our level.

As no data augmentation will occur, we will not use any Keras generators.

We will train the model and Keras network using the raw data itself, and the same will be fitted inside our memory, that is, RAM.

How to run and fit data with Keras?

While running and fitting our Keras model, we need to follow certain steps mentioned below –

Required libraries should be imported at the top of the program, which may include pandas, NumPy, sampleEducba, which is nothing but our training dataset(you can select your dataset that is to be used for training), datasets, model_selection, Keras models, layers such as dense, dropout and sequential.

Dataset should be loaded – We will be using the x_sample_educba_data and y_sample_educba_data along with our imported sampleEducba dataset as shown below by using this statement – (x_sample_educba_data, y_sample_educba_data), (x_test_educba_data, y_test_educba_data) = sampleEdcuab.load_data()

The next steps will be to create the model and add the required layers to our model.

After creating the model, we will compile the same using the compile method function.

To fit our model, we will be simply using a healthy function. Say for example,, y_sample_educba_data), batch_size = 32, epochs = 5, verbose = 1, validation_data = (x_test_educba_data, y_test_educba_data)

Keras fit function

sampleEducbaModel. Fit (x = None, y = None, batch_size = None, epochs = 1 = verbose = “auto”, 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_multiprocesing = False)

Various arguments used in the above syntax are explained below –

X – The data is passed as input and can have the value of NumPy array or Numpy arraylike, TensorFlow tensor value, dict mapping names of input or list of tensors. A numpy array is specified when we need to pass multiple inputs to the model. A tensor or tensor list is also used in the same scenario. In contrast, dict mapping is used for specifying the names of the inputs corresponding to the tensors or arrays provided if the model contains the inputs named ones.

Y – It is the value of the data that is targeted. Even this value can be a Numpy array, tensor, or list of tensors, but the condition is that the value should be consistent with the x parameter. So, for example, we cannot have tensor inputs and NumPy targets or vice versa.

Batch_size – It has the value set to either None or integer and is used to specify sample count per update of a gradient. When not specified, the default value is considered as 32. When the input data is going to create generators of data sets, there is no need to mention the batch size.

Epochs – It is an integer number where we specify the epochs we must carry out to train the model. Epoch consists of an iteration for x and y data that are mentioned.

Verbose – It can have the value of 0, 1, 2, or auto where one is for the progress, 0 for silent, and auto is the default value set to 1, 2 is the value that specifies one line to be considered per epoch or iteration.

Callbacks – It is the list of the instances of callbacks that are implemented during training.

Validation_split – It has the float value and can be either 1 or 0, which is the specification of a fraction of training data that will be used for validation.

Validation_data – It is the data that will be considered for the loss evaluation and metrics for the model.

Class_weight is the optional parameter for mapping dictionary indices of class with the corresponding float or weight values that are used further for calculating the loss function.

Steps_per_epoch – It helps in mentioning the count of the steps that need to be used while jumping from one epoch completion to the beginning of the new epoch. When not mentioned, the default value is treated as Null.


Keras fit model is used for training the Keras model by passing the required training data and iterating it for a necessary number of times as specified by epoch, which aims to update the mathematical variables present internally by the model.

Recommended Articles

This is a guide to Keras fit. Here we discuss the definition, how to use, run and fit data with Keras models, and its function. You may also have a look at the following articles to learn more –

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