Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to use Model.fit which supports generators (after fit_generator deprecation)** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How to use Model.fit which supports generators (after fit_generator deprecation)?

**How to use Model.fit which supports generators (after fit_generator deprecation)?**`Model.fit_generator`

is deprecated starting from tensorflow 2.1.0 which is currently is in**rc1**. You can find the documentation for tf-2.1.0-rc1**use Model.fit which supports generators (after fit_generator deprecation)**`Model.fit_generator`

is deprecated starting from tensorflow 2.1.0 which is currently is in**rc1**. You can find the documentation for tf-2.1.0-rc1

## Method 1

`Model.fit_generator`

is deprecated starting from tensorflow 2.1.0 which is currently is in **rc1**. You can find the documentation for tf-2.1.0-rc1

As you can see the first argument of the `Model.fit`

can take a generator so just pass it your generator.

## Method 2

As mentioned in the documentation (emphasis mine):

x: Input data. It could be

- A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
- A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
- A tf.data dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights)
*A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample weights). A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given below.*

you can simply pass the generator to `Model.fit`

as similar to `Model.fit_generator`

data_gen_train = ImageDataGenerator(rescale=1/255.) data_gen_valid = ImageDataGenerator(rescale=1/255.) train_generator = data_gen_train.flow_from_directory(train_dir, target_size=(128,128), batch_size=128, class_mode="binary") valid_generator = data_gen_valid.flow_from_directory(validation_dir, target_size=(128,128), batch_size=128, class_mode="binary") model.fit(train_generator, epochs=2, validation_data=valid_generator)

**Summery**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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