# python-tensorflowHow do I use the Xception model in TensorFlow with Python?

The Xception model is a deep learning model developed by Google and introduced in their paper [1] in 2017. It is a deep convolutional neural network (CNN) architecture that uses depthwise separable convolutions to reduce the number of parameters and increase the performance of the network. The Xception model can be used in TensorFlow with Python using the following steps:

- Import the necessary libraries:

```
import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras.applications.xception import Xception
```

- Load the Xception model with weights pre-trained on ImageNet:

`model = Xception(weights='imagenet')`

- Compile the model with a suitable optimizer and loss function:

`model.compile(optimizer='adam', loss='categorical_crossentropy')`

- Fit the model on the training data:

`model.fit(x_train, y_train)`

- Evaluate the model on the test data:

```
score = model.evaluate(x_test, y_test)
print("Test accuracy:", score[1])
```

- Make predictions on new data:

`predictions = model.predict(x_new)`

- Save the model for future use:

`model.save('xception.h5')`

[1] Chollet, Francois. "Xception: Deep Learning with Depthwise Separable Convolutions." arXiv preprint arXiv:1610.02357 (2016).

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