# How can Tensorflow and pre-trained model be used for evaluation and prediction of data using Python?

Tensorflow and the pre-trained model can be used for evaluation and prediction of data using the ‘evaluate’ and ‘predict’ methods. The batch of input images is first flattened. The sigmoid function is applied on the model so that it would return logit values.

A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model.

We will understand how to classify images of cats and dogs with the help of transfer learning from a pre-trained network. The intuition behind transfer learning for image classification is, if a model is trained on a large and general dataset, this model can be used to effectively serve as a generic model for the visual world. It would have learned the feature maps, which means the user won’t have to start from scratch by training a large model on a large dataset.

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.

## Example

print("Evaluation and prediction")
loss, accuracy = model.evaluate(test_dataset)
print('Test accuracy is :', accuracy)
print("The batch of image from test set is retrieved")
image_batch, label_batch = test_dataset.as_numpy_iterator().next()
predictions = model.predict_on_batch(image_batch).flatten()
print("The sigmoid function is applied on the model, it returns logits")
predictions = tf.nn.sigmoid(predictions)
predictions = tf.where(predictions < 0.5, 0, 1)
print('Predictions are:\n', predictions.numpy())
print('Labels are:\n', label_batch)

## Output

Evaluation and prediction
6/6 [==============================] - 3s 516ms/step - loss: 0.0276 - accuracy: 0.9844
Test accuracy is : 0.984375
The batch of image from test set is retrieved
The sigmoid function is applied on the model, it returns logits
Predictions are:
[1 1 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 1 0 0 1 1 1 0 1 0 1]
Labels are:
[1 1 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 1 0 0 1 1 1 0 1 0 1]

## Explanation

• The model can now be used to predict and evaluate the data.
• The prediction is done when an image is passed as input.
• The prediction has to be whether the image is a dog or a cat.