Using Pretrained models for prediction

Using Pretrained models for prediction


So today, I will tell you how to use pre-trained models.

 

For your existing images, you must follow specific steps.

step one

load the model

step two,

load image to be predicted. Step three, resize it. We resize since models were trained, using a specific size of pictures, and our target images must be resized before prediction.

Step Four

convert to NumPy array. We are doing this because images can be all in different formats. This can raise specific issues. So converting it to a form that Python can understand is a good idea for all images. Now we can predict after prediction, and we have to use the code predictions and find out the top few predictions. Now show to the user

 

 Code:


from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
model = ResNet50(weights='imagenet')
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(preds, top=3)[0])
# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)]


 

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