8.2 Testing Alexnet Model

 Program:


import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
#from tensorflow.keras.applications.resnet50 import preprocess_input
import matplotlib.pyplot as plt

# Load the saved model
model = load_model('/content/drive/MyDrive/8/alexnet_cifar10.h5')
d={0:"Airplane",1:"Automobile",2:"Bird",3:"Cat",4:"Deer",5:"Dog",6:"Frog",
   7:"Horse",8:"Ship",9:"Truck"}
# Load and preprocess an image from a local path for prediction
local_image_path = '/content/drive/MyDrive/8/a.jpg'  # Replace with the path to your local image
local_image = load_img(local_image_path, target_size=(32, 32))
local_image1 = load_img(local_image_path)
local_image_array = img_to_array(local_image)
local_image_array = np.expand_dims(local_image_array, axis=0)
local_image_array /= 255.0  # Apply simple normalization, not preprocess_input

# Make a prediction for the local image
local_predictions = model.predict(local_image_array)
local_predicted_class = np.argmax(local_predictions[0])

# Display the image and prediction
plt.imshow(local_image)
plt.title(f"Predicted Class: {d[local_predicted_class]}")
plt.axis('off')
plt.show()

plt.imshow(local_image1, interpolation='nearest')
plt.title(f"Predicted Class: {d[local_predicted_class]}")
plt.axis('off')
plt.show()


link to images to predict:

https://drive.google.com/drive/folders/1S34ART1LbDlOJsEwxogRxsLCtq_BVAhc?usp=sharing


The CIFAR-10 dataset consists of 10 classes of images. Here is the list of classes in the CIFAR-10 dataset:

  1. Airplane
  2. Automobile
  3. Bird
  4. Cat
  5. Deer
  6. Dog
  7. Frog
  8. Horse
  9. Ship
  10. Truck
So one should test only these Images

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