VGG16 CNN Model Architecture | Transfer Learning

# VGG16
 
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import decode_predictions
from keras.applications.vgg16 import preprocess_input
 
from keras.preprocessing import image
import numpy as np
import matplotlib.pyplot as plt
import os
from os import listdir
from PIL import Image as PImage
 
img_width, img_height = 224, 224
 
model_pretrained = VGG16(weights='imagenet', 
                      include_top=True, 
                      input_shape=(img_height, img_width, 3))
 
# Insert correct path of your image below
img_path = '/content/drive/My Drive/My ML Project /DL Project/Transfer Learning/images/lemon.png'
img = image.load_img(img_path, target_size=(img_width, img_height))
img_data = image.img_to_array(img)
img_data = np.expand_dims(img_data, axis=0)
img_data = preprocess_input(img_data)
 
#predict the result
cnn_feature = model_pretrained.predict(img_data,verbose=0)
# decode the results into a list of tuples (class, description, probability)
label = decode_predictions(cnn_feature)
label = label[0][0]
 
 
plt.imshow(img)
 
stringprint ="%.1f" % round(label[2]*100,1)
plt.title(label[1] + " " + str(stringprint) + "%", fontsize=20)
plt.axis('off')
plt.show()
 
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(cnn_feature, top=3)[0])
 
label
 
# Insert correct path of your image below
img_path = '/content/drive/My Drive/My ML Project /DL Project/Transfer Learning/images/flowers-5463475_640.jpg'
img = image.load_img(img_path, target_size=(img_width, img_height))
img_data = image.img_to_array(img)
img_data = np.expand_dims(img_data, axis=0)
img_data = preprocess_input(img_data)
 
#predict the result
cnn_feature = model_pretrained.predict(img_data,verbose=0)
# decode the results into a list of tuples (class, description, probability)
label = decode_predictions(cnn_feature)
label = label[0][0]
 
 
plt.imshow(img)
 
stringprint ="%.1f" % round(label[2]*100,1)
plt.title(label[1] + " " + str(stringprint) + "%", fontsize=20)
plt.axis('off')
plt.show()
 
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(cnn_feature, top=3)[0])
 
# Insert correct path of your image folder below
 
folder_path = '/content/drive/My Drive/My ML Project /DL Project/Transfer Learning/images/'
images = os.listdir(folder_path)
fig = plt.figure(figsize=(16,20))
i=0
rows=4
columns=3
 
for image1 in images:
    i+=1
    img = image.load_img(folder_path+image1, target_size=(img_width, img_height))
    img_data = image.img_to_array(img)
    img_data = np.expand_dims(img_data, axis=0)
    img_data = preprocess_input(img_data)
 
    cnn_feature = model_pretrained.predict(img_data,verbose=0)
    label = decode_predictions(cnn_feature)
    label = label[0][0]
     
    fig.add_subplot(rows,columns,i)
    fig.subplots_adjust(hspace=.5)
 
    plt.imshow(img)
    stringprint ="%.1f" % round(label[2]*100,1)
    plt.title(label[1] + " " + str(stringprint) + "%", fontsize=20)
    plt.axis('off')
plt.show()