predict code.txt

from keras.models import load_model
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np

# (1)PREDICTION USING VGG16

model = load_model('C:\\Users\\MCHOME\\Desktop\\PBL B5\\PBL_VGG16\\model_vgg16.h5')

img=image.load_img('E:\\chest_xray\\val\\NORMAL\\NORMAL2-IM-1436-0001.jpeg',target_size=(224,224))

x=image.img_to_array(img)
x=np.expand_dims(x,axis=0)
img_data=preprocess_input(x)
classes=model.predict(img_data)


# (2)PREDICTION USING VGG19

model = load_model('C:\\Users\\MCHOME\\Desktop\\PBL B5\\PBL_VGG19\\model_vgg19.h5')

img=image.load_img('E:\\chest_xray\\val\\NORMAL\\NORMAL2-IM-1436-0001.jpeg',target_size=(224,224))

x=image.img_to_array(img)
x=np.expand_dims(x,axis=0)
img_data=preprocess_input(x)
classes=model.predict(img_data)


# (3)PREDICTION USING iNCEPTION v3

model = load_model('C:\\Users\\MCHOME\\Desktop\\PBL B5\\PBL_InceptionV3\\model_inceptionV3.h5')

img=image.load_img('E:\\chest_xray\\val\\NORMAL\\NORMAL2-IM-1436-0001.jpeg',target_size=(224,224))

x=image.img_to_array(img)
x=np.expand_dims(x,axis=0)
img_data=preprocess_input(x)
classes=model.predict(img_data)


# (4)PREDICTION USING RESNET50

model = load_model('C:\\Users\\MCHOME\\Desktop\\PBL B5\\PBL_Resnet50\\model_resnet50.h5')

img=image.load_img('E:\\chest_xray\\val\\NORMAL\\NORMAL2-IM-1436-0001.jpeg',target_size=(224,224))

x=image.img_to_array(img)
x=np.expand_dims(x,axis=0)
img_data=preprocess_input(x)
classes=model.predict(img_data)


# (5)PREDICTION USING DENSENET121

model = load_model('C:\\Users\\MCHOME\\Desktop\\PBL B5\\PBL_DenseNet121\\model_densenet121.h5')

img=image.load_img('E:\\chest_xray\\val\\NORMAL\\NORMAL2-IM-1436-0001.jpeg',target_size=(224,224))

x=image.img_to_array(img)
x=np.expand_dims(x,axis=0)
img_data=preprocess_input(x)
classes=model.predict(img_data)