def get_classes(file_path):
model = myModel(weights="imagenet")
img = image.load_img(file_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.array([x])
x = preprocess_input(x)
preds = model.predict(x)
predictions = decode_predictions(preds, top=3)[0]
return predictions
def get_classes(file_path):
# Create an instance of 'myModel' imported above
model = myModel(weights="imagenet")
# Load image and preprocess it
img = image.load_img(file_path, target_size=(224, 224))
x = image.img_to_array(img)
x= np.array([x])
x = preprocess_input(x)
# This is the inference time. Given an instance, it produces the predictions.
preds = model.predict(x)
predictions = decode_predictions(preds, top=3)[0]
return predictions
from tensorflow.keras.applications.resnet50 import ResNet50 as myModel
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
model = myModel(weights="imagenet")
def get_classes(file_path):
img = image.load_img(file_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.array([x])
x = preprocess_input(x)
preds = model.predict(x)
predictions = decode_predictions(preds, top=3)[0]
print(predictions)
return predictions
if __name__ == "__main__":
name = '/cxldata/projects/image-class/dog.png'
get_classes(name)