ResNet50.py
def ResNet50_predict_labels(self, img_path):
        """
        Use the model to extract the predictions and get the highest predicted object classes
        Parameters:
            img_path: a string-valued file path to an image
        Return:
            an integer corresponding to the model's predicted object class
        """
        # returns prediction vector for image located at img_path
        img = preprocess_input(self.path_to_tensor(img_path))
        return np.argmax(ResNet50_model.predict(img))

        Use the model to extract the predictions and get the highest predicted object classes
        Parameters:
            img_path: a string-valued file path to an image
        Return:
            an integer corresponding to the model's predicted object class
        
Use the model to extract the predictions and get the highest predicted object classes
Use the model to extract the predictions and get the highest predicted object classes
def ResNet50_predict_labels(self, img_path):
        img = preprocess_input(self.path_to_tensor(img_path))
        return np.argmax(ResNet50_model.predict(img))
def ResNet50_predict_labels ( self , img_path ) : img = preprocess_input ( self . path_to_tensor ( img_path ) ) return np . argmax ( ResNet50_model . predict ( img ) )
Use the model to extract the predictions and get the highest predicted object classes
Use the model to extract the predictions and get the highest predicted object classes
"""
        Use the model to extract the predictions and get the highest predicted object classes
        Parameters:
            img_path: a string-valued file path to an image
        Return:
            an integer corresponding to the model's predicted object class
        """ # returns prediction vector for image located at img_path
self
img_path
a string-valued file path to an image
a string - valued file path to an image