code: import tensorflow as tf
  import numpy as np
  import os,glob,cv2
  import sys,argparse
  
  
  # First, pass the path of the image
  dir_path = os.path.dirname(os.path.realpath(__file__))
  image_path=sys.argv[1] 
  filename = dir_path +'/' +image_path
  image_size=128
  num_channels=3
  images = []
  # Reading the image using OpenCV
  image = cv2.imread(filename)
  # Resizing the image to our desired size and preprocessing will be done exactly as done during training
  image = cv2.resize(image, (image_size, image_size),0,0, cv2.INTER_LINEAR)
  images.append(image)
  images = np.array(images, dtype=np.uint8)
  images = images.astype('float32')
  images = np.multiply(images, 1.0/255.0) 
  #The input to the network is of shape [None image_size image_size num_channels]. Hence we reshape.
  x_batch = images.reshape(1, image_size,image_size,num_channels)
  
  ## Let us restore the saved model 
  sess = tf.Session()
  # Step-1: Recreate the network graph. At this step only graph is created.
  saver = tf.train.import_meta_graph('ore-mine-model.meta')
  # Step-2: Now let's load the weights saved using the restore method.
  saver.restore(sess, tf.train.latest_checkpoint('./'))
  
  # Accessing the default graph which we have restored
  graph = tf.get_default_graph()
  
  # Now, let's get hold of the op that we can be processed to get the output.
  # In the original network y_pred is the tensor that is the prediction of the network
  y_pred = graph.get_tensor_by_name("y_pred:0")
  
  ## Let's feed the images to the input placeholders
  x= graph.get_tensor_by_name("x:0") 
  y_true = graph.get_tensor_by_name("y_true:0") 
  y_test_images = np.zeros((1, len(os.listdir('training_data')))) 
  
  
  ### Creating the feed_dict that is required to be fed to calculate y_pred 
  feed_dict_testing = {x: x_batch, y_true: y_test_images}
  result=sess.run(y_pred, feed_dict=feed_dict_testing)
  # result is of this format [probabiliy_of_rose probability_of_sunflower]
  print(result)
  
apis: numpy.multiply tensorflow.train.latest_checkpoint tensorflow.get_default_graph tensorflow.Session numpy.array tensorflow.train.import_meta_graph