How to infer an image classification with pre-trained frozen tensorflow pb file

mobilenet_pb_inference.py

import tensorflow as tf
from tensorflow.python.platform import gfile
import numpy as np

from imagenet_classes import class_names
from scipy.misc import imread, imresize

dir_name = 'mobilenet_v1_1.0_224'
with tf.Graph().as_default() as graph:
	with tf.Session() as sess:
		with gfile.FastGFile(dir_name + "/mobilenet_v1_1.0_224_frozen.pb") as f:
			file = 'file1.jpg'
			input = imread(file, mode='RGB')
			input = imresize(input, (224, 224)).reshape(1, 224, 224, 3).astype(float)
			input/=127.5
			input-=1.
			
			graph_def = tf.GraphDef()
			graph_def.ParseFromString(f.read())
			sess.graph.as_default()

			tf.import_graph_def(graph_def, input_map=None, return_elements=None,
				name="", op_dict=None, producer_op_list=None)
			for op in graph.get_operations():
				print("Operation Name :" + op.name)
				print("Tensor Stats :" + str(op.values()))
			l_input = graph.get_tensor_by_name('input:0')
			intermediate = graph.get_tensor_by_name('MobilenetV1/MobilenetV1/Conv2d_0/Relu6:0')
			l_output = graph.get_tensor_by_name('MobilenetV1/Predictions/Reshape_1:0')
			tf.global_variables_initializer()
			inter_out = sess.run(intermediate, feed_dict = {l_input : input})
			print(inter_out)
			op_prob = sess.run(l_output, feed_dict = {l_input : input})
			preds = (np.argsort(op_prob[0])[::-1])[0:5]
			for p in preds:
			    print(class_names[p-1], op_prob[0][p])