Restoring a model trained with tf.estimator and feeding input through feed_dict

Question

I trained a resnet with tf.estimator, the model was saved during the training process. The saved files consist of .data, .index and .meta

Answer 1

If you have model pb or pb.txt then inference is easy. Using predictor module, we can do an inference. Check out here for more information. For image data it will be something to similar to below example. Hope this helps !!

Example code:

import numpy as np
import matplotlib.pyplot as plt

def extract_data(index=0, filepath='data/cifar-10-batches-bin/data_batch_5.bin'):
    bytestream = open(filepath, mode='rb')
    label_bytes_length = 1
    image_bytes_length = (32 ** 2) * 3
    record_bytes_length = label_bytes_length + image_bytes_length
    bytestream.seek(record_bytes_length * index, 0)
    label_bytes = bytestream.read(label_bytes_length)
    image_bytes = bytestream.read(image_bytes_length)
    label = np.frombuffer(label_bytes, dtype=np.uint8)  
    image = np.frombuffer(image_bytes, dtype=np.uint8)
    image = np.reshape(image, [3, 32, 32])
    image = np.transpose(image, [1, 2, 0])
    image = image.astype(np.float32)
   result = {
     'image': image,
     'label': label,
   }
   bytestream.close()
   return result


    predictor_fn = tf.contrib.predictor.from_saved_model(
  export_dir = saved_model_dir, signature_def_key='predictions')
    N = 1000
    labels = []
    images = []
    for i in range(N):
       result = extract_data(i)
       images.append(result['image'])
       labels.append(result['label'][0])
    output = predictor_fn(
      {
        'images': images,
      }
    )