title: Running trained tensorflow model in C++
body: <p>I have trained an image classification network in python using tensorflow. The trained model was saved as a <code>.pb</code>. Now, I want to test the model, I need this to be done in C++. </p>
  
  <p>I had used <code>numpy</code> in manipulating and handling data. During training phase the image is passed in as a numpy array. The image is stretched out as a 1D array and the class label is prepended to this array. </p>
  
  <p>I'm confused as to how to pass the image data while running the model in C++, where <code>numpy</code> isn't available to me. I use <code>numpy</code> operations to manipulate and handle the data. In what format should I pass in the data if I have to execute it in C++.</p>
  
  <p>Below is how I train and save my model</p>
  
  <pre><code>def trainModel(data):
      global_step = tf.Variable(0, name='global_step', trainable=False)
      X, y,keep_prob = modelInputs((741, 620, 1),4)
      logits = cnnModel(X,keep_prob)
      cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y), name="cost")
      optimizer = tf.train.AdamOptimizer(.0001, name='Adam').minimize(cost)
      prediction = tf.argmax(logits, 1, name="prediction")
      correct_pred = tf.equal(prediction, tf.argmax(y, 1), name="correct_pred")
      accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
      with tf.Session() as sess:
          sess.run(tf.global_variables_initializer())
          saver = tf.train.Saver()
          batch_size = 30
          for e in range(11):
              batch_x, batch_y = data.next_batch(batch_size)
              batch_y = batch_y.astype('int32')
              x = np.reshape(batch_x, [batch_size, 741, 620, 1])
              labels = np.zeros(shape=(batch_size,4))
              labels[np.arange(len(labels)),batch_y]=1
              sess.run(optimizer, feed_dict={X: x, y: labels,keep_prob:0.5})
              global_step.assign(e).eval()
          saver.save(sess, './my_test_model',global_step=global_step)
  </code></pre>
  
  <p>*741x620 is the size of the image</p>