title: Load checkpoint and evaluate single image with tensorflow DNN
body: <p>For research at university I am examining the oxford 17 flowers alexnet example. The example uses the API tflearn based on tensorflow. Training is working very well on my GPU, reaching an accuracy of ~ 97% after a while.</p>
  
  <p>Unfortunately evaluating single images isn't working yet in tflearn, I would have to use model.predict(...) to predict all my data per batch, and loop over all my test set and calculate accuracy by myself. </p>
  
  <p>My training code so far:</p>
  
  <pre><code>...
  import image_loader
  X, Y = image_loader.load_data(one_hot=True, shuffle=False)
  
  X = X.reshape(244,244)
  
  # Build network
  network = input_data(shape=[None, 224, 224, 3])
  
  network = conv_2d(network, 96, 11, strides=4, activation='relu')
  network = max_pool_2d(network, 3, strides=2)
  network = local_response_normalization(network)
  
  network = conv_2d(network, 256, 5, activation='relu')
  network = max_pool_2d(network, 3, strides=2)
  network = local_response_normalization(network)
  
  network = conv_2d(network, 384, 3, activation='relu')
  network = conv_2d(network, 384, 3, activation='relu')
  network = conv_2d(network, 256, 3, activation='relu')
  network = max_pool_2d(network, 3, strides=2)
  network = local_response_normalization(network)
  
  network = fully_connected(network, 4096, activation='tanh')
  network = dropout(network, 0.5)
  
  network = fully_connected(network, 4096, activation='tanh')
  network = dropout(network, 0.5)
  
  network = fully_connected(network, 17, activation='softmax')
  network = regression(network, optimizer='momentum',
                   loss='categorical_crossentropy',
                   learning_rate=0.01)
  
  # Training
  model = tflearn.DNN(network, checkpoint_path='model_ba',
                  max_checkpoints=1, tensorboard_verbose=0)
  model.fit(X, Y, n_epoch=3, validation_set=0.1, shuffle=True,
        show_metric=True, batch_size=32, snapshot_step=400,
        snapshot_epoch=False, run_id='ba_soccer_network')
  </code></pre>
  
  <p>The code is saving a checkpoint "model_ba" and also the network in form of a .meta-file.
  Is there a possibility of loading that saved checkpoint and evaluate a single image with tensorflow?</p>
  
  <p>Thanks in advance,
  Arno</p>