title: how to use my own trained model with facenet implemented in tensorflow?
body: <p>I train the model with the shell command:</p>
  
  <pre><code>python src/facenet_train.py \
             --batch_size 15 \
             --gpu_memory_fraction 0.25 \
             --models_base_dir trained_model_2017_05_15_10_24 \
             --pretrained_model trained_model_2017_05_15_10_24/20170515-121856/model-20170515-121856.ckpt-182784 \
             --model_def models.nn2 \
             --logs_base_dir logs \
             --data_dir /data/user_set/training/2017_05_15_10_24 \
             --lfw_pairs /data/user_set/lfw_pairs.txt \
             --image_size 224 \
             --lfw_dir /data/user_set/lfw \
             --optimizer ADAM \
             --max_nrof_epochs 1000 \
             --learning_rate 0.00001
  </code></pre>
  
  <p>but i get error infomation like this when use my own trained model:</p>
  
  <blockquote>
    <p>2017-05-17 14:23:05.448285: W
    tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
    library wasn't compiled to use SSE4.1 instructions, but these are
    available on your machine and could speed up CPU computations.
        2017-05-17 14:23:05.448318: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
    library wasn't compiled to use SSE4.2 instructions, but these are
    available on your machine and could speed up CPU computations.
        2017-05-17 14:23:05.448324: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
    library wasn't compiled to use AVX instructions, but these are
    available on your machine and could speed up CPU computations.
        2017-05-17 14:23:05.448329: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
    library wasn't compiled to use AVX2 instructions, but these are
    available on your machine and could speed up CPU computations.
        2017-05-17 14:23:05.448334: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
    library wasn't compiled to use FMA instructions, but these are
    available on your machine and could speed up CPU computations.
        2017-05-17 14:23:05.674872: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0
    with properties:
        name: Quadro M4000
        major: 5 minor: 2 memoryClockRate (GHz) 0.7725
        pciBusID 0000:03:00.0
        Total memory: 7.93GiB
        Free memory: 2.89GiB
        2017-05-17 14:23:05.674917: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
        2017-05-17 14:23:05.674935: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y
        2017-05-17 14:23:05.674957: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating
    TensorFlow device (/gpu:0) -> (device: 0, name: Quadro M4000, pci bus
    id: 0000:03:00.0)
        Traceback (most recent call last):
          File "forward.py", line 21, in 
            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
          File "/home/chen/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2563, in get_tensor_by_name
            return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
          File "/home/chen/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2414, in as_graph_element
            return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
          File "/home/chen/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2456, in _as_graph_element_locked
            "graph." % (repr(name), repr(op_name)))
        KeyError: "The name 'input:0' refers to a Tensor which does not exist. The operation, 'input', does not exist in the graph."</p>
  </blockquote>
  
  <p>get feature code:</p>
  
  <pre><code>import tensorflow as tf
  import facenet
  w_MODEL_PATH_='/home/chen/demo_dir/facenet_tensorflow_train/trained_model_2017_05_15_10_24/20170515-121856'
  
  with tf.Graph().as_default():
      with tf.Session() as sess:
          # load the model
          meta_file, ckpt_file = facenet.get_model_filenames(w_MODEL_PATH_)
          facenet.load_model(w_MODEL_PATH_, meta_file, ckpt_file)
          # print("model_path:", w_MODEL_PATH_,"meta_file:", meta_file,"ckpt_file:", ckpt_file)
  
          # Get input and output tensors
          # ops = tf.get_default_graph().get_operations()
          #
          # print(ops)
  
          images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
          embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
          phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
  
          image_size = images_placeholder.get_shape()[1]
          embedding_size = embeddings.get_shape()[1]
  
          # print(image_size)
  
          paths = ['one.png', 'two.png']
  
          # Run forward pass to calculate embeddings
          images = facenet.load_data(paths, do_random_crop=False, do_random_flip=False, image_size=image_size,
                                     do_prewhiten=True)
          # print("images:", idx, images)
          feed_dict = {images_placeholder: images, phase_train_placeholder: False}
          # print(idx,"embeddings:", embeddings)
          emb_array = sess.run(embeddings, feed_dict=feed_dict)
          # print(idx, "emb_array:", emb_array)
          print(emb_array)
  </code></pre>
  
  <p>I don't know how to use my own trained model, please help.</p>