【TensorFlow学习笔记】get_tensor_by_name

tf.get_default_graph().get_tensor_by_name("<name>:0")

1、基础知识

# 创建图
a = tf.constant([[1.0, 2.0], [3.0, 4.0]], name="a")
b = tf.constant([[1.0, 1.0], [0.0, 1.0]], name="b")
c = tf.matmul(a, b, name='example')
with tf.Session() as sess:
    print(c.name)
    # example:0
    # <name>:0 (0 refers to endpoint which is somewhat redundant)
    # 形如'conv1'是节点名称，而'conv1:0'是张量名称，表示节点的第一个输出张量
    tensor = tf.get_default_graph().get_tensor_by_name("example:0")
    print(tensor)
    # Tensor("example:0", shape=(2, 2), dtype=float32)

    all_tensor = tf.get_default_graph().as_graph_def().node
    print(all_tensor)

2、应用：

def load_model(model):
    # Check if the model is a model directory (containing a metagraph and a checkpoint file)
    # or if it is a protobuf file with a frozen graph
    model_exp = os.path.expanduser(model)
    if (os.path.isfile(model_exp)):
        print('Model filename: %s' % model_exp)
        with tf.gfile.FastGFile(model_exp, 'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            tf.import_graph_def(graph_def, name='')
    else:
        ckpt = tf.train.get_checkpoint_state(model_exp)
        if ckpt and ckpt.model_checkpoint_path:
        # 也可以使用 tf.train.import_meta_graph()
            saver.restore(sess, ckpt.model_checkpoint_path)

with tf.Graph().as_default():

        with tf.Session() as sess:      
            # Load the model
    		load_model(model_file)
            # Get input and output tensors
            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")

            # Run forward pass to calculate embeddings
            feed_dict = { images_placeholder: images, phase_train_placeholder:False }
            emb = sess.run(embeddings, feed_dict=feed_dict)

最后

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