BaseModel.py 文件源码

def show_embedding(self,name,save_model="model.ckpt",meta_path='metadata.tsv'):
        self._build()
        self._write_meta()
        from tensorflow.contrib.tensorboard.plugins import projector
        # Use the same LOG_DIR where you stored your checkpoint.
        with tf.Session() as sess:
            self.sess = sess
            sess.run(tf.global_variables_initializer())
            sess.run(tf.local_variables_initializer())
            summary_writer = tf.summary.FileWriter(self.flags.log_path, sess.graph)
            saver = tf.train.Saver()
            saver.save(sess, os.path.join(self.flags.log_path, save_model), 0)
        # Format: tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto
        config = projector.ProjectorConfig()
        # You can add multiple embeddings. Here we add only one.
        embedding = config.embeddings.add()
        embedding.tensor_name = name
        # Link this tensor to its metadata file (e.g. labels).
        embedding.metadata_path = os.path.join(self.flags.log_path, meta_path)
        # Saves a configuration file that TensorBoard will read during startup.
        projector.visualize_embeddings(summary_writer, config)