深度学习辅助工具tensorboard可视化实现训练过程的动态监视

1.首先，找到linux服务器中tensorflow-gpu的安装位置

pip show tensorflow-gpu
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Name: tensorflow-gpuVersion: 1.0.1Summary: TensorFlow helps the tensors flowHome-page: http://tensorflow.org/Author: Google Inc.Author-email: [e-mail]: Apache 2.0Location: /home/bids/.local/lib/python2.7/site-packagesRequires: mock, numpy, protobuf, wheel, six
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或者

pythonimport tensorflowtensorflow.__path__dir(tensorflow)

在找到tensorflow-gpu地址后，在改地址目录下寻找tensorboard/tensorboard.py路径。在后面对rensorboard的可视化中会用到该路径

2.在网络训练的代码中(train.py):

log_filepath ='/tmp/keras_log' model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.001), metrics=['accuracy']) tb_cb = keras.callbacks.TensorBoard(log_dir=log_filepath, write_images=1, histogram_freq=1)# 设置log的存储位置，将网络权值以图片格式保持在tensorboard中显示，设置每一个周期计算一次网络的#权值，每层输出值的分布直方图cbks = [tb_cb] history = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, callbacks=cbks, validation_data=(X_test, Y_test))

3.重新开启一个新的终端，输入命令，使得train_history可视化：

python /home/bids/.local/lib/python2.7/site-packages/tensorboard/tensorboard.py--logdir='/tmp/keras_log'

Starting TensorBoard 54athttp://bids:6006(Press CTRL+C to quit)

右键上面的网址打开链接即可。

转载参考来自blog：http://blog.csdn.net/jiandanjinxin/article/details/77155565