TensorBoard: Embedding Visualization
In machine learning, to improve something you often need to be able to measure it. TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more. This quickstart will show how to quickly
TensorFlowとTensorBoardでニューラルネットワークを可視化 - Qiita
$ python fully_connected_feed.py Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. Extracting data/train-images-idx3-ubyte.gz Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. Extracting data/train-labels-idx1-ubyte.gz Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. Extracting data/t10k-images-idx3-ubyte.gz Succesfully downloaded t10k-labels-idx1-ubyte.g
TensorBoardでデータフローを可視化する - Qiita
-supervisor_labels_placeholder = tf.placeholder("float", [None, 3], name="supervisor_labels_placeholder") -input_placeholder = tf.placeholder("float", [None, 3], name="input_labels_placeholder") -feed_dict={input_placeholder: input, supervisor_labels_placeholder: winning_hands} -with tf.Session() as sess: - summary_writer = tf.train.SummaryWriter('data', graph_def=sess.graph_def) +with tf.Graph().
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