Deploy ML on mobile, microcontrollers and other edge devices
$ 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
-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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