{"action":"created","issue":{"url":"https://api.github.com/repos/samjabrahams/tensorflow-on-raspberry-pi/issues/14","repository_url":"https://api.github.com/repos/samjabrahams/tensorflow-on-raspberry-pi","labels_url":"https://api.github.com/repos/samjabrahams/tensorflow-on-raspberry-pi/issues/14/labels{/name}","comments_url":"https://api.github.com/repos/samjabrahams/tensorflow-on-raspberry-pi/issues/14/comments","events_url":"https://api.github.com/repos/samjabrahams/tensorflow-on-raspberry-pi/issues/14/events","html_url":"https://github.com/samjabrahams/tensorflow-on-raspberry-pi/issues/14","id":154573768,"number":14,"title":"Inception-v3 works, AlexNet causes OOM","user":{"login":"ArnoXf","id":19186660,"avatar_url":"https://avatars.githubusercontent.com/u/19186660?v=3","gravatar_id":"","url":"https://api.github.com/users/ArnoXf","html_url":"https://github.com/ArnoXf","followers_url":"https://api.github.com/users/ArnoXf/followers","following_url":"https://api.github.com/users/ArnoXf/following{/other_user}","gists_url":"https://api.github.com/users/ArnoXf/gists{/gist_id}","starred_url":"https://api.github.com/users/ArnoXf/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ArnoXf/subscriptions","organizations_url":"https://api.github.com/users/ArnoXf/orgs","repos_url":"https://api.github.com/users/ArnoXf/repos","events_url":"https://api.github.com/users/ArnoXf/events{/privacy}","received_events_url":"https://api.github.com/users/ArnoXf/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":5,"created_at":"2016-05-12T20:28:24Z","updated_at":"2016-06-10T19:46:07Z","closed_at":null,"body":"Hi! First of all, many thanks for your work! Installation with pip on Pi 3 with Jessie worked without any issues. I first tried the Inception-v3 classification you provided and it worked very well. Now I am trying to get AlexNet working on the Oxford 17 flowers dataset. I have the following configuration:\r\n\r\n    input_layer = input_data(shape=[None, 224, 224, 3])\r\n\r\n    conv1 = conv_2d(input_layer, 96, 11, strides=4, activation='relu')\r\n    pool1 = max_pool_2d(conv1, 3, strides=2)\r\n    network = local_response_normalization(pool1)\r\n\r\n    conv2 = conv_2d(network, 256, 5, activation='relu')\r\n    pool2 = max_pool_2d(conv2, 3, strides=2)\r\n    network = local_response_normalization(pool2)\r\n\r\n    conv3 = conv_2d(network, 384, 3, activation='relu')\r\n    conv4 = conv_2d(conv3, 384, 3, activation='relu')\r\n    conv5 = conv_2d(conv4, 256, 3, activation='relu')\r\n    pool3 = max_pool_2d(conv5, 3, strides=2)\r\n    network = local_response_normalization(pool3)\r\n\r\n    fc1 = fully_connected(network, 4096, activation='tanh')\r\n    dropout1 = dropout(fc1, 0.5)\r\n    fc2 = fully_connected(dropout1, 4096, activation='tanh')\r\n    dropout2 = dropout(fc2, 0.5)\r\n    fc3 = fully_connected(dropout2, 2, activation='softmax')\r\n    network = regression(fc3, optimizer='momentum', loss='categorical_crossentropy',\r\n                         learning_rate=0.01)\r\n\r\nThis was written using the TFLearn API, but I think it gives a good overview over the layers and configuration. This code is working on my desktop computer but fails with OOM error in the fully connected layers on the Pi. Reducing the fc layers doesn't give OOM error with 1024 instead of 4096. Unfortunately it is still not training but just quitting after building up the network.\r\n\r\nAny ideas how to solve this? Isn't the loaded Inception graph bigger than AlexNet?"},"comment":{"url":"https://api.github.com/repos/samjabrahams/tensorflow-on-raspberry-pi/issues/comments/225277975","html_url":"https://github.com/samjabrahams/tensorflow-on-raspberry-pi/issues/14#issuecomment-225277975","issue_url":"https://api.github.com/repos/samjabrahams/tensorflow-on-raspberry-pi/issues/14","id":225277975,"user":{"login":"mrubashkin-svds","id":17418749,"avatar_url":"https://avatars.githubusercontent.com/u/17418749?v=3","gravatar_id":"","url":"https://api.github.com/users/mrubashkin-svds","html_url":"https://github.com/mrubashkin-svds","followers_url":"https://api.github.com/users/mrubashkin-svds/followers","following_url":"https://api.github.com/users/mrubashkin-svds/following{/other_user}","gists_url":"https://api.github.com/users/mrubashkin-svds/gists{/gist_id}","starred_url":"https://api.github.com/users/mrubashkin-svds/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mrubashkin-svds/subscriptions","organizations_url":"https://api.github.com/users/mrubashkin-svds/orgs","repos_url":"https://api.github.com/users/mrubashkin-svds/repos","events_url":"https://api.github.com/users/mrubashkin-svds/events{/privacy}","received_events_url":"https://api.github.com/users/mrubashkin-svds/received_events","type":"User","site_admin":false},"created_at":"2016-06-10T19:46:07Z","updated_at":"2016-06-10T19:46:07Z","body":"Hey Sam, Thanks for answering @ArnoXf 's questions. Have you been able to successfully train any part or whole model on the Pi3? If not, do you know of any other places where things like \"learning_rate\" need to be turned off to avoid building model errors?"}}
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