TensorFlowで 趣味の画像収集サーバーを作る 4月号

TensorFlow勉強会（３）の発表資料です。

Transcript

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3. C-LIS CO., LTD.  IUUQGVLVZVLJOFUQPTU ;͘Ώ͖ϒϩά AlphaGoͷαʔόʔྉۚ͸࠷௿60ԯԁ!!? #dltfb
4. C-LIS CO., LTD.  IUUQGVLVZVLJOFUQPTU ;͘Ώ͖ϒϩά AlphaGoͷαʔόʔྉۚ͸࠷௿60ԯԁ!!? #dltfb
5. C-LIS CO., LTD. #dltfb
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23. C-LIS CO., LTD. # local3 with tf.variable_scope('local3') as scope: # Move everything into depth so we can perform a single matrix multiply. dim = 1 for d in pool2.get_shape()[1:].as_list(): dim *= d reshape = tf.reshape(pool2, [FLAGS.batch_size, dim]) weights = _variable_with_weight_decay('weights', shape=[dim, 384], stddev=0.04, wd=0.004) biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1)) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) _activation_summary(local3) # local4 with tf.variable_scope('local4') as scope: weights = _variable_with_weight_decay('weights', shape=[384, 192], stddev=0.04, wd=0.004) biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1)) local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name) _activation_summary(local4)  DJGBSQZ
24. C-LIS CO., LTD. 
25. C-LIS CO., LTD. 
26. C-LIS CO., LTD.  "This kind of layer is just like a convolutional layer, but without any weight-sharing. That is to say, a different set of filters is applied at every (x, y) location in the input image. Aside from that, it behaves exactly as a convolutional layer." IUUQTDPEFHPPHMFDPNQDVEBDPOWOFUXJLJ-BZFS1BSBNT-PDBMMZ DPOOFDUFE@MBZFS@XJUI@VOTIBSFE@XFJHIUT
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28. C-LIS CO., LTD. ંΓ৞Έ૚Λ૿΍ͯ͠Έͨ
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    ύϥϝʔλʔΛௐ੔ # conv0
 with tf.variable_scope('conv0') as scope:
 kernel = _variable_with_weight_decay('weights', shape=[32, 32, 3, 32],
 stddev=1e-4, wd=0.0)
 conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
 biases = _variable_on_cpu('biases', [32], tf.constant_initializer(0.0))
 bias = tf.nn.bias_add(conv, biases)
 conv0 = tf.nn.relu(bias, name=scope.name)
 _activation_summary(conv0)
 
 # pool0
 pool0 = tf.nn.max_pool(conv0, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
 padding='SAME', name='pool0')
 # norm0
 norm0 = tf.nn.lrn(pool0, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,
 name='norm0')
 
 # conv1
 with tf.variable_scope('conv1') as scope:
 kernel = _variable_with_weight_decay('weights', shape=[16, 16, 32, 64],
 stddev=1e-4, wd=0.0)
 conv = tf.nn.conv2d(norm0, kernel, [1, 1, 1, 1], padding='SAME')
 biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
 bias = tf.nn.bias_add(conv, biases)
 conv1 = tf.nn.relu(bias, name=scope.name)
 _activation_summary(conv1)
29. C-LIS CO., LTD.
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38. C-LIS CO., LTD. ໰୊ൃੜ ΦϑΟεͷαʔόʔϚγϯͷ(16͕࢖͑ͳ͘ͳΔ  $ python3 megane_co/cifar10_train.py I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally Filling queue with 400 CIFAR images before starting to train. This will take a few minutes. E tensorflow/stream_executor/cuda/cuda_driver.cc:481] failed call to cuInit: CUDA_ERROR_NO_DEVICE
39. C-LIS CO., LTD. ଎౓ͷൺֱ  $16
    2016-04-13 10:40:51.188290: step 0, loss = 11.15 (65.8 examples/sec; 1.946 sec/batch) 2016-04-13 10:41:10.546296: step 10, loss = 10.67 (74.0 examples/sec; 1.731 sec/batch) 2016-04-13 10:41:29.646204: step 20, loss = 10.29 (74.0 examples/sec; 1.729 sec/batch) (16
    2016-03-06 00:42:54.454566: step 90, loss = 10.39 (286.9 examples/sec; 0.446 sec/batch) 2016-03-06 00:42:59.533350: step 100, loss = 10.32 (290.1 examples/sec; 0.441 sec/batch) 2016-03-06 00:43:04.834940: step 110, loss = 10.27 (292.9 examples/sec; 0.437 sec/batch)
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