Luonic
/ my_net_log.txt
Last active
November 12, 2016 21:12
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| I1112 07:17:41.890250 17193 solver.cpp:337] Iteration 0, Testing net (#0) | |
| I1112 07:17:41.896697 17193 net.cpp:693] Ignoring source layer drop1 | |
| I1112 07:17:41.901717 17193 net.cpp:693] Ignoring source layer prob | |
| I1112 07:17:49.161095 17193 solver.cpp:404] Test net output #0: accuracy = 0.02056 | |
| I1112 07:17:49.283001 17193 solver.cpp:228] Iteration 0, loss = 5.03651 | |
| I1112 07:17:49.283051 17193 solver.cpp:244] Train net output #0: loss = 5.03651 (* 1 = 5.03651 loss) | |
| I1112 07:17:49.283078 17193 sgd_solver.cpp:106] Iteration 0, lr = 0.01 | |
| I1112 07:19:59.706967 17193 solver.cpp:228] Iteration 1000, loss = 1.17764 | |
| I1112 07:19:59.707039 17193 solver.cpp:244] Train net output #0: loss = 1.17765 (* 1 = 1.17765 loss) | |
| I1112 07:19:59.707053 17193 sgd_solver.cpp:106] Iteration 1000, lr = 0.000187328 |
Luonic
/ orig_net_log.txt
Created
November 12, 2016 21:12
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| I1112 18:26:20.476073 22658 solver.cpp:337] Iteration 0, Testing net (#0) | |
| I1112 18:26:20.486620 22658 net.cpp:693] Ignoring source layer prob | |
| I1112 18:26:22.844864 22658 solver.cpp:404] Test net output #0: accuracy = 0.02416 | |
| I1112 18:26:22.862859 22658 solver.cpp:228] Iteration 0, loss = 38.3147 | |
| I1112 18:26:22.862937 22658 solver.cpp:244] Train net output #0: loss = 38.3147 (* 1 = 38.3147 loss) | |
| I1112 18:26:22.862980 22658 sgd_solver.cpp:106] Iteration 0, lr = 0.01 | |
| I1112 18:27:25.690100 22658 solver.cpp:228] Iteration 1000, loss = 4.31486 | |
| I1112 18:27:25.690229 22658 solver.cpp:244] Train net output #0: loss = 4.31486 (* 1 = 4.31486 loss) | |
| I1112 18:27:25.690245 22658 sgd_solver.cpp:106] Iteration 1000, lr = 0.01 | |
| I1112 18:28:28.546875 22658 solver.cpp:228] Iteration 2000, loss = 4.29191 |
Luonic
/ log_12312.txt
Created
November 13, 2016 14:48
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| I1113 13:59:14.466996 15355 solver.cpp:337] Iteration 0, Testing net (#0) | |
| I1113 13:59:14.481719 15355 net.cpp:693] Ignoring source layer prob | |
| I1113 13:59:44.028913 15355 solver.cpp:404] Test net output #0: accuracy = 0.013875 | |
| I1113 13:59:44.168041 15355 solver.cpp:228] Iteration 0, loss = 4.52858 | |
| I1113 13:59:44.168081 15355 solver.cpp:244] Train net output #0: loss = 4.52858 (* 1 = 4.52858 loss) | |
| I1113 13:59:44.168093 15355 sgd_solver.cpp:106] Iteration 0, lr = 0.001 | |
| I1113 14:03:18.589840 15355 solver.cpp:228] Iteration 1000, loss = 0.235305 | |
| I1113 14:03:18.589912 15355 solver.cpp:244] Train net output #0: loss = 0.235305 (* 1 = 0.235305 loss) | |
| I1113 14:03:18.589920 15355 sgd_solver.cpp:106] Iteration 1000, lr = 0.001 | |
| I1113 14:06:52.377185 15355 solver.cpp:228] Iteration 2000, loss = 0.0354668 |
Luonic
/ caffe_log.txt
Last active
March 20, 2017 14:32
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| I0320 14:39:22.460748 8660 solver.cpp:219] Iteration 0 (-6.57593e+33 iter/s, 0.322088s/250 iters), loss = 0.901091 | |
| I0320 14:39:22.460778 8660 solver.cpp:238] Train net output #0: loss1 = 0.693134 (* 0.3 = 0.20794 loss) | |
| I0320 14:39:22.460798 8660 solver.cpp:238] Train net output #1: loss2 = 0.693151 (* 1 = 0.693151 loss) | |
| I0320 14:39:22.460814 8660 sgd_solver.cpp:105] Iteration 0, lr = 0.0001 | |
| I0320 14:40:56.452066 8660 solver.cpp:219] Iteration 250 (2.65983 iter/s, 93.9909s/250 iters), loss = 0.133513 | |
| I0320 14:40:56.452141 8660 solver.cpp:238] Train net output #0: loss1 = 0.119867 (* 0.3 = 0.0359602 loss) | |
| I0320 14:40:56.452162 8660 solver.cpp:238] Train net output #1: loss2 = 0.0975531 (* 1 = 0.0975531 loss) | |
| I0320 14:40:56.452167 8660 sgd_solver.cpp:105] Iteration 250, lr = 0.0001 | |
| I0320 14:42:58.538004 8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_500.caffemodel | |
| I0320 14:42:58.732300 8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file sn |
Luonic
/ caffe_log_2.txt
Created
December 20, 2016 15:02
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| I1220 16:01:43.502776 3961783232 solver.cpp:228] Iteration 0, loss = 5.0178 | |
| I1220 16:01:43.502820 3961783232 solver.cpp:244] Train net output #0: loss = 5.0178 (* 1 = 5.0178 loss) | |
| I1220 16:01:43.502847 3961783232 sgd_solver.cpp:106] Iteration 0, lr = 0.001 | |
| I1220 16:02:51.024165 3961783232 solver.cpp:228] Iteration 100, loss = 1.87306 | |
| I1220 16:02:51.063248 3961783232 solver.cpp:244] Train net output #0: loss = 1.87306 (* 1 = 1.87306 loss) | |
| I1220 16:02:51.063267 3961783232 sgd_solver.cpp:106] Iteration 100, lr = 0.001 | |
| I1220 16:03:58.084491 3961783232 solver.cpp:228] Iteration 200, loss = 1.01183 | |
| I1220 16:03:58.084777 3961783232 solver.cpp:244] Train net output #0: loss = 1.01183 (* 1 = 1.01183 loss) | |
| I1220 16:03:58.084800 3961783232 sgd_solver.cpp:106] Iteration 200, lr = 0.001 | |
| I1220 16:05:05.282796 3961783232 solver.cpp:228] Iteration 300, loss = 0.772216 |
Luonic
/ img_ocr_keras.py
Created
February 14, 2017 22:51
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| '''This example uses a convolutional stack followed by a recurrent stack | |
| and a CTC logloss function to perform optical character recognition | |
| of generated text images. I have no evidence of whether it actually | |
| learns general shapes of text, or just is able to recognize all | |
| the different fonts thrown at it...the purpose is more to demonstrate CTC | |
| inside of Keras. Note that the font list may need to be updated | |
| for the particular OS in use. | |
| This starts off with 4 letter words. For the first 12 epochs, the | |
| difficulty is gradually increased using the TextImageGenerator class |
Luonic
/ subpixel.py
Created
June 21, 2017 21:09
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| import matplotlib.pyplot as plt | |
| import tensorflow as tf | |
| import numpy as np | |
| def _phase_shift(I, r): | |
| bsize, a, b, c = I.get_shape().as_list() | |
| bsize = tf.shape(I)[0] # Handling Dimension(None) type for undefined batch dim | |
| X = tf.reshape(I, (bsize, a, b, r, r)) | |
| X = tf.transpose(X, (0, 1, 2, 4, 3)) # bsize, a, b, 1, 1 |
Luonic
/ group_norm.py
Created
June 15, 2018 09:57
Group normalization for Tensorflow with adaptive groups count and data format 'channels_last'
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| def group_normalization(input_tensor, num_groups, gamma=1.0, beta=0.0, epsilon=1e-5): | |
| channels_int = input_tensor.get_shape().as_list()[3] | |
| while channels_int % num_groups != 0 and num_groups != 0: | |
| num_groups -= 1 | |
| batch, height, width, channels = input_tensor.shape | |
| input_tensor = tf.reshape(input_tensor, shape=(batch, height, width, channels // num_groups, num_groups)) | |
| mean, var = tf.nn.moments(input_tensor, [1, 2, 3], keep_dims=True) | |
| input_tensor = (input_tensor - mean) / tf.sqrt(var + epsilon) | |
| input_tensor = tf.reshape(input_tensor, [batch, height, width, channels]) |
Luonic
/ ilm_norm.py
Created
May 29, 2019 20:31
Instance-Level Meta Normalization for Tensorflow 1.1x [https://arxiv.org/abs/1904.03516]
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| import tensorflow as tf | |
| def group_norm(x, G=32, eps=1e-5): | |
| # x: input features with shape [N,C,H,W] | |
| # G: number of groups for GN | |
| N, H, W, C = x.shape | |
| gamma = tf.get_variable('gamma', [1, 1, 1, C], initializer=tf.initializers.ones) | |
| beta = tf.get_variable('B_beta', [1, 1, 1, C], initializer=tf.initializers.zeros) | |
| x = tf.reshape(x, [N, H, W, G, C // G]) | |
| mean, var = tf.nn.moments(x, [1, 2, 4], keep_dims=True) |