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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,10 @@ | ||
| # Test results | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,100 @@ | ||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
|
||
| import tensorflow as tf | ||
|
|
||
| ###################### | ||
| # data set | ||
| #################### | ||
|
|
||
| tf.app.flags.DEFINE_string( | ||
| 'dataset_tfrecord', | ||
| '../data/tfrecords', | ||
| 'tfrecord of fruits dataset' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'new_img_size', | ||
| 224, | ||
| 'the value of new height and new width, new_height = new_width' | ||
| ) | ||
|
|
||
| ########################### | ||
| # data batch | ||
| ########################## | ||
| tf.app.flags.DEFINE_integer( | ||
| 'num_classes', | ||
| 100, | ||
| 'num of classes' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'batch_size', | ||
| 32, #64 | ||
| 'num of imgs in a batch' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'val_batch_size', | ||
| 16, | ||
| 'val or test batch' | ||
| ) | ||
| ########################### | ||
| ## learning rate | ||
| ######################### | ||
| tf.app.flags.DEFINE_float( | ||
| 'lr_begin', | ||
| 0.0001, # 0.01 # 0.001 for without prepocess | ||
| 'the value of learning rate start with' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'decay_steps', | ||
| 2000, # 5000 | ||
| "after 'decay_steps' steps, learning rate begin decay" | ||
| ) | ||
| tf.app.flags.DEFINE_float( | ||
| 'decay_rate', | ||
| 0.1, | ||
| 'decay rate' | ||
| ) | ||
|
|
||
| ############################### | ||
| # optimizer-- MomentumOptimizer | ||
| ############################### | ||
| tf.app.flags.DEFINE_float( | ||
| 'momentum', | ||
| 0.9, | ||
| 'accumulation = momentum * accumulation + gradient' | ||
| ) | ||
|
|
||
| ############################ | ||
| # train | ||
| ######################## | ||
| tf.app.flags.DEFINE_integer( | ||
| 'max_steps', | ||
| 30050, | ||
| 'max iterate steps' | ||
| ) | ||
|
|
||
| tf.app.flags.DEFINE_string( | ||
| 'pretrained_model_path', | ||
| '../data/pretrained_weights/inception_resnet_v2_2016_08_30.ckpt', | ||
| 'the path of pretrained weights' | ||
| ) | ||
| tf.app.flags.DEFINE_float( | ||
| 'weight_decay', | ||
| 0.00004, | ||
| 'weight_decay in regulation' | ||
| ) | ||
| ################################ | ||
| # summary and save_weights_checkpoint | ||
| ################################## | ||
| tf.app.flags.DEFINE_string( | ||
| 'summary_path', | ||
| '../output/inception_res_summary', | ||
| 'the path of summary write to ' | ||
| ) | ||
| tf.app.flags.DEFINE_string( | ||
| 'trained_checkpoint', | ||
| '../output/inception_res_trainedweights', | ||
| 'the path to save trained_weights' | ||
| ) | ||
| FLAGS = tf.app.flags.FLAGS |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
|
||
| import tensorflow as tf | ||
|
|
||
| ###################### | ||
| # data set | ||
| #################### | ||
| tf.app.flags.DEFINE_string( | ||
| 'dataset_tfrecord', | ||
| '../data/tfrecords', | ||
| 'tfrecord of fruits dataset' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'new_img_size', | ||
| 224, | ||
| 'the value of new height and new width, new_height = new_width' | ||
| ) | ||
|
|
||
| ########################### | ||
| # data batch | ||
| ########################## | ||
| tf.app.flags.DEFINE_integer( | ||
| 'num_classes', | ||
| 134, | ||
| 'num of classes' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'batch_size', | ||
| 64, #64 | ||
| 'num of imgs in a batch' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'val_batch_size', | ||
| 32, | ||
| 'val or test batch' | ||
| ) | ||
| ########################### | ||
| ## learning rate | ||
| ######################### | ||
| tf.app.flags.DEFINE_float( | ||
| 'lr_begin', | ||
| 0.001, # 0.01 # 0.001 for without prepocess | ||
| 'the value of learning rate start with' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'decay_steps', | ||
| 20000, # 5000 | ||
| "after 'decay_steps' steps, learning rate begin decay" | ||
| ) | ||
| tf.app.flags.DEFINE_float( | ||
| 'decay_rate', | ||
| 0.1, | ||
| 'decay rate' | ||
| ) | ||
|
|
||
| ############################### | ||
| # optimizer-- MomentumOptimizer | ||
| ############################### | ||
| tf.app.flags.DEFINE_float( | ||
| 'momentum', | ||
| 0.9, | ||
| 'accumulation = momentum * accumulation + gradient' | ||
| ) | ||
|
|
||
| ############################ | ||
| # train | ||
| ######################## | ||
| tf.app.flags.DEFINE_integer( | ||
| 'max_steps', | ||
| 4003, | ||
| 'max iterate steps' | ||
| ) | ||
|
|
||
| tf.app.flags.DEFINE_string( | ||
| 'pretrained_model_path', | ||
| '../data/pretrained_weights/resnet_v1_101.ckpt', | ||
| 'the path of pretrained weights' | ||
| ) | ||
| tf.app.flags.DEFINE_float( | ||
| 'weight_decay', | ||
| 0.0001, | ||
| 'weight_decay in regulation' | ||
| ) | ||
| ################################ | ||
| # summary and save_weights_checkpoint | ||
| ################################## | ||
| tf.app.flags.DEFINE_string( | ||
| 'summary_path', | ||
| '../output/res101_summary', | ||
| 'the path of summary write to ' | ||
| ) | ||
| tf.app.flags.DEFINE_string( | ||
| 'trained_checkpoint', | ||
| '../output/res101_trained_weights', | ||
| 'the path to save trained_weights' | ||
| ) | ||
| FLAGS = tf.app.flags.FLAGS |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,73 @@ | ||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
|
||
| import tensorflow as tf | ||
|
|
||
| tf.app.flags.DEFINE_string( | ||
| 'dataset_tfrecord', | ||
| '../data/tfrecords', | ||
| 'tfrecord of fruits dataset' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'shortside_size', | ||
| 600, | ||
| 'the value of new height and new width, new_height = new_width' | ||
| ) | ||
|
|
||
| ########################### | ||
| # data batch | ||
| ########################## | ||
| tf.app.flags.DEFINE_integer( | ||
| 'num_classes', | ||
| 20, | ||
| 'num of classes' | ||
| ) | ||
| tf.app.flags.DEFINE_integer( | ||
| 'batch_size', | ||
| 1, #64 | ||
| 'num of imgs in a batch' | ||
| ) | ||
|
|
||
| ############################### | ||
| # optimizer-- MomentumOptimizer | ||
| ############################### | ||
| tf.app.flags.DEFINE_float( | ||
| 'momentum', | ||
| 0.9, | ||
| 'accumulation = momentum * accumulation + gradient' | ||
| ) | ||
|
|
||
| ############################ | ||
| # train | ||
| ######################## | ||
| tf.app.flags.DEFINE_integer( | ||
| 'max_steps', | ||
| 900000, | ||
| 'max iterate steps' | ||
| ) | ||
|
|
||
| tf.app.flags.DEFINE_string( | ||
| 'pretrained_model_path', | ||
| '../data/pretrained_weights/resnet_50.ckpt', | ||
| 'the path of pretrained weights' | ||
| ) | ||
| tf.app.flags.DEFINE_float( | ||
| 'weight_decay', | ||
| 0.0001, | ||
| 'weight_decay in regulation' | ||
| ) | ||
| ################################ | ||
| # summary and save_weights_checkpoint | ||
| ################################## | ||
| tf.app.flags.DEFINE_string( | ||
| 'summary_path', | ||
| '../output/resnet_summary', | ||
| 'the path of summary write to ' | ||
| ) | ||
| tf.app.flags.DEFINE_string( | ||
| 'trained_checkpoint', | ||
| '../output/resnet_trained_weights', | ||
| 'the path to save trained_weights' | ||
| ) | ||
| FLAGS = tf.app.flags.FLAGS |
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