Tensorflow Colab上的gdrive问题_Tensorflow_Deep Learning_Google Drive Api_Google Colaboratory

Tensorflow Colab上的gdrive问题

tensorflow deep-learning google-drive-api google-colaboratory

Tensorflow Colab上的gdrive问题,tensorflow,deep-learning,google-drive-api,google-colaboratory,Tensorflow,Deep Learning,Google Drive Api,Google Colaboratory,在安装google drive之后，我正在使用colab在cifar10上训练resnet 我克隆了存储库，并且能够运行脚本。但是，Tensorflow已加载，数据文件已传递到网络，但我以以下内容结束： tensorflow.python.framework.errors\u impl.NotFoundError: /内容/驱动/我的；没有这样的文件或目录我的路径似乎有问题，因为它包含一个空格“/content/gdrive/my Drive/apps/PocketFlow”。我如何改变gd

在安装google drive之后，我正在使用colab在cifar10上训练resnet 我克隆了存储库，并且能够运行脚本。但是，Tensorflow已加载，数据文件已传递到网络，但我以以下内容结束：

tensorflow.python.framework.errors\u impl.NotFoundError: /内容/驱动/我的；没有这样的文件或目录

我的路径似乎有问题，因为它包含一个空格“/content/gdrive/my Drive/apps/PocketFlow”。我如何改变gdrive的安装方式，换句话说，我可以将“我的驱动器”更改为其他内容以再次运行测试

您可以在下面找到代码和日志文件：

from google.colab import drive
drive.mount('/content/gdrive')
import os
os.chdir("/content/gdrive/My Drive/apps/PocketFlow")
!chmod 755 ./scripts/run_local.sh
!./scripts/run_local.sh nets/resnet_at_cifar10_run.py

日志：

Python script: nets/resnet_at_cifar10_run.py
# of GPUs: 1
extra arguments:  --model_http_url https://api.ai.tencent.com/pocketflow --data_dir_local /content/drive/My Drive/apps/datasets/cifar10
'nets/resnet_at_cifar10_run.py' -> 'main.py'
multi-GPU training disabled
[WARNING] TF-Plus & Horovod cannot be imported; multi-GPU training is unsupported
INFO:tensorflow:FLAGS:
INFO:tensorflow:data_disk: local
INFO:tensorflow:data_hdfs_host: None
INFO:tensorflow:data_dir_local: /content/drive/My
INFO:tensorflow:data_dir_hdfs: None
INFO:tensorflow:cycle_length: 4
INFO:tensorflow:nb_threads: 8
INFO:tensorflow:buffer_size: 1024
INFO:tensorflow:prefetch_size: 8
INFO:tensorflow:nb_classes: 10
INFO:tensorflow:nb_smpls_train: 50000
INFO:tensorflow:nb_smpls_val: 5000
INFO:tensorflow:nb_smpls_eval: 10000
INFO:tensorflow:batch_size: 128
INFO:tensorflow:batch_size_eval: 100
INFO:tensorflow:resnet_size: 20
INFO:tensorflow:lrn_rate_init: 0.1
INFO:tensorflow:batch_size_norm: 128.0
INFO:tensorflow:momentum: 0.9
INFO:tensorflow:loss_w_dcy: 0.0002
INFO:tensorflow:model_http_url: https://api.ai.tencent.com/pocketflow
INFO:tensorflow:summ_step: 100
INFO:tensorflow:save_step: 10000
INFO:tensorflow:save_path: ./models/model.ckpt
INFO:tensorflow:save_path_eval: ./models_eval/model.ckpt
INFO:tensorflow:enbl_dst: False
INFO:tensorflow:enbl_warm_start: False
INFO:tensorflow:loss_w_dst: 4.0
INFO:tensorflow:tempr_dst: 4.0
INFO:tensorflow:save_path_dst: ./models_dst/model.ckpt
INFO:tensorflow:nb_epochs_rat: 1.0
INFO:tensorflow:ddpg_actor_depth: 2
INFO:tensorflow:ddpg_actor_width: 64
INFO:tensorflow:ddpg_critic_depth: 2
INFO:tensorflow:ddpg_critic_width: 64
INFO:tensorflow:ddpg_noise_type: param
INFO:tensorflow:ddpg_noise_prtl: tdecy
INFO:tensorflow:ddpg_noise_std_init: 1.0
INFO:tensorflow:ddpg_noise_dst_finl: 0.01
INFO:tensorflow:ddpg_noise_adpt_rat: 1.03
INFO:tensorflow:ddpg_noise_std_finl: 1e-05
INFO:tensorflow:ddpg_rms_eps: 0.0001
INFO:tensorflow:ddpg_tau: 0.01
INFO:tensorflow:ddpg_gamma: 0.9
INFO:tensorflow:ddpg_lrn_rate: 0.001
INFO:tensorflow:ddpg_loss_w_dcy: 0.0
INFO:tensorflow:ddpg_record_step: 1
INFO:tensorflow:ddpg_batch_size: 64
INFO:tensorflow:ddpg_enbl_bsln_func: True
INFO:tensorflow:ddpg_bsln_decy_rate: 0.95
INFO:tensorflow:ws_save_path: ./models_ws/model.ckpt
INFO:tensorflow:ws_prune_ratio: 0.75
INFO:tensorflow:ws_prune_ratio_prtl: optimal
INFO:tensorflow:ws_nb_rlouts: 200
INFO:tensorflow:ws_nb_rlouts_min: 50
INFO:tensorflow:ws_reward_type: single-obj
INFO:tensorflow:ws_lrn_rate_rg: 0.03
INFO:tensorflow:ws_nb_iters_rg: 20
INFO:tensorflow:ws_lrn_rate_ft: 0.0003
INFO:tensorflow:ws_nb_iters_ft: 400
INFO:tensorflow:ws_nb_iters_feval: 25
INFO:tensorflow:ws_prune_ratio_exp: 3.0
INFO:tensorflow:ws_iter_ratio_beg: 0.1
INFO:tensorflow:ws_iter_ratio_end: 0.5
INFO:tensorflow:ws_mask_update_step: 500.0
INFO:tensorflow:cp_lasso: True
INFO:tensorflow:cp_quadruple: False
INFO:tensorflow:cp_reward_policy: accuracy
INFO:tensorflow:cp_nb_points_per_layer: 10
INFO:tensorflow:cp_nb_batches: 30
INFO:tensorflow:cp_prune_option: auto
INFO:tensorflow:cp_prune_list_file: ratio.list
INFO:tensorflow:cp_channel_pruned_path: ./models/pruned_model.ckpt
INFO:tensorflow:cp_best_path: ./models/best_model.ckpt
INFO:tensorflow:cp_original_path: ./models/original_model.ckpt
INFO:tensorflow:cp_preserve_ratio: 0.5
INFO:tensorflow:cp_uniform_preserve_ratio: 0.6
INFO:tensorflow:cp_noise_tolerance: 0.15
INFO:tensorflow:cp_lrn_rate_ft: 0.0001
INFO:tensorflow:cp_nb_iters_ft_ratio: 0.2
INFO:tensorflow:cp_finetune: False
INFO:tensorflow:cp_retrain: False
INFO:tensorflow:cp_list_group: 1000
INFO:tensorflow:cp_nb_rlouts: 200
INFO:tensorflow:cp_nb_rlouts_min: 50
INFO:tensorflow:dcp_save_path: ./models_dcp/model.ckpt
INFO:tensorflow:dcp_save_path_eval: ./models_dcp_eval/model.ckpt
INFO:tensorflow:dcp_prune_ratio: 0.5
INFO:tensorflow:dcp_nb_stages: 3
INFO:tensorflow:dcp_lrn_rate_adam: 0.001
INFO:tensorflow:dcp_nb_iters_block: 10000
INFO:tensorflow:dcp_nb_iters_layer: 500
INFO:tensorflow:uql_equivalent_bits: 4
INFO:tensorflow:uql_nb_rlouts: 200
INFO:tensorflow:uql_w_bit_min: 2
INFO:tensorflow:uql_w_bit_max: 8
INFO:tensorflow:uql_tune_layerwise_steps: 100
INFO:tensorflow:uql_tune_global_steps: 2000
INFO:tensorflow:uql_tune_save_path: ./rl_tune_models/model.ckpt
INFO:tensorflow:uql_tune_disp_steps: 300
INFO:tensorflow:uql_enbl_random_layers: True
INFO:tensorflow:uql_enbl_rl_agent: False
INFO:tensorflow:uql_enbl_rl_global_tune: True
INFO:tensorflow:uql_enbl_rl_layerwise_tune: False
INFO:tensorflow:uql_weight_bits: 4
INFO:tensorflow:uql_activation_bits: 32
INFO:tensorflow:uql_use_buckets: False
INFO:tensorflow:uql_bucket_size: 256
INFO:tensorflow:uql_quant_epochs: 60
INFO:tensorflow:uql_save_quant_model_path: ./uql_quant_models/uql_quant_model.ckpt
INFO:tensorflow:uql_quantize_all_layers: False
INFO:tensorflow:uql_bucket_type: channel
INFO:tensorflow:uqtf_save_path: ./models_uqtf/model.ckpt
INFO:tensorflow:uqtf_save_path_eval: ./models_uqtf_eval/model.ckpt
INFO:tensorflow:uqtf_weight_bits: 8
INFO:tensorflow:uqtf_activation_bits: 8
INFO:tensorflow:uqtf_quant_delay: 0
INFO:tensorflow:uqtf_freeze_bn_delay: None
INFO:tensorflow:uqtf_lrn_rate_dcy: 0.01
INFO:tensorflow:nuql_equivalent_bits: 4
INFO:tensorflow:nuql_nb_rlouts: 200
INFO:tensorflow:nuql_w_bit_min: 2
INFO:tensorflow:nuql_w_bit_max: 8
INFO:tensorflow:nuql_tune_layerwise_steps: 100
INFO:tensorflow:nuql_tune_global_steps: 2101
INFO:tensorflow:nuql_tune_save_path: ./rl_tune_models/model.ckpt
INFO:tensorflow:nuql_tune_disp_steps: 300
INFO:tensorflow:nuql_enbl_random_layers: True
INFO:tensorflow:nuql_enbl_rl_agent: False
INFO:tensorflow:nuql_enbl_rl_global_tune: True
INFO:tensorflow:nuql_enbl_rl_layerwise_tune: False
INFO:tensorflow:nuql_init_style: quantile
INFO:tensorflow:nuql_opt_mode: weights
INFO:tensorflow:nuql_weight_bits: 4
INFO:tensorflow:nuql_activation_bits: 32
INFO:tensorflow:nuql_use_buckets: False
INFO:tensorflow:nuql_bucket_size: 256
INFO:tensorflow:nuql_quant_epochs: 60
INFO:tensorflow:nuql_save_quant_model_path: ./nuql_quant_models/model.ckpt
INFO:tensorflow:nuql_quantize_all_layers: False
INFO:tensorflow:nuql_bucket_type: split
INFO:tensorflow:log_dir: ./logs
INFO:tensorflow:enbl_multi_gpu: False
INFO:tensorflow:learner: full-prec
INFO:tensorflow:exec_mode: train
INFO:tensorflow:debug: False
INFO:tensorflow:h: False
INFO:tensorflow:help: False
INFO:tensorflow:helpfull: False
INFO:tensorflow:helpshort: False
2018-11-16 12:53:20.147847: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-11-16 12:53:20.148287: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties: 
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 0000:00:04.0
totalMemory: 11.17GiB freeMemory: 11.10GiB
2018-11-16 12:53:20.148358: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2018-11-16 12:53:20.565167: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-11-16 12:53:20.565235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 
2018-11-16 12:53:20.565262: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N 
2018-11-16 12:53:20.565561: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2018-11-16 12:53:20.565637: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10758 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)
WARNING:tensorflow:From /content/gdrive/My Drive/apps/PocketFlow/datasets/abstract_dataset.py:85: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.experimental.parallel_interleave(...)`.
WARNING:tensorflow:From /content/gdrive/My Drive/apps/PocketFlow/datasets/abstract_dataset.py:106: shuffle_and_repeat (from tensorflow.contrib.data.python.ops.shuffle_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.experimental.shuffle_and_repeat(...)`.
2018-11-16 12:53:23.066723: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2018-11-16 12:53:23.066814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-11-16 12:53:23.066857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 
2018-11-16 12:53:23.066882: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N 
2018-11-16 12:53:23.067168: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10758 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)
2018-11-16 12:53:24.963790: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at matching_files_op.cc:49 : Not found: /content/drive/My; No such file or directory
2018-11-16 12:53:24.964542: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at matching_files_op.cc:49 : Not found: /content/drive/My; No such file or directory
2018-11-16 12:53:24.964744: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at iterator_ops.cc:947 : Not found: /content/drive/My; No such file or directory
     [[{{node ShuffleDataset/data/list_files/MatchingFiles}} = MatchingFiles[](ShuffleDataset/data/list_files/file_pattern)]]
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1334, in _do_call
    return fn(*args)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1319, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.NotFoundError: /content/drive/My; No such file or directory
     [[{{node ShuffleDataset/data/list_files/MatchingFiles}} = MatchingFiles[](ShuffleDataset/data/list_files/file_pattern)]]
     [[{{node data/OneShotIterator}} = OneShotIterator[container="", dataset_factory=_make_dataset_E02JEaYNEAE[], output_shapes=[[?,32,32,3], [?,10]], output_types=[DT_FLOAT, DT_FLOAT], shared_name="", _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
     [[{{node data/IteratorGetNext/_3}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_107_data/IteratorGetNext", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "main.py", line 69, in <module>
    tf.app.run()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "main.py", line 55, in main
    learner.train()
  File "/content/gdrive/My Drive/apps/PocketFlow/learners/full_precision/learner.py", line 71, in train
    self.sess_train.run(self.train_op)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 929, in run
    run_metadata_ptr)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1328, in _do_run
    run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: /content/drive/My; No such file or directory
     [[{{node ShuffleDataset/data/list_files/MatchingFiles}} = MatchingFiles[](ShuffleDataset/data/list_files/file_pattern)]]
     [[node data/OneShotIterator (defined at /content/gdrive/My Drive/apps/PocketFlow/datasets/abstract_dataset.py:109)  = OneShotIterator[container="", dataset_factory=_make_dataset_E02JEaYNEAE[], output_shapes=[[?,32,32,3], [?,10]], output_types=[DT_FLOAT, DT_FLOAT], shared_name="", _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
     [[{{node data/IteratorGetNext/_3}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_107_data/IteratorGetNext", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]

Python脚本：nets/resnet\u at\u cifar10\u run.py
#GPU数量：1
额外参数：--model\u http\u urlhttps://api.ai.tencent.com/pocketflow --数据目录本地/内容/驱动器/我的驱动器/应用程序/数据集/cifar10
'nets/resnet_at_cifar10_run.py'->'main.py'
多GPU训练禁用
[警告]TF Plus和Horovod无法导入；不支持多GPU培训
信息：tensorflow:标志：
信息：tensorflow:数据\磁盘：本地
信息：tensorflow:数据\u hdfs\u主机：无
信息：tensorflow:data\u dir\u local:/content/drive/My
信息：tensorflow:数据\u目录\u hdfs:无
信息：tensorflow:循环长度：4
信息：tensorflow:nb_线程：8
信息：tensorflow:缓冲区大小：1024
信息：tensorflow:预取大小：8
信息：tensorflow:nb_类别：10
信息：tensorflow:nb\U smpls\U列车：50000
信息：tensorflow:nb\U smpls\U val:5000
信息：tensorflow:nb\U smpls\U eval:10000
信息：tensorflow:批量大小：128
信息：tensorflow：批次大小评估：100
信息：tensorflow:resnet\u尺寸：20
信息：tensorflow:lrn\u速率\u初始值：0.1
信息：tensorflow:批量大小规格：128.0
信息：张量流：动量：0.9
信息：tensorflow:损耗w\u dcy:0.0002
信息：tensorflow:model\u http\u url:https://api.ai.tencent.com/pocketflow
信息：tensorflow:总和步长：100
信息：tensorflow:保存步骤：10000
信息：tensorflow:保存路径：./models/model.ckpt
信息：tensorflow:保存路径评估：./models评估/model.ckpt
信息：tensorflow:enbl_dst:False
信息：tensorflow:enbl\u warm\u start:False
信息：tensorflow:loss_w_dst:4.0
信息：tensorflow:tempr_dst:4.0
信息：tensorflow:保存路径：./models\dst/model.ckpt
信息：tensorflow:nb_时代鼠：1.0
信息：tensorflow:ddpg\u actor\u深度：2
信息：tensorflow:ddpg\u actor\u宽度：64
信息：tensorflow:ddpg\u Critical\u深度：2
信息：tensorflow:ddpg\u Critical\u宽度：64
信息：tensorflow:ddpg_噪音_类型：参数
信息：tensorflow:ddpg\u noise\u prtl:tdecy
信息：tensorflow:ddpg_noise_std_init:1.0
信息：tensorflow:ddpg_noise_dst_finl:0.01
信息：tensorflow:ddpg\u噪音\u adpt\u鼠：1.03
信息：tensorflow:ddpg_noise_std_finl:1e-05
信息：tensorflow:ddpg\U rms\U eps:0.0001
信息：tensorflow:ddpg_tau:0.01
信息：tensorflow:ddpg_gamma:0.9
信息：tensorflow:ddpg\U lrn\U比率：0.001
信息：tensorflow:ddpg\u损耗\u dcy:0.0
信息：tensorflow:ddpg\u记录\u步骤：1
信息：tensorflow:ddpg\u批量大小：64
信息：tensorflow:ddpg_enbl_bsln_func:True
信息：tensorflow:ddpg\u bsln\u decy\u费率：0.95
信息：tensorflow:ws\u保存路径：./models\u ws/model.ckpt
信息：tensorflow:ws_prune_比率：0.75
信息：tensorflow:ws\u prune\u比率\u prtl:最佳
信息：tensorflow:ws_nb_rlout:200
信息：tensorflow:ws_nb_rlout_min:50
信息：tensorflow:ws\u奖励\u类型：单个对象
信息：tensorflow:ws\u lrn\u比率\u rg:0.03
信息：tensorflow:ws\u nb\u iters\u rg:20
信息：tensorflow:ws\u lrn\u速率\u ft:0.0003
信息：tensorflow:ws\u nb\u iters\u ft:400
信息：tensorflow:ws\u nb\u iters\u feval:25
信息：tensorflow:ws\u prune\u ratio\u exp:3.0
信息：tensorflow:ws_iter_比率：0.1
信息：tensorflow:ws_iter_比率_end:0.5
信息：tensorflow:ws\u mask\u update\u步骤：500.0
信息：tensorflow:cp_lasso:True
信息：tensorflow:cp_四倍：错误
信息：tensorflow:cp\U奖励\U政策：准确性
信息：tensorflow:cp\u nb\u每层点数：10
信息：tensorflow:cp\U nb\U批次：30
信息：tensorflow:cp\u prune\u选项：自动
信息：tensorflow:cp\u prune\u list\u文件：ratio.list
信息：tensorflow:cp\u通道\u修剪路径：./models/pruned\u model.ckpt
信息：tensorflow:cp\u最佳路径：./models/best\u model.ckpt
信息：tensorflow:cp\u原始路径：./models/original\u model.ckpt
信息：tensorflow:cp\u保存率：0.5
信息：tensorflow:cp\u均匀\u保存\u比率：0.6
信息：tensorflow:cp\u噪音\u容差：0.15
信息：tensorflow:cp\u lrn\u比率\u ft:0.0001
信息：tensorflow:cp_nb_iters_ft_比率：0.2
信息：tensorflow:cp\U finetune:False
信息：tensorflow:cp\U再培训：错误
信息：tensorflow:cp\u列表\u组：1000
信息：tensorflow:cp\u nb\u rlout:200
信息：tensorflow:cp\u nb\u rlout\u min:50
信息：tensorflow:dcp\u save\u path:./models\u dcp/model.ckpt
信息：tensorflow:dcp\u save\u path\u eval:./models\u dcp\u eval/model.ckpt
信息：tensorflow:dcp\U修剪比例：0.5
信息：tensorflow:dcp\U nb\U阶段：3
信息：tensorflow:dcp\U lrn\U比率\U adam:0.001
信息：tensorflow:dcp\U nb\U iters\U区块：10000
信息：tensorflow:dcp\U nb\U iters\U层：500
信息：tensorflow:uql\u等效\u位：4
信息：tensorflow:uql_nb_rlout:200
信息：tensorflow:uql\u w\u bit\u min:2
信息：tensorflow:uql\u w\u位\u最大值：8
信息：tensorflow:uql\u tune\u layerwise\u步骤：100
信息：tensorflow:uql\u tune\u global\u步骤：2000
信息：tensorflow:uql\u tune\u save\u路径：./rl\u tune\u models/model.ckpt
信息：tensorflow:uql\u tune\u disp\u步骤：300
信息：tensorflow:uql\u enbl\u random\u layers:True
信息：tensorflow:uql\u enbl\u rl\u代理：False
信息：tensorflow:uql\u enbl\u rl\u global\u tune:True
信息：tensorflow:uql\u enbl\u rl\u layerwise\u tune:False
信息：tensorflow:uql\u权重\u位：4
信息：tensorflow:uql\u激活\u位：32
信息：tensorflow:uql\u use\u bucket:False
信息：tensorflow:uql\u bucket\u大小：256
信息：tensorflow:uql\u quant\u时代：60
信息：tensorflow:uql\u save\u quant\u model\u路径：./uql\u quant\u models/uql\u quant\u model.ckpt
信息：tensorflow:uql\u量化\u所有层：False
信息：tensorflow:uql\u bucket\u类型：通道
信息：tensorflow:uqtf\u保存路径：./models\u uqtf/model.ckpt
信息：tensorflow:uqtf\u save\u path\u eval:./models\u qtf\u eval/model.ckpt
信息：tensorflow:uqtf\u重量\u位：8
信息：tensorflow:uqtf\u激活\u位：8
信息：tensorflow:uqtf\u数量\u延迟：0
信息：tensorflow:uqtf\u冻结\u bn\u延迟：无
信息：tensorflow:uqtf\u lrn\u比率\u dcy:0.01
信息：tensorflow:nuql\u等效\u位：4
信息：tensorflow:nuql_nb_rlout:200
信息：tensorflow:numql\u w\u bit\u min:2
from google.colab import drive
drive.mount('/content/gdrive')
!ln -s "/content/gdrive/My Drive" "/content/mydrive"
import os
os.chdir("/content/mydrive/apps/PocketFlow")
!chmod 755 ./scripts/run_local.sh
!./scripts/run_local.sh nets/resnet_at_cifar10_run.py