tensorflow模型ckpt转化为pb后,pb文件1kb

转换后的pb文件也无法调用,不知道是我转换代码问题还是什么问题。
请大神赐教,如果是代码问题也请大神提点一下转换代码

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def freeze_graph(input_checkpoint, output_graph): ''' :param input_checkpoint: :param output_graph: PB模型保存路径 ''' # 输出节点名称 output_node_names = ''
在训练Tensorflow模型(object_detection)时,训练在第一次评估后退出,怎么使训练继续下去?
当我进行ssd模型训练时,训练进行了10分钟,然后进入评估阶段,评估之后程序就自动退出了,没有看到误和警告,这是为什么,怎么让程序一直训练下去? 训练命令: ``` python object_detection/model_main.py --pipeline_config_path=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/pipeline.config --model_dir=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/saved_model --num_train_steps=50000 --alsologtostderr ``` 配置文件: ``` training exit after the first evaluation(only one evaluation) in Tensorflow model(object_detection) without error and waring System information What is the top-level directory of the model you are using:models/research/object_detection/ Have I written custom code (as opposed to using a stock example script provided in TensorFlow):NO OS Platform and Distribution (e.g., Linux Ubuntu 16.04):Windows-10(64bit) TensorFlow installed from (source or binary):conda install tensorflow-gpu TensorFlow version (use command below):1.13.1 Bazel version (if compiling from source):N/A CUDA/cuDNN version:cudnn-7.6.0 GPU model and memory:GeForce GTX 1060 6GB Exact command to reproduce:See below my command for training : python object_detection/model_main.py --pipeline_config_path=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/pipeline.config --model_dir=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/saved_model --num_train_steps=50000 --alsologtostderr This is my config : train_config { batch_size: 24 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { ssd_random_crop { } } optimizer { rms_prop_optimizer { learning_rate { exponential_decay_learning_rate { initial_learning_rate: 0.00400000018999 decay_steps: 800720 decay_factor: 0.949999988079 } } momentum_optimizer_value: 0.899999976158 decay: 0.899999976158 epsilon: 1.0 } } fine_tune_checkpoint: "D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/model.ckpt" from_detection_checkpoint: true num_steps: 200000 train_input_reader { label_map_path: "D:/gitcode/models/research/object_detection/idol/tf_label_map.pbtxt" tf_record_input_reader { input_path: "D:/gitcode/models/research/object_detection/idol/train/Iframe_??????.tfrecord" } } eval_config { num_examples: 8000 max_evals: 10 use_moving_averages: false } eval_input_reader { label_map_path: "D:/gitcode/models/research/object_detection/idol/tf_label_map.pbtxt" shuffle: false num_readers: 1 tf_record_input_reader { input_path: "D:/gitcode/models/research/object_detection/idol/eval/Iframe_??????.tfrecord" } ``` 窗口输出: (default) D:\gitcode\models\research>python object_detection/model_main.py --pipeline_config_path=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/pipeline.config --model_dir=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/saved_model --num_train_steps=50000 --alsologtostderr WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see: https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md https://github.com/tensorflow/addons If you depend on functionality not listed there, please file an issue. WARNING:tensorflow:Forced number of epochs for all eval validations to be 1. WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered eval_on_train_input_config.num_epochs = 0. Overwriting num_epochs to 1. WARNING:tensorflow:Estimator's model_fn (<function create_model_fn..model_fn at 0x0000027CBAB7BB70>) includes params argument, but params are not passed to Estimator. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\builders\dataset_builder.py:86: 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 C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\core\preprocessor.py:196: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: seed2 arg is deprecated.Use sample_distorted_bounding_box_v2 instead. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\builders\dataset_builder.py:158: batch_and_drop_remainder (from tensorflow.contrib.data.python.ops.batching) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.Dataset.batch(..., drop_remainder=True). WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\ops\losses\losses_impl.py:448: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\ops\array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. 2019-08-14 16:29:31.607841: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845 pciBusID: 0000:04:00.0 totalMemory: 6.00GiB freeMemory: 4.97GiB 2019-08-14 16:29:31.621836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 2019-08-14 16:29:32.275712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-08-14 16:29:32.283072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 2019-08-14 16:29:32.288675: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 2019-08-14 16:29:32.293514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4714 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:04:00.0, compute capability: 6.1) WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\eval_util.py:796: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\utils\visualization_utils.py:498: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. Instructions for updating: tf.py_func is deprecated in TF V2. Instead, use tf.py_function, which takes a python function which manipulates tf eager tensors instead of numpy arrays. It's easy to convert a tf eager tensor to an ndarray (just call tensor.numpy()) but having access to eager tensors means tf.py_functions can use accelerators such as GPUs as well as being differentiable using a gradient tape. 2019-08-14 16:41:44.736212: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 2019-08-14 16:41:44.741242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-08-14 16:41:44.747522: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 2019-08-14 16:41:44.751256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 2019-08-14 16:41:44.755548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4714 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:04:00.0, compute capability: 6.1) WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\training\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix. creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=2.43s). Accumulating evaluation results... DONE (t=0.14s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.287 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.529 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.162 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.061 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384 (default) D:\gitcode\models\research>
tensorflow模型载入失败
载入模型的时候不知道怎么出现这种报错 DataLossError (see above for traceback): Unable to open table file .\eye-model\eye_kaggle.ckpt-192.meta: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator?
tensorflow 报错You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,32,32,3],但是怎么看数据都没错,请大神指点
调试googlenet的代码,总是报错 InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,32,32,3],但是我怎么看喂的数据都没问题,请大神们指点 ``` # -*- coding: utf-8 -*- """ GoogleNet也被称为InceptionNet Created on Mon Feb 10 12:15:35 2020 @author: 月光下的云海 """ import tensorflow as tf from keras.datasets import cifar10 import numpy as np import tensorflow.contrib.slim as slim tf.reset_default_graph() tf.reset_default_graph() (x_train,y_train),(x_test,y_test) = cifar10.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') y_train = y_train.astype('int32') y_test = y_test.astype('int32') y_train = y_train.reshape(y_train.shape[0]) y_test = y_test.reshape(y_test.shape[0]) x_train = x_train/255 x_test = x_test/255 #************************************************ 构建inception ************************************************ #构建一个多分支的网络结构 #INPUTS: # d0_1:最左边的分支,分支0,大小为1*1的卷积核个数 # d1_1:左数第二个分支,分支1,大小为1*1的卷积核的个数 # d1_3:左数第二个分支,分支1,大小为3*3的卷积核的个数 # d2_1:左数第三个分支,分支2,大小为1*1的卷积核的个数 # d2_5:左数第三个分支,分支2,大小为5*5的卷积核的个数 # d3_1:左数第四个分支,分支3,大小为1*1的卷积核的个数 # scope:参数域名称 # reuse:是否重复使用 #*************************************************************************************************************** def inception(x,d0_1,d1_1,d1_3,d2_1,d2_5,d3_1,scope = 'inception',reuse = None): with tf.variable_scope(scope,reuse = reuse): #slim.conv2d,slim.max_pool2d的默认参数都放在了slim的参数域里面 with slim.arg_scope([slim.conv2d,slim.max_pool2d],stride = 1,padding = 'SAME'): #第一个分支 with tf.variable_scope('branch0'): branch_0 = slim.conv2d(x,d0_1,[1,1],scope = 'conv_1x1') #第二个分支 with tf.variable_scope('branch1'): branch_1 = slim.conv2d(x,d1_1,[1,1],scope = 'conv_1x1') branch_1 = slim.conv2d(branch_1,d1_3,[3,3],scope = 'conv_3x3') #第三个分支 with tf.variable_scope('branch2'): branch_2 = slim.conv2d(x,d2_1,[1,1],scope = 'conv_1x1') branch_2 = slim.conv2d(branch_2,d2_5,[5,5],scope = 'conv_5x5') #第四个分支 with tf.variable_scope('branch3'): branch_3 = slim.max_pool2d(x,[3,3],scope = 'max_pool') branch_3 = slim.conv2d(branch_3,d3_1,[1,1],scope = 'conv_1x1') #连接 net = tf.concat([branch_0,branch_1,branch_2,branch_3],axis = -1) return net #*************************************** 使用inception构建GoogleNet ********************************************* #使用inception构建GoogleNet #INPUTS: # inputs-----------输入 # num_classes------输出类别数目 # is_trainning-----batch_norm层是否使用训练模式,batch_norm和is_trainning密切相关 # 当is_trainning = True 时候,它使用一个batch数据的平均移动,方差值 # 当is_trainning = Flase时候,它就使用固定的值 # verbos-----------控制打印信息 # reuse------------是否重复使用 #*************************************************************************************************************** def googlenet(inputs,num_classes,reuse = None,is_trainning = None,verbose = False): with slim.arg_scope([slim.batch_norm],is_training = is_trainning): with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d], padding = 'SAME',stride = 1): net = inputs #googlnet的第一个块 with tf.variable_scope('block1',reuse = reuse): net = slim.conv2d(net,64,[5,5],stride = 2,scope = 'conv_5x5') if verbose: print('block1 output:{}'.format(net.shape)) #googlenet的第二个块 with tf.variable_scope('block2',reuse = reuse): net = slim.conv2d(net,64,[1,1],scope = 'conv_1x1') net = slim.conv2d(net,192,[3,3],scope = 'conv_3x3') net = slim.max_pool2d(net,[3,3],stride = 2,scope = 'max_pool') if verbose: print('block2 output:{}'.format(net.shape)) #googlenet第三个块 with tf.variable_scope('block3',reuse = reuse): net = inception(net,64,96,128,16,32,32,scope = 'inception_1') net = inception(net,128,128,192,32,96,64,scope = 'inception_2') net = slim.max_pool2d(net,[3,3],stride = 2,scope = 'max_pool') if verbose: print('block3 output:{}'.format(net.shape)) #googlenet第四个块 with tf.variable_scope('block4',reuse = reuse): net = inception(net,192,96,208,16,48,64,scope = 'inception_1') net = inception(net,160,112,224,24,64,64,scope = 'inception_2') net = inception(net,128,128,256,24,64,64,scope = 'inception_3') net = inception(net,112,144,288,24,64,64,scope = 'inception_4') net = inception(net,256,160,320,32,128,128,scope = 'inception_5') net = slim.max_pool2d(net,[3,3],stride = 2,scope = 'max_pool') if verbose: print('block4 output:{}'.format(net.shape)) #googlenet第五个块 with tf.variable_scope('block5',reuse = reuse): net = inception(net,256,160,320,32,128,128,scope = 'inception1') net = inception(net,384,182,384,48,128,128,scope = 'inception2') net = slim.avg_pool2d(net,[2,2],stride = 2,scope = 'avg_pool') if verbose: print('block5 output:{}'.format(net.shape)) #最后一块 with tf.variable_scope('classification',reuse = reuse): net = slim.flatten(net) net = slim.fully_connected(net,num_classes,activation_fn = None,normalizer_fn = None,scope = 'logit') if verbose: print('classification output:{}'.format(net.shape)) return net #给卷积层设置默认的激活函数和batch_norm with slim.arg_scope([slim.conv2d],activation_fn = tf.nn.relu,normalizer_fn = slim.batch_norm) as sc: conv_scope = sc is_trainning_ph = tf.placeholder(tf.bool,name = 'is_trainning') #定义占位符 x_train_ph = tf.placeholder(shape = (None,x_train.shape[1],x_train.shape[2],x_train.shape[3]),dtype = tf.float32) x_test_ph = tf.placeholder(shape = (None,x_test.shape[1],x_test.shape[2],x_test.shape[3]),dtype = tf.float32) y_train_ph = tf.placeholder(shape = (None,),dtype = tf.int32) y_test_ph = tf.placeholder(shape = (None,),dtype = tf.int32) #实例化网络 with slim.arg_scope(conv_scope): train_out = googlenet(x_train_ph,10,is_trainning = is_trainning_ph,verbose = True) val_out = googlenet(x_test_ph,10,is_trainning = is_trainning_ph,reuse = True) #定义loss和acc with tf.variable_scope('loss'): train_loss = tf.losses.sparse_softmax_cross_entropy(labels = y_train_ph,logits = train_out,scope = 'train') val_loss = tf.losses.sparse_softmax_cross_entropy(labels = y_test_ph,logits = val_out,scope = 'val') with tf.name_scope('accurcay'): train_acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(train_out,axis = -1,output_type = tf.int32),y_train_ph),tf.float32)) val_acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(val_out,axis = -1,output_type = tf.int32),y_test_ph),tf.float32)) #定义训练op lr = 1e-2 opt = tf.train.MomentumOptimizer(lr,momentum = 0.9) #通过tf.get_collection获得所有需要更新的op update_op = tf.get_collection(tf.GraphKeys.UPDATE_OPS) #使用tesorflow控制流,先执行update_op再进行loss最小化 with tf.control_dependencies(update_op): train_op = opt.minimize(train_loss) #开启会话 sess = tf.Session() saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) batch_size = 64 #开始训练 for e in range(10000): batch1 = np.random.randint(0,50000,size = batch_size) t_x_train = x_train[batch1][:][:][:] t_y_train = y_train[batch1] batch2 = np.random.randint(0,10000,size = batch_size) t_x_test = x_test[batch2][:][:][:] t_y_test = y_test[batch2] sess.run(train_op,feed_dict = {x_train_ph:t_x_train, is_trainning_ph:True, y_train_ph:t_y_train}) # if(e%1000 == 999): # loss_train,acc_train = sess.run([train_loss,train_acc], # feed_dict = {x_train_ph:t_x_train, # is_trainning_ph:True, # y_train_ph:t_y_train}) # loss_test,acc_test = sess.run([val_loss,val_acc], # feed_dict = {x_test_ph:t_x_test, # is_trainning_ph:False, # y_test_ph:t_y_test}) # print('STEP{}:train_loss:{:.6f} train_acc:{:.6f} test_loss:{:.6f} test_acc:{:.6f}' # .format(e+1,loss_train,acc_train,loss_test,acc_test)) saver.save(sess = sess,save_path = 'VGGModel\model.ckpt') print('Train Done!!') print('--'*60) sess.close() ``` 报错信息是 ``` Using TensorFlow backend. block1 output:(?, 16, 16, 64) block2 output:(?, 8, 8, 192) block3 output:(?, 4, 4, 480) block4 output:(?, 2, 2, 832) block5 output:(?, 1, 1, 1024) classification output:(?, 10) Traceback (most recent call last): File "<ipython-input-1-6385a760fe16>", line 1, in <module> runfile('F:/Project/TEMP/LearnTF/GoogleNet/GoogleNet.py', wdir='F:/Project/TEMP/LearnTF/GoogleNet') File "D:\ANACONDA\Anaconda3\envs\spyder\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile execfile(filename, namespace) File "D:\ANACONDA\Anaconda3\envs\spyder\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile exec(compile(f.read(), filename, 'exec'), namespace) File "F:/Project/TEMP/LearnTF/GoogleNet/GoogleNet.py", line 177, in <module> y_train_ph:t_y_train}) File "D:\ANACONDA\Anaconda3\envs\spyder\lib\site-packages\tensorflow\python\client\session.py", line 900, in run run_metadata_ptr) File "D:\ANACONDA\Anaconda3\envs\spyder\lib\site-packages\tensorflow\python\client\session.py", line 1135, in _run feed_dict_tensor, options, run_metadata) File "D:\ANACONDA\Anaconda3\envs\spyder\lib\site-packages\tensorflow\python\client\session.py", line 1316, in _do_run run_metadata) File "D:\ANACONDA\Anaconda3\envs\spyder\lib\site-packages\tensorflow\python\client\session.py", line 1335, in _do_call raise type(e)(node_def, op, message) InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,32,32,3] [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,32,32,3], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]] [[Node: gradients/block4/inception_4/concat_grad/ShapeN/_45 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_23694_gradients/block4/inception_4/concat_grad/ShapeN", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]] ``` 看了好多遍都不是喂数据的问题,百度说是summary出了问题,可是我也没有summary呀,头晕~~~~
google发布的预训练的经典神经网络模型文件中为啥不包括 M.ckpt.meta文件,仅仅有M.ckpt?
[链接中](https://github.com/tensorflow/models/tree/master/research/slim "模型")的模型集合,大部分下载下来的模型文件中只有ckpt文件,只有mobilenet模型中 给出了 .ckpt.meta 文件。当我想把inception-v3模型加载到我的网络中并对网络的 部分结构作出修改,但是它又没有给出.ckpt.meta 文件,请问该如何加载inception-v3模型结构呢?
tensorflow载入训练好的模型进行预测,同一张图片预测的结果却不一样????
最近在跑deeplabv1,在测试代码的时候,跑通了训练程序,但是用训练好的模型进行与测试却发现相同的图片预测的结果不一样??请问有大神知道怎么回事吗? 用的是saver.restore()方法载入模型。代码如下: ``` def main(): """Create the model and start the inference process.""" args = get_arguments() # Prepare image. img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3) # Convert RGB to BGR. img_r, img_g, img_b = tf.split(value=img, num_or_size_splits=3, axis=2) img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]), dtype=tf.float32) # Extract mean. img -= IMG_MEAN # Create network. net = DeepLabLFOVModel() # Which variables to load. trainable = tf.trainable_variables() # Predictions. pred = net.preds(tf.expand_dims(img, dim=0)) # Set up TF session and initialize variables. config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) #init = tf.global_variables_initializer() sess.run(tf.global_variables_initializer()) # Load weights. saver = tf.train.Saver(var_list=trainable) load(saver, sess, args.model_weights) # Perform inference. preds = sess.run([pred]) print(preds) if not os.path.exists(args.save_dir): os.makedirs(args.save_dir) msk = decode_labels(np.array(preds)[0, 0, :, :, 0]) im = Image.fromarray(msk) im.save(args.save_dir + 'mask1.png') print('The output file has been saved to {}'.format( args.save_dir + 'mask.png')) if __name__ == '__main__': main() ``` 其中load是 ``` def load(saver, sess, ckpt_path): '''Load trained weights. Args: saver: TensorFlow saver object. sess: TensorFlow session. ckpt_path: path to checkpoint file with parameters. ''' ckpt = tf.train.get_checkpoint_state(ckpt_path) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) print("Restored model parameters from {}".format(ckpt_path)) ``` DeepLabLFOVMode类如下: ``` class DeepLabLFOVModel(object): """DeepLab-LargeFOV model with atrous convolution and bilinear upsampling. This class implements a multi-layer convolutional neural network for semantic image segmentation task. This is the same as the model described in this paper: https://arxiv.org/abs/1412.7062 - please look there for details. """ def __init__(self, weights_path=None): """Create the model. Args: weights_path: the path to the cpkt file with dictionary of weights from .caffemodel. """ self.variables = self._create_variables(weights_path) def _create_variables(self, weights_path): """Create all variables used by the network. This allows to share them between multiple calls to the loss function. Args: weights_path: the path to the ckpt file with dictionary of weights from .caffemodel. If none, initialise all variables randomly. Returns: A dictionary with all variables. """ var = list() index = 0 if weights_path is not None: with open(weights_path, "rb") as f: weights = cPickle.load(f) # Load pre-trained weights. for name, shape in net_skeleton: var.append(tf.Variable(weights[name], name=name)) del weights else: # Initialise all weights randomly with the Xavier scheme, # and # all biases to 0's. for name, shape in net_skeleton: if "/w" in name: # Weight filter. w = create_variable(name, list(shape)) var.append(w) else: b = create_bias_variable(name, list(shape)) var.append(b) return var def _create_network(self, input_batch, keep_prob): """Construct DeepLab-LargeFOV network. Args: input_batch: batch of pre-processed images. keep_prob: probability of keeping neurons intact. Returns: A downsampled segmentation mask. """ current = input_batch v_idx = 0 # Index variable. # Last block is the classification layer. for b_idx in xrange(len(dilations) - 1): for l_idx, dilation in enumerate(dilations[b_idx]): w = self.variables[v_idx * 2] b = self.variables[v_idx * 2 + 1] if dilation == 1: conv = tf.nn.conv2d(current, w, strides=[ 1, 1, 1, 1], padding='SAME') else: conv = tf.nn.atrous_conv2d( current, w, dilation, padding='SAME') current = tf.nn.relu(tf.nn.bias_add(conv, b)) v_idx += 1 # Optional pooling and dropout after each block. if b_idx < 3: current = tf.nn.max_pool(current, ksize=[1, ks, ks, 1], strides=[1, 2, 2, 1], padding='SAME') elif b_idx == 3: current = tf.nn.max_pool(current, ksize=[1, ks, ks, 1], strides=[1, 1, 1, 1], padding='SAME') elif b_idx == 4: current = tf.nn.max_pool(current, ksize=[1, ks, ks, 1], strides=[1, 1, 1, 1], padding='SAME') current = tf.nn.avg_pool(current, ksize=[1, ks, ks, 1], strides=[1, 1, 1, 1], padding='SAME') elif b_idx <= 6: current = tf.nn.dropout(current, keep_prob=keep_prob) # Classification layer; no ReLU. # w = self.variables[v_idx * 2] w = create_variable(name='w', shape=[1, 1, 1024, n_classes]) # b = self.variables[v_idx * 2 + 1] b = create_bias_variable(name='b', shape=[n_classes]) conv = tf.nn.conv2d(current, w, strides=[1, 1, 1, 1], padding='SAME') current = tf.nn.bias_add(conv, b) return current def prepare_label(self, input_batch, new_size): """Resize masks and perform one-hot encoding. Args: input_batch: input tensor of shape [batch_size H W 1]. new_size: a tensor with new height and width. Returns: Outputs a tensor of shape [batch_size h w 18] with last dimension comprised of 0's and 1's only. """ with tf.name_scope('label_encode'): # As labels are integer numbers, need to use NN interp. input_batch = tf.image.resize_nearest_neighbor( input_batch, new_size) # Reducing the channel dimension. input_batch = tf.squeeze(input_batch, squeeze_dims=[3]) input_batch = tf.one_hot(input_batch, depth=n_classes) return input_batch def preds(self, input_batch): """Create the network and run inference on the input batch. Args: input_batch: batch of pre-processed images. Returns: Argmax over the predictions of the network of the same shape as the input. """ raw_output = self._create_network( tf.cast(input_batch, tf.float32), keep_prob=tf.constant(1.0)) raw_output = tf.image.resize_bilinear( raw_output, tf.shape(input_batch)[1:3, ]) raw_output = tf.argmax(raw_output, dimension=3) raw_output = tf.expand_dims(raw_output, dim=3) # Create 4D-tensor. return tf.cast(raw_output, tf.uint8) def loss(self, img_batch, label_batch): """Create the network, run inference on the input batch and compute loss. Args: input_batch: batch of pre-processed images. Returns: Pixel-wise softmax loss. """ raw_output = self._create_network( tf.cast(img_batch, tf.float32), keep_prob=tf.constant(0.5)) prediction = tf.reshape(raw_output, [-1, n_classes]) # Need to resize labels and convert using one-hot encoding. label_batch = self.prepare_label( label_batch, tf.stack(raw_output.get_shape()[1:3])) gt = tf.reshape(label_batch, [-1, n_classes]) # Pixel-wise softmax loss. loss = tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=gt) reduced_loss = tf.reduce_mean(loss) return reduced_loss ``` 按理说载入模型应该没有问题,可是不知道为什么结果却不一样? 图片:![图片说明](https://img-ask.csdn.net/upload/201911/15/1573810836_83106.jpg) ![图片说明](https://img-ask.csdn.net/upload/201911/15/1573810850_924663.png) 预测的结果: ![图片说明](https://img-ask.csdn.net/upload/201911/15/1573810884_985680.png) ![图片说明](https://img-ask.csdn.net/upload/201911/15/1573810904_577649.png) 两次结果不一样,与保存的模型算出来的结果也不一样。 我用的是GitHub上这个人的代码: https://github.com/minar09/DeepLab-LFOV-TensorFlow 急急急,请问有大神知道吗???
Tensorflow object detection api 训练自己数据 map一直是 -1
使用Tensorflow object detection api 训练自己的数据 map 一直是-1.loss一直也很低。 结果是这样的: ![图片说明](https://img-ask.csdn.net/upload/201910/14/1571048294_159662.png) loss: ![图片说明](https://img-ask.csdn.net/upload/201910/15/1571105856_282377.jpg) 使用的模型是:model zoo的这个![图片说明](https://img-ask.csdn.net/upload/201910/14/1571047856_17157.jpg) piplineConfig 如下: ``` model { faster_rcnn { num_classes: 25 image_resizer { keep_aspect_ratio_resizer { min_dimension: 720 max_dimension: 1280 } } feature_extractor { type: "faster_rcnn_resnet50" first_stage_features_stride: 16 } first_stage_anchor_generator { grid_anchor_generator { height_stride: 16 width_stride: 16 scales: 0.25 scales: 0.5 scales: 1.0 scales: 2.0 aspect_ratios: 0.5 aspect_ratios: 1.0 aspect_ratios: 2.0 } } first_stage_box_predictor_conv_hyperparams { op: CONV regularizer { l2_regularizer { weight: 0.0 } } initializer { truncated_normal_initializer { stddev: 0.00999999977648 } } } first_stage_nms_score_threshold: 0.0 first_stage_nms_iou_threshold: 0.699999988079 first_stage_max_proposals: 100 first_stage_localization_loss_weight: 2.0 first_stage_objectness_loss_weight: 1.0 initial_crop_size: 14 maxpool_kernel_size: 2 maxpool_stride: 2 second_stage_box_predictor { mask_rcnn_box_predictor { fc_hyperparams { op: FC regularizer { l2_regularizer { weight: 0.0 } } initializer { variance_scaling_initializer { factor: 1.0 uniform: true mode: FAN_AVG } } } use_dropout: false dropout_keep_probability: 1.0 } } second_stage_post_processing { batch_non_max_suppression { score_threshold: 0.300000011921 iou_threshold: 0.600000023842 max_detections_per_class: 100 max_total_detections: 100 } score_converter: SOFTMAX } second_stage_localization_loss_weight: 2.0 second_stage_classification_loss_weight: 1.0 } } train_config { batch_size: 1 data_augmentation_options { random_horizontal_flip { } } optimizer { momentum_optimizer { learning_rate { manual_step_learning_rate { initial_learning_rate: 0.000300000014249 schedule { step: 900000 learning_rate: 2.99999992421e-05 } schedule { step: 1200000 learning_rate: 3.00000010611e-06 } } } momentum_optimizer_value: 0.899999976158 } use_moving_average: false } gradient_clipping_by_norm: 10.0 fine_tune_checkpoint: "/home/yons/code/自动驾驶视觉综合感知/faster_rcnn_resnet50_coco_2018_01_28/model.ckpt" from_detection_checkpoint: true num_steps: 200000 } train_input_reader { label_map_path: "/home/yons/code/自动驾驶视觉综合感知/pascal_label_map.pbtxt" tf_record_input_reader { input_path: "/home/yons/data/自动驾驶视觉综合感知/train_dataset/tfRecord/train/coco_train.record" } } eval_config { num_examples: 200 max_evals: 10 use_moving_averages: false metrics_set: "coco_detection_metrics" } eval_input_reader { label_map_path: "/home/yons/code/自动驾驶视觉综合感知/pascal_label_map.pbtxt" shuffle: false num_readers: 1 tf_record_input_reader { input_path: "/home/yons/data/自动驾驶视觉综合感知/train_dataset/tfRecord/val/coco_val.record" } } ``` label_map配置: ``` item { id: 1 name: 'red' } item { id: 2 name: 'green' } item { id: 3 name: 'yellow' } item { id: 4 name: 'red_left' } item { id: 5 name: 'red_right' } item { id: 6 name: 'yellow_left' } item { id: 7 name: 'yellow_right' } item { id: 8 name: 'green_left' } item { id: 9 name: 'green_right' } item { id: 10 name: 'red_forward' } item { id: 11 name: 'green_forward' } item { id: 12 name: 'yellow_forward' } item { id:13 name: 'horizon_red' } item { id: 14 name: 'horizon_green' } item { id: 15 name: 'horizon_yellow' } item { id: 16 name: 'off' } item { id: 17 name: 'traffic_sign' } item { id: 18 name: 'car' } item { id: 19 name: 'motor' } item { id: 20 name: 'bike' } item { id: 21 name: 'bus' } item { id: 22 name: 'truck' } item { id: 23 name: 'suv' } item { id: 24 name: 'express' } item { id: 25 name: 'person' } ``` 自己解析数据tfrecord: ![图片说明](https://img-ask.csdn.net/upload/201910/14/1571048123_764545.png) ![图片说明](https://img-ask.csdn.net/upload/201910/14/1571048152_258259.png)
Tensorflow实现手写数字识别,使用训练模型进行预测时,为什么精确度远不如训练精确度??
用BP神经网络算法,基于Tensorflow训练了mnist数据集,在训练的python脚本中可以得到test上的精确度为96%,然后在另一个python脚本中,恢复出这个模型,输入手写数字的图片进行预测,100张图片识别的精确度只有70%多,请教各路大神帮忙解决一下,预测脚本的代码如下: ``` import sys import tensorflow as tf import os from PIL import Image, ImageFilter from pylab import * def predictint(imvalue): with tf.Graph().as_default(): def addlayer(input_data,insize,outsize,act_function=None): W=tf.Variable(tf.random_normal([insize,outsize])) b=tf.Variable(tf.zeros([outsize]))+0.1 out_data=tf.matmul(input_data,W)+b if act_function==None: return out_data elif act_function=="relu": return tf.nn.relu(out_data) elif act_function=="softmax": #result=tf.nn.softmax(out_data) return tf.nn.softmax(out_data) else: return tf.nn.sigmoid(out_data) x_input=tf.placeholder(tf.float32,[None,784]) #y_input=tf.placeholder(tf.float32,[None,10]) l1=addlayer(x_input,784,64,act_function="relu") l2=addlayer(l1,64,10,act_function="softmax") init_op = tf.initialize_all_variables() saver = tf.train.Saver() with tf.Session() as sess: sess.run(init_op) saver.restore(sess, "./model.ckpt") prediction=tf.argmax(l2,1) return prediction.eval(feed_dict={x_input: [imvalue]}, session=sess) def imageprepare(argv): im = Image.open(argv).convert('L') width = float(im.size[0]) height = float(im.size[1]) newImage = Image.new('L', (28, 28), (255)) #creates white canvas of 28x28 pixels if width > height: #check which dimension is bigger #Width is bigger. Width becomes 20 pixels. nheight = int(round((20.0/width*height),0)) #resize height according to ratio width if (nheigth == 0): #rare case but minimum is 1 pixel nheigth = 1 # resize and sharpen img = im.resize((20,nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN) wtop = int(round(((28 - nheight)/2),0)) #caculate horizontal pozition newImage.paste(img, (4, wtop)) #paste resized image on white canvas else: #Height is bigger. Heigth becomes 20 pixels. nwidth = int(round((20.0/height*width),0)) #resize width according to ratio height if (nwidth == 0): #rare case but minimum is 1 pixel nwidth = 1 # resize and sharpen img = im.resize((nwidth,20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN) wleft = int(round(((28 - nwidth)/2),0)) #caculate vertical pozition newImage.paste(img, (wleft, 4)) #paste resized image on white canvas newImage.show() tv = list(newImage.getdata()) #get pixel values #normalize pixels to 0 and 1. 0 is pure white, 1 is pure black. tva = [ (255-x)*1.0/255.0 for x in tv] return tva #print(tva) def main(argv): imvalue = imageprepare(argv) predint = predictint(imvalue) print (predint[0]) #first value in list f1 = open('/home/wch/MNIST_data/traindata_predict.txt','a') f1.writelines('%d ' % predint[0]) f1.close() def VisitDir(path): for root,dirs,files in os.walk(path): for filepath in files: print(os.path.join(root,filepath)) main(os.path.join(root,filepath)) if __name__ == "__main__": path = r"/home/wch/MNIST_data/data_convert2" VisitDir(path) ```
tensorflow 在feed的时候报错could not convert string to float
报错ValueError: could not convert string to float: 'C:/Users/Administrator/Desktop/se//12.21/FV-USM/Published_database_FV-USM_Dec2013/1st_session/extractedvein/vein001_1/01.jpg' 以下是我的代码 ```#!/usr/bin/env python # -*- coding:utf-8 -*- import tensorflow as tf from PIL import Image import model import numpy as np import os def get_one_image(): i=1 n=1 k=1 i = str(i) n = str(n) k = str(k) a = i.zfill(3) b = n.zfill(1) c = k.zfill(2) files='C:/Users/Administrator/Desktop/se//12.21/FV-USM/Published_database_FV-USM_Dec2013/1st_session/extractedvein/vein' + a + '_' + b + '/' + c + '.jpg' # image=image.resize([60,175]) # image=np.array(image) return files def evaluate_one_image(): image_array = get_one_image() with tf.Graph().as_default(): BATCH_SIZE=1 N_CLASS=123 image = tf.image.decode_jpeg(image_array, channels=1) image = tf.image.resize_image_with_crop_or_pad(image,60, 175) image = tf.image.per_image_standardization(image) ###图片调整完成 # image=tf.cast(image,tf.string) # image1 = tf.image.decode_jpeg(image, channels=1) # image1 = tf.image.resize_image_with_crop_or_pad(image1, image_w, image_h) #image1 = tf.image.per_image_standardization(image) ###图片调整完成 #image=tf.image.per_image_standardization(image) image = tf.cast(image, tf.float32) image=tf.reshape(image,[1,60,175,1]) logit = model.inference(image, BATCH_SIZE, N_CLASS) logit=tf.nn.softmax(logit) # 因为 inference 的返回没有用激活函数,所以在这里对结果用softmax 激活。但是为什么要激活?? x=tf.placeholder(tf.float32,shape=[60,175,1]) logs_model_dir='C:/Users/Administrator/Desktop/python的神经网络/model/' saver=tf.train.Saver() with tf.Session() as sess: print("从指定路径加载模型。。。") ckpt=tf.train.get_checkpoint_state(logs_model_dir) if ckpt and ckpt.model_checkpoint_path: global_step=ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] saver.restore(sess,ckpt.model_checkpoint_path) print('模型加载成功,训练步数为 %s'%global_step) else: print('模型加载失败,,,,文件没有找到') ###IMG = sess.run(image) prediction=sess.run(logit,feed_dict={x:image_array}) max_index=np.argmax(prediction) print(max_index) evaluate_one_image() ```
用tensorflow做机器翻译时训练代码有问题
``` # -*- coding:UTF-8 -*- import tensorflow as tf src_path = 'D:/Python37/untitled1/train.tags.en-zh.en.deletehtml' trg_path = 'D:/Python37/untitled1/train.tags.en-zh.zh.deletehtml' SRC_TRAIN_DATA = 'D:/Python37/untitled1/train.tags.en-zh.en.deletehtml.segment' # 源语言输入文件 TRG_TRAIN_DATA = 'D:/Python37/untitled1/train.tags.en-zh.zh.deletehtml.segment' # 目标语言输入文件 CHECKPOINT_PATH = './model/seq2seq_ckpt' # checkpoint保存路径 HIDDEN_SIZE = 1024 # LSTM的隐藏层规模 NUM_LAYERS = 2 # 深层循环神经网络中LSTM结构的层数 SRC_VOCAB_SIZE = 10000 # 源语言词汇表大小 TRG_VOCAB_SIZE = 4000 # 目标语言词汇表大小 BATCH_SIZE = 100 # 训练数据batch的大小 NUM_EPOCH = 5 # 使用训练数据的轮数 KEEP_PROB = 0.8 # 节点不被dropout的概率 MAX_GRAD_NORM = 5 # 用于控制梯度膨胀的梯度大小上限 SHARE_EMB_AND_SOFTMAX = True # 在softmax层和词向量层之间共享参数 MAX_LEN = 50 # 限定句子的最大单词数量 SOS_ID = 1 # 目标语言词汇表中<sos>的ID """ function: 数据batching,产生最后输入数据格式 Parameters: file_path-数据路径 Returns: dataset- 每个句子-对应的长度组成的TextLineDataset类的数据集对应的张量 """ def MakeDataset(file_path): dataset = tf.data.TextLineDataset(file_path) # map(function, sequence[, sequence, ...]) -> list # 通过定义可以看到,这个函数的第一个参数是一个函数,剩下的参数是一个或多个序列,返回值是一个集合。 # function可以理解为是一个一对一或多对一函数,map的作用是以参数序列中的每一个元素调用function函数,返回包含每次function函数返回值的list。 # lambda argument_list: expression # 其中lambda是Python预留的关键字,argument_list和expression由用户自定义 # argument_list参数列表, expression 为函数表达式 # 根据空格将单词编号切分开并放入一个一维向量 dataset = dataset.map(lambda string: tf.string_split([string]).values) # 将字符串形式的单词编号转化为整数 dataset = dataset.map(lambda string: tf.string_to_number(string, tf.int32)) # 统计每个句子的单词数量,并与句子内容一起放入Dataset dataset = dataset.map(lambda x: (x, tf.size(x))) return dataset """ function: 从源语言文件src_path和目标语言文件trg_path中分别读取数据,并进行填充和batching操作 Parameters: src_path-源语言,即被翻译的语言,英语. trg_path-目标语言,翻译之后的语言,汉语. batch_size-batch的大小 Returns: dataset- 每个句子-对应的长度 组成的TextLineDataset类的数据集 """ def MakeSrcTrgDataset(src_path, trg_path, batch_size): # 首先分别读取源语言数据和目标语言数据 src_data = MakeDataset(src_path) trg_data = MakeDataset(trg_path) # 通过zip操作将两个Dataset合并为一个Dataset,现在每个Dataset中每一项数据ds由4个张量组成 # ds[0][0]是源句子 # ds[0][1]是源句子长度 # ds[1][0]是目标句子 # ds[1][1]是目标句子长度 #https://blog.csdn.net/qq_32458499/article/details/78856530这篇博客看一下可以细致了解一下Dataset这个库,以及.map和.zip的用法 dataset = tf.data.Dataset.zip((src_data, trg_data)) # 删除内容为空(只包含<eos>)的句子和长度过长的句子 def FilterLength(src_tuple, trg_tuple): ((src_input, src_len), (trg_label, trg_len)) = (src_tuple, trg_tuple) # tf.logical_and 相当于集合中的and做法,后面两个都为true最终结果才会为true,否则为false # tf.greater Returns the truth value of (x > y),所以以下所说的是句子长度必须得大于一也就是不能为空的句子 # tf.less_equal Returns the truth value of (x <= y),所以所说的是长度要小于最长长度 src_len_ok = tf.logical_and(tf.greater(src_len, 1), tf.less_equal(src_len, MAX_LEN)) trg_len_ok = tf.logical_and(tf.greater(trg_len, 1), tf.less_equal(trg_len, MAX_LEN)) return tf.logical_and(src_len_ok, trg_len_ok) #两个都满足才返回true # filter接收一个函数Func并将该函数作用于dataset的每个元素,根据返回值True或False保留或丢弃该元素,True保留该元素,False丢弃该元素 # 最后得到的就是去掉空句子和过长的句子的数据集 dataset = dataset.filter(FilterLength) # 解码器需要两种格式的目标句子: # 1.解码器的输入(trg_input), 形式如同'<sos> X Y Z' # 2.解码器的目标输出(trg_label), 形式如同'X Y Z <eos>' # 上面从文件中读到的目标句子是'X Y Z <eos>'的形式,我们需要从中生成'<sos> X Y Z'形式并加入到Dataset # 编码器只有输入,没有输出,而解码器有输入也有输出,输入为<sos>+(除去最后一位eos的label列表) # 例如train.en最后都为2,id为2就是eos def MakeTrgInput(src_tuple, trg_tuple): ((src_input, src_len), (trg_label, trg_len)) = (src_tuple, trg_tuple) # tf.concat用法 https://blog.csdn.net/qq_33431368/article/details/79429295 trg_input = tf.concat([[SOS_ID], trg_label[:-1]], axis=0) return ((src_input, src_len), (trg_input, trg_label, trg_len)) dataset = dataset.map(MakeTrgInput) # 随机打乱训练数据 dataset = dataset.shuffle(10000) # 规定填充后的输出的数据维度 padded_shapes = ( (tf.TensorShape([None]), # 源句子是长度未知的向量 tf.TensorShape([])), # 源句子长度是单个数字 (tf.TensorShape([None]), # 目标句子(解码器输入)是长度未知的向量 tf.TensorShape([None]), # 目标句子(解码器目标输出)是长度未知的向量 tf.TensorShape([])) # 目标句子长度(输出)是单个数字 ) # 调用padded_batch方法进行padding 和 batching操作 batched_dataset = dataset.padded_batch(batch_size, padded_shapes) return batched_dataset """ function: seq2seq模型 Parameters: Returns: """ class NMTModel(object): """ function: 模型初始化 Parameters: Returns: """ def __init__(self): # 定义编码器和解码器所使用的LSTM结构 self.enc_cell = tf.nn.rnn_cell.MultiRNNCell( [tf.nn.rnn_cell.LSTMCell(HIDDEN_SIZE) for _ in range(NUM_LAYERS)]) self.dec_cell = tf.nn.rnn_cell.MultiRNNCell( [tf.nn.rnn_cell.LSTMCell(HIDDEN_SIZE) for _ in range(NUM_LAYERS)]) # 为源语言和目标语言分别定义词向量 self.src_embedding = tf.get_variable('src_emb', [SRC_VOCAB_SIZE, HIDDEN_SIZE]) self.trg_embedding = tf.get_variable('trg_emb', [TRG_VOCAB_SIZE, HIDDEN_SIZE]) # 定义softmax层的变量 if SHARE_EMB_AND_SOFTMAX: self.softmax_weight = tf.transpose(self.trg_embedding) else: self.softmax_weight = tf.get_variable('weight', [HIDDEN_SIZE, TRG_VOCAB_SIZE]) self.softmax_bias = tf.get_variable('softmax_loss', [TRG_VOCAB_SIZE]) """ function: 在forward函数中定义模型的前向计算图 Parameters:   MakeSrcTrgDataset函数产生的五种张量如下(全部为张量) src_input: 编码器输入(源数据) src_size : 输入大小 trg_input:解码器输入(目标数据) trg_label:解码器输出(目标数据) trg_size: 输出大小 Returns: """ def forward(self, src_input, src_size, trg_input, trg_label, trg_size): batch_size = tf.shape(src_input)[0] # 将输入和输出单词转为词向量(rnn中输入数据都要转换成词向量) # 相当于input中的每个id对应的embedding中的向量转换 src_emb = tf.nn.embedding_lookup(self.src_embedding, src_input) trg_emb = tf.nn.embedding_lookup(self.trg_embedding, trg_input) # 在词向量上进行dropout src_emb = tf.nn.dropout(src_emb, KEEP_PROB) trg_emb = tf.nn.dropout(trg_emb, KEEP_PROB) # 使用dynamic_rnn构造编码器 # 编码器读取源句子每个位置的词向量,输出最后一步的隐藏状态enc_state # 因为编码器是一个双层LSTM,因此enc_state是一个包含两个LSTMStateTuple类的tuple, # 每个LSTMStateTuple对应编码器中一层的状态 # enc_outputs是顶层LSTM在每一步的输出,它的维度是[batch_size, max_time, HIDDEN_SIZE] # seq2seq模型中不需要用到enc_outputs,而attention模型会用到它 with tf.variable_scope('encoder'): enc_outputs, enc_state = tf.nn.dynamic_rnn(self.enc_cell, src_emb, src_size, dtype=tf.float32) # 使用dynamic_rnn构造解码器 # 解码器读取目标句子每个位置的词向量,输出的dec_outputs为每一步顶层LSTM的输出 # dec_outputs的维度是[batch_size, max_time, HIDDEN_SIZE] # initial_state=enc_state表示用编码器的输出来初始化第一步的隐藏状态 # 编码器最后编码结束最后的状态为解码器初始化的状态 with tf.variable_scope('decoder'): dec_outputs, _ = tf.nn.dynamic_rnn(self.dec_cell, trg_emb, trg_size, initial_state=enc_state) # 计算解码器每一步的log perplexity # 输出重新转换成shape为[,HIDDEN_SIZE] output = tf.reshape(dec_outputs, [-1, HIDDEN_SIZE]) # 计算解码器每一步的softmax概率值 logits = tf.matmul(output, self.softmax_weight) + self.softmax_bias # 交叉熵损失函数,算loss loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.reshape(trg_label, [-1]), logits=logits) # 在计算平均损失时,需要将填充位置的权重设置为0,以避免无效位置的预测干扰模型的训练 label_weights = tf.sequence_mask(trg_size, maxlen=tf.shape(trg_label)[1], dtype=tf.float32) label_weights = tf.reshape(label_weights, [-1]) cost = tf.reduce_sum(loss * label_weights) cost_per_token = cost / tf.reduce_sum(label_weights) # 定义反向传播操作 trainable_variables = tf.trainable_variables() # 控制梯度大小,定义优化方法和训练步骤 # 算出每个需要更新的值的梯度,并对其进行控制 grads = tf.gradients(cost / tf.to_float(batch_size), trainable_variables) grads, _ = tf.clip_by_global_norm(grads, MAX_GRAD_NORM) # 利用梯度下降优化算法进行优化.学习率为1.0 optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0) # 相当于minimize的第二步,正常来讲所得到的list[grads,vars]由compute_gradients得到,返回的是执行对应变量的更新梯度操作的op train_op = optimizer.apply_gradients(zip(grads, trainable_variables)) return cost_per_token, train_op """ function: 使用给定的模型model上训练一个epoch,并返回全局步数,每训练200步便保存一个checkpoint Parameters: session : 会议 cost_op : 计算loss的操作op train_op: 训练的操作op saver:  保存model的类 step:   训练步数 Returns: """ def run_epoch(session, cost_op, train_op, saver, step): # 训练一个epoch # 重复训练步骤直至遍历完Dataset中所有数据 while True: try: # 运行train_op并计算cost_op的结果也就是损失值,训练数据在main()函数中以Dataset方式提供 cost, _ = session.run([cost_op, train_op]) # 步数为10的倍数进行打印 if step % 10 == 0: print('After %d steps, per token cost is %.3f' % (step, cost)) # 每200步保存一个checkpoint if step % 200 == 0: saver.save(session, CHECKPOINT_PATH, global_step=step) step += 1 except tf.errors.OutOfRangeError: break return step """ function: 主函数 Parameters: Returns: """ def main(): # 定义初始化函数 initializer = tf.random_uniform_initializer(-0.05, 0.05) # 定义训练用的循环神经网络模型 with tf.variable_scope('nmt_model', reuse=None, initializer=initializer): train_model = NMTModel() # 定义输入数据 data = MakeSrcTrgDataset(SRC_TRAIN_DATA, TRG_TRAIN_DATA, BATCH_SIZE) iterator = data.make_initializable_iterator() (src, src_size), (trg_input, trg_label, trg_size) = iterator.get_next() # 定义前向计算图,输入数据以张量形式提供给forward函数 cost_op, train_op = train_model.forward(src, src_size, trg_input, trg_label, trg_size) # 训练模型 # 保存模型 saver = tf.train.Saver() step = 0 with tf.Session() as sess: # 初始化全部变量 tf.global_variables_initializer().run() # 进行NUM_EPOCH轮数 for i in range(NUM_EPOCH): print('In iteration: %d' % (i + 1)) sess.run(iterator.initializer) step = run_epoch(sess, cost_op, train_op, saver, step) if __name__ == '__main__': main() ``` 问题如下,不知道怎么解决,谢谢! Traceback (most recent call last): File "D:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1334, in _do_call return fn(*args) File "D:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1319, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "D:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1407, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError: StringToNumberOp could not correctly convert string: This [[{{node StringToNumber}}]] [[{{node IteratorGetNext}}]] During handling of the above exception, another exception occurred: Traceback (most recent call last): File "D:/Python37/untitled1/train_model.py", line 277, in <module> main() File "D:/Python37/untitled1/train_model.py", line 273, in main step = run_epoch(sess, cost_op, train_op, saver, step) File "D:/Python37/untitled1/train_model.py", line 231, in run_epoch cost, _ = session.run([cost_op, train_op]) File "D:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 929, in run run_metadata_ptr) File "D:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1152, in _run feed_dict_tensor, options, run_metadata) File "D:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1328, in _do_run run_metadata) File "D:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1348, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: StringToNumberOp could not correctly convert string: This [[{{node StringToNumber}}]] [[node IteratorGetNext (defined at D:/Python37/untitled1/train_model.py:259) ]]
我在用facenet模型中的classifier.py文件中训练自己的数据集的时候配置了相关参数所在的路径,但是却发生了如下报错,请问是什么原因?
作为一个刚入门的小白在这方面的学习还有许多的不懂,我在用facenet模型中的classifier.py文件中训练自己的数据集的时候配置了相关参数所在的路径,但是却发生了如下报错,请问是什么原因? ``` def parse_arguments(argv): parser = argparse.ArgumentParser() parser.add_argument('mode', type=str, choices=['TRAIN', 'CLASSIFY'], help='Indicates if a new classifier should be trained or a classification ' + 'model should be used for classification', default='TRAIN')#训练模型 parser.add_argument('data_dir', type=str, help='Path to the data directory containing aligned LFW face patches.',default='D:/pyCharm/facenet/data_dir/African_convert')#添加数据集文件夹 parser.add_argument('model', type=str, help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file',default='D:/pyCharm/facenet/20170512-110547') parser.add_argument('classifier_filename', help='Classifier model file name as a pickle (.pkl) file. ' + 'For training this is the output and for classification this is an input.' ,default='pick/classifier.pkl') parser.add_argument('--use_split_dataset', help='Indicates that the dataset specified by data_dir should be split into a training and test set. ' + 'Otherwise a separate test set can be specified using the test_data_dir option.', action='store_true') parser.add_argument('--test_data_dir', type=str, help='Path to the test data directory containing aligned images used for testing.') parser.add_argument('--batch_size', type=int, help='Number of images to process in a batch.', default=90) parser.add_argument('--image_size', type=int, help='Image size (height, width) in pixels.', default=160) parser.add_argument('--seed', type=int, help='Random seed.', default=666) parser.add_argument('--min_nrof_images_per_class', type=int, help='Only include classes with at least this number of images in the dataset', default=20) parser.add_argument('--nrof_train_images_per_class', type=int, help='Use this number of images from each class for training and the rest for testing', default=10) return parser.parse_args(argv) if __name__ == '__main__': main(parse_arguments(sys.argv[1:])) ``` 报错信息如下: usage: classifier.py [-h] [--use_split_dataset] [--test_data_dir TEST_DATA_DIR] [--batch_size BATCH_SIZE] [--image_size IMAGE_SIZE] [--seed SEED] [--min_nrof_images_per_class MIN_NROF_IMAGES_PER_CLASS] [--nrof_train_images_per_class NROF_TRAIN_IMAGES_PER_CLASS] {TRAIN,CLASSIFY} data_dir model classifier_filename classifier.py: error: the following arguments are required: mode, data_dir, model, classifier_filename ![图片说明](https://img-ask.csdn.net/upload/201910/12/1570866719_655865.jpg) ![图片说明](https://img-ask.csdn.net/upload/201910/12/1570867095_477948.jpg) 谢谢!!!
TensorFlow SSD训练自己的数据 checkpoint问题
1、参考教程:https://blog.csdn.net/liuyan20062010/article/details/78905517 2、一直到训练成功! 3、导入模型测试,代码 ``` # Restore SSD model. ckpt_filename = '../train_model/model.ckpt-100' # ckpt_filename = '../checkpoints/VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt' isess.run(tf.global_variables_initializer()) saver = tf.train.Saver() saver.restore(isess, ckpt_filename) ``` 4、错误信息: ![图片说明](https://img-ask.csdn.net/upload/201903/31/1553997360_295222.png) 5、经网上查阅,说是修改了模型结构的问题,但是只是按教程上面修改了类别数,且训练成功了。其他均未修改。
oracle自动关闭服务,然后手动stratup启动,又能正常使用!
Thu Dec 05 09:37:05 2019 System state dump requested by (instance=1, osid=44223 (DBW0)), summary=[abnormal instance termination]. System State dumped to trace file /oracle/app/oracle/diag/rdbms/orcl/orcl/trace/orcl_diag_44215_20191205093705.trc Instance terminated by DBW0, pid = 44223 Thu Dec 05 09:47:25 2019 Adjusting the default value of parameter parallel_max_servers from 320 to 120 due to the value of parameter processes (150) Starting ORACLE instance (normal) ************************ Large Pages Information ******************* Per process system memlock (soft) limit = 64 KB Total Shared Global Region in Large Pages = 0 KB (0%) Large Pages used by this instance: 0 (0 KB) Large Pages unused system wide = 0 (0 KB) Large Pages configured system wide = 0 (0 KB) Large Page size = 2048 KB RECOMMENDATION: Total System Global Area size is 9634 MB. For optimal performance, prior to the next instance restart: 1. Increase the number of unused large pages by at least 4817 (page size 2048 KB, total size 9634 MB) system wide to get 100% of the System Global Area allocated with large pages 2. Large pages are automatically locked into physical memory. Increase the per process memlock (soft) limit to at least 9642 MB to lock 100% System Global Area's large pages into physical memory ******************************************************************** LICENSE_MAX_SESSION = 0 LICENSE_SESSIONS_WARNING = 0 Initial number of CPU is 8 Number of processor cores in the system is 8 Number of processor sockets in the system is 1 CELL communication is configured to use 0 interface(s): CELL IP affinity details: NUMA status: non-NUMA system cellaffinity.ora status: N/A CELL communication will use 1 IP group(s): Grp 0: Picked latch-free SCN scheme 3 Autotune of undo retention is turned on. IMODE=BR ILAT =27 LICENSE_MAX_USERS = 0 SYS auditing is disabled Starting up: Oracle Database 11g Enterprise Edition Release 11.2.0.4.0 - 64bit Production With the Partitioning, OLAP, Data Mining and Real Application Testing options. ORACLE_HOME = /oracle/app/oracle/product/11.2.0/dbhome_1 System name: Linux Node name: easdatabase Release: 2.6.32-642.el6.x86_64 Version: #1 SMP Tue May 10 17:27:01 UTC 2016 Machine: x86_64 Using parameter settings in server-side spfile /oracle/app/oracle/product/11.2.0/dbhome_1/dbs/spfileorcl.ora System parameters with non-default values: processes = 150 sga_target = 9632M control_files = "/oracle/app/oracle/oradata/orcl/control01.ctl" control_files = "/oracle/app/oracle/fast_recovery_area/orcl/control02.ctl" db_block_size = 8192 compatible = "11.2.0.4.0" log_archive_dest_1 = "location=/mnt1/archivelog" db_recovery_file_dest = "/oracle/app/oracle/fast_recovery_area" db_recovery_file_dest_size= 35G undo_tablespace = "UNDOTBS1" remote_login_passwordfile= "EXCLUSIVE" db_domain = "" dispatchers = "(PROTOCOL=TCP) (SERVICE=orclXDB)" audit_file_dest = "/oracle/app/oracle/admin/orcl/adump" audit_trail = "DB" db_name = "orcl" open_cursors = 300 _b_tree_bitmap_plans = FALSE _no_or_expansion = TRUE optimizer_index_cost_adj = 50 optimizer_index_caching = 90 pga_aggregate_target = 3210M diagnostic_dest = "/oracle/app/oracle" Thu Dec 05 09:47:26 2019 PMON started with pid=2, OS id=47601 Thu Dec 05 09:47:26 2019 PSP0 started with pid=3, OS id=47603 Thu Dec 05 09:47:28 2019 VKTM started with pid=4, OS id=47607 at elevated priority VKTM running at (1)millisec precision with DBRM quantum (100)ms Thu Dec 05 09:47:28 2019 GEN0 started with pid=5, OS id=47611 Thu Dec 05 09:47:28 2019 DIAG started with pid=6, OS id=47613 Thu Dec 05 09:47:28 2019 DBRM started with pid=7, OS id=47615 Thu Dec 05 09:47:28 2019 DIA0 started with pid=8, OS id=47617 Thu Dec 05 09:47:28 2019 MMAN started with pid=9, OS id=47619 Thu Dec 05 09:47:28 2019 DBW0 started with pid=10, OS id=47621 Thu Dec 05 09:47:28 2019 LGWR started with pid=11, OS id=47623 Thu Dec 05 09:47:28 2019 CKPT started with pid=12, OS id=47625 Thu Dec 05 09:47:28 2019 SMON started with pid=13, OS id=47627 Thu Dec 05 09:47:28 2019 RECO started with pid=14, OS id=47629 Thu Dec 05 09:47:28 2019 MMON started with pid=15, OS id=47631 Thu Dec 05 09:47:28 2019 MMNL started with pid=16, OS id=47634 starting up 1 dispatcher(s) for network address '(ADDRESS=(PARTIAL=YES)(PROTOCOL=TCP))'... starting up 1 shared server(s) ... ORACLE_BASE from environment = /oracle/app/oracle Thu Dec 05 09:47:28 2019 ALTER DATABASE MOUNT Successful mount of redo thread 1, with mount id 1553952944 Database mounted in Exclusive Mode Lost write protection disabled Completed: ALTER DATABASE MOUNT Thu Dec 05 09:47:33 2019 ALTER DATABASE OPEN Beginning crash recovery of 1 threads parallel recovery started with 7 processes Started redo scan Completed redo scan read 10546 KB redo, 1690 data blocks need recovery Started redo application at Thread 1: logseq 47164, block 39943 Recovery of Online Redo Log: Thread 1 Group 3 Seq 47164 Reading mem 0 Mem# 0: /oracle/app/oracle/oradata/orcl/redo03.log Recovery of Online Redo Log: Thread 1 Group 2 Seq 47165 Reading mem 0 Mem# 0: /oracle/app/oracle/oradata/orcl/redo02.log Completed redo application of 5.86MB Completed crash recovery at Thread 1: logseq 47165, block 4932, scn 500814159 1690 data blocks read, 1688 data blocks written, 10546 redo k-bytes read Thu Dec 05 09:47:40 2019 LGWR: STARTING ARCH PROCESSES Thu Dec 05 09:47:40 2019 ARC0 started with pid=27, OS id=47696 ARC0: Archival started LGWR: STARTING ARCH PROCESSES COMPLETE ARC0: STARTING ARCH PROCESSES Thu Dec 05 09:47:41 2019 ARC1 started with pid=28, OS id=47711 Thu Dec 05 09:47:41 2019 ARC2 started with pid=30, OS id=47713 Thu Dec 05 09:47:41 2019 ARC3 started with pid=29, OS id=47715 ARC1: Archival started ARC2: Archival started ARC1: Becoming the 'no FAL' ARCH ARC1: Becoming the 'no SRL' ARCH ARC2: Becoming the heartbeat ARCH Thread 1 advanced to log sequence 47166 (thread open) Thread 1 opened at log sequence 47166 Current log# 1 seq# 47166 mem# 0: /oracle/app/oracle/oradata/orcl/redo01.log Successful open of redo thread 1 MTTR advisory is disabled because FAST_START_MTTR_TARGET is not set Thu Dec 05 09:47:41 2019 SMON: enabling cache recovery Archived Log entry 44784 added for thread 1 sequence 47165 ID 0x58d8a86f dest 1: [47655] Successfully onlined Undo Tablespace 2. Undo initialization finished serial:0 start:1111127512 end:1111127762 diff:250 (2 seconds) Verifying file header compatibility for 11g tablespace encryption.. Verifying 11g file header compatibility for tablespace encryption completed SMON: enabling tx recovery Database Characterset is AL32UTF8 No Resource Manager plan active replication_dependency_tracking turned off (no async multimaster replication found) Thu Dec 05 09:47:43 2019 Starting background process QMNC Thu Dec 05 09:47:43 2019 QMNC started with pid=31, OS id=47739 ARC3: Archival started ARC0: STARTING ARCH PROCESSES COMPLETE Thu Dec 05 09:47:44 2019 db_recovery_file_dest_size of 35840 MB is 0.00% used. This is a user-specified limit on the amount of space that will be used by this database for recovery-related files, and does not reflect the amount of space available in the underlying filesystem or ASM diskgroup. Completed: ALTER DATABASE OPEN Thu Dec 05 09:47:45 2019 Starting background process CJQ0 Thu Dec 05 09:47:45 2019 CJQ0 started with pid=32, OS id=47753 Thu Dec 05 09:48:03 2019 Starting background process SMCO Thu Dec 05 09:48:03 2019 SMCO started with pid=47, OS id=47850
tensorflow.GraphDef was modified concurrently during serialization
![图片说明](https://img-ask.csdn.net/upload/201909/17/1568650242_211346.png) # Create a saver object which will save all the variables saver = tf.train.Saver(keep_checkpoint_every_n_hours=1.0) if FLAGS.pre_trained_checkpoint: train_utils.restore_fn(FLAGS) start_epoch = 0 # Get the number of training/validation steps per epoch tr_batches = int(MODELNET_TRAIN_DATA_SIZE / FLAGS.batch_size) if MODELNET_TRAIN_DATA_SIZE % FLAGS.batch_size > 0: tr_batches += 1 val_batches = int(MODELNET_VALIDATE_DATA_SIZE / FLAGS.batch_size) if MODELNET_VALIDATE_DATA_SIZE % FLAGS.batch_size > 0: val_batches += 1 # The filenames argument to the TFRecordDataset initializer can either be a string, # a list of strings, or a tf.Tensor of strings. training_filenames = os.path.join(FLAGS.dataset_dir, 'train.record') validate_filenames = os.path.join(FLAGS.dataset_dir, 'validate.record') ################## # Training loop. ################## for training_epoch in range(start_epoch, FLAGS.how_many_training_epochs): print("-------------------------------------") print(" Epoch {} ".format(training_epoch)) print("-------------------------------------") sess.run(iterator.initializer, feed_dict={filenames: training_filenames}) for step in range(tr_batches): # Pull the image batch we'll use for training. train_batch_xs, train_batch_ys = sess.run(next_batch) handle = sess.partial_run_setup([d_scores, final_desc, learning_rate, summary_op, accuracy, total_loss, grad_summ_op, train_op], [X, final_X, ground_truth, grouping_scheme, grouping_weight, is_training, is_training2, dropout_keep_prob]) scores, final = sess.partial_run(handle, [d_scores, final_desc], feed_dict={ X: train_batch_xs, is_training: True} ) schemes = gvcnn.grouping_scheme(scores, NUM_GROUP, FLAGS.num_views) weights = gvcnn.grouping_weight(scores, schemes) # Run the graph with this batch of training data. lr, train_summary, train_accuracy, train_loss, grad_vals, _ = \ sess.partial_run(handle, [learning_rate, summary_op, accuracy, total_loss, grad_summ_op, train_op], feed_dict={ final_X: final, ground_truth: train_batch_ys, grouping_scheme: schemes, grouping_weight: weights, is_training2: True, dropout_keep_prob: 0.8} ) train_writer.add_summary(train_summary, training_epoch) train_writer.add_summary(grad_vals, training_epoch) tf.logging.info('Epoch #%d, Step #%d, rate %.10f, accuracy %.1f%%, loss %f' % (training_epoch, step, lr, train_accuracy * 100, train_loss)) # Save the model checkpoint periodically. if (training_epoch <= FLAGS.how_many_training_epochs-1): checkpoint_path = os.path.join(FLAGS.train_logdir, FLAGS.ckpt_name_to_save) tf.logging.info('Saving to "%s-%d"', checkpoint_path, training_epoch) saver.save(sess, checkpoint_path, global_step=training_epoch) ``` ```
tensorflow训练过程权重不更新,loss不下降,输出保持不变,只有bias在非常缓慢地变化?
模型里没有参数被初始化为0 ,学习率从10的-5次方试到了0.1,输入数据都已经被归一化为了0-1之间,模型是改过的vgg16,有四个输出,使用了vgg16的预训练模型来初始化参数,输出中间结果也没有nan或者inf值。是不是不能自定义损失函数呢?但输出中间梯度发现并不是0,非常奇怪。 **train.py的部分代码** ``` def train(): x = tf.placeholder(tf.float32, [None, 182, 182, 2], name = 'image_input') y_ = tf.placeholder(tf.float32, [None, 8], name='label_input') global_step = tf.Variable(0, trainable=False) learning_rate = tf.train.exponential_decay(learning_rate=0.0001,decay_rate=0.9, global_step=TRAINING_STEPS, decay_steps=50,staircase=True) # 读取图片数据,pos是标签为1的图,neg是标签为0的图 pos, neg = get_data.get_image(img_path) #输入标签固定,输入数据每个batch前4张放pos,后4张放neg label_batch = np.reshape(np.array([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0]),[1, 8]) vgg = vgg16.Vgg16() vgg.build(x) #loss函数的定义在后面 loss = vgg.side_loss( y_,vgg.output1, vgg.output2, vgg.output3, vgg.output4) train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=global_step) init_op = tf.global_variables_initializer() saver = tf.train.Saver() with tf.device('/gpu:0'): with tf.Session() as sess: sess.run(init_op) for i in range(TRAINING_STEPS): #在train.py的其他部分定义了batch_size= 4 start = i * batch_size end = start + batch_size #制作输入数据,前4个是标签为1的图,后4个是标签为0的图 image_list = [] image_list.append(pos[start:end]) image_list.append(neg[start:end]) image_batch = np.reshape(np.array(image_list),[-1,182,182,2]) _,loss_val,step = sess.run([train_step,loss,global_step], feed_dict={x: image_batch,y_:label_batch}) if i % 50 == 0: print("the step is %d,loss is %f" % (step, loss_val)) if loss_val < min_loss: min_loss = loss_val saver.save(sess, 'ckpt/vgg.ckpt', global_step=2000) ``` **Loss 函数的定义** ``` **loss函数的定义(写在了Vgg16类里)** ``` class Vgg16: #a,b,c,d都是vgg模型里的输出,是多输出模型 def side_loss(self,yi,a,b,c,d): self.loss1 = self.f_indicator(yi, a) self.loss2 = self.f_indicator(yi, b) self.loss3 = self.f_indicator(yi, c) self.loss_fuse = self.f_indicator(yi, d) self.loss_side = self.loss1 + self.loss2 + self.loss3 + self.loss_fuse res_loss = tf.reduce_sum(self.loss_side) return res_loss #损失函数的定义,标签为0时为log(1-yj),标签为1时为log(yj) def f_indicator(self,yi,yj): b = tf.where(yj>=1,yj*50,tf.abs(tf.log(tf.abs(1 - yj)))) res=tf.where(tf.equal(yi , 0.0), b,tf.abs(tf.log(tf.clip_by_value(yj, 1e-8, float("inf"))))) return res ```
tensorflow 怎么预训练 微调自己的数据
我在github上找了一个代码:https://github.com/MrCPlusPlus/MobileFaceNet_Tensorflow_Pretrain/blob/master/train_nets.py 我想要尝试训练自己的数据集,我知道好像只要加几行代码就行, 但是我小白不知道怎么加,好像只要把最后一层输出层改下,其它层固定就可以了, 请大家帮忙,谢谢! 我自己瞎折腾报错 :Key Mean_1/avg not found in checkpoint, 不知道为什么?? 最后一层好像是这个: fc7/biases (DT_FLOAT) [85164] fc7/weights (DT_FLOAT) [128,85164] saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=args.saver_maxkeep) # init all variables sess.run(tf.global_variables_initializer()) sess.run(tf.local_variables_initializer()) # load pretrained model if pretrained_model: print('Restoring pretrained model: %s' % pretrained_model) ckpt = tf.train.get_checkpoint_state(pretrained_model) #tf.reset_default_graph() saver.restore(sess, ckpt.model_checkpoint_path) print('Successfully restored!') exclude = ['fc7'] variables_to_restore = slim.get_variables_to_restore(exclude=exclude) init_fn = slim.assign_from_checkpoint_fn('./output/ckpt_best/MobileFaceNet_pretrain.ckpt', variables_to_restore) init_fn(sess) 我加的代码是这样的,但是报错lhs=[1156], rhs=[85164 也就是说原来模型的输出 和自己训练的数据集类数不同, 但是我不是已经排除模型最后一层了吗 ? 我找不到的变量是这种形式的,好像是global variables中的: fc7/weights/ExponentialMovingAverage:0 fc7/biases/ExponentialMovingAverage:0 不同于网络结构中打出来的变量: fc7/biases (DT_FLOAT) [85164] fc7/weights (DT_FLOAT) [128,85164] 还有报错很多变量未初始化,但是我也添加了初始化代码啊 很郁闷. 请大家帮帮忙,谢谢了. 或者可以加我微信沟通:1886 2232 309 ,谢谢! 急 急急!
tensorflow,python运行时报错在reshape上,求大神解答
代码来自于一篇博客,用tensorflow判断拨号图标和短信图标的分类,训练已经成功运行,以下为测试代码,错误出现在38行 image = tf.reshape(image, [1, 208, 208, 3]) 我的测试图片是256*256的,也测试了48*48的 ``` #!/usr/bin/python # -*- coding:utf-8 -*- # @Time : 2018/3/31 0031 17:50 # @Author : scw # @File : main.py # 进行图片预测方法调用的文件 import numpy as np from PIL import Image import tensorflow as tf import matplotlib.pyplot as plt from shenjingwangluomodel import inference # 定义需要进行分类的种类,这里我是进行分两种,因为一种为是拨号键,另一种就是非拨号键 CallPhoneStyle = 2 # 进行测试的操作处理========================== # 加载要进行测试的图片 def get_one_image(img_dir): image = Image.open(img_dir) # Image.open() # 好像一次只能打开一张图片,不能一次打开一个文件夹,这里大家可以去搜索一下 plt.imshow(image) image = image.resize([208, 208]) image_arr = np.array(image) return image_arr # 进行测试处理------------------------------------------------------- def test(test_file): # 设置加载训练结果的文件目录(这个是需要之前就已经训练好的,别忘记) log_dir = '/home/administrator/test_system/calldata2/' # 打开要进行测试的图片 image_arr = get_one_image(test_file) with tf.Graph().as_default(): # 把要进行测试的图片转为tensorflow所支持的格式 image = tf.cast(image_arr, tf.float32) # 将图片进行格式化的处理 image = tf.image.per_image_standardization(image) # 将tensorflow的图片的格式参数,转变为shape格式的,好像就是去掉-1这样的列表 image = tf.reshape(image, [1, 208, 208, 3]) # print(image.shape) # 参数CallPhoneStyle:表示的是分为两类 p = inference(image, 1, CallPhoneStyle) # 这是训练出一个神经网络的模型 # 这里用到了softmax这个逻辑回归模型的处理 logits = tf.nn.softmax(p) x = tf.placeholder(tf.float32, shape=[208, 208, 3]) saver = tf.train.Saver() with tf.Session() as sess: # 对tensorflow的训练参数进行初始化,使用默认的方式 sess.run(tf.global_variables_initializer()) # 判断是否有进行训练模型的设置,所以一定要之前就进行了模型的训练 ckpt = tf.train.get_checkpoint_state(log_dir) if ckpt and ckpt.model_checkpoint_path: # global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] saver.restore(sess, ckpt.model_checkpoint_path) # 调用saver.restore()函数,加载训练好的网络模型 print('Loading success') else: print('No checkpoint') prediction = sess.run(logits, feed_dict={x: image_arr}) max_index = np.argmax(prediction) print('预测的标签为:') if max_index == 0: print("是拨号键图片") else: print("是短信图片") # print(max_index) print('预测的分类结果每种的概率为:') print(prediction) # 我用0,1表示两种分类,这也是我在训练的时候就设置好的 if max_index == 0: print('图片是拨号键图标的概率为 %.6f' %prediction[:, 0]) elif max_index == 1: print('图片是短信它图标的概率为 %.6f' %prediction[:, 1]) # 进行图片预测 test('/home/administrator/Downloads/def.png') ''' # 测试自己的训练集的图片是不是已经加载成功(因为这个是进行训练的第一步) train_dir = 'E:/tensorflowdata/calldata/' BATCH_SIZE = 5 # 生成批次队列中的容量缓存的大小 CAPACITY = 256 # 设置我自己要对图片进行统一大小的高和宽 IMG_W = 208 IMG_H = 208 image_list,label_list = get_files(train_dir) # 加载训练集的图片和对应的标签 image_batch,label_batch = get_batch(image_list,label_list,IMG_W,IMG_H,BATCH_SIZE,CAPACITY) # 进行批次图片加载到内存中 # 这是打开一个session,主要是用于进行图片的显示效果的测试 with tf.Session() as sess: i = 0 coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) try: while not coord.should_stop() and i < 2: # 提取出两个batch的图片并可视化。 img, label = sess.run([image_batch, label_batch]) for j in np.arange(BATCH_SIZE): print('label: %d' % label[j]) plt.imshow(img[j, :, :, :]) plt.show() i += 1 except tf.errors.OutOfRangeError: print('done!') finally: coord.request_stop() coord.join(threads) ''' ```
跑AlexNet猫狗遇到IndexError: too many indices for array的问题
小弟最近在看OpenCV+TensorFlow这本书的案例 照着输进去了结果发现运行不下去, 问题应该是出现在第二块内容,但是真的不太明白!求各位大神赐教,如何修改! ![图片说明](https://img-ask.csdn.net/upload/201906/30/1561889895_192060.png) 第一块,修改照片尺寸,为啥呀 ``` import cv2 import os def resize(dir): for root, dirs, files in os.walk(dir): for file in files: filepath = os.path.join(root, file) try: image = cv2.imread(filepath) dim = (227, 227) resized = cv2.resize(image, dim) path = "C:\\Users\\Telon_Hu\\Desktop\\ANNs\\train1\\" + file cv2.imwrite(path, resized) except: print(filepath) # os.remove(filepath) cv2.waitKey(0) resize('C:\\Users\\Telon_Hu\\Desktop\\ANNs\\train') ``` ``` import os import numpy as np import tensorflow as tf import cv2 def get_file(file_dir): images=[] temp=[] for root,sub_folders,files in os.walk(file_dir): ''' os.walk(path)---返回的是一个三元组(root,dirs,files): root 所指的是当前正在遍历的这个文件夹的本身的地址 dirs 是一个 list ,内容是该文件夹中所有的目录的名字(不包括子目录) files 同样是 list , 内容是该文件夹中所有的文件(不包括子目录) ''' for name in files: images.append(os.path.join(root,name)) for name in sub_folders: temp.append(os.path.join(root,name)) labels=[] for one_folder in temp: n_img=len(os.listdir(one_folder)) #s.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表 letter=one_folder.split('\\')[-1] #split() 通过指定分隔符对字符串进行切片,默认为-1, 即分隔所有。 if letter=='cat': labels=np.append(labels,n_img*[0]) else: labels=np.append(labels,n_img*[1]) temp=np.array([images, labels]) temp=temp.transpose() #矩阵转置 np.random.shuffle(temp) #随机排序 image_list=list(temp[:, 0]) label_list=list(temp[:, 1]) label_list=[int(float(i)) for i in label_list] return image_list,label_list def get_batch(image_list,label_list,img_width,img_height,batch_size,capacity): image=tf.cast(image_list,tf.string) label=tf.cast(label_list,tf.int32) input_queue=tf.train.slice_input_producer([image,label]) label=input_queue[1] image_contents=tf.read_file(input_queue[0]) #通过图片地址读取图片 image=tf.image.decode_jpeg(image_contents,channels=3) #解码图片成矩阵 image=tf.image.resize_image_with_crop_or_pad(image,img_width,img_height) ''' tf.image.resize_images 不能保证图像的纵横比,这样用来做抓取位姿的识别,可能受到影响 tf.image.resize_image_with_crop_or_pad可让纵横比不变 ''' image=tf.image.per_image_standardization(image) #将图片标准化 image_batch,label_batch=tf.train.batch([image,label],batch_size=batch_size,num_threads=64,capacity=capacity) ''' tf.train.batch([example, label], batch_size=batch_size, capacity=capacity): 1.[example, label]表示样本和样本标签,这个可以是一个样本和一个样本标签 2.batch_size是返回的一个batch样本集的样本个数 3.num_threads是线程 4.capacity是队列中的容量。 ''' label_batch=tf.reshape(label_batch,[batch_size]) return image_batch,label_batch def one_hot(labels): '''one-hot 编码''' n_sample=len(labels) n_class=max(labels)+1 onehot_labels=np.zeros((n_sample,n_class)) onehot_labels[np.arange(n_sample),labels]=1 return onehot_labels get_file('C:\\Users\\Telon_Hu\\Desktop\\ANNs\\dogs_vs_cats\\') ``` ``` import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import time import os import sys import creat_and_read_TFReacord as reader x_train,y_train=reader.get_file('dogs_vs_cats') image_batch,label_batch=reader.get_batch(x_train,y_train,227,227,50,2048) #Batch_Normalization正则化 def batch_norm(inputs,is_train,is_conv_out=True,decay=0.999): scale=tf.Variable(tf.ones([inputs.get_shape()[-1]])) beta = tf.Variable(tf.zeros([inputs.get_shape()[-1]])) pop_mean = tf.Variable(tf.zeros([inputs.get_shape()[-1]]), trainable=False) pop_var = tf.Variable(tf.ones([inputs.get_shape()[-1]]), trainable=False) if is_train: if is_conv_out: batch_mean, batch_var = tf.nn.moments(inputs, [0, 1, 2]) else: batch_mean, batch_var = tf.nn.moments(inputs, [0]) train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - 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start_time)) start_time = end_time print("---------------%d onpech is finished-------------------" % i) print("Optimization Finished!") # checkpoint_path = os.path.join(".//model", 'model.ckpt') # 输出模型的地方 saver.save(sess, save_model) print("Model Save Finished!") coord.request_stop() coord.join(threads) plt.plot(c) plt.xlabel('Iter') plt.ylabel('loss') plt.title('lr=%f, ti=%d, bs=%d' % (learning_rate, training_iters, batch_size)) plt.tight_layout() plt.savefig('cat_and_dog_AlexNet.jpg', dpi=200) train(training_iters) ```
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