tensorboard

问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

问题 i have started a sagemaker job: from sagemaker.tensorflow import TensorFlow mytraining= TensorFlow(entry_point='model.py', role=role, train_instance_count=1, train_instance_type='ml.p2.xlarge', framework_version='2.0.0', py_version='py3', distributions={'parameter_server'{'enabled':False}}) training_data_uri ='s3://path/to/my/data' mytraining.fit(training_data_uri,run_tensorboard_locally=True) using run_tesorboard_locally=True gave me Tensorboard is not supported with script mode. You can run

问题 now, i'm studying tensorflow. but, i can't draw dot graph using tensorboard. if i have sample data for training, like that train_X = numpy.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779]) train_Y = numpy.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366]) i want to show scatter plots using tensorboard. i know "import matplotlib.pyplot as plt" can do that. but i can just use console (putty). so can't use this method. can i see dot graph, like scatter plots using tensorboard. can anyone

问题 now, i'm studying tensorflow. but, i can't draw dot graph using tensorboard. if i have sample data for training, like that train_X = numpy.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779]) train_Y = numpy.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366]) i want to show scatter plots using tensorboard. i know "import matplotlib.pyplot as plt" can do that. but i can just use console (putty). so can't use this method. can i see dot graph, like scatter plots using tensorboard. can anyone

问题 now, i'm studying tensorflow. but, i can't draw dot graph using tensorboard. if i have sample data for training, like that train_X = numpy.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779]) train_Y = numpy.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366]) i want to show scatter plots using tensorboard. i know "import matplotlib.pyplot as plt" can do that. but i can just use console (putty). so can't use this method. can i see dot graph, like scatter plots using tensorboard. can anyone

问题 I have followed the TensorFlow Layers tutorial to create a CNN for MNIST digit classification using TensorFlow's tf.layers module. Now I'm trying to learn how to use TensorBoard from TensorBoard: Visualizing Learning. Perhaps this tutorial hasn't been updated recently, because it says its example code is a modification of that tutorial's and links to it, but the code is completely different: it manually defines a single-hidden-layer fully-connected network. The TensorBoard tutorial shows how

问题 I have followed the TensorFlow Layers tutorial to create a CNN for MNIST digit classification using TensorFlow's tf.layers module. Now I'm trying to learn how to use TensorBoard from TensorBoard: Visualizing Learning. Perhaps this tutorial hasn't been updated recently, because it says its example code is a modification of that tutorial's and links to it, but the code is completely different: it manually defines a single-hidden-layer fully-connected network. The TensorBoard tutorial shows how

问题 I'm following Sentdex's DQN tutorial. I'm stuck trying to rewrite custom TensorBoard in TF 2.0. The point is to add **stats to a file, for example: {'reward_avg': -99.0, 'reward_min': -200, 'reward_max': 2, 'epsilon': 1} Original code: class ModifiedTensorBoard(TensorBoard): def __init__(self, **kwargs): super().__init__(**kwargs) self.step = 1 self.writer = tf.summary.FileWriter(self.log_dir) # Custom method for saving own metrics # Creates writer, writes custom metrics and closes writer def

问题 I'm following Sentdex's DQN tutorial. I'm stuck trying to rewrite custom TensorBoard in TF 2.0. The point is to add **stats to a file, for example: {'reward_avg': -99.0, 'reward_min': -200, 'reward_max': 2, 'epsilon': 1} Original code: class ModifiedTensorBoard(TensorBoard): def __init__(self, **kwargs): super().__init__(**kwargs) self.step = 1 self.writer = tf.summary.FileWriter(self.log_dir) # Custom method for saving own metrics # Creates writer, writes custom metrics and closes writer def

问题 I'm following Sentdex's DQN tutorial. I'm stuck trying to rewrite custom TensorBoard in TF 2.0. The point is to add **stats to a file, for example: {'reward_avg': -99.0, 'reward_min': -200, 'reward_max': 2, 'epsilon': 1} Original code: class ModifiedTensorBoard(TensorBoard): def __init__(self, **kwargs): super().__init__(**kwargs) self.step = 1 self.writer = tf.summary.FileWriter(self.log_dir) # Custom method for saving own metrics # Creates writer, writes custom metrics and closes writer def