tensorboard

问题 I am training a ResNet50 on Audioset2017 dataset,with tensorflow during training and validating results,my loss function fluctuating,the overall trend is going down,but I am afraid of this. I have run with 100 epochs,with batch size=100. and have decrease and increase the learning rate,but had no effect. want to know is my training correct,can I use this network?or it causes wrong results.Can I remove them with some tricks? these are my train and validation(eval) loss and other metrics

问题 I am studying tensorboard code from Dandelion Mane specificially: https://github.com/dandelionmane/tf-dev-summit-tensorboard-tutorial/blob/master/mnist.py His convolution layer is specifically defined as: def conv_layer(input, size_in, size_out, name="conv"): with tf.name_scope(name): w = tf.Variable(tf.truncated_normal([5, 5, size_in, size_out], stddev=0.1), name="W") b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B") conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding=

问题 I"m going through this tutorial to set up pytorch (v1.3.0 through conda) with tensorboard https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html# but on the step from torch.utils.tensorboard import SummaryWriter # default `log_dir` is "runs" - we'll be more specific here writer = SummaryWriter('runs/fashion_mnist_experiment_1') I keep getting the error --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last

问题 I run Tensorboard from local with: tensorboard --port 8080 --logdir gs://root-album-8512/output/summaries/ --debug and then I access: http://arynas:8080/ I got this error message: enter image description here In my local terminal there is error like that: tensorflow/core/platform/cloud/retrying_utils.cc:77] The operation failed and will be automatically retried in 1.15606 seconds (attempt 1 out of 10), caused by: Unavailable: Error executing an HTTP request (HTTP response code 0, error code

问题 I run my detection model on google cloud ml and got this error while running the evaluation script. I found this link that mentioned about this issue, but it seems like the issue's till not be solved. Anyone knows how to fix this? Any helps would be appreciated. Thanks. ERROR 2018-02-04 12:53:10 -0600 master-replica-0 Couldn't match files for checkpoint gs://obj-detection/train/model.ckpt-0 INFO 2018-02-04 12:53:10 -0600 master-replica-0 No model found in gs://obj-detection/train. Will try

问题 I'm trying to run tensorboard but it keeps showing the same error. tensorboard --logdir=tensorflow/logdir File "<stdin>", line 1 SyntaxError: can't assign to operator I'm using Ubuntu 16.04 and installed tensorflow-gpu by virtualenv. 回答1: You are running tensorboard --logdir=tensorflow/logdir from an interactive python shell or ipython/jupyter-notebook. As mentioned in the comment above, you need to run this command from the terminal. Alternatively, you can run the command from ipython

问题 I'm trying to display my embeddings in tensorboard. When I open embeddings tab of tensorboard I get: "Computing PCA..." and tensorboard hangs infinitely. Before that it does load my tensor of shape 200x128. It does find the metadata file too. I tried that on TF versions 0.12 and 1.1 with the same result. features = np.zeros(shape=(num_batches*batch_size, 128), dtype=float) embedding_var = tf.Variable(features, name='feature_embedding') config = projector.ProjectorConfig() embedding = config

问题 In order to learn tensorflow, I executed this tensorflow official mnist script (cnn_mnist.py) and displayed the graph with tensorboard. The following is part of the code. This network contains two conv layers and two dense layers. conv1 = tf.layers.conv2d(inputs=input_layer,filters=32,kernel_size=[5, 5], padding="same",activation=tf.nn.relu) pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) conv2 = tf.layers.conv2d(inputs=pool1,filters=64,kernel_size=[5, 5], padding=

问题 I'm trying to a simple plot on tensorboard, just like they have it on homepage, something like this: To understand how this is working I've wrote the following : import tensorflow as tf import numpy as np x = tf.placeholder('float',name='X') y= tf.placeholder('float',name='y') addition = tf.add(x,y) with tf.Session() as sess: for i in range(100): var1= np.random.rand() var2= np.random.rand() print(var1,var2) tf.summary.scalar('addition',sess.run(addition, feed_dict={x:var1,y:var2})) writer =

问题 I am reading a book on Tensorflow and I find this code: from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf const1 = tf.constant(2) const2 = tf.constant(3) add_opp = tf.add(const1,const2) mul_opp = tf.mul(add_opp, const2) with tf.Session() as sess: result, result2 = sess.run([mul_opp,add_opp]) print(result) print(result2) tf.train.SummaryWriter('./',sess.graph) so it is very simple, nothing fancy and it is