tf-slim | lightweight library for defining , training | Graph Database library
kandi X-RAY | tf-slim Summary
kandi X-RAY | tf-slim Summary
TF-Slim is a lightweight library for defining, training and evaluating complex models in TensorFlow. Components of tf-slim can be freely mixed with native tensorflow, as well as other frameworks.. Note: Latest version of TF-Slim, 1.1.0, was tested with TF 1.15.2 py2, TF 2.0.1, TF 2.1 and TF 2.2.
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Top functions reviewed by kandi - BETA
- Batch normalization
- Build variable getter for model variable scope
- Creates a getter for a model variable
- Adds variable to collection
- Optimizes a loss
- Add noise to gradients
- Clips gradients by global norm
- Train the model
- Wait for a single step
- Cluster the cluster loss
- Assigns variables from a checkpoint
- Inception v2
- Computes the loss of a weighted struct loss
- Creates a fully connected layer
- R Compute the mean squared squared error between predictions
- Generate a ROC curve with a ROC curve
- Instance Normalization
- Group norm
- Creates a separable convolution layer
- Wrapper for inception_v3
- Legacy fully connected layer
- Evaluate a checkpoint
- Create a train op
- Convert a list of indices to a sparse matrix
- Transpose input tensor
- Transpose input tensors
tf-slim Key Features
tf-slim Examples and Code Snippets
Community Discussions
Trending Discussions on tf-slim
QUESTION
I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:
...ANSWER
Answered 2022-Feb-07 at 09:19It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason
QUESTION
I have not seen anything in the rllib documentation that would allow me to print a quick summary of the model like print(model.summary()) in keras. I tried using tf-slim and
ANSWER
Answered 2022-Jan-04 at 08:20The training agent can return the policy which gives you access to the model:
QUESTION
I am trying to install the Tensorflow Object Detection API on a Google Colab and the part that installs the API, shown below, takes a very long time to execute (in excess of one hour) and eventually fails to install.
...ANSWER
Answered 2021-Nov-19 at 00:16I have solved this problem with
QUESTION
import cv2
import numpy as np
from tensorflow.keras.layers import Concatenate
import tensorflow as tf
im=cv2.imread(val_path)#image reading
im = np.array(im) # no channel
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) # no channel
dx = cv2.Sobel(gray, cv2.CV_32F, 1, 0) # float
dy = cv2.Sobel(gray, cv2.CV_32F, 0, 1)
mag = cv2.magnitude(dx, dy) # gradient magnitude , no channel
mag = np.clip(mag, 0, 255).astype(np.uint8)
mag = cv2.cvtColor(mag, cv2.COLOR_GRAY2RGB) # convert mag to RGBA
mag = np.array(mag)#(282, 366, 3)
concat = Concatenate([im, mag])# using Concatenate of tensorflow.keras.layers
concat = tf.cast( concat, dtype=tf.float32)
#concat:(564, 366, 3)
input_image=np.expand_dims(concat, axis=0)/255.0)#
output_image_t, output_image_r = sess.run([transmission_layer, reflection_layer],feed_dict={input: input_image})###error
#transmission_layer, reflection_layer is fine
ANSWER
Answered 2021-Feb-18 at 05:55I fixed your code. It was just a small mistake. You missed a ()
this is the line a changed
QUESTION
I wanted to try creating an object detection model, so I followed this official tutorial. Everything worked fine. I could use GPU, and no error while I was installing COCO API. As I checked by "Verify the installation" step in the tutorial.
"Verify the installation" tell me to run below code
...ANSWER
Answered 2021-Jan-11 at 04:48Tensorflow 2.2 uses CUDA 10.1 but after running the object-detection api your tensorflow is getting updated to 2.4, at which point GPU no longer is used to run the default test program.
This is because, Tensorflow 2.4.0 uses CUDA 11.0. If you simply update cuda to the right version, you will have this working.
QUESTION
As in the subject I am trying to implement sending e-mail with multiple language versions recognized on the basis of the language in the receiver browser.
I would like to send the same email message in English and Spanish for example, but the receiver will see only one option in his preferred (by email provider settings) language version.
I am trying to build it on the basis of https://tools.ietf.org/id/draft-ietf-slim-multilangcontent-14.html#Examples, but I am not sure if it is even possible because I didn't find any working implementation of this example. I am using Python and Pyramid Mailer.
I could not set properly 2 different Content-Language in one email message.
Please help me if you have any idea if it is possible to send this type of emails.
...ANSWER
Answered 2020-Sep-29 at 09:47Like the example you link to shows, you have to have multiple MIME parts, each with a separate Content-Language: header.
Here's an attempt at recreating the sample. Unfortunately, Python seems to scrub the Content-Language: header if you add it with suben['Content-Language'] = 'en-GB' and move it to the headers of the attachment with add_header, so I'm not sure this is going to work at all. There is also a pesky conflict between the Content-Disposition: I add and the one added by Python. If I use msg.add_attachment()suben, 'rfc822', 'inline') I get an angry error message saying 'inline' is not supported for message/rfc822 parts. (Maybe you'd need to create a new content manager for this type?)
QUESTION
I need to classify small images in 4 different categories, +1 "background" for false detection.
While training the loss quickly drop to 0.7, but stay there even after 800k steps. In the end, the frozen graph seems to classify most images with the background label.
I'm probably missing something, I'll detail the steps I used below, and any feedback is welcomed. I'm new to tf-slim, so it can be an obvious mistake, maybe too little samples ? I'm not looking for top accuracy, just something working for prototyping.
Source materials can be found there : https://www.dropbox.com/s/k55xoygdzb2efag/TilesDataset.zip?dl=0
I used tensorflow-gpu 1.15.3 on windows 10.
I created the dataset using :
python ./createTfRecords.py --tfrecord_filename=tilesV2_40 --dataset_dir=.\tilesV2\Tiles_40I added a dataset provider in models-master\research\slim\datasets based on the flowers provider.
I modified the mobilnet_v2.py in models-master\research\slim\nets\mobilenet, changed num_classes=5 and mobilenet.default_image_size = 40
I trained the net with :
python ./models-master/research/slim/train_image_classifier.py --model_name "mobilenet_v2" --learning_rate 0.045 --preprocessing_name "inception_v2" --label_smoothing 0.1 --moving_average_decay 0.9999 --batch_size 96 --learning_rate_decay_factor 0.98 --num_epochs_per_decay 2.5 --train_dir ./weight --dataset_name Tiles_40 --dataset_dir .\tilesV2\Tiles_40When I try this
python .\models-master\research\slim\eval_image_classifier.py --alsologtostderr --checkpoint_path ./weight/model.ckpt-XXX --dataset_dir ./tilesV2/Tiles_40 --dataset_name Tiles_40 --dataset_split_name validation --model_name mobilenet_v2I geteval/Recall_5[1]eval/Accuracy[1]I then export the graph with
python .\models-master\research\slim\export_inference_graph.py --alsologtostderr --model_name mobilenet_v2 --image_size 40 --output_file .\export\output.pb --dataset_name Tiles_40And freeze it with
freeze_graph --input_graph .\export\output.pb --input_checkpoint .\weight\model.ckpt-XXX --input_binary true --output_graph .\export\frozen.pb --output_node_names MobilenetV2/Predictions/Reshape_1I then try the net with images from the dataset with
python .\label_image.py --graph .\export\frozen.pb --labels .\tilesV2\Tiles_40\labels.txt --image .\tilesV2\Tiles_40\photos\lac\1_1.png --input_layer input --output_layer MobilenetV2/Predictions/Reshape_1. This is where I get wrong classifications., like0:background 0.92839915 2:lac 0.020171663 1:house 0.019106707 3:road 0.01677236 4:start 0.0155500565for a "lac" image of the dataset
I tried changing the depth_multiplier, the learning rate, learning on a cpu, removing --preprocessing_name "inception_v2" from the learning command. I don't have any idea left...
ANSWER
Answered 2020-Jul-19 at 15:32Change your learning rate, maybe start from the usual choice of 3e-5.
QUESTION
I'm trying to make a custom object-detection model following this tutorial: https://towardsdatascience.com/custom-object-detection-using-tensorflow-from-scratch-e61da2e10087
Everything worked fine until the Step 10, where I have to train my model using the train.py script (with tensorflow 1).
When I run the script (macOS) :
...ANSWER
Answered 2020-Jun-29 at 15:06Your link shows the problem. There the programmer had to find bottleneck function at main. So in your case you have to find depth_multiplier at nets.mobilenet.mobilenet. IF the last one is something that is downloaded during selection of backbone for the model then you are at a dead end. Sorry but you have to train with Tensorflow v2. and better to follow instructions at Tensorflow github. I am happy to help if you have.... a dataset.
If I wanted to train I would follow this. It is the safest solution with examples. Inside you can find how to bring your own dataset....which is the hardest part as it takes time to prepare it right!
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Install tf-slim
You can use tf-slim like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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