I am new to Tensorflow and TFLearn and when I was following some tutorials I found the tool Projector https://www.youtube.com/watch?v=eBbEDRsCmv4&t=629s. I was trying to use it with TFLearn but I couldn't found any example in the internet and the documentation in the Tensorflow page is not the very intuitive https://www.tensorflow.org/programmers_guide/embedding. Can somebody help me with a proper example that integrate TFLearn and projector.
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I have a Deep Learning Code for Object Detection. What I did is that I ran the code on Google Colab and then Exported the model to use it locally. Now to run the model I have to again install whole Tensorflow package which is quite heavy for my system.
I want to ask if there is a way to download and run only specific parts of Tensorflow Library?
I am using Tensorflow at only 2 places in my code and I have to install whole Tensorflow library for it.
This is where I am loading the model.
detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL)
This is where I am using Tensorflow 2nd time.
input_tensor = tf.convert_to_tensor(image_rgb)
These are the only 2 functions required to me from the Tensorflow Library and not the whole library... Thanks in anticipation.
Though I'm not entirely sure on the library as a whole, there is a Lite version of Tensorflow (I guess they realised 430MB is a bit much too).
Information regarding this can be found here:
https://www.tensorflow.org/lite/
A guide here seems to detail how to pick and choose parts of the Lite library and although not used myself, I should expect some degree of compatibility between the two...
https://www.tensorflow.org/lite/guide/reduce_binary_size
I am very confused after reading a lot about keras and tensorFlow, and still have some basic questions in my mind.
My confusion started from the answer of this question, where he writes keras standalone and from tensorflow.keras import keras.
1- (Python case):
Does keras use any backend when I write this line of code import keras, and No single line of code related to tensorflow e.g tf.keras or tf.keras.layers in my full implementation of the model, but only import keras? if it does, then is there any way to check what backend is being used?
2- Same question in the case of R Language.
3 - Is TensorFlow only used as backend when we write import tensorflow as tf and import tf.keras ?
4- Does import keras and import tf.keras have any discrepency in performance and accuracy in case of python?
5- Does versions of keras and tensorFlow have an impact in performance and accuracy in both language (R and Python) ?
6- What could be the reasons to have 5% accuracy difference in R and Python. Python gives 94%, while the same implementation in R gives 89% accuracy. The versions of keras & tensorFlow in R are 2.3.0, 2.2.0, while the versions in Python are : tf: 2.3.0, keras: 2.4.3. Please see this one.
I am using https://github.com/fizyr/keras-retinanet this implementation of retinanet which is implemented with tensorflow and keras. So I want to use produced model in c++ for inference. But when I search for it, I can't find anything to try for tensorflow version>=2.0 . There is a good documentation for this operation for pytorch https://pytorch.org/tutorials/advanced/cpp_export.html . I am looking for tensorflow version of it. Thanks.
I have trained segmentation and classification network in python using Tensorflow 2.1. The Model is saved using SavedModel (.pb). Now, I want to test the model, I need this to be done in C++.
I saw many information about C++ API for Tensorflow 1.x, except TF 2.x.
The official tensorflow site says "Note: There is no libtensorflow support for TensorFlow 2 yet. It is expected in a future release.".
Does anyone know of a possible way?
It will be great help to me.
Thanks.
I am following a deep learning tutorial book in Japanese and it is using MNIST for its handwritten images. It has the code from dataset.mnist import load_dataset, and when I tried it, it did not work, gave an error saying no such module named dataset.mnist. I have downloaded the modules dataset and mnist individually using pip. The book recommended to use Anaconda, but I have tried it to no success.
How can I use the module dataset.mnist?
The first question I want to ask you is which deep learning framework are you working with for solving your problem.
There are many deep learning frameworks. PyTorch, Tensorflow, and Keras are examples of such frameworks.
1) If you are working with PyTorch framework then you should import the torch framework using the command.
import torch
and then you can import MNIST dataset using the command
from torchvision.datasets import MNIST
2) For Keras framework use the following commands for importing MNIST dataset.
import keras
from keras.datasets import mnist
NOTE: This can be written as well for better understanding of your problem.
import keras
from keras.datasets as datasets
and then you can import MNIST dataset using thedatasets which is an alias of keras.datasets
Similarly, you can import MNIST dataset in other frameworks as well.
Hope this will be of your help.
Adding on to #SauravRai's Answer
For tensorflow :
from tensorflow.examples.tutorials.mnist import input_data
input_data.read_data_sets('my/directory')