kandi X-RAY | tensorflow Summary
kandi X-RAY | tensorflow Summary
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well. TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages. Keep up-to-date with release announcements and security updates by subscribing to email@example.com. See all the mailing lists.
Top functions reviewed by kandi - BETA
- Run a single model iteration .
- Splits the given computation into multiple tensors .
- Decorate a function .
- Compute an RNN layer .
- Compute the eigenvalues of a Hermitagonal matrix .
- Decorator for functions .
- Produce an RNN layer .
- Return the gradient of an einsum operator .
- Creates a csv dataset .
- Extracts inputs and attrs .
tensorflow Key Features
tensorflow Examples and Code Snippets
img_final = np.reshape(img, (1,784))
# !pip install keras-rl2 import tensorflow as tf from keras.layers import Dense, Flatten import gym from rl.agents.dqn import DQNAgent from rl.policy import BoltzmannQPolicy from rl.memory import SequentialMemory env = gym.make('CartPole-
(x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data() print(x_trian.shape) # (60000, 28, 28) # train set / data x_train = np.expand_dims(x_train, axis=-1) x_train = tf.image.resize(x_train, [224,224]) print(x_train.shape) #
predict = model.predict(np.array([image])) print(predict)
modelWeights = model.get_weights() model.set_weights(modelWeights)
import tensorflow as tf from tensorflow.python.ops import resource_variable_ops class MyModule(tf.Module): def __init__(self): pass @tf.function(input_signature=[ tf.TensorSpec(shape=[None], dtype=
x_train = np.array([cv2.resize(img, dim) for img in x_train[:,:,:]])
x_train = np.array([cv2.resize(img, dim) for img in x_train])
import tensorflow as tf import matplotlib.pyplot as plt (X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data() train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train)) test_dataset = tf.data.Dataset.f
callbacks = [ tf.keras.callbacks.ModelCheckpoint( filepath=ckpt_path, monitor="val_accuracy", mode='max', save_best_only=True, save_weights_only=False, verbose=1 ) ]
shapes ├── circle │ ├── shared │ └── unshared ├── square │ ├── shared │ └── unshared └── triangle ├── shared └── unshared
import pathlib # Get project root depending on your project structure. PROJE
Trending Discussions on tensorflow
What's the XLA class
XlaBuilder for? The docs describe its interface but don't provide a motivation.
The presentation in the docs, and indeed the comment above
XlaBuilder in the source code
ANSWERAnswered 2021-Dec-15 at 01:32
XlaBuilder is the C++ API for building up XLA computations -- conceptually this is like building up a function, full of various operations, that you could execute over and over again on different input data.
Some background, XLA serves as an abstraction layer for creating executable blobs that run on various target accelerators (CPU, GPU, TPU, IPU, ...), conceptually kind of an "accelerator virtual machine" with conceptual similarities to earlier systems like PeakStream or the line of work that led to ArBB.
XlaBuilder is a way to enqueue operations into a "computation" (similar to a function) that you want to run against the various set of accelerators that XLA can target. The operations at this level are often referred to as "High Level Operations" (HLOs).
XlaOp represents the result of the operation you've just enqueued. (Aside/nerdery: this is a classic technique used in "builder" APIs that represent the program in "Static Single Assignment" form under the hood, the operation itself and the result of the operation can be unified as one concept!)
XLA computations are very similar to functions, so you can think of what you're doing with an
XlaBuilder like building up a function. (Aside: they're called "computations" because they do a little bit more than a straightforward function -- conceptually they are coroutines that can talk to an external "host" world and also talk to each other via networking facilities.)
So the fact
XlaOps can't be used across
XlaBuilders may make more sense with that context -- in the same way that when building up a function you can't grab intermediate results in the internals of other functions, you have to compose them with function calls / parameters. In
XlaBuilder you can
Call another built computation, which is a reason you might use multiple builders.
As you note, you can choose to inline everything into one "mega builder", but often programs are structured as functions that get composed together, and ultimately get called from a few different "entry points". XLA currently aggressively specializes for the entry points it sees API users using, but this is a design artifact similar to inlining decisions, XLA can conceptually reuse computations built up / invoked from multiple callers if it thought that was the right thing to do. Usually it's most natural to enqueue things into XLA however is convenient for your description from the "outside world", and allow XLA to inline and aggressively specialize the "entry point" computations you've built up as you execute them, in Just-in-Time compilation fashion.
I am implementing a simple chatbot using keras and WebSockets. I now have a model that can make a prediction about the user input and send the according answer.
When I do it through command line it works fine, however when I try to send the answer through my WebSocket, the WebSocket doesn't even start anymore.
Here is my working WebSocket code:...
ANSWERAnswered 2022-Feb-16 at 19:53
There is no problem with your websocket route. Could you please share how you are triggering this route? Websocket is a different protocol and I'm suspecting that you are using a HTTP client to test websocket. For example in Postman:
HTTP requests are different than websocket requests. So, you should use appropriate client to test websocket.
It was a project that used to work well in the past, but after updating, the following errors appear....
ANSWERAnswered 2021-Sep-17 at 11:03
Add mavenCentral() in Build Script
For the last 5 days, I am trying to make Keras/Tensorflow packages work in R. I am using RStudio for installation and have used
virtualenv but it crashes each time in the end. Installing a library should not be a nightmare especially when we are talking about R (one of the best statistical languages) and TensorFlow (one of the best deep learning libraries). Can someone share a reliable way to install Keras/Tensorflow on CentOS 7?
Following are the steps I am using to install
tensorflow in RStudio.
Since RStudio simply crashes each time I run
tensorflow::tf_config() I have no way to check what is going wrong.
ANSWERAnswered 2022-Jan-16 at 00:08
Perhaps my failed attempts will help someone else solve this problem; my approach:
- boot up a clean CentOS 7 vm
- install R and some dependencies
I am getting an error when trying to save a model with data augmentation layers with Tensorflow version 2.7.0.
Here is the code of data augmentation:...
ANSWERAnswered 2022-Feb-04 at 17:25
This seems to be a bug in Tensorflow 2.7 when using
model.save combined with the parameter
save_format="tf", which is set by default. The layers
RandomContrast are causing the problems, since they are not serializable. Interestingly, the
Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format
I have created a working CNN model in Keras/Tensorflow, and have successfully used the CIFAR-10 & MNIST datasets to test this model. The functioning code as seen below:...
ANSWERAnswered 2021-Dec-16 at 10:18
If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.
I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).
Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.
That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).
Some further ideas:
- If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
- If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.
i have an import problem when executing my code:...
ANSWERAnswered 2021-Oct-06 at 20:27
You're using outdated imports for
tf.keras. Layers can now be imported directly from
I am writing a small code to calculate the fourth derivative using the method of finite differences in tensorflow. This is as follows:...
ANSWERAnswered 2021-Sep-16 at 13:01
The issue is related to the choice of floating-point types.
tf.float32as its type, while
float64array, which has much more precision.
Making the following modification:
I wrote a unit-test in order to safe a model after noticing that I am not able to do so (anymore) during training....
ANSWERAnswered 2021-Sep-06 at 13:25
Your issue is not related to
The error code tells you this element is referring to a non trackable element. It seems the non-trackable object is not directly assigned to an attribute of this conv2d/kernel:0.
To solve your issue, we need to localize
Tensor("77040:0", shape=(), dtype=resource) from this error code:
I am using the pre-built deep learning VM instances offered by google cloud, with an Nvidia tesla K80 GPU attached. I choose to have Tensorflow 2.5 and CUDA 11.0 automatically installed. When I start the instance, everything works great - I can run:...
ANSWERAnswered 2021-Jun-25 at 09:11
Some people (sadly not me) are able to resolve this by setting the following at the beginning of their script/main:
No vulnerabilities reported
You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.
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