Contribute to ALFA-group/lipizzaner-gan development by creating an account on GitHub. Utilities for deep neural network in chainer. Contribute to tochikuji/chainer-libDNN development by creating an account on GitHub. Tensorflow bindings for the Elixir programming language :muscle: - anshuman23/tensorflex Codes for Layer-wise Optimal Brain Surgeon. Contribute to csyhhu/L-OBS development by creating an account on GitHub.
Machine learning, computer vision, statistics and general scientific computing for .NET - accord-net/framework
img_array1 = np.load(‘images_test.npy’) x = img_array1.reshape(-1,28,28,1) p = model.predict(x[index:index+1]) print(np.argmax(p)) plt.imshow(x[index].reshape((28,28))) plt.show() We show an example of image classification on the Mnist dataset, which is a famous benchmark image dataset for hand-written digits classification. from __future__ import absolute_import, division, print_function !pip install tensorflow==2.0.0-alpha0 import tensorflow as tf from matplotlib import pyplot as plt import numpy as np file = tf.keras.utils.get_file( "grace_hopper.jpg… A curated list of awesome C++ frameworks, libraries and software. - uhub/awesome-cpp Machine learning, computer vision, statistics and general scientific computing for .NET - accord-net/framework Experiments to analyze the decision boundaries of ML models. - yashkant/Decision-Flip-Experiments test.txt - Free download as Text File (.txt), PDF File (.pdf) or read online for free.
A Tensorflow implementation of a Capsule Network that allows you to separate/export the decoder and modify or tweak the dimensions on an Android app - JsFlo/CapsNet
Mnist using ConvNet. Contribute to sharan-dce/CNN-Mnist development by creating an account on GitHub. Generating Images from Captions with Attention. Contribute to mansimov/text2image development by creating an account on GitHub. Keras implementation of Balancing GAN (Bagan) applied to the Mnist example. - IBM/Bagan Trades (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization) - yaodongyu/Trades Updated to the Keras 2.0 API. GitHub Gist: instantly share code, notes, and snippets. Let's suppose that the data is stored in x_tr.npy, y_tr.npy, x_te.npy and y_te.npy files. We will assume that x_tr.npy and x_te.npy have shapes of the form (?, 8, 8, 1). We can then define the class corresponding to this dataset in bgan_util… Contribute to daib13/TwoStageVAE development by creating an account on GitHub.
Learn how to build machine learning models with TensorFlow and put them into your apps
After converting them to NumPy arrays, you can store the raw data somehow in something like a npz, pickle, or HDF5 file. You'll want to make this decision Download. If you're going to use this dataset, please cite the tech report at the def unpickle(file): import pickle with open(file, 'rb') as fo: dict = pickle.load(fo,
Contribute to daib13/TwoStageVAE development by creating an account on GitHub. #!/usr/bin/env sh Caffe_ROOT=/path/to/caffe mkdir dogvscat DOG_VS_CAT_Folder=/path/to/dogvscat cd $DOG_VS_CAT_Folder ## Download datasets (requires first a login) #https://www.kaggle.com/c/dogs-vs-cats/download/train.zip #https://www.kaggle… The implementation of Temporal Generative Adversarial Nets with Singular Value Clipping - pfnet-research/tgan The LRP Toolbox provides simple and accessible stand-alone implementations of LRP for artificial neural networks supporting Matlab and Python. The Toolbox realizes LRP functionality for the Caffe Deep Learning Framework as an extension of…
#!/usr/bin/env sh Caffe_ROOT=/path/to/caffe mkdir dogvscat DOG_VS_CAT_Folder=/path/to/dogvscat cd $DOG_VS_CAT_Folder ## Download datasets (requires first a login) #https://www.kaggle.com/c/dogs-vs-cats/download/train.zip #https://www.kaggle…
code for "Residual Flows for Invertible Generative Modeling". - rtqichen/residual-flows Coding exercises for UPC QML course. Contribute to adauphin/QML-Course-UPC-2018 development by creating an account on GitHub. Code for reproducing work of ICML 2019 paper: Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications - agural/memory-optimal-direct-convolutions