| import torch | |
| import torch.nn as nn | |
| def log_sum_exp(x): | |
| # See implementation detail in | |
| # http://timvieira.github.io/blog/post/2014/02/11/exp-normalize-trick/ | |
| # b is a shift factor. see link. | |
| # x.size() = [N, C]: | |
| b, _ = torch.max(x, 1) |
| from keras.layers.merge import Concatenate | |
| from keras.layers.core import Lambda | |
| from keras.models import Model | |
| import tensorflow as tf | |
| def make_parallel(model, gpu_count): | |
| def get_slice(data, idx, parts): | |
| shape = tf.shape(data) | |
| size = tf.concat([ shape[:1] // parts, shape[1:] ],axis=0) |
| from keras.layers import Conv2D, Activation, BatchNormalization, Add, Input, GlobalAveragePooling2D, Dense | |
| from keras.models import Model | |
| from keras.optimizers import SGD | |
| from keras.datasets import cifar10 | |
| from keras.utils import to_categorical | |
| from keras.initializers import he_normal | |
| from keras.preprocessing.image import ImageDataGenerator | |
| from keras.callbacks import Callback, LearningRateScheduler | |
| from keras.regularizers import l2 | |
| import numpy as np |
#VPNGate for OS X This script is a simple script for using to connect vpn server list provided VPNGate through OpenVPN on OS X environment.
It inspired by VPNGate Python script wrtten by Andrea Lazzarotto.
##Requirements If you use the script, Tunnelblick needs to be installed first.
##Installation Open terminal app and copy & paste the command below.
This script allows to use the free VPN service provided by VPNGate in an easy way. The user just needs to provide the desidered output country, and the script automatically chooses the best server.
After this step, OpenVPN is launched with the proper configuration. The VPN can be terminated by pressing Ctrl+C.
Run the script by providing the desired output country:
| """ | |
| Clean and simple Keras implementation of network architectures described in: | |
| - (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf). | |
| - (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf). | |
| Python 3. | |
| """ | |
| from keras import layers | |
| from keras import models |
| #!/usr/bin/env python | |
| """ | |
| Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction. | |
| """ | |
| from __future__ import print_function, division | |
| import numpy as np | |
| from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten | |
| from keras.models import Sequential |