| ''' | |
| Implementation for https://arxiv.org/abs/1906.00672 | |
| Tips: The code could be directly used in place of BadahnauMonotonicAttention in Tensorflow codes. Similar to its | |
| base class in the Tensorflow seq2seq codebase, you may use "hard" for hard inference, or "parallel" for training or | |
| soft inference. "recurrent" mode in BadahnauMonotonicAttention is not supported. | |
| If you have already trained another model using BadahnauMonotonicAttention, the model could be reused, otherwise you | |
| possibly have to tune the score_bias_init, which, similar to that in Raffel et al., 2017, is determined a priori to | |
| suit the moving speed of the alignments, i.e. speed of speech of your training corpus in TTS cases. So |
This dataset contains ~443k anime face images of size 256x256 drawn by ~7,000 artists, obtained from Danbooru
We first downloaded JSON files of all existing posts numbered from 1 to 2,800,000 using their API. We filtered the posts by the following criteria:
| """ | |
| 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 |
| import sys, getopt | |
| import tensorflow as tf | |
| usage_str = 'python tensorflow_rename_variables.py --checkpoint_dir=path/to/dir/ ' \ | |
| '--replace_from=substr --replace_to=substr --add_prefix=abc --dry_run' | |
| def rename(checkpoint_dir, replace_from, replace_to, add_prefix, dry_run): | |
| checkpoint = tf.train.get_checkpoint_state(checkpoint_dir) |
| #!/usr/bin/env python | |
| from __future__ import print_function | |
| from keras.models import Sequential | |
| from keras.layers import TimeDistributed | |
| from keras.layers.core import Dense, Activation, Dropout, RepeatVector, TimeDistributedDense | |
| from keras.layers.recurrent import LSTM | |
| from keras.utils.data_utils import get_file | |
| import numpy as np | |
| import random,string | |
| import sys |
| git fetch --all | |
| git reset --hard origin/master | |
| git pull origin master |