| #!/usr/bin/python | |
| #-*- coding: latin-1 -*- | |
| """This module contains pure Python implementations of the | |
| Levenberg-Marquardt algorithm for data fitting. | |
| """ | |
| import numpy | |
| from numpy import inner, max, diag, eye, Inf, dot | |
| from numpy.linalg import norm, solve |
| import numpy as np | |
| import tensorflow as tf | |
| __author__ = "Sangwoong Yoon" | |
| def np_to_tfrecords(X, Y, file_path_prefix, verbose=True): | |
| """ | |
| Converts a Numpy array (or two Numpy arrays) into a tfrecord file. | |
| For supervised learning, feed training inputs to X and training labels to Y. | |
| For unsupervised learning, only feed training inputs to X, and feed None to Y. |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman