| import numpy as np | |
| import matplotlib as mpl | |
| mpl.use('pgf') | |
| def figsize(scale): | |
| fig_width_pt = 469.755 # Get this from LaTeX using \the\textwidth | |
| inches_per_pt = 1.0/72.27 # Convert pt to inch | |
| golden_mean = (np.sqrt(5.0)-1.0)/2.0 # Aesthetic ratio (you could change this) | |
| fig_width = fig_width_pt*inches_per_pt*scale # width in inches | |
| fig_height = fig_width*golden_mean # height in inches |
##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
| # Instructions for 4.14 and cuda 9.1 | |
| # If upgrading from 4.13 and cuda 9.0 | |
| $ sudo apt-get purge --auto-remove libcud* | |
| $ sudo apt-get purge --auto-remove cuda* | |
| $ sudo apt-get purge --auto-remove nvidia* | |
| # also remove the container directory direcotory at /usr/local/cuda-9.0/ | |
| # Important libs required with 4.14.x with Cuda 9.X | |
| $ sudo apt install libelf1 libelf-dev |