| from __future__ import print_function | |
| import argparse | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.optim as optim | |
| from torchvision import datasets, transforms | |
| import datetime | |
| def five_convnet(X, model, y=None, reg=0.0): | |
| W1, b1, W2, b2 = model['W1'], model['b1'], model['W2'], model['b2'] | |
| W3, b3, W4, b4 = model['W3'], model['b3'], model['W4'], model['b4'] | |
| W5, b5, W6, b6 = model['W5'], model['b5'], model['W6'], model['b6'] | |
| conv_filter_height, conv_filter_width = W1.shape[2:] | |
| assert conv_filter_height == conv_filter_width, 'Conv filter must be square' | |
| assert conv_filter_height % 2 == 1, 'Conv filter height must be odd' |
| mkdir /truba/home/{username}/python3 | |
| cd python3 | |
| wget https://www.python.org/ftp/python/3.5.2/Python-3.5.2.tgz | |
| tar zxfv Python-3.5.2.tgz | |
| cd Python-3.5.2.tgz | |
| ./configure --prefix=/truba/home/{username}/python3 | |
| make && make install | |
| export PATH=/truba/home/{username}/python3/Python-3.5.2/:$PATH | |
| export PYTHONPATH=/truba/home/{username}/python3/Python-3.5.2 |
| import json | |
| data = json.load(open('instances_train2017.json')) | |
| print data.keys() | |
| #OUTPUT : | |
| # [u'info', u'licenses', u'images', u'annotations', u'categories'] | |
| print data['categories'] | |
| ''' |
| 7 | |
| 2 | |
| 1 | |
| 0 | |
| 4 | |
| 1 | |
| 4 | |
| 9 | |
| 5 | |
| 9 |
| # Written by MSK | |
| import glob | |
| import cv2 | |
| pngs = glob('./*.png') | |
| for png in pngs: | |
| img = cv2.imread(png) | |
| cv2.imwrite(png[:-3] + 'jpg', img) |
| import numpy as np | |
| from pycocotools.coco import COCO | |
| import os | |
| import math | |
| import keras | |
| from keras.models import Sequential, Model | |
| from keras.layers import Dense, Activation, Input, Flatten, Dropout | |
| from keras.utils import plot_model | |
| import cv2 | |
| import matplotlib.pyplot as plt |
| y_true = 2 | |
| y_pred = 0.2 | |
| if(K.backend()=="tensorflow"): | |
| import tensorflow as tf | |
| sess= tf.Session() | |
| with tf.device('/cpu:0'): | |
| pt = tf.where(tf.equal(y_true, 1), y_pred, 1 - y_pred) | |
| print(sess.run(pt)) | |
| if(K.backend()=="theano"): |
##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
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