Code for Keras plays catch blog post
python qlearn.py
- Generate figures
from __future__ import print_function | |
import imageio | |
from PIL import Image | |
import numpy as np | |
import keras | |
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation | |
from keras.models import Model | |
from keras.regularizers import l2 | |
from keras.optimizers import SGD |
Code for Keras plays catch blog post
python qlearn.py
# Context manager to generate batches in the background via a process pool | |
# Usage: | |
# | |
# def batch(seed): | |
# .... # generate minibatch | |
# return minibatch | |
# | |
# with BatchGenCM(batch) as bg: | |
# minibatch = next(bg) | |
# .... # do something with minibatch |
##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
import subprocess, itertools, numpy | |
import matplotlib.pyplot as plt | |
command = 'git log --shortstat --log-size --format=oneline --no-merges'.split() | |
data = subprocess.check_output(command).split('\n') | |
def read_groups(): | |
buf = [] | |
for line in data: | |
buf.append(line) |