Code for Keras plays catch blog post
python qlearn.py
- Generate figures
import tensorflow as tf | |
import numpy as np | |
import os | |
import gym | |
import time | |
import sklearn | |
import itertools | |
import sklearn.pipeline | |
import sklearn.preprocessing | |
from sklearn.kernel_approximation import RBFSampler |
import matplotlib.pyplot as plt | |
import numpy as np | |
def show_images(images, cols = 1, titles = None): | |
"""Display a list of images in a single figure with matplotlib. | |
Parameters | |
--------- | |
images: List of np.arrays compatible with plt.imshow. | |
Code for Keras plays catch blog post
python qlearn.py
"""Illustration for various types of namespace scopes in TensorFlow. | |
> python tf_scopes.py | |
foo_name_scoped : | |
v.name= v:0 | |
v2.name= foo_name_scoped/v2:0 | |
a.name= Variable:0 | |
b.name= Variable_1:0 | |
result_op.name= foo_name_scoped/Add:0 | |
foo_op_scoped : |
""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
# You need to install scikit-learn: | |
# sudo pip install scikit-learn | |
# | |
# Dataset: Polarity dataset v2.0 | |
# http://www.cs.cornell.edu/people/pabo/movie-review-data/ | |
# | |
# Full discussion: | |
# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn | |
# first: | |
lsbom -f -l -s -pf /var/db/receipts/org.nodejs.pkg.bom | while read f; do sudo rm /usr/local/${f}; done | |
sudo rm -rf /usr/local/lib/node /usr/local/lib/node_modules /var/db/receipts/org.nodejs.* | |
# To recap, the best way (I've found) to completely uninstall node + npm is to do the following: | |
# go to /usr/local/lib and delete any node and node_modules | |
cd /usr/local/lib | |
sudo rm -rf node* |
# By Jake VanderPlas | |
# License: BSD-style | |
import matplotlib.pyplot as plt | |
import numpy as np | |
def discrete_cmap(N, base_cmap=None): | |
"""Create an N-bin discrete colormap from the specified input map""" |
ZIP,LAT,LNG | |
00601,18.180555, -66.749961 | |
00602,18.361945, -67.175597 | |
00603,18.455183, -67.119887 | |
00606,18.158345, -66.932911 | |
00610,18.295366, -67.125135 | |
00612,18.402253, -66.711397 | |
00616,18.420412, -66.671979 | |
00617,18.445147, -66.559696 | |
00622,17.991245, -67.153993 |