One Paragraph of project description goes here
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
import java.io.IOException; | |
import java.net.URLClassLoader; | |
import java.nio.file.Files; | |
import java.nio.file.Paths; | |
import java.nio.file.Path; | |
/** | |
* Example demonstrating a ClassLoader leak. | |
* | |
* <p>To see it in action, copy this file to a temp directory somewhere, |
import matplotlib.pyplot as plt | |
def draw_neural_net(ax, left, right, bottom, top, layer_sizes): | |
''' | |
Draw a neural network cartoon using matplotilb. | |
:usage: | |
>>> fig = plt.figure(figsize=(12, 12)) | |
>>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2]) | |
import pandas as pd | |
import pymysql | |
from sqlalchemy import create_engine | |
engine = create_engine("mysql+pymysql://USER:PASSWORD@HOST:PORT/DBNAME") | |
df = pd.read_sql_query("SELECT * FROM table", engine) | |
df.head() |
===
[core] | |
editor = "code --wait" | |
[diff] | |
tool = default-difftool | |
[difftool "default-difftool"] | |
cmd = "code --wait --diff $LOCAL $REMOTE" | |
[merge] |
#!/usr/bin/env python3 | |
""" | |
Created by @author: craffel | |
Modified on Sun Jan 15, 2017 by anbrjohn | |
Modifications: | |
-Changed xrange to range for python 3 | |
-Added functionality to annotate nodes | |
""" |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, |
"""An example of how to use tf.Dataset in Keras Model""" | |
import tensorflow as tf # only work from tensorflow==1.9.0-rc1 and after | |
_EPOCHS = 5 | |
_NUM_CLASSES = 10 | |
_BATCH_SIZE = 128 | |
def training_pipeline(): | |
# ############# | |
# Load Dataset |