I hereby claim:
- I am nategeorge on github.
- I am ngeorge (https://keybase.io/ngeorge) on keybase.
- I have a public key ASBN62tlMiBh7pp7XiZdcynW33evbt0WQ_b5c9JwWV6H1Qo
To claim this, I am signing this object:
import numpy as np | |
from sklearn.ensemble import RandomForestRegressor | |
# simulate data | |
# 12 rows train, 6 rows test, 5 features, 3 columns for target | |
features = np.random.random((12, 5)) | |
targets = np.random.random((12, 3)) | |
test_features = np.random.random((6, 5)) | |
rfr = RandomForestRegressor(random_state=42) |
I hereby claim:
To claim this, I am signing this object:
#!/usr/bin/env python | |
""" | |
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction. | |
""" | |
from __future__ import print_function, division | |
import numpy as np | |
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten | |
from keras.models import Sequential |
##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
# taken from here: http://web.archive.org/web/20110527163743/https://svn.enthought.com/enthought/browser/sandbox/docs/coding_standard.py | |
""" This module is an example of the Enthought Python coding standards. | |
It was adapted from the Python Enhancement Proposal 8 (aka PEP 8) titled | |
'Style Guide for Python Code' (http://www.python.org/peps/pep-0008.html). | |
The first item in a module must be a documentation string (docstring). The | |
first line of the docstring should be a one line summary. If a more | |
detailed description is required, put an empty line before it. |