kastnerkyle

kastnerkyle

#!/usr/bin/env python

'''
Pure Python implementation of some numerical optimizers

Created on Jan 21, 2011

@author Jiahao Chen
'''

kastnerkyle

class SPPLayer(lasagne.layers.Layer):
    def __init__(self, incoming, **kwargs):
        super(SPPLayer, self).__init__(incoming, **kwargs)

        # divide by 4 gives 16 patches
        self.win1 = (int(np.floor(incoming.output_shape[2]/4.0)), int(np.floor(incoming.output_shape[3]/4.0)))
        self.str1 = (int(np.ceil(incoming.output_shape[2]/4.0)), int(np.ceil(incoming.output_shape[3]/4.0)))

        # divide by 2 gives 4 patches
        self.win2 = (int(np.floor(incoming.output_shape[2]/2.0)), int(np.floor(incoming.output_shape[3]/2.0)))

kastnerkyle

 def gaussian_density_batch(x, mean, stddev, correlation, compute_derivatives=False): 
  """
  Compute the Gaussian density at x for a 2D normal distribution with parameters mean, stddev, correlation.
  
  This works simultaneously on a batch of inputs.  The inputs should have dimensions: 
    x.shape = (n, 1, 2)
    mean.shape = stddev.shape = (n, m, 2)
    correlation.shape = (n, m, 1)
  where n*m is the number of different Gaussian density functions that we want to evaluate, on n input points x.  
  So the same input x is plugged into the density for m Gaussian pdfs.  (This is convenient for evaluating a 

kastnerkyle

# smidi.py: A simple midi-file library
# make a single file version for midi-outfile only.
# Original code is from Max M's site here (GPL)
# http://www.mxm.dk/products/public/pythonmidi
# Modified by korakot,
# http://snippets.dzone.com/posts/show/572

"""
include source codes from
- constants.py

kastnerkyle

kastnerkyle

import os
import requests
from bs4 import BeautifulSoup


class GSheet(object):
    base_sheet_url = 'https://spreadsheets.google.com/feeds/worksheets/{0}/private/full'
    list_worksheet_url = 'https://spreadsheets.google.com/feeds/list/{0}/{{}}/private/full'
    cell_worksheet_url = 'https://spreadsheets.google.com/feeds/cells/{0}/{{}}/private/full'
    HTTPheaders = {'content-type': 'application/atom+xml'}

kastnerkyle

import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):

kastnerkyle

kastnerkyle

"""
Maxout implementation in Theano
"""

# W,b        - Parameters in neural network layer
# activation - Activation function
# maxoutsize - Number of input neurons to maxout units

# Output activation function
output = activation(T.dot(input,W) + b)