kastnerkyle

# Author: Kyle Kastner
# License: BSD 3-clause
# THEANO_FLAGS="optimizer=None,compute_test_value=raise" python tanh_rnn.py
import numpy as np
import theano
import theano.tensor as T
from scipy import linalg


class sgd(object):

kastnerkyle

# skip hidden files and sed replace
find . -not -path '*/\.*' -type f -exec sed -i -e 's|from pelican|from ../lib/pelican|' {} +

# replace in every file recursive
find . -type f -exec sed -i -e 's/foo/bar/g' {} +

# replace in all files in current dir 
sed -i -- 's/foo/bar/g' *

# tmp_wav.txt is raw text from dropbox downloads list - pull out wav files, making sure to strip leading whitespace

kastnerkyle

import numpy as np

# Broadcast tricks to repeat a matrix
a = np.arange(100 * 10).reshape((100, 10))
# Number of times to clone each entry
clone_count = 2
# axis 0 clone
b = np.ones((1, clone_count, a.shape[1]))
c = (a[:, None, :] * b).reshape((-1, a.shape[-1]))
# axis 1 clone

kastnerkyle

kastnerkyle

#!/usr/bin/env python

'''
Pure Python implementation of some numerical optimizers

Created on Jan 21, 2011

@author Jiahao Chen
'''

kastnerkyle

"""
bitmap utils and much of the ctc code modified
From Shawn Tan, Rakesh and Mohammad Pezeshki
"""
# Author: Kyle Kastner
# License: BSD 3-clause
from theano import tensor
from scipy import linalg
import theano
import numpy as np

kastnerkyle

from scipy.linalg import svd
from scipy.io import wavfile
import matplotlib.pyplot as plt
import numpy as np
from numpy.lib.stride_tricks import as_strided
import theano
import os
try:
    import urllib.request as urllib  # for backwards compatibility
except ImportError:

kastnerkyle

import numpy as np
import matplotlib.pyplot as plt
from scipy import linalg
from sklearn.externals import joblib

mem = joblib.Memory(cachedir='.')


def plot_gp_confidence(gp, show_gp_points=True, X_low=-1, X_high=1,
                       X_count=1000, xlim=None, ylim=None, show=False):

kastnerkyle

# Stacked LSTMs
# Author: Kyle Kastner
# Based on script from /u/siblbombs
# License: BSD 3-Clause
import tensorflow as tf
from tensorflow.models.rnn import rnn
from tensorflow.models.rnn.rnn_cell import LSTMCell
import numpy as np
import time

kastnerkyle

# Author: Kyle Kastner
# License: BSD 3-clause
# Based on example blog from
# http://kawahara.ca/matlab-jaccard-similarity-coefficient-between-images/
import numpy as np
import matplotlib.pyplot as plt


def _check_01(arr):
    """