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Option Explicit | |
Sub AddElements() | |
Dim shp As Shape | |
Dim i As Integer, n As Integer | |
n = ActivePresentation.Slides.Count | |
For i = 1 To n | |
Dim s As Slide | |
Set s = ActivePresentation.Slides(i) |
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"""akmtdfgen: A Keras multithreaded dataframe generator. | |
Works with Python 2.7 and Keras 2.x. | |
For Python 3.x, need to fiddle with the threadsafe generator code. | |
Test the generator_from_df() functions by running this file: | |
python akmtdfgen.py |
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""" | |
This is a batched LSTM forward and backward pass | |
""" | |
import numpy as np | |
import code | |
class LSTM: | |
@staticmethod | |
def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
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""" | |
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) |
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def adam(loss, all_params, learning_rate=0.0002, beta1=0.1, beta2=0.001, | |
epsilon=1e-8, gamma=1-1e-7): | |
""" | |
ADAM update rules | |
Default values are taken from [Kingma2014] | |
References: | |
[Kingma2014] Kingma, Diederik, and Jimmy Ba. | |
"Adam: A Method for Stochastic Optimization." | |
arXiv preprint arXiv:1412.6980 (2014). |
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def adam(loss, all_params, learning_rate=0.001, b1=0.9, b2=0.999, e=1e-8, | |
gamma=1-1e-8): | |
""" | |
ADAM update rules | |
Default values are taken from [Kingma2014] | |
References: | |
[Kingma2014] Kingma, Diederik, and Jimmy Ba. | |
"Adam: A Method for Stochastic Optimization." | |
arXiv preprint arXiv:1412.6980 (2014). |
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""" | |
The MIT License (MIT) | |
Copyright (c) 2015 Alec Radford | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
of this software and associated documentation files (the "Software"), to deal | |
in the Software without restriction, including without limitation the rights | |
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
copies of the Software, and to permit persons to whom the Software is |
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import theano | |
import theano.tensor as T | |
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams | |
from theano.tensor.signal.downsample import max_pool_2d | |
from theano.tensor.extra_ops import repeat | |
from theano.sandbox.cuda.dnn import dnn_conv | |
from time import time | |
import numpy as np | |
from matplotlib import pyplot as plt |
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import os | |
import numpy as np | |
from matplotlib import pyplot as plt | |
from time import time | |
from foxhound import activations | |
from foxhound import updates | |
from foxhound import inits | |
from foxhound.theano_utils import floatX, sharedX |
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import sys | |
sys.path.append('..') | |
import os | |
import json | |
from time import time | |
import numpy as np | |
from tqdm import tqdm | |
from matplotlib import pyplot as plt | |
from sklearn.externals import joblib |
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