<commit_before>"""
Authors: 
<NAME> - <<EMAIL>>
<NAME> - <<EMAIL>>

#License: MIT
"""

"""This script trains an auto-encoder on the MNIST dataset and keeps track of the lowerbound"""

#python trainmnist.py -s mnist.npy

import VariationalAutoencoder
import numpy as np
import argparse
import time
import gzip, cPickle
import scipy.io.wavfile
import os
from pydub import AudioSegment
from preprocess import pickle_loader,pickle_saver,map_to_range
import matplotlib.pyplot as plt
import numpy
from sn_plot import plot_reconstructed

parser = argparse.ArgumentParser()
parser.add_argument("-d","--double", help="Train on hidden layer of previously trained AE - specify params", default = False)

args = parser.parse_args()

print "Loading MNIST data"
#Retrieved from: http://deeplearning.net/data/mnist/mnist.pkl.gz

f = gzip.open('mnist.pkl.gz', 'r')
(x_train, t_train), (x_valid, t_valid), (x_test, t_test)  = cPickle.load(f)
f.close()

#TOFO: Make all input convensions N*D
#x_train = pickle_loader("sound/test_data.pkl")
x_train = pickle_loader("sound/puretone_data.pkl")

#print all_data.shape
x_test = x_train

t = np.linspace(0, 2, 4001)

#x_train = np.array([numpy.sin(t*100), numpy.sin(t*200), numpy.sin(t*40), numpy.sin(t*50)])*.5

n_steps = 400

dimZ = 4<commit_msg>Silly bug<commit_after>"""
Authors: 
<NAME> - <<EMAIL>>
<NAME> - <<EMAIL>>

#License: MIT
"""

"""This script trains an auto-encoder on the MNIST dataset and keeps track of the lowerbound"""

#python trainmnist.py -s mnist.npy

import VariationalAutoencoder
import numpy as np
import argparse
import time
import gzip, cPickle
import scipy.io.wavfile
import os
from pydub import AudioSegment
from preprocess import pickle_loader,pickle_saver,map_to_range
import matplotlib.pyplot as plt
import numpy
from sn_plot import plot_reconstructed

parser = argparse.ArgumentParser()
parser.add_argument("-d","--double", help="Train on hidden layer of previously trained AE - specify params", default = False)

args = parser.parse_args()

print "Loading MNIST data"
#Retrieved from: http://deeplearning.net/data/mnist/mnist.pkl.gz

f = gzip.open('mnist.pkl.gz', 'r')
(x_train, t_train), (x_valid, t_valid), (x_test, t_test)  = cPickle.load(f)
f.close()

#TOFO: Make all input convensions N*D
#x_train = pickle_loader("sound/test_data.pkl")
x_train = pickle_loader("sound/puretone_data.pkl")

#print all_data.shape
x_test = x_train

t = np.linspace(0, 2, 4001)

#x_train = np.array([numpy.sin(t*100), numpy.sin(t*200), numpy.sin(t*40), numpy.sin(t*50)])*.5

n_steps = 10

dimZ = 4
{'fraction_non_alphanumeric': 0.1321274085725521, 'fraction_numerical': 0.016122689736531654, 'mean_word_length': 5.456852791878172, 'pattern_counts': {'":': 0, '<': 15, '<?xml version=': 0, '>': 15, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2988267', 'n_tokens_mistral': 984, 'n_tokens_neox': 942, 'n_words': 248}