chmodsss

import csv
import numpy as np
from collections import Counter
from nltk.corpus import brown
from mittens import GloVe, Mittens
from sklearn.feature_extraction import stop_words
from sklearn.feature_extraction.text import CountVectorizer


def glove2dict(glove_filename):

chmodsss

import torch
from torchvision.datasets import MNIST
from torch import nn
from torchvision import transforms
import torch.optim as optim
from matplotlib import pyplot as plt
import matplotlib.cm as cm

import warnings
warnings.filterwarnings("ignore")

chmodsss

class Discriminator(nn.Module):
    def __init__(self):
        super().__init__()
        
        ip_emb = 784
        emb1 = 256
        emb2 = 128
        out_emb = 1
        
        self.layer1 = nn.Sequential(

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import collections
import pandas as pd
import numpy as np

sents = [s for s in cdata.split()]

vocab = sorted(collections.Counter(sents))
vocab2idx = {v:idx for idx,v in enumerate(vocab)}
idx2vocab = {idx:v for idx,v in enumerate(vocab)}

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import string

puncts = string.punctuation.replace('.','')
punct = str.maketrans('','', puncts)

data = open('story.txt','r').read()

# data pre-processing only for alphabet strings
def clean(xx):
    return ' '.join(x for x in xx.split() if not x.isnumeric())

chmodsss

chmodsss

import pandas as pd
import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

dig = load_digits()
onehot_target = pd.get_dummies(dig.target)
x_train, x_val, y_train, y_val = train_test_split(dig.data, onehot_target, test_size=0.1, random_state=20)

def sigmoid(s):

chmodsss

import pandas as pd
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import RMSprop, Adadelta, Adam
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

dig = load_digits()
onehot_target = pd.get_dummies(dig.target)
x_train, x_val, y_train, y_val = train_test_split(dig.data, onehot_target, test_size=0.1, random_state=20)

chmodsss

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

%matplotlib inline

dig = load_digits()
plt.gray()
plt.matshow(dig.images[25])

chmodsss

#!/bin/sh
SAVEIFS=$IFS
IFS=$(echo -en "\n\b")
SRC_PATH=path/to/source/folder
DST_PATH=path/to/destination/folder
for src_dir in $(ls $SRC_PATH)
	do 
		for src_file in $(ls $SRC_PATH/$src_dir)
			do 
				src_fpath=$SRC_PATH/$src_dir/$src_file