ReiiSky

import java.io.File;
import java.io.FileNotFoundException;
import java.util.PriorityQueue;
import java.util.Scanner;
import java.util.TreeMap;

/* Huffman coding , decoding */

public class Huffman {
    static final boolean readFromFile = false;

ReiiSky

import tensorflow as tf

class testCell(tf.nn.rnn_cell.RNNCell):

    def __init__(self, input_size=1, state_size=1):
        self.input_size = input_size
        self._state_size = state_size

    @property
    def state_size(self):

ReiiSky

from keras import layers
from keras import models
import tensorflow as tf


#
# generator input params
#

rand_dim = (1, 1, 2048)  # dimension of the generator's input tensor (gaussian noise)

ReiiSky

/**
* Apache License 2.0
* see https://www.apache.org/licenses/LICENSE-2.0
*/
import org.apache.commons.math3.distribution.EnumeratedDistribution;
import org.apache.commons.math3.util.Pair;
import org.nd4j.linalg.api.iter.NdIndexIterator;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.SetRange;
import org.nd4j.linalg.api.ops.impl.transforms.SoftMax;

ReiiSky

from keras.layers import Dense, Dropout, LSTM, Embedding
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
import pandas as pd
import numpy as np

input_file = 'input.csv'

def load_data(test_split = 0.2):
    print ('Loading data...')

ReiiSky

from keras.layers import Dense, Dropout, LSTM, Embedding
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
import pandas as pd
import numpy as np

input_file = 'input.csv'

def load_data(test_split = 0.2):
    print ('Loading data...')

ReiiSky

from keras.layers import Dense, Dropout, LSTM, Embedding
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
import pandas as pd
import numpy as np

input_file = 'input.csv'

def load_data(test_split = 0.2):
    print ('Loading data...')

ReiiSky

from keras.layers import Dense, Dropout, LSTM, Embedding
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
import pandas as pd
import numpy as np

input_file = 'input.csv'

def load_data(test_split = 0.2):
    print ('Loading data...')

ReiiSky

"""
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)

ReiiSky

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
 Denoising Autoencoders (dA)

 References :
   - P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol: Extracting and
   Composing Robust Features with Denoising Autoencoders, ICML'08, 1096-1103,
   2008