def generate_continuous_reberwords(number, minimumlength):
D = []
for i in range(number):
dataset = ''
while len(dataset) < minimumlength:
embeddedStep = random.choice('TP')
dataset += 'B' + embeddedStep + make_reber(
1)[0] + embeddedStep + 'E'
D.append(dataset)
return D
def test_reber_wrod_generation(self):
words = make_reber(50)
self.assertEqual(50, len(words))
for word in words:
self.assertTrue(is_valid_by_reber(word))
import numpy as np
from neupy.datasets import make_reber
import matplotlib.pyplot as plt
import matplotlib
n = [100, 200,500, 1000,2000,5000, 10000, 20000, 50000, 100000, 500000, 1000000, 5000000, 10000000]
x = np.zeros(len(n))
for j, u in enumerate(n):
data = make_reber(u)
for word in data:
x[j] += len(word)
x[j] /= u
plt.semilogx(n,abs(x-6))
plt.xlabel('N reber words')
plt.ylabel(' err = |average - 6|')
plt.title('Convergence of average reber word length')
matplotlib.rcParams.update({'font.size': 12})
plt.show()
from neupy.datasets import make_reber
# notes: zero padds asks for a mask to neglect zeros
# We will not padd
# look for to categorical from np.utils
# use roll to shift y from x
# Check fit models that are stateful, must forget between batches
# must not forget between steps
batch_size = 20
n_batch = 50
#redefine this. IT is to make 1000 reber words
iteration = 10 #learning
dataset = make_reber(batch_size * n_batch)
dataset = ''.join(dataset)
chars = sorted(list(set(dataset)))
char_indices = dict((c, i) for i, c in enumerate(chars))
print('DataSet concatenated lenght:', len(dataset))
batches = []
next_char = []
step = 1
for i in range(0, len(dataset) - batch_size, step):
batches.append(dataset[i:i + batch_size])
next_char.append(dataset[i + batch_size])
print('nb batches of size {}:'.format(batch_size), len(batches))
print('Vectorization...')
X = np.zeros((len(batches), batch_size, len(chars)), dtype=np.bool)
N_Epoch = 80
print('Making dataset of {} chars * {} steps, with {} times batches of {}'.\
format(step_size,n_step,100,batch_size))
D = []
for i in range(n_batch * batch_size):
dataset = ''
while len(dataset) < step_size * n_step + 1 - 0.1 * step_size * n_step:
embeddedStep = random.choice('TP')
custom = 'P'
if (embeddedStep == 'P'):
custom = 'X'
#dataset += 'B' + embeddedStep + 'B' + make_reber(1)[0] + custom + 'E' + embeddedStep + 'E'
dataset += 'B' + embeddedStep + 'B' + make_reber(
1)[0] + 'E' + embeddedStep + 'E'
D.append(dataset)
#print(len(zip(*D)[0]))
chars = sorted(list(set(D[0])))
char_indices = dict((c, i) for i, c in enumerate(chars))
print(char_indices)
batches = []
next_char = []
print('Vectorization...')
#X = np.zeros((n_batch*batch_size, step_size*n_step+1, len(chars)), dtype=np.bool)
#for j, word in enumerate(D):
# for i in range(step_size*n_step+1):
# if(i<len(word)):
def test_reber_word_generation(self):
words = make_reber(50)
self.assertEqual(50, len(words))
for word in words:
self.assertTrue(is_valid_by_reber(word))
def test_reber_expcetions(self):
with self.assertRaises(ValueError):
make_reber(n_words=0)
with self.assertRaises(ValueError):
make_reber(n_words=-1)
def test_reber_expcetions(self):
with self.assertRaises(ValueError):
make_reber(n_words=0)
with self.assertRaises(ValueError):
make_reber(n_words=-1)
from keras.optimizers import RMSprop
from neupy.datasets import make_reber
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Activation, TimeDistributed
from keras.layers import LSTM
from keras.utils import np_utils, plot_model
import pydot
import graphviz
import numpy as np
samples = make_reber(1000)
chars = sorted(list(set(samples)))
concatenated_samples = 'B' + 'EB'.join(samples) + 'E'
X = concatenated_samples[0:(len(concatenated_samples) - 1)]
Y = concatenated_samples[1:len(concatenated_samples)]
chars = sorted(list(set(X)))
print('total chars:', len(chars))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
chars = sorted(list(set(X)))
def batchify(X, Y, num_batches, batch_size, batch_length):
retX = np.ndarray(
shape=np.append([num_batches, batch_size, batch_length], X.shape[1:]))
retY = np.ndarray(
shape=np.append([num_batches, batch_size, batch_length], Y.shape[1:]))
from keras.optimizers import RMSprop
from neupy.datasets import make_reber
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, TimeDistributed
from keras.layers import LSTM
from keras.utils import np_utils, plot_model
import numpy as np
samples = make_reber(1000)
chars = sorted(list(set(samples)))
concatenated_samples = 'B'+'EB'.join(samples)+'E'
X = concatenated_samples[0:(len(concatenated_samples)-1)]
Y = concatenated_samples[1:len(concatenated_samples)]
chars = sorted(list(set(X)))
print('total chars:', len(chars))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
#Array = np.asarray([char_indices[c] for c in X])
categorized = np_utils.to_categorical([char_indices[c] for c in X])
categorizedY = np_utils.to_categorical([char_indices[c] for c in Y])
decategorized = np.argmax(categorized,axis=1)
decoded = ''.join([indices_char[i] for i in decategorized])
def batchify(X,Y,num_batches,batch_size,batch_length):
