char-rnn (многослойная повторяющаяся нейронная сеть) реализация получает IndexError

Question

Я пытаюсь реализовать это char-rnn.py с небольшими изменениями в моей системе.

Это мой полный код:

from keras.models import Sequential 
from keras.layers import Dense, Activation,TimeDistributedDense, Dropout 
from keras.layers import LSTM 
from keras.optimizers import RMSprop 
from keras.utils.data_utils import get_file 
import numpy 

# Obtain the corpus of character sequence to train from. 
# Here it is just the sequence 123456789 repeated 100000 times. 
x = "123456789"*1000 

# Construct a dictionary, and the reverse dictionary for the participating chars. 
# '*" is a 'start-sequence' character. 
dct = ['*'] + list(set(x)) 
max_features = len(dct) 
rev_dct = [(j, i) for i, j in enumerate(dct)] 
rev_dct = dict(rev_dct) 

# Convert the characters to their dct indexes. 
x = [rev_dct[ch] for ch in x] 

# Divide the corpuse to substrings of length 200. 
n_timestamps = 200 
x = x[:len(x)- len(x) % n_timestamps] 
x = numpy.array(x, dtype='int32').reshape((-1, n_timestamps)) 

# Generate input and ouput per substring, as an indicator matrix. 
y = numpy.zeros((x.shape[0], x.shape[1], max_features), dtype='int32') 
for i in numpy.arange(x.shape[0]): 
    for j in numpy.arange(x.shape[1]): 
     y[i, j, x[i, j]] = 1 

# Shift-1 the input sequences to the right, and make them start with '*'. 
x = numpy.roll(y, 1, axis=1) 
x[:, 0, :] = 0 
x[:, 0, 0] = 1 

# Build the model. 

model = Sequential() 
model.add(LSTM(256, return_sequences=True, batch_input_shape=x.shape)) 
model.add(Dense(max_features)) 
model.add(Activation('softmax')) 

optimizer = RMSprop(lr=0.01) 
model.compile(loss='categorical_crossentropy', optimizer=optimizer) 


model.fit(x, y, batch_size=100, nb_epoch=1) 

# Sample 128 sentences (200 characters each) from model. 

def mnrnd(probs): 
    rnd = numpy.random.random() 
    for i in xrange(len(probs)): 
     rnd -= probs[i] 
     if rnd <= 0: 
      return i 
    return i 

sentences = numpy.zeros((45, n_timestamps, max_features)) 
sentences[:, 0, 0] = 1 

# Start sampling char-sequences. At each iteration i the probability over 
# the i-th character of each sequences is computed. 
for i in numpy.arange(n_timestamps): 
    probs = model.predict_proba(sentences)[:,i,:] 
    # Go over each sequence and sample the i-th character. 
    for j in numpy.arange(len(sentences)): 
     sentences[j, i+1, mnrnd(probs[j, :])] = 1 
sentences = [sentence[1:].nonzero()[1] for sentence in sentences] 

# Convert to readable text. 
text = [] 
for sentence in sentences: 
    text.append(''.join([dct[word] for word in sentence])) 

Но я получаю эту ошибку:

Traceback (most recent call last): 
    File "char-rnn.py", line 70, in <module> 
    sentences[j, i+1, mnrnd(probs[j, :])] = 1 
IndexError: index 200 is out of bounds for axis 1 with size 200 

Answer 1

Похоже, он пытается задавить последовательность, которая больше, чем данные.

Глядя на ваш код, это область бросает ошибку:

for i in numpy.arange(n_timestamps): 
probs = model.predict_proba(sentences)[:,i,:] 
# Go over each sequence and sample the i-th character. 
for j in numpy.arange(len(sentences)): 
    sentences[j, i+1, mnrnd(probs[j, :])] = 1 

Проблема, вероятно, что ваши данные n_timestamps долго, но вы пытаетесь предсказать n_timestamps + 1 символ (если вы предсказание i +1).

попытка уменьшить длину вашего цикла один следующим образом:

for i in numpy.arange(n_timestamps - 1): 
probs = model.predict_proba(sentences)[:,i,:] 
# Go over each sequence and sample the i-th character. 
for j in numpy.arange(len(sentences)): 
    sentences[j, i+1, mnrnd(probs[j, :])] = 1 

Answer 2

Там может быть проблема с тем, как вы манипулировать X, я попытался запустить свой код, но столкнулись с разными ошибками, чем те, которые вы упомянули.

Я добавляю другой пример LSTM, который делает то, что вы хотели, и основан на примере here.

Вот код:

from __future__ import print_function 
from keras.models import Sequential 
from keras.layers import Dense, Activation 
from keras.layers import LSTM 
from keras.optimizers import RMSprop 
import numpy as np 
import random 
import sys 

text = "123456789"*1000 
print('corpus length:', len(text)) 

chars = sorted(list(set(text))) 
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)) 

# cut the text in semi-redundant sequences of maxlen characters 
maxlen = 200 
sentences = [] 
next_chars = [] 
for i in range(0, len(text) - maxlen): 
    sentences.append(text[i: i + maxlen]) 
    next_chars.append(text[i + maxlen]) 
print('nb sequences:', len(sentences)) 

print('Vectorization...') 
X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool) 
y = np.zeros((len(sentences), len(chars)), dtype=np.bool) 
for i, sentence in enumerate(sentences): 
    for t, char in enumerate(sentence): 
     X[i, t, char_indices[char]] = 1 
    y[i, char_indices[next_chars[i]]] = 1 


# build the model: a single LSTM 
print('Build model...') 
model = Sequential() 
model.add(LSTM(128, input_shape=(maxlen, len(chars)))) 
model.add(Dense(len(chars))) 
model.add(Activation('softmax')) 

optimizer = RMSprop(lr=0.01) 
model.compile(loss='categorical_crossentropy', optimizer=optimizer) 


def sample(preds, temperature=1.0): 
    # helper function to sample an index from a probability array 
    preds = np.asarray(preds).astype('float64') 
    preds = np.log(preds)/temperature 
    exp_preds = np.exp(preds) 
    preds = exp_preds/np.sum(exp_preds) 
    probas = np.random.multinomial(1, preds, 1) 
    return np.argmax(probas) 

# train the model, output generated text after each iteration 
for iteration in range(1, 60): 
    print() 
    print('-' * 50) 
    print('Iteration', iteration) 
    model.fit(X, y, batch_size=128, nb_epoch=1) 

    start_index = random.randint(0, len(text) - maxlen - 1) 

    for diversity in [0.2, 0.5, 1.0, 1.2]: 
     print() 
     print('----- diversity:', diversity) 

     generated = '' 
     sentence = text[start_index: start_index + maxlen] 
     generated += sentence 
     print('----- Generating with seed: "' + sentence + '"') 
     sys.stdout.write(generated) 

     for i in range(400): 
      x = np.zeros((1, maxlen, len(chars))) 
      for t, char in enumerate(sentence): 
       x[0, t, char_indices[char]] = 1. 

      preds = model.predict(x, verbose=0)[0] 
      next_index = sample(preds, diversity) 
      next_char = indices_char[next_index] 

      generated += next_char 
      sentence = sentence[1:] + next_char 

      sys.stdout.write(next_char) 
      sys.stdout.flush() 
     print()