Question

我一直在尝试创建文本预测模型。我浏览了http://curiousily.com/data-science/2017/05/23/tensorflow-for-hackers-part-5.html中的一篇文章。该代码可以正常工作，但是当我将SEQUENCE_LENGTH从40更改为其他值时遇到了问题。请帮助我找到一种修改长度而又不会收到错误的方法。我运行的代码是：

#Setup
import numpy as np
import tensorflow as tf
from numpy.core.multiarray import dtype
from keras.models import Sequential, load_model
from keras.layers import Dense, Activation
from keras.layers import LSTM, Dropout
from keras.layers import TimeDistributed
from keras.layers.core import Dense, Activation, Dropout, RepeatVector
from keras.optimizers import RMSprop
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import pickle
import sys
import heapq
import seaborn as sns
from pylab import rcParams
np.random.seed(42)
tf.set_random_seed(42)

sns.set(style='whitegrid', palette='muted', font_scale=1.5)
rcParams['figure.figsize'] = 12, 5


#Loading the data
path = 'nietzsche.txt'
text = open(path).read().lower()
print ('Corpus length: ',len(text))

#Preprocessing
#Finding all the unique characters in the corpus
chars = sorted(list(set(text)))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))

print ("unique chars: ",len(chars))

#Cutting the corpus into chunks of 39 chars, spacing the sequences by 3 characters
#We will additionally store the next character (the one we need to predict) for every sequence

SEQUENCE_LENGTH = 39
step = 3
sentences = []
next_chars = []
for i in range(0, len(text) - SEQUENCE_LENGTH, step):
    sentences.append(text[i:i+SEQUENCE_LENGTH])
    next_chars.append(text[i+SEQUENCE_LENGTH])
print 'num training examples: ',len(sentences)

#Generating features and labels.
#Using previously generated sequences and characters that need to be predicted to create one-hot encoded vectors

X = np.zeros((len(sentences), SEQUENCE_LENGTH, 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



#Building the model

model = Sequential();
model.add(LSTM(128, input_shape=(SEQUENCE_LENGTH, len(chars))))
model.add(Dense(len(chars)))
model.add(Activation('softmax'))

"""
#Training
optimizer = RMSprop(lr= 0.01)
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

history = model.fit(X, y, validation_split=0.05, batch_size=128, epochs=20, shuffle=True).history


#Saving
model.save('keras_model'+str(SEQUENCE_LENGTH)+'.h5')
pickle.dump(history, open('history'+str(SEQUENCE_LENGTH)+'.p', 'wb'))
"""

#Loading back the saved weights and history

model = load_model('keras_model'+str(SEQUENCE_LENGTH)+'.h5')
history = pickle.load(open('history'+str(SEQUENCE_LENGTH)+'.p', 'rb'))


#Evaluation
plt.plot(history['acc'])
plt.plot(history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc= 'upper left')

plt.savefig("01.Accuracy.png")

plt.plot(history['loss'])
plt.plot(history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc= 'upper left')

plt.savefig("02.Loss.png")


#Testing
def prepare_input(text):
    x = np.zeros((1, SEQUENCE_LENGTH, len(chars)))
    for t, char in enumerate(text):
        x[0, t, char_indices[char]] = 1
    return x
#The sequences must be 40 chars long and the tensor is of the shape (1, 40, 57)


#The sample function
#This function allows us to ask our model what are the next probable characters (The heap simplifies the job)
def sample(preds, top_n = 3):
    preds = np.asarray(preds).astype('float64')
    preds = np.log(preds)
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
    return heapq.nlargest(top_n, range(len(preds)), preds.take)


#Prediction function
def predict_completion(text):
    original_text = text
    generalised = text
    completion = ''
    while True:
        x = prepare_input(text)
        preds = model.predict(x, verbose=0)[0]
        next_index = sample(preds, top_n=1)[0]
        next_char = indices_char[next_index]

        text = text[1:] + next_char
        completion += next_char

        if len(original_text + completion) + 2 > len(original_text) and next_char == ' ':
            return completion

#This methods wraps everything and allows us to predict multiple completions
def predict_completions(text, n = 3):
    x = prepare_input(text)
    preds = model.predict(x, verbose=0)[0]
    next_indices = sample(preds, n)
    return [indices_char[idx] + predict_completion(text[1:] + indices_char[idx]) for idx in next_indices]

quotes = [
    "It is not a lack of love, but a lack of friendship that makes unhappy marriages.",
    "That which does not kill us makes us stronger.",
    "I'm not upset that you lied to me, I'm upset that from now on I can't believe you.",
    "And those who were seen dancing were thought to be insane by those who could not hear the music.",
    "It is hard enough to remember my opinions, without also remembering my reasons for them!"
]

for q in quotes:
    seq = q[:40].lower()
    print seq
    print predict_completions(seq, 5)
    print

        return [indices_char[idx] + predict_completion(text[1:] + indices_char[idx]) for idx in next_indices]

    quotes = [
        "It is not a lack of love, but a lack of friendship that makes unhappy marriages.",
        "That which does not kill us makes us stronger.",
        "I'm not upset that you lied to me, I'm upset that from now on I can't believe you.",
        "And those who were seen dancing were thought to be insane by those who could not hear the music.",
        "It is hard enough to remember my opinions, without also remembering my reasons for them!"
    ]

    for q in quotes:
        seq = q[:40].lower()
        print seq
        print predict_completions(seq, 5)
        print

我得到的错误是索引超出范围： Screen Shot of the error

Answer 1

在很多地方使用下面的代码行。

seq = q[:40].lower()

将其更改为

seq = q[:SEQUENCE_LENGTH].lower()

我认为您假设SEQUENCE_LENGTH为40，并在代码中对其进行了硬编码

更改序列长度时出错

1 个答案: