Python 改变'；温度'；在RNN中生成文本

我最近遵循了一个生成RNN以生成文本的步骤： 我准确地复制了python代码，并对其进行了理解。 我的模型已经训练了20个时代，它产生了一个由3个单词组成的长循环：

"and the wour and the wour and the wour..."

我在Andrej Kapathy的书中读到，改变RNN的温度会改变其可信度：

将温度从1降低到某个较低的数值（例如0.5）可以使RNN更加可靠，但其样本也更加保守

我想改变这个温度水平来降低RNN的可信度，以便它创建新的模式，但由于这是我的第一个机器学习项目，我不知道如何进行

以下是我的Python/keras代码：

正在生成文本文件：

# Generate Text
import sys
import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils

filename = "king_lear.txt"
raw_text = open(filename).read()
raw_text = raw_text.lower()

chars = sorted(list(set(raw_text)))
char_to_int = dict((c, i) for i, c in enumerate(chars))
int_to_char = dict((i, c) for i, c in enumerate(chars))

n_chars = len(raw_text)
n_vocab = len(chars)
print "Total Characters: ", n_chars
print "Total Vocab: ", n_vocab

seq_length = 100
dataX = []
dataY = []
for i in range(0, n_chars - seq_length, 1):
    seq_in = raw_text[i:i + seq_length]
    seq_out = raw_text[i + seq_length]
    dataX.append([char_to_int[char] for char in seq_in])
    dataY.append(char_to_int[seq_out])
n_patterns = len(dataX)
print "Total Patterns: ", n_patterns

X = numpy.reshape(dataX, (n_patterns, seq_length, 1))

X = X / float(n_vocab)

y = np_utils.to_categorical(dataY)

model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(256))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))

filename = "weights-improvement-08-2.0298-bigger.hdf5"
model.load_weights(filename)
model.compile(loss='categorical_crossentropy', optimizer='adam')

start = numpy.random.randint(0, len(dataX)-1)
pattern = dataX[start]
print "Seed:"
print "\"", ''.join([int_to_char[value] for value in pattern]), "\""

for i in range(60):
    x = numpy.reshape(pattern, (1, len(pattern), 1))
    x = x / float(n_vocab)
    prediction = model.predict(x, verbose=0)
    index = numpy.argmax(prediction)
    result = int_to_char[index]
    seq_in = [int_to_char[value] for value in pattern]
    sys.stdout.write(result)
    pattern.append(index)
    pattern = pattern[1:len(pattern)]

print "\nDone."

学习档案：

# Learn Sentences
import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils



filename = "king_lear.txt"
raw_text = open(filename).read()
raw_text = raw_text.lower()

chars = sorted(list(set(raw_text)))
char_to_int = dict((c, i) for i, c in enumerate(chars))

n_chars = len(raw_text)
n_vocab = len(chars)
print "Total Characters: ", n_chars
print "Total Vocab: ", n_vocab

seq_length = 100
dataX = []
dataY = []
for i in range(0, n_chars - seq_length, 1):
    seq_in = raw_text[i:i + seq_length]
    seq_out = raw_text[i + seq_length]
    dataX.append([char_to_int[char] for char in seq_in])
    dataY.append(char_to_int[seq_out])
n_patterns = len(dataX)
print "Total Patterns: ", n_patterns

X = numpy.reshape(dataX, (n_patterns, seq_length, 1))

X = X / float(n_vocab)

y = np_utils.to_categorical(dataY)

model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(256))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

filepath="weights-improvement-{epoch:02d}-{loss:.4f}-bigger.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]

model.fit(X, y, epochs=50, batch_size=64, callbacks=callbacks_list)

请帮我做这个。如果这篇文章有什么问题，请不要犹豫纠正我，因为这是我的第一个问题。 非常感谢。

查看Keras GitHub。可以在softmax之前添加Lambda层，以除以温度：

model.add(Lambda(lambda x: x / temp))

根据：

对于高温，所有行为的概率几乎相同，温度越低，预期回报对概率的影响越大。对于低温，预期回报最高的行为概率趋于1

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非常感谢。我是否在生成输出的generate_text.py中添加这一行。还是在培训代码中？而且，如果我想让温度高，我是使用一个高值，还是一个低值，因为在训练代码中，在softmax层之前，它除以。高温=高值，低温=低值。