SimpleRNN实现生成文本

　　尝试使用Keras构建我们的第一个RNN实例，在《爱丽丝梦游仙境》的文本上训练一个基于字符的语言模型，这个模型将通过给定的前10个字符预测下一个字符。我们选择一个基于字符的模型，是因为它的字典较小，并可以训练的更快，这和基于词的语言模型的想法是一样的。

1. 文本预处理

首先我们先获取《爱丽丝梦游仙境的》输入文本
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导入必要的库，读入文件并作基本的处理

from keras.layers.recurrent import SimpleRNN
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np

INPUT_FILE = "./alice_in_wonderland.txt"

# extract the input as a stream of characters
print("Extracting text from input...")
fin = open(INPUT_FILE, 'rb')
lines = []
for line in fin:
    line = line.strip().lower()
    line = line.decode("ascii", "ignore")
    if len(line) == 0:
        continue
    lines.append(line)
fin.close()
text = " ".join(lines)

因为我们在构建一个字符级水平的RNN，我们将字典设置为文本中出现的所有字符。因为我们将要处理的是这些字符的索引而非字符本身，于是我们要创建必要的查询表：

chars = set([c for c in text])
nb_chars = len(chars)
char2index = dict((c, i) for i, c in enumerate(chars))
index2char = dict((i, c) for i, c in enumerate(chars))

2. 创建输入和标签文本

我们通过STEP变量给出字符数目（本例为1）来步进便利文本，并提取出一段大小为SEQLEN变量定义值（本例为10）的文本段。文本段的下一字符是我们的标签字符。

#   例如：输入"The sky was falling"，输出如下（前5个）：
#   The sky wa -> s
#   he sky was ->  
#   e sky was  -> f
#    sky was f -> a
#   sky was fa -> l
print("Creating input and label text...")
SEQLEN = 10
STEP = 1

input_chars = []
label_chars = []
for i in range(0, len(text) - SEQLEN, STEP):
    input_chars.append(text[i:i + SEQLEN])
    label_chars.append(text[i + SEQLEN])

3. 输入和标签文本向量化

RNN输入中的每行都对应了前面展示的一个输入文本。输入中共有SEQLEN个字符，因为我们的字典大小是nb_chars给定的，我们把每个输入字符表示成one-hot编码的大小为(nb_chars)的向量。这样每行输入就是一个大小为(SEQLEN, nb_chars)的张量。我们的输出标签是一个单个的字符，所以和输入中的每个字符的表示类似。我们将输出标签表示成大小为(nb_chars)的one-hot编码的向量。因此，每个标签的形状就是nb_chars。

print("Vectorizing input and label text...")
X = np.zeros((len(input_chars), SEQLEN, nb_chars), dtype=np.bool)
y = np.zeros((len(input_chars), nb_chars), dtype=np.bool)
for i, input_char in enumerate(input_chars):
    for j, ch in enumerate(input_char):
        X[i, j, char2index[ch]] = 1
    y[i, char2index[label_chars[i]]] = 1

4. 构建模型

设定超参数BATCH_SIZE = 128
我们想返回一个字符作为输出，而非字符序列，因而设置return_sequences=False
输入形状为(SEQLEN, nb_chars)
为了改善TensorFlow后端性能，设置unroll=True

HIDDEN_SIZE = 128
BATCH_SIZE = 128
NUM_ITERATIONS = 25
NUM_EPOCHS_PER_ITERATION = 1
NUM_PREDS_PER_EPOCH = 100

model = Sequential()
model.add(SimpleRNN(HIDDEN_SIZE, return_sequences=False,
                    input_shape=(SEQLEN, nb_chars),
                    unroll=True))
model.add(Dense(nb_chars))
model.add(Activation("softmax"))

model.compile(loss="categorical_crossentropy", optimizer="rmsprop")

5. 模型的训练和测试

我们分批训练模型，每一步都测试输出
测试模型：我们先从输入里随机选一个，然后用它去预测接下来的100个字符

for iteration in range(NUM_ITERATIONS):
    print("=" * 50)
    print("Iteration #: %d" % (iteration))
    model.fit(X, y, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS_PER_ITERATION, verbose=0)

    test_idx = np.random.randint(len(input_chars))
    test_chars = input_chars[test_idx]
    print("Generating from seed: %s" % (test_chars))
    print(test_chars, end="")
    for i in range(NUM_PREDS_PER_EPOCH):
        Xtest = np.zeros((1, SEQLEN, nb_chars))
        for i, ch in enumerate(test_chars):
            Xtest[0, i, char2index[ch]] = 1
        pred = model.predict(Xtest, verbose=0)[0]
        ypred = index2char[np.argmax(pred)]
        print(ypred, end="")
        # move forward with test_chars + ypred
        test_chars = test_chars[1:] + ypred
    print()

6. 输出测试结果

模型刚开始的预测毫无意义，当随着训练轮数的增加，它已经可以进行正确的拼写，尽管在表达语义上还有困难。毕竟我们这个模型是基于字符的，它对词没有任何认识，然而它的表现也足够令人惊艳了。

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完整代码如下：

from keras.layers.recurrent import SimpleRNN
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np

INPUT_FILE = "./alice_in_wonderland.txt"

# extract the input as a stream of characters
print("Extracting text from input...")
fin = open(INPUT_FILE, 'rb')
lines = []
for line in fin:
    line = line.strip().lower()
    line = line.decode("ascii", "ignore")
    if len(line) == 0:
        continue
    lines.append(line)
fin.close()
text = " ".join(lines)

chars = set([c for c in text])
nb_chars = len(chars)
char2index = dict((c, i) for i, c in enumerate(chars))
index2char = dict((i, c) for i, c in enumerate(chars))

#   例如：输入"The sky was falling"，输出如下（前5个）：
#   The sky wa -> s
#   he sky was ->
#   e sky was  -> f
#    sky was f -> a
#   sky was fa -> l
print("Creating input and label text...")
SEQLEN = 10
STEP = 1

input_chars = []
label_chars = []
for i in range(0, len(text) - SEQLEN, STEP):
    input_chars.append(text[i:i + SEQLEN])
    label_chars.append(text[i + SEQLEN])

print("Vectorizing input and label text...")
X = np.zeros((len(input_chars), SEQLEN, nb_chars), dtype=np.bool)
y = np.zeros((len(input_chars), nb_chars), dtype=np.bool)
for i, input_char in enumerate(input_chars):
    for j, ch in enumerate(input_char):
        X[i, j, char2index[ch]] = 1
    y[i, char2index[label_chars[i]]] = 1

HIDDEN_SIZE = 128
BATCH_SIZE = 128
NUM_ITERATIONS = 25
NUM_EPOCHS_PER_ITERATION = 1
NUM_PREDS_PER_EPOCH = 100

model = Sequential()
model.add(SimpleRNN(HIDDEN_SIZE, return_sequences=False,
                    input_shape=(SEQLEN, nb_chars),
                    unroll=True))
model.add(Dense(nb_chars))
model.add(Activation("softmax"))

model.compile(loss="categorical_crossentropy", optimizer="rmsprop")

for iteration in range(NUM_ITERATIONS):
    print("=" * 50)
    print("Iteration #: %d" % (iteration))
    model.fit(X, y, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS_PER_ITERATION, verbose=0)

    test_idx = np.random.randint(len(input_chars))
    test_chars = input_chars[test_idx]
    print("Generating from seed: %s" % (test_chars))
    print(test_chars, end="")
    for i in range(NUM_PREDS_PER_EPOCH):
        Xtest = np.zeros((1, SEQLEN, nb_chars))
        for i, ch in enumerate(test_chars):
            Xtest[0, i, char2index[ch]] = 1
        pred = model.predict(Xtest, verbose=0)[0]
        ypred = index2char[np.argmax(pred)]
        print(ypred, end="")
        # move forward with test_chars + ypred
        test_chars = test_chars[1:] + ypred
    print()