import numpy as np
from keras.models import Model
from keras.models import load_model
from keras.layers import Input,LSTM,Dense
batch_size = 64 # Batch size for training.
epochs = 100 # Number of epochs to train for.
latent_dim = 256 # Latent dimensionality of the encoding space.
num_samples = 10000 # Number of samples to train on.
# Path to the data txt file on disk.
data_path = 'fra.txt'input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
lines = open(data_path,encoding='utf-8').read().split('\n')
for index,line in enumerate(lines[: min(num_samples, len(lines) - 1)]):
input_text, target_text = line.split('\t')
target_text = '\t' + target_text + '\n'
input_texts.append(input_text)
target_texts.append(target_text)
for char in input_text:
if char not in input_characters:
input_characters.add(char)
for char in target_text:
if char not in target_characters:
target_characters.add(char)
input_characters = sorted(list(input_characters))
target_characters = sorted(list(target_characters))
# 统计source和target的字符数
num_encoder_tokens = len(input_characters)
num_decoder_tokens = len(target_characters)
# 取出最长的句子的长度
max_encoder_seq_length = max([len(txt) for txt in input_texts])
max_decoder_seq_length = max([len(txt) for txt in target_texts])
# 打印具体的信息
print('Number of samples:', len(input_texts))
print('Number of unique input tokens:', num_encoder_tokens)
print('Number of unique output tokens:', num_decoder_tokens)
print('Max sequence length for inputs:', max_encoder_seq_length)
print('Max sequence length for outputs:', max_decoder_seq_length)
# 将它们转化为id的形式存储（char-to-id）
input_token_index = dict(
[(char, i) for i, char in enumerate(input_characters)])
target_token_index = dict(
[(char, i) for i, char in enumerate(target_characters)])
# 初始化
encoder_input_data = np.zeros(
(len(input_texts), max_encoder_seq_length, num_encoder_tokens),
dtype='float32')
decoder_input_data = np.zeros(
(len(input_texts), max_decoder_seq_length, num_decoder_tokens),
dtype='float32')
decoder_target_data = np.zeros(
(len(input_texts), max_decoder_seq_length, num_decoder_tokens),
dtype='float32')
print(encoder_input_data.shape)
# 训练测试
for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
for t, char in enumerate(input_text):
encoder_input_data[i, t, input_token_index[char]] = 1.
for t, char in enumerate(target_text):
# decoder_target_data比decoder_input_data提前一个时间步长
decoder_input_data[i, t, target_token_index[char]] = 1.
if t > 0:
# decoder_target_data will be ahead by one timestep
# and will not include the start character.
decoder_target_data[i, t - 1, target_token_index[char]] = 1.
# 定义输入序列并处理它
encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
# 我们丢弃' encoder_output '，只保留状态
encoder_states = [state_h, state_c]# 设置解码器，使用' encoder_states '作为初始状态
decoder_inputs = Input(shape=(None, num_decoder_tokens))
# 我们设置解码器以返回完整的输出序列，并返回内部状态。我们不在训练模型中使用返回状态，但是我们将在推理中使用它们。
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs,
initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
#model.load_weights('s2s.h5')
# Run training
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
# 保存模型
model.save('s2s.h5')# 接下来:推理模式(抽样)
# Here's the drill:
# 1)编码输入，检索初始解码器状态
# 2)以初始状态和“序列开始”token作为目标运行一个解码器步骤。输出将是下一个目标token
# 3)重复当前目标token和当前状态# 定义抽样模型
encoder_model = Model(encoder_inputs, encoder_states)
decoder_state_input_h = Input(shape=(latent_dim,))
decoder_state_input_c = Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_outputs, state_h, state_c = decoder_lstm(
decoder_inputs, initial_state=decoder_states_inputs)
decoder_states = [state_h, state_c]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = Model(
[decoder_inputs] + decoder_states_inputs,
[decoder_outputs] + decoder_states)
# 反向查找令牌索引，将序列解码回可读的内容。
reverse_input_char_index = dict(
(i, char) for char, i in input_token_index.items())
reverse_target_char_index = dict(
(i, char) for char, i in target_token_index.items())def decode_sequence(input_seq):
# 将输入编码为状态向量
states_value = encoder_model.predict(input_seq)
# 生成长度为1的空目标序列
target_seq = np.zeros((1, 1, num_decoder_tokens))
# 用起始字符填充目标序列的第一个字符。
target_seq[0, 0, target_token_index['\t']] = 1.
# 对一批序列的抽样循环(为了简化，这里我们假设批大小为1)
stop_condition = False
decoded_sentence = ''
while not stop_condition:
output_tokens, h, c = decoder_model.predict(
[target_seq] + states_value)
# Sample a token
sampled_token_index = np.argmax(output_tokens[0, -1, :])
sampled_char = reverse_target_char_index[sampled_token_index]
decoded_sentence += sampled_char
# 退出条件:到达最大长度或找到停止字符。
if (sampled_char == '\n' or len(decoded_sentence) > max_decoder_seq_length):
stop_condition = True
# 更新目标序列(长度1)
target_seq = np.zeros((1, 1, num_decoder_tokens))
target_seq[0, 0, sampled_token_index] = 1.
# 更新状态
states_value = [h, c]
return decoded_sentence
for seq_index in range(100):
# 取一个序列(训练测试的一部分)来尝试解码
input_seq = encoder_input_data[seq_index: seq_index + 1]
decoded_sentence = decode_sequence(input_seq)
print('-')
print('Input sentence:', input_texts[seq_index])
print('Decoded sentence:', decoded_sentence)
数据集下载：http://www.manythings.org/anki/fra-eng.zip
