字符级递归序列到序列模型
作者: fchollet
创建日期: 2017/09/29
最后修改日期: 2023/11/22
描述: 字符级递归序列到序列模型。
介绍
本示例演示如何实现一个基本的字符级递归序列到序列模型。我们将其应用于将短英语句子逐字符翻译成短法语句子。请注意，进行字符级机器翻译相对不寻常，因为在该领域单词级模型更为常见。
算法摘要
- 我们从一个领域（例如英语句子）开始输入序列，并从另一个领域（例如法语句子）获取相应的目标序列。
- 编码器LSTM将输入序列转换为2个状态向量（我们保留最后的LSTM状态并丢弃输出）。
- 解码器LSTM被训练将目标序列转换为相同的序列，但时间上向未来偏移一个时间步，这个训练过程在这个上下文中称为“教师强迫”。它使用来自编码器的状态向量作为初始状态。有效地，解码器学习生成
targets[t+1...]，给定targets[...t]，并以输入序列为条件。 - 在推理模式下，当我们想要解码未知输入序列时，我们： - 将输入序列编码为状态向量 - 从大小为1的目标序列（仅起始字符）开始 - 将状态向量和1字符目标序列输入解码器，以生成下一个字符的预测 - 使用这些预测来抽样下一个字符（我们简单地使用argmax）。 - 将抽样的字符附加到目标序列 - 重复此过程，直到生成结束字符或达到字符限制。
设置
import numpy as np
import keras
import os
from pathlib import Path
下载数据
fpath = keras.utils.get_file(origin="http://www.manythings.org/anki/fra-eng.zip")
dirpath = Path(fpath).parent.absolute()
os.system(f"unzip -q {fpath} -d {dirpath}")
0
配置
batch_size = 64 # 训练的批次大小。
epochs = 100 # 训练的周期数。
latent_dim = 256 # 编码空间的潜在维度。
num_samples = 10000 # 用于训练的样本数。
# 磁盘上数据文本文件的路径。
data_path = os.path.join(dirpath, "fra.txt")
准备数据
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
with open(data_path, "r", encoding="utf-8") as f:
lines = f.read().split("\n")
for line in lines[: min(num_samples, len(lines) - 1)]:
input_text, target_text, _ = line.split("\t")
# 我们使用"tab"作为目标的"开始序列"字符
# 使用"\n"作为"结束序列"字符。
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))
num_encoder_tokens = len(input_characters)
num_decoder_tokens = len(target_characters)
max_encoder_seq_length = max([len(txt) for txt in input_texts])
max_decoder_seq_length = max([len(txt) for txt in target_texts])
print("样本数:", len(input_texts))
print("唯一输入标记数:", num_encoder_tokens)
print("唯一输出标记数:", num_decoder_tokens)
print("输入的最大序列长度:", max_encoder_seq_length)
print("输出的最大序列长度:", max_decoder_seq_length)
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)])
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",
)
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]] = 1.0
encoder_input_data[i, t + 1 :, input_token_index[" "]] = 1.0
for t, char in enumerate(target_text):
# decoder_target_data比decoder_input_data提前一个时间步
decoder_input_data[i, t, target_token_index[char]] = 1.0
if t > 0:
# decoder_target_data会提前一个时间步
# 并且不会包含开始字符。
decoder_target_data[i, t - 1, target_token_index[char]] = 1.0
decoder_input_data[i, t + 1 :, target_token_index[" "]] = 1.0
decoder_target_data[i, t:, target_token_index[" "]] = 1.0
样本数: 10000
唯一输入标记数: 70
唯一输出标记数: 93
输入的最大序列长度: 14
输出的最大序列长度: 59
构建模型
# 定义输入序列并处理它。
encoder_inputs = keras.Input(shape=(None, num_encoder_tokens))
encoder = keras.layers.LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
# 我们丢弃 `encoder_outputs` 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 = keras.Input(shape=(None, num_decoder_tokens))
# 我们设置解码器返回完整输出序列，
# 并返回内部状态。我们在训练模型中不使用返回的状态，
# 但在推理中会使用它们。
decoder_lstm = keras.layers.LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)
decoder_dense = keras.layers.Dense(num_decoder_tokens, activation="softmax")
decoder_outputs = decoder_dense(decoder_outputs)
# 定义将
# `encoder_input_data` 和 `decoder_input_data` 转换为 `decoder_target_data` 的模型
model = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)
训练模型
model.compile(
optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"]
)
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_model.keras")
Epoch 1/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.7338 - loss: 1.5405 - val_accuracy: 0.7138 - val_loss: 1.0745
Epoch 2/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.7470 - loss: 0.9546 - val_accuracy: 0.7188 - val_loss: 1.0219
Epoch 3/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.7590 - loss: 0.8659 - val_accuracy: 0.7482 - val_loss: 0.8677
Epoch 4/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.7878 - loss: 0.7588 - val_accuracy: 0.7744 - val_loss: 0.7864
Epoch 5/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.7957 - loss: 0.7092 - val_accuracy: 0.7904 - val_loss: 0.7256
Epoch 6/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.8151 - loss: 0.6375 - val_accuracy: 0.8003 - val_loss: 0.6926
Epoch 7/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.8217 - loss: 0.6095 - val_accuracy: 0.8081 - val_loss: 0.6633
Epoch 8/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8299 - loss: 0.5818 - val_accuracy: 0.8146 - val_loss: 0.6355
Epoch 9/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8346 - loss: 0.5632 - val_accuracy: 0.8179 - val_loss: 0.6285
Epoch 10/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8378 - loss: 0.5496 - val_accuracy: 0.8233 - val_loss: 0.6056
Epoch 11/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8450 - loss: 0.5301 - val_accuracy: 0.8300 - val_loss: 0.5913
Epoch 12/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8487 - loss: 0.5148 - val_accuracy: 0.8324 - val_loss: 0.5805
Epoch 13/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8537 - loss: 0.4996 - val_accuracy: 0.8354 - val_loss: 0.5718
Epoch 14/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8570 - loss: 0.4874 - val_accuracy: 0.8388 - val_loss: 0.5535
Epoch 15/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8603 - loss: 0.4749 - val_accuracy: 0.8428 - val_loss: 0.5451
Epoch 16/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8636 - loss: 0.4642 - val_accuracy: 0.8448 - val_loss: 0.5332
Epoch 17/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8658 - loss: 0.4551 - val_accuracy: 0.8473 - val_loss: 0.5260
Epoch 18/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8689 - loss: 0.4443 - val_accuracy: 0.8465 - val_loss: 0.5236
Epoch 19/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8711 - loss: 0.4363 - val_accuracy: 0.8531 - val_loss: 0.5078
Epoch 20/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8731 - loss: 0.4285 - val_accuracy: 0.8508 - val_loss: 0.5121
Epoch 21/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8759 - loss: 0.4180 - val_accuracy: 0.8546 - val_loss: 0.5005
Epoch 22/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8788 - loss: 0.4075 - val_accuracy: 0.8550 - val_loss: 0.4981
Epoch 23/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8799 - loss: 0.4043 - val_accuracy: 0.8563 - val_loss: 0.4918
Epoch 24/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8820 - loss: 0.3960 - val_accuracy: 0.8584 - val_loss: 0.4870
Epoch 25/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8830 - loss: 0.3927 - val_accuracy: 0.8605 - val_loss: 0.4794
Epoch 26/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8852 - loss: 0.3862 - val_accuracy: 0.8607 - val_loss: 0.4784
Epoch 27/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8877 - loss: 0.3767 - val_accuracy: 0.8616 - val_loss: 0.4753
Epoch 28/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8890 - loss: 0.3730 - val_accuracy: 0.8633 - val_loss: 0.4685
Epoch 29/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8897 - loss: 0.3695 - val_accuracy: 0.8633 - val_loss: 0.4685
Epoch 30/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8924 - loss: 0.3604 - val_accuracy: 0.8648 - val_loss: 0.4664
Epoch 31/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8946 - loss: 0.3538 - val_accuracy: 0.8658 - val_loss: 0.4613
Epoch 32/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8948 - loss: 0.3526 - val_accuracy: 0.8668 - val_loss: 0.4618
Epoch 33/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8972 - loss: 0.3442 - val_accuracy: 0.8662 - val_loss: 0.4597
Epoch 34/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8969 - loss: 0.3435 - val_accuracy: 0.8672 - val_loss: 0.4594
Epoch 35/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.8996 - loss: 0.3364 - val_accuracy: 0.8673 - val_loss: 0.4569
Epoch 36/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9003 - loss: 0.3340 - val_accuracy: 0.8677 - val_loss: 0.4601
Epoch 37/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9024 - loss: 0.3260 - val_accuracy: 0.8671 - val_loss: 0.4569
Epoch 38/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9048 - loss: 0.3200 - val_accuracy: 0.8685 - val_loss: 0.4540
Epoch 39/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9051 - loss: 0.3187 - val_accuracy: 0.8692 - val_loss: 0.4545
Epoch 40/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9071 - loss: 0.3119 - val_accuracy: 0.8708 - val_loss: 0.4490
Epoch 41/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9085 - loss: 0.3064 - val_accuracy: 0.8706 - val_loss: 0.4506
Epoch 42/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9092 - loss: 0.3061 - val_accuracy: 0.8711 - val_loss: 0.4484
Epoch 43/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9100 - loss: 0.3011 - val_accuracy: 0.8718 - val_loss: 0.4485
Epoch 44/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9101 - loss: 0.3007 - val_accuracy: 0.8716 - val_loss: 0.4509
Epoch 45/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9126 - loss: 0.2920 - val_accuracy: 0.8723 - val_loss: 0.4474
Epoch 46/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9144 - loss: 0.2881 - val_accuracy: 0.8714 - val_loss: 0.4505
Epoch 47/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9155 - loss: 0.2829 - val_accuracy: 0.8727 - val_loss: 0.4487
Epoch 48/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9158 - loss: 0.2816 - val_accuracy: 0.8725 - val_loss: 0.4519
Epoch 49/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9174 - loss: 0.2763 - val_accuracy: 0.8739 - val_loss: 0.4454
Epoch 50/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9188 - loss: 0.2706 - val_accuracy: 0.8738 - val_loss: 0.4473
Epoch 51/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9199 - loss: 0.2682 - val_accuracy: 0.8716 - val_loss: 0.4542
Epoch 52/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9202 - loss: 0.2665 - val_accuracy: 0.8725 - val_loss: 0.4533
Epoch 53/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9228 - loss: 0.2579 - val_accuracy: 0.8735 - val_loss: 0.4485
Epoch 54/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9230 - loss: 0.2580 - val_accuracy: 0.8735 - val_loss: 0.4507
Epoch 55/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9237 - loss: 0.2546 - val_accuracy: 0.8737 - val_loss: 0.4579
Epoch 56/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9253 - loss: 0.2482 - val_accuracy: 0.8749 - val_loss: 0.4496
Epoch 57/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9264 - loss: 0.2448 - val_accuracy: 0.8755 - val_loss: 0.4503
Epoch 58/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9271 - loss: 0.2426 - val_accuracy: 0.8747 - val_loss: 0.4526
Epoch 59/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9289 - loss: 0.2380 - val_accuracy: 0.8750 - val_loss: 0.4543
Epoch 60/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9292 - loss: 0.2358 - val_accuracy: 0.8745 - val_loss: 0.4563
Epoch 61/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9297 - loss: 0.2339 - val_accuracy: 0.8750 - val_loss: 0.4555
Epoch 62/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9308 - loss: 0.2299 - val_accuracy: 0.8741 - val_loss: 0.4590
Epoch 63/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9324 - loss: 0.2259 - val_accuracy: 0.8761 - val_loss: 0.4611
Epoch 64/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9329 - loss: 0.2247 - val_accuracy: 0.8751 - val_loss: 0.4608
Epoch 65/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9344 - loss: 0.2187 - val_accuracy: 0.8756 - val_loss: 0.4628
Epoch 66/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9354 - loss: 0.2156 - val_accuracy: 0.8750 - val_loss: 0.4664
Epoch 67/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9360 - loss: 0.2136 - val_accuracy: 0.8751 - val_loss: 0.4665
Epoch 68/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9370 - loss: 0.2093 - val_accuracy: 0.8751 - val_loss: 0.4688
Epoch 69/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9385 - loss: 0.2057 - val_accuracy: 0.8747 - val_loss: 0.4757
Epoch 70/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9388 - loss: 0.2039 - val_accuracy: 0.8752 - val_loss: 0.4748
Epoch 71/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9393 - loss: 0.2020 - val_accuracy: 0.8749 - val_loss: 0.4749
Epoch 72/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9403 - loss: 0.1991 - val_accuracy: 0.8756 - val_loss: 0.4754
Epoch 73/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9417 - loss: 0.1946 - val_accuracy: 0.8752 - val_loss: 0.4774
Epoch 74/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9427 - loss: 0.1911 - val_accuracy: 0.8746 - val_loss: 0.4809
Epoch 75/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9430 - loss: 0.1900 - val_accuracy: 0.8746 - val_loss: 0.4809
Epoch 76/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9443 - loss: 0.1856 - val_accuracy: 0.8749 - val_loss: 0.4836
Epoch 77/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9438 - loss: 0.1867 - val_accuracy: 0.8759 - val_loss: 0.4866
Epoch 78/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9454 - loss: 0.1811 - val_accuracy: 0.8751 - val_loss: 0.4869
Epoch 79/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9462 - loss: 0.1788 - val_accuracy: 0.8767 - val_loss: 0.4899
Epoch 80/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9467 - loss: 0.1777 - val_accuracy: 0.8754 - val_loss: 0.4932
Epoch 81/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9474 - loss: 0.1748 - val_accuracy: 0.8758 - val_loss: 0.4932
Epoch 82/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9481 - loss: 0.1731 - val_accuracy: 0.8751 - val_loss: 0.5027
Epoch 83/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9484 - loss: 0.1708 - val_accuracy: 0.8748 - val_loss: 0.5012
Epoch 84/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9491 - loss: 0.1675 - val_accuracy: 0.8748 - val_loss: 0.5091
Epoch 85/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9514 - loss: 0.1624 - val_accuracy: 0.8744 - val_loss: 0.5082
Epoch 86/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9508 - loss: 0.1627 - val_accuracy: 0.8733 - val_loss: 0.5159
Epoch 87/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9517 - loss: 0.1606 - val_accuracy: 0.8749 - val_loss: 0.5139
Epoch 88/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9519 - loss: 0.1579 - val_accuracy: 0.8746 - val_loss: 0.5189
Epoch 89/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9526 - loss: 0.1565 - val_accuracy: 0.8752 - val_loss: 0.5171
Epoch 90/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9531 - loss: 0.1549 - val_accuracy: 0.8750 - val_loss: 0.5169
Epoch 91/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9543 - loss: 0.1506 - val_accuracy: 0.8740 - val_loss: 0.5182
Epoch 92/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9547 - loss: 0.1497 - val_accuracy: 0.8752 - val_loss: 0.5207
Epoch 93/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9554 - loss: 0.1471 - val_accuracy: 0.8750 - val_loss: 0.5293
Epoch 94/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9560 - loss: 0.1467 - val_accuracy: 0.8749 - val_loss: 0.5298
Epoch 95/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9563 - loss: 0.1449 - val_accuracy: 0.8746 - val_loss: 0.5309
Epoch 96/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9571 - loss: 0.1421 - val_accuracy: 0.8728 - val_loss: 0.5391
Epoch 97/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9577 - loss: 0.1390 - val_accuracy: 0.8755 - val_loss: 0.5318
Epoch 98/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9583 - loss: 0.1375 - val_accuracy: 0.8744 - val_loss: 0.5433
Epoch 99/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9591 - loss: 0.1363 - val_accuracy: 0.8746 - val_loss: 0.5359
Epoch 100/100
125/125 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9592 - loss: 0.1351 - val_accuracy: 0.8738 - val_loss: 0.5482
运行推理（采样）
- 编码输入并检索初始解码器状态
- 使用该初始状态和“序列开始”标记作为目标运行解码器的一个步骤。 输出将是下一个目标标记。
- 使用当前目标标记和当前状态重复上述步骤
# 定义采样模型
# 恢复模型并构建编码器和解码器。
model = keras.models.load_model("s2s_model.keras")
encoder_inputs = model.input[0] # input_1
encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output # lstm_1
encoder_states = [state_h_enc, state_c_enc]
encoder_model = keras.Model(encoder_inputs, encoder_states)
decoder_inputs = model.input[1] # input_2
decoder_state_input_h = keras.Input(shape=(latent_dim,))
decoder_state_input_c = keras.Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_lstm = model.layers[3]
decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(
decoder_inputs, initial_state=decoder_states_inputs
)
decoder_states = [state_h_dec, state_c_dec]
decoder_dense = model.layers[4]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = keras.Model(
[decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states
)
# 反向查找标记索引以将序列解码为
# 可读内容。
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, verbose=0)
# 生成一个长度为1的空目标序列。
target_seq = np.zeros((1, 1, num_decoder_tokens))
# 用开始字符填充目标序列的第一个字符。
target_seq[0, 0, target_token_index["\t"]] = 1.0
# 针对一批序列的采样循环
# （为了简化，这里假设批大小为1）。
stop_condition = False
decoded_sentence = ""
while not stop_condition:
output_tokens, h, c = decoder_model.predict(
[target_seq] + states_value, verbose=0
)
# 采样一个标记
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：达到最大长度
# 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（长度为1）。
target_seq = np.zeros((1, 1, num_decoder_tokens))
target_seq[0, 0, sampled_token_index] = 1.0
# 更新状态
states_value = [h, c]
return decoded_sentence
您现在可以生成解码的句子，如下所示：
for seq_index in range(20):
# 取一个序列（训练集的一部分）
# 尝试解码。
input_seq = encoder_input_data[seq_index : seq_index + 1]
decoded_sentence = decode_sequence(input_seq)
print("-")
print("输入句子：", input_texts[seq_index])
print("解码句子：", decoded_sentence)
-
输入句子： Go.
解码句子： Va !
-
输入句子： Go.
解码句子： Va !
-
输入句子： Go.
解码句子： Va !
-
输入句子： Go.
解码句子： Va !
-
输入句子： Hi.
解码句子： Salut.
-
输入句子： Hi.
解码句子： Salut.
-
输入句子： Run!
解码句子： Fuyez !
-
输入句子： Run!
解码句子： Fuyez !
-
输入句子： Run!
解码句子： Fuyez !
-
输入句子： Run!
解码句子： Fuyez !
-
输入句子： Run!
解码句子： Fuyez !
-
输入句子： Run!
解码句子： Fuyez !
-
输入句子： Run!
解码句子： Fuyez !
-
输入句子： Run!
解码句子： Fuyez !
-
输入句子： Run.
解码句子： Courez !
-
输入句子： Run.
解码句子： Courez !
-
输入句子： Run.
解码句子： Courez !
-
输入句子：跑。
解码句子：快跑 ！
-
输入句子：跑。
解码句子：快跑 ！
-
输入句子：跑。
解码句子：快跑 ！