基于字符级循环神经网络的序列到序列模型
作者： fchollet
创建时间 2017/09/29
上次修改时间 2023/11/22
描述：基于字符级循环神经网络的序列到序列模型。
引言
本示例演示如何实现一个基本的字符级循环神经网络序列到序列模型。我们将它应用于将简短的英文句子逐字符翻译成法文句子。需要注意的是，字符级机器翻译并不常见，因为在这个领域中词级模型更为普遍。
算法概述
- 我们从一个领域（例如英文句子）的输入序列和另一个领域（例如法文句子）的对应目标序列开始。
- 编码器 LSTM 将输入序列转换为 2 个状态向量（我们保留最后一个 LSTM 状态并丢弃输出）。
- 解码器 LSTM 经过训练，将目标序列转换为相同的序列，但时间步长偏移一个，在这种情况下，训练过程称为“强制教学”。它使用编码器中的状态向量作为初始状态。实际上，解码器学习在给定输入序列的情况下，根据
targets[...t]生成targets[t+1...]。 - 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 - 以大小为 1 的目标序列开始（仅是序列开始字符） - 将状态向量和 1 字符目标序列馈送到解码器以产生下一个字符的预测 - 使用这些预测对下一个字符进行采样（我们简单地使用 argmax）。 - 将采样字符追加到目标序列 - 重复此过程，直到我们生成序列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 # 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 = os.path.join(dirpath, "fra.txt")
准备数据
# Vectorize the data.
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")
# We use "tab" as the "start sequence" character
# for the targets, and "\n" as "end sequence" character.
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("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)
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 is ahead of decoder_input_data by one timestep
decoder_input_data[i, t, target_token_index[char]] = 1.0
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]] = 1.0
decoder_input_data[i, t + 1 :, target_token_index[" "]] = 1.0
decoder_target_data[i, t:, target_token_index[" "]] = 1.0
Number of samples: 10000
Number of unique input tokens: 70
Number of unique output tokens: 93
Max sequence length for inputs: 14
Max sequence length for outputs: 59
构建模型
# Define an input sequence and process it.
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)
# We discard `encoder_outputs` and only keep the states.
encoder_states = [state_h, state_c]
# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = keras.Input(shape=(None, num_decoder_tokens))
# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the
# return states in the training model, but we will use them in inference.
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)
# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
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,
)
# Save model
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）
- 编码输入并检索解码器的初始状态
- 使用此初始状态和“序列开始”标记作为目标运行解码器的一步。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此过程
# Define sampling models
# Restore the model and construct the encoder and decoder.
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
)
# Reverse-lookup token index to decode sequences back to
# something readable.
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):
# Encode the input as state vectors.
states_value = encoder_model.predict(input_seq, verbose=0)
# Generate empty target sequence of length 1.
target_seq = np.zeros((1, 1, num_decoder_tokens))
# Populate the first character of target sequence with the start character.
target_seq[0, 0, target_token_index["\t"]] = 1.0
# Sampling loop for a batch of sequences
# (to simplify, here we assume a batch of size 1).
stop_condition = False
decoded_sentence = ""
while not stop_condition:
output_tokens, h, c = decoder_model.predict(
[target_seq] + states_value, verbose=0
)
# 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
# Exit condition: either hit max length
# or find stop character.
if sampled_char == "\n" or len(decoded_sentence) > max_decoder_seq_length:
stop_condition = True
# Update the target sequence (of length 1).
target_seq = np.zeros((1, 1, num_decoder_tokens))
target_seq[0, 0, sampled_token_index] = 1.0
# Update states
states_value = [h, c]
return decoded_sentence
您现在可以像这样生成解码后的句子
for seq_index in range(20):
# Take one sequence (part of the training set)
# for trying out decoding.
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)
-
Input sentence: Go.
Decoded sentence: Va !
-
Input sentence: Go.
Decoded sentence: Va !
-
Input sentence: Go.
Decoded sentence: Va !
-
Input sentence: Go.
Decoded sentence: Va !
-
Input sentence: Hi.
Decoded sentence: Salut.
-
Input sentence: Hi.
Decoded sentence: Salut.
-
Input sentence: Run!
Decoded sentence: Fuyez !
-
Input sentence: Run!
Decoded sentence: Fuyez !
-
Input sentence: Run!
Decoded sentence: Fuyez !
-
Input sentence: Run!
Decoded sentence: Fuyez !
-
Input sentence: Run!
Decoded sentence: Fuyez !
-
Input sentence: Run!
Decoded sentence: Fuyez !
-
Input sentence: Run!
Decoded sentence: Fuyez !
-
Input sentence: Run!
Decoded sentence: Fuyez !
-
Input sentence: Run.
Decoded sentence: Courez !
-
Input sentence: Run.
Decoded sentence: Courez !
-
Input sentence: Run.
Decoded sentence: Courez !
-
Input sentence: Run.
Decoded sentence: Courez !
-
Input sentence: Run.
Decoded sentence: Courez !
-
Input sentence: Run.
Decoded sentence: Courez !