网站群发推广软件,接私活做网站要不要签合同,做网站加入广告联盟,广东做网站文章目录 一、完整代码二、过程实现2.1 导包2.2 数据准备2.3 字符分词2.4 构建数据集2.5 定义模型2.6 模型训练2.7 模型推理 三、整体总结 采用RNN和unicode分词进行文本生成 一、完整代码 作者在文章开头地址中使用C实现了这一过程#xff0c;为了便于理解#xff0c;这里我… 文章目录 一、完整代码二、过程实现2.1 导包2.2 数据准备2.3 字符分词2.4 构建数据集2.5 定义模型2.6 模型训练2.7 模型推理 三、整体总结 采用RNN和unicode分词进行文本生成 一、完整代码 作者在文章开头地址中使用C实现了这一过程为了便于理解这里我们使用python代码进行实现 # 完整代码在这里 import tensorflow as tf import keras_nlp import numpy as nptokenizer keras_nlp.tokenizers.UnicodeCodepointTokenizer(vocabulary_size400)# tokens - ids ids tokenizer([Why are you so funny?, how can i get you])# ids - tokens tokenizer.detokenize(ids)def split_input_target(sequence):input_text sequence[:-1]target_text sequence[1:]return input_text, target_text# 准备数据 text open(./shakespeare.txt, rb).read().decode(encodingutf-8) dataset tf.data.Dataset.from_tensor_slices(tokenizer(text)) dataset dataset.batch(64, drop_remainderTrue) dataset dataset.map(split_input_target).batch(64)input, ouput dataset.take(1).get_single_element()# 定义模型d_model 512 rnn_units 1025class CustomModel(tf.keras.Model):def __init__(self, vocabulary_size, d_model, rnn_units):super().__init__(self)self.embedding tf.keras.layers.Embedding(vocabulary_size, d_model)self.gru tf.keras.layers.GRU(rnn_units, return_sequencesTrue, return_stateTrue)self.dense tf.keras.layers.Dense(vocabulary_size, activationsoftmax)def call(self, inputs, statesNone, return_stateFalse, trainingFalse):x inputsx self.embedding(x)if states is None:states self.gru.get_initial_state(x)x, states self.gru(x, initial_statestates, trainingtraining)x self.dense(x, trainingtraining)if return_state:return x, stateselse:return xmodel CustomModel(tokenizer.vocabulary_size(), d_model, rnn_units)# 查看模型结构 model(input) model.summary()# 模型配置 model.compile(loss tf.losses.SparseCategoricalCrossentropy(),optimizeradam,metrics[accuracy] )# 模型训练 model.fit(dataset, epochs3)# 模型推理 class InferenceModel(tf.keras.Model):def __init__(self, model, tokenizer):super().__init__(self)self.model modelself.tokenizer tokenizerdef generate(self, inputs, length, return_statesFalse):inputs inputs tf.constant(inputs)[tf.newaxis]states Noneinput_ids self.tokenizer(inputs).to_tensor()outputs []for i in range(length):predicted_logits, states model(inputsinput_ids, statesstates, return_stateTrue)input_ids tf.argmax(predicted_logits, axis-1)outputs.append(input_ids[0][-1].numpy())outputs self.tokenizer.detokenize(lst).numpy().decode(utf-8)if return_states:return outputs, stateselse:return outputsinfere InferenceModel(model, tokenizer)# 开始推理 start_chars hello outputs infere.generate(start_chars, 1000) print(start_chars outputs)二、过程实现 2.1 导包 先导包tensorflow, keras_nlp, numpy import tensorflow as tf import keras_nlp import numpy as np2.2 数据准备 数据来自莎士比亚的作品 storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt我们将其下载下来存储为shakespeare.txt 2.3 字符分词 这里我们使用unicode分词将所有字符都作为一个词来进行分词 tokenizer keras_nlp.tokenizers.UnicodeCodepointTokenizer(vocabulary_size400)# tokens - ids ids tokenizer([Why are you so funny?, how can i get you])# ids - tokens tokenizer.detokenize(ids)2.4 构建数据集 利用tokenizer和text数据构建数据集 def split_input_target(sequence):input_text sequence[:-1]target_text sequence[1:]return input_text, target_texttext open(./shakespeare.txt, rb).read().decode(encodingutf-8) dataset tf.data.Dataset.from_tensor_slices(tokenizer(text)) dataset dataset.batch(64, drop_remainderTrue) dataset dataset.map(split_input_target).batch(64)input, ouput dataset.take(1).get_single_element()2.5 定义模型 d_model 512 rnn_units 1025class CustomModel(tf.keras.Model):def __init__(self, vocabulary_size, d_model, rnn_units):super().__init__(self)self.embedding tf.keras.layers.Embedding(vocabulary_size, d_model)self.gru tf.keras.layers.GRU(rnn_units, return_sequencesTrue, return_stateTrue)self.dense tf.keras.layers.Dense(vocabulary_size, activationsoftmax)def call(self, inputs, statesNone, return_stateFalse, trainingFalse):x inputsx self.embedding(x)if states is None:states self.gru.get_initial_state(x)x, states self.gru(x, initial_statestates, trainingtraining)x self.dense(x, trainingtraining)if return_state:return x, stateselse:return xmodel CustomModel(tokenizer.vocabulary_size(), d_model, rnn_units)# 查看模型结构 model(input) model.summary()2.6 模型训练 model.compile(loss tf.losses.SparseCategoricalCrossentropy(),optimizeradam,metrics[accuracy] )model.fit(dataset, epochs3)2.7 模型推理 定义一个InferenceModel进行模型推理配置 class InferenceModel(tf.keras.Model):def __init__(self, model, tokenizer):super().__init__(self)self.model modelself.tokenizer tokenizerdef generate(self, inputs, length, return_statesFalse):inputs inputs tf.constant(inputs)[tf.newaxis]states Noneinput_ids self.tokenizer(inputs).to_tensor()outputs []for i in range(length):predicted_logits, states model(inputsinput_ids, statesstates, return_stateTrue)input_ids tf.argmax(predicted_logits, axis-1)outputs.append(input_ids[0][-1].numpy())outputs self.tokenizer.detokenize(lst).numpy().decode(utf-8)if return_states:return outputs, stateselse:return outputsinfere InferenceModel(model, tokenizer)start_chars hello outputs infere.generate(start_chars, 1000) print(start_chars outputs)生成结果如下所示感觉很差 hellonofur us: medous, teserwomador. walled o y. as t aderemowate tinievearetyedust. manonels, w? workeneastily. watrenerdores anershra palathermalod, te a y, s adousced an ptit: mamerethus: bas as t: uaruriryedinesms lesoureris lares palit al ancoup, maly thitts? b veatrt watyeleditenchitr sts, on fotearen, medan ur tiblainou-lele priniseryo, ofonet manad plenerulyo thilyrt th palezedorine. ti dous slas, sed, ang atad t, wanti shew. e upede wadraredorenksenche: wedemen stamesly ateara tiafin t t pes: t: tus mo at io my. ane hbrelely berenerusedus m tr; p outellilid ng ait tevadwantstry. arafincara, es fody es pra aluserelyonine pales corseryea aburures angab: sunelyothe: s al, chtaburoly o oonis s tioute tt, pro. tedeslenali: s t ing h sh, age de, anet: hathes: s estht, as: wedly at s serinechamai: mored t. t monatht t athoumonches le. chededondirineared ter p y letinalys ani aconen, t rs: t;et, tes- luste aly, thonort aly one telus, s mpsantenam ranthinarrame! a pul; bon s fofuly三、整体总结 RNN结合unicode分词能进行文本生成但是效果一言难尽