Python机器学习NLP自然语言解决基本操作电影影评分析
发布时间：2021-11-08 01:35:14 所属栏目：站长百科 来源：互联网
导读：目录 概述RNN权重共享计算过程LSTM阶段代码预处理主函数 概述 从今天开始我们将开启一段自然语言处理 (NLP) 的旅程. 自然语言处理可以让来处理, 理解, 以及运用人类的语言, 实现机器语言和人类语言之间的沟通桥梁. 在这里插入图片描述 RNN RNN (Recurrent Neu
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目录 概述RNN权重共享计算过程LSTM阶段代码预处理主函数 概述 从今天开始我们将开启一段自然语言处理 (NLP) 的旅程. 自然语言处理可以让来处理, 理解, 以及运用人类的语言, 实现机器语言和人类语言之间的沟通桥梁. 在这里插入图片描述 RNN RNN (Recurrent Neural Network), 即循环神经网络. RNN 相较于 CNN, 可以帮助我们更好的处理序列信息, 挖掘前后信息之间的联系. 对于 NLP 这类的任务, 语料的前后概率有极大的联系. 比如: “明天天气真好” 的概率 > “明天天气篮球”. 在这里插入图片描述 权重共享 传统神经网络: 在这里插入图片描述 RNN: 在这里插入图片描述 RNN 的权重共享和 CNN 的权重共享类似, 不同时刻共享一个权重, 大大减少了参数数量. 计算过程 在这里插入图片描述 计算状态 (State) 在这里插入图片描述 计算输出: 在这里插入图片描述 LSTM LSTM (Long Short Term Memory), 即长短期记忆模型. LSTM 是一种特殊的 RNN 模型, 解决了长序列训练过程中的梯度消失和梯度爆炸的问题. 相较于普通 RNN, LSTM 能够在更长的序列中有更好的表现. 相比 RNN 只有一个传递状态 ht, LSTM 有两个传递状态： ct (cell state) 和 ht (hidden state). 在这里插入图片描述 阶段 LSTM 通过门来控制传输状态。 LSTM 总共分为三个阶段: 忘记阶段: 对上一个节点传进来的输入进行选择性忘记 选择记忆阶段: 将这个阶段的记忆有选择性的进行记忆. 哪些重要则着重记录下来, 哪些不重要, 则少记录一些 输出阶段: 决定哪些将会被当成当前状态的输出 代码 预处理 import pandas as pd import re from bs4 import BeautifulSoup from sklearn.model_selection import train_test_split import tensorflow as tf # 停用词 stop_words = pd.read_csv("data/stopwords.txt", index_col=False, quoting=3, sep="n", names=["stop_words"]) stop_words = [word.strip() for word in stop_words["stop_words"].values] # 用pandas读取训练数据 def load_data(): # 语料 data = pd.read_csv("data/labeledTrainData.tsv", sep="t", escapechar="") print(data[:5]) print("评论数量:", len(data)) return data def pre_process(text): # 去除网页链接 text = BeautifulSoup(text, "html.parser").get_text() # 去除标点 text = re.sub("[^a-zA-Z]", " ", text) # 分词 words = text.lower().split() # 去除停用词 words = [w for w in words if w not in stop_words] return " ".join(words) def split_data(): # 读取文件 data = pd.read_csv("data/train.csv") print(data.head()) # 实例化 tokenizer = tf.keras.preprocessing.text.Tokenizer() # 拟合 tokenizer.fit_on_texts(data["review"]) # 词袋 word_index = tokenizer.word_index print(word_index) print(len(word_index)) # 转换成数组 sequence = tokenizer.texts_to_sequences(data["review"]) # 填充 character = tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=200) # 标签转换 labels = tf.keras.utils.to_categorical(data["sentiment"]) # 分割数据集 X_train, X_test, y_train, y_test = train_test_split(character, labels, test_size=0.2, random_state=0) return X_train, X_test, y_train, y_test if __name__ == '__main__': # # # data = load_data() # data["review"] = data["review"].apply(pre_process) # print(data.head()) # # # 保存 # data.to_csv("data.csv") split_data() 主函数 import tensorflow as tf from lstm_pre_processing import split_data def main(): # 读取数据 X_train, X_test, y_train, y_test = split_data() print(X_train[:5]) print(y_train[:5]) # 超参数 EMBEDDING_DIM = 200 # embedding 维度 optimizer = tf.keras.optimizers.RMSprop() # 优化器 loss = tf.losses.CategoricalCrossentropy(from_logits=True) # 损失 # 模型 model = tf.keras.Sequential([ tf.keras.layers.Embedding(73424, EMBEDDING_DIM), tf.keras.layers.LSTM(200, dropout=0.2, recurrent_dropout=0.2), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(64, activation="relu"), tf.keras.layers.Dense(2, activation="softmax") ]) model.build(input_shape=[None, 20]) print(model.summary()) # 组合 model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"]) # 训练 model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=32) # 保存模型 model.save("movie_model.h5") if __name__ == '__main__': # 主函数 main() 输出结果: 2021-09-14 22:20:56.974310: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0 Unnamed: 0 id sentiment review 0 0 5814_8 1 stuff moment mj ve started listening music wat... 1 1 2381_9 1 classic war worlds timothy hines entertaining ... 2 2 7759_3 0 film starts manager nicholas bell investors ro... 3 3 3630_4 0 assumed praised film filmed opera didn read do... 4 4 9495_8 1 superbly trashy wondrously unpretentious explo... 73423 [[15958 623 12368 4459 622 835 30 152 2097 2408 35364 57143 892 2997 766 42223 967 266 25276 157 108 696 1631 198 2576 9850 3745 27 52 3789 9503 696 526 52 354 862 474 38 2 101 11027 696 6456 22390 969 5873 5376 4044 623 1401 2069 718 618 92 96 138 1345 714 96 18 123 1770 518 3314 354 983 1888 520 83 73 983 2 28 28635 1044 2054 401 1071 85 8565 8957 7226 804 46 224 447 2113 2691 5742 10 5 3217 943 5045 980 373 28 873 438 389 41 23 19 56 122 9 253 27176 2149 19 90 57144 53 4874 696 6558 136 2067 10682 48 518 1482 9 3668 1587 3786 2 110 10 506 25150 20744 340 33 316 17 4824 3892 978 14 10150 2596 766 42223 5082 4784 700 198 6276 5254 700 198 2334 696 20879 5 86 30 2 583 2872 30601 30 86 28 83 73 32 96 18 2 224 708 30 167 7 3791 216 45 513 2 2310 513 1860 4536 1925 414 1321 578 7434 851 696 997 5354 57145 162 30 2 91 1839] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 357 684 28 3027 10371 5801 20987 21481 19800 1 3027 10371 21481 19800 1719 204 49 168 250 7355 1547 374 401 5415 24 1719 24 49 168 7355 1547 3610 21481 19800 123 204 49 168 1102 1547 656 213 5432 5183 61 4 66166 20 36 56 7 5183 2025 116 5031 11 45 782] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2189 1 586 2189 15 1855 615 400 5394 3797 23866 2892 481 2892 810 22020 17820 1 741 231 20 746 2028 1040 6089 816 5555 41772 1762 26 811 288 8 796 45] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 85 310 1734 78 1906 78 1906 1412 1985 78 7644 1412 244 9287 7092 6374 2584 6183 3795 3080 1288 2217 3534 6005 4851 1543 762 1797 26144 699 237 6745 7 1288 1415 9003 5623 237 1669 17987 874 421 234 1278 347 9287 1609 7100 1065 75 9800 3344 76 5021 47 380 3015 14366 6523 1396 851 22330 3465 20861 7106 6374 340 60 19035 3089 5081 3 7 1695 10735 3582 92 6374 176 8348 60 1491 11540 28826 1847 464 4099 22 3561 51 22 1538 1027 38926 2195 1966 3089 33 19894 287 142 6374 184 37 4025 67 325 37 421 549 21976 28 7744 2466 31533 27 2836 1339 6374 14805 1670 4666 60 33 12] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 27 52 4639 9 5774 1545 8575 855 10463 2688 21019 1542 1701 653 9765 9 189 706 2212 18342 566 437 2639 4311 4504 26110 307 496 893 317 1 27 52 587]] [[0. 1.] [0. 1.] [0. 1.] [1. 0.] [0. 1.]] 2021-09-14 22:21:02.212681: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set 2021-09-14 22:21:02.213245: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: /usr/lib/x86_64-linux-gnu/libcuda.so.1: file too short; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64/:/usr/lib/x86_64-linux-gnu 2021-09-14 22:21:02.213268: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303) 2021-09-14 22:21:02.213305: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (5aa046a4f47b): /proc/driver/nvidia/version does not exist 2021-09-14 22:21:02.213624: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX512F To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-09-14 22:21:02.216309: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding (Embedding) (None, None, 200) 14684800 _________________________________________________________________ lstm (LSTM) (None, 200) 320800 _________________________________________________________________ dropout (Dropout) (None, 200) 0 _________________________________________________________________ dense (Dense) (None, 64) 12864 _________________________________________________________________ dense_1 (Dense) (None, 2) 130 ================================================================= Total params: 15,018,594 Trainable params: 15,018,594 Non-trainable params: 0 _________________________________________________________________ None 2021-09-14 22:21:02.515404: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2) 2021-09-14 22:21:02.547745: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2300000000 Hz Epoch 1/2 313/313 [==============================] - 97s 302ms/step - loss: 0.5112 - accuracy: 0.7510 - val_loss: 0.3607 - val_accuracy: 0.8628 Epoch 2/2 313/313 [==============================] - 94s 300ms/step - loss: 0.2090 - accuracy: 0.9236 - val_loss: 0.3078 - val_accuracy: 0.8790 以上就是Python机器学习NLP自然语言处理基本操作电影影评分析的详细内容，更多关于NLP自然语言处理资料请关注易采站长站其它相关文章！ （编辑：厦门网） 【声明】本站内容均来自网络，其相关言论仅代表作者个人观点，不代表本站立场。若无意侵犯到您的权利，请及时与联系站长删除相关内容! |
