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這期內(nèi)容當(dāng)中小編將會給大家?guī)碛嘘P(guān)Python中學(xué)習(xí)NLP自然語言處理電影影評的操作方法，文章內(nèi)容豐富且以專業(yè)的角度為大家分析和敘述，閱讀完這篇文章希望大家可以有所收獲。
從今天開始我們將開啟一段自然語言處理 (NLP) 的旅程. 自然語言處理可以讓來處理, 理解, 以及運用人類的語言, 實現(xiàn)機器語言和人類語言之間的溝通橋梁.
RNN (Recurrent Neural Network), 即循環(huán)神經(jīng)網(wǎng)絡(luò). RNN 相較于 CNN, 可以幫助我們更好的處理序列信息, 挖掘前后信息之間的聯(lián)系. 對于 NLP 這類的任務(wù), 語料的前后概率有極大的聯(lián)系. 比如: “明天天氣真好” 的概率 > “明天天氣籃球”.
傳統(tǒng)神經(jīng)網(wǎng)絡(luò):
RNN:
RNN 的權(quán)重共享和 CNN 的權(quán)重共享類似, 不同時刻共享一個權(quán)重, 大大減少了參數(shù)數(shù)量.
計算狀態(tài) (State)
計算輸出:
LSTM (Long Short Term Memory), 即長短期記憶模型. LSTM 是一種特殊的 RNN 模型, 解決了長序列訓(xùn)練過程中的梯度消失和梯度爆炸的問題. 相較于普通 RNN, LSTM 能夠在更長的序列中有更好的表現(xiàn). 相比 RNN 只有一個傳遞狀態(tài) ht, LSTM 有兩個傳遞狀態(tài)： ct (cell state) 和 ht (hidden state).
LSTM 通過門來控制傳輸狀態(tài)。
LSTM 總共分為三個階段:
忘記階段: 對上一個節(jié)點傳進來的輸入進行選擇性忘記
選擇記憶階段: 將這個階段的記憶有選擇性的進行記憶. 哪些重要則著重記錄下來, 哪些不重要, 則少記錄一些
輸出階段: 決定哪些將會被當(dāng)成當(dāng)前狀態(tài)的輸出
import pandas as pd
import re
from bs4 import BeautifulSoup
from sklearn.model_selection import train_test_split
import tensorflow as tf
# 停用詞
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讀取訓(xùn)練數(shù)據(jù)
def load_data():
# 語料
data = pd.read_csv("data/labeledTrainData.tsv", sep="\t", escapechar="\\")
print(data[:5])
print("評論數(shù)量:", len(data))
return data
def pre_process(text):
# 去除網(wǎng)頁鏈接
text = BeautifulSoup(text, "html.parser").get_text()
# 去除標(biāo)點
text = re.sub("[^a-zA-Z]", " ", text)
# 分詞
words = text.lower().split()
# 去除停用詞
words = [w for w in words if w not in stop_words]
return " ".join(words)
def split_data():
# 讀取文件
data = pd.read_csv("data/train.csv")
print(data.head())
# 實例化
tokenizer = tf.keras.preprocessing.text.Tokenizer()
# 擬合
tokenizer.fit_on_texts(data["review"])
# 詞袋
word_index = tokenizer.word_index
print(word_index)
print(len(word_index))
# 轉(zhuǎn)換成數(shù)組
sequence = tokenizer.texts_to_sequences(data["review"])
# 填充
character = tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=200)
# 標(biāo)簽轉(zhuǎn)換
labels = tf.keras.utils.to_categorical(data["sentiment"])
# 分割數(shù)據(jù)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()import tensorflow as tf
from lstm_pre_processing import split_data
def main():
# 讀取數(shù)據(jù)
X_train, X_test, y_train, y_test = split_data()
print(X_train[:5])
print(y_train[:5])
# 超參數(shù)
EMBEDDING_DIM = 200 # embedding 維度
optimizer = tf.keras.optimizers.RMSprop() # 優(yōu)化器
loss = tf.losses.CategoricalCrossentropy(from_logits=True) # 損失
# 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())
# 組合
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
# 訓(xùn)練
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.h6")
if __name__ == '__main__':
# 主函數(shù)
main()輸出結(jié)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
上述就是小編為大家分享的Python中學(xué)習(xí)NLP自然語言處理電影影評的操作方法了，如果剛好有類似的疑惑，不妨參照上述分析進行理解。如果想知道更多相關(guān)知識，歡迎關(guān)注億速云行業(yè)資訊頻道。
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