تصنيف النص مع مراجعات الفيلم

عرض على TensorFlow.org

تشغيل في Google Colab

عرض على جيثب

تحميل دفتر

انظر نماذج TF Hub

يستعرض هذا يصنف دفتر الفيلم كما إيجابية أو سلبية باستخدام نص المراجعة. وهذا مثال من ثنائي -أو من الدرجة يومين تصنيف، وهو نوع مهم وقابلة للتطبيق على نطاق واسع من آلة مشكلة التعلم.

سنستخدم بيانات IMDB الذي يحتوي على النص من 50000 يستعرض الفيلم من قاعدة بيانات الأفلام على الإنترنت . يتم تقسيم هذه إلى 25000 مراجعة للتدريب و 25000 مراجعة للاختبار. ومتوازنة تدريب واختبار مجموعات، وهذا يعني أنها تحتوي على عدد متساو من الاستعراضات إيجابية وسلبية.

يستخدم هذا الكمبيوتر الدفتري tf.keras ، وAPI رفيع المستوى لبناء وتدريب النماذج في TensorFlow، و TensorFlow المحور ، ومكتبة ومنصة للتعلم النقل. لوضع نص أكثر تقدما تصنيف البرنامج التعليمي باستخدام tf.keras ، راجع MLCC نص دليل تصنيف .

المزيد من النماذج

هنا يمكنك العثور على نماذج أكثر تعبيرا أو performant للالتي يمكن استخدامها لتوليد تضمين النص.

يثبت

import numpy as np

import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds

import matplotlib.pyplot as plt

print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print("Hub version: ", hub.__version__)
print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE")

Version:  2.7.0
Eager mode:  True
Hub version:  0.12.0
GPU is available

قم بتنزيل مجموعة بيانات IMDB

مجموعة البيانات IMDB متاح في قواعد البيانات TensorFlow . الكود التالي يقوم بتنزيل مجموعة بيانات IMDB على جهازك (أو وقت تشغيل colab):

train_data, test_data = tfds.load(name="imdb_reviews", split=["train", "test"], 
                                  batch_size=-1, as_supervised=True)

train_examples, train_labels = tfds.as_numpy(train_data)
test_examples, test_labels = tfds.as_numpy(test_data)

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_datasets/core/dataset_builder.py:622: get_single_element (from tensorflow.python.data.experimental.ops.get_single_element) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.get_single_element()`.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_datasets/core/dataset_builder.py:622: get_single_element (from tensorflow.python.data.experimental.ops.get_single_element) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.get_single_element()`.

استكشف البيانات

دعنا نتوقف لحظة لفهم تنسيق البيانات. كل مثال عبارة عن جملة تمثل مراجعة الفيلم والتسمية المقابلة. لم يتم تنفيذ الجملة بأي شكل من الأشكال. التصنيف عبارة عن قيمة عدد صحيح إما 0 أو 1 ، حيث يمثل 0 مراجعة سلبية و 1 مراجعة إيجابية.

print("Training entries: {}, test entries: {}".format(len(train_examples), len(test_examples)))

Training entries: 25000, test entries: 25000

دعنا نطبع أول 10 أمثلة.

train_examples[:10]

array([b"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.",
       b'I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However on this occasion I fell asleep because the film was rubbish. The plot development was constant. Constantly slow and boring. Things seemed to happen, but with no explanation of what was causing them or why. I admit, I may have missed part of the film, but i watched the majority of it and everything just seemed to happen of its own accord without any real concern for anything else. I cant recommend this film at all.',
       b'Mann photographs the Alberta Rocky Mountains in a superb fashion, and Jimmy Stewart and Walter Brennan give enjoyable performances as they always seem to do. <br /><br />But come on Hollywood - a Mountie telling the people of Dawson City, Yukon to elect themselves a marshal (yes a marshal!) and to enforce the law themselves, then gunfighters battling it out on the streets for control of the town? <br /><br />Nothing even remotely resembling that happened on the Canadian side of the border during the Klondike gold rush. Mr. Mann and company appear to have mistaken Dawson City for Deadwood, the Canadian North for the American Wild West.<br /><br />Canadian viewers be prepared for a Reefer Madness type of enjoyable howl with this ludicrous plot, or, to shake your head in disgust.',
       b'This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful performances from Cher and Nicolas Cage (as always) gently row the plot along. There are no rapids to cross, no dangerous waters, just a warm and witty paddle through New York life at its best. A family film in every sense and one that deserves the praise it received.',
       b'As others have mentioned, all the women that go nude in this film are mostly absolutely gorgeous. The plot very ably shows the hypocrisy of the female libido. When men are around they want to be pursued, but when no "men" are around, they become the pursuers of a 14 year old boy. And the boy becomes a man really fast (we should all be so lucky at this age!). He then gets up the courage to pursue his true love.',
       b"This is a film which should be seen by anybody interested in, effected by, or suffering from an eating disorder. It is an amazingly accurate and sensitive portrayal of bulimia in a teenage girl, its causes and its symptoms. The girl is played by one of the most brilliant young actresses working in cinema today, Alison Lohman, who was later so spectacular in 'Where the Truth Lies'. I would recommend that this film be shown in all schools, as you will never see a better on this subject. Alison Lohman is absolutely outstanding, and one marvels at her ability to convey the anguish of a girl suffering from this compulsive disorder. If barometers tell us the air pressure, Alison Lohman tells us the emotional pressure with the same degree of accuracy. Her emotional range is so precise, each scene could be measured microscopically for its gradations of trauma, on a scale of rising hysteria and desperation which reaches unbearable intensity. Mare Winningham is the perfect choice to play her mother, and does so with immense sympathy and a range of emotions just as finely tuned as Lohman's. Together, they make a pair of sensitive emotional oscillators vibrating in resonance with one another. This film is really an astonishing achievement, and director Katt Shea should be proud of it. The only reason for not seeing it is if you are not interested in people. But even if you like nature films best, this is after all animal behaviour at the sharp edge. Bulimia is an extreme version of how a tormented soul can destroy her own body in a frenzy of despair. And if we don't sympathise with people suffering from the depths of despair, then we are dead inside.",
       b'Okay, you have:<br /><br />Penelope Keith as Miss Herringbone-Tweed, B.B.E. (Backbone of England.) She\'s killed off in the first scene - that\'s right, folks; this show has no backbone!<br /><br />Peter O\'Toole as Ol\' Colonel Cricket from The First War and now the emblazered Lord of the Manor.<br /><br />Joanna Lumley as the ensweatered Lady of the Manor, 20 years younger than the colonel and 20 years past her own prime but still glamourous (Brit spelling, not mine) enough to have a toy-boy on the side. It\'s alright, they have Col. Cricket\'s full knowledge and consent (they guy even comes \'round for Christmas!) Still, she\'s considerate of the colonel enough to have said toy-boy her own age (what a gal!)<br /><br />David McCallum as said toy-boy, equally as pointlessly glamourous as his squeeze. Pilcher couldn\'t come up with any cover for him within the story, so she gave him a hush-hush job at the Circus.<br /><br />and finally:<br /><br />Susan Hampshire as Miss Polonia Teacups, Venerable Headmistress of the Venerable Girls\' Boarding-School, serving tea in her office with a dash of deep, poignant advice for life in the outside world just before graduation. Her best bit of advice: "I\'ve only been to Nancherrow (the local Stately Home of England) once. I thought it was very beautiful but, somehow, not part of the real world." Well, we can\'t say they didn\'t warn us.<br /><br />Ah, Susan - time was, your character would have been running the whole show. They don\'t write \'em like that any more. Our loss, not yours.<br /><br />So - with a cast and setting like this, you have the re-makings of "Brideshead Revisited," right?<br /><br />Wrong! They took these 1-dimensional supporting roles because they paid so well. After all, acting is one of the oldest temp-jobs there is (YOU name another!)<br /><br />First warning sign: lots and lots of backlighting. They get around it by shooting outdoors - "hey, it\'s just the sunlight!"<br /><br />Second warning sign: Leading Lady cries a lot. When not crying, her eyes are moist. That\'s the law of romance novels: Leading Lady is "dewy-eyed."<br /><br />Henceforth, Leading Lady shall be known as L.L.<br /><br />Third warning sign: L.L. actually has stars in her eyes when she\'s in love. Still, I\'ll give Emily Mortimer an award just for having to act with that spotlight in her eyes (I wonder . did they use contacts?)<br /><br />And lastly, fourth warning sign: no on-screen female character is "Mrs." She\'s either "Miss" or "Lady."<br /><br />When all was said and done, I still couldn\'t tell you who was pursuing whom and why. I couldn\'t even tell you what was said and done.<br /><br />To sum up: they all live through World War II without anything happening to them at all.<br /><br />OK, at the end, L.L. finds she\'s lost her parents to the Japanese prison camps and baby sis comes home catatonic. Meanwhile (there\'s always a "meanwhile,") some young guy L.L. had a crush on (when, I don\'t know) comes home from some wartime tough spot and is found living on the street by Lady of the Manor (must be some street if SHE\'s going to find him there.) Both war casualties are whisked away to recover at Nancherrow (SOMEBODY has to be "whisked away" SOMEWHERE in these romance stories!)<br /><br />Great drama.',
       b'The film is based on a genuine 1950s novel.<br /><br />Journalist Colin McInnes wrote a set of three "London novels": "Absolute Beginners", "City of Spades" and "Mr Love and Justice". I have read all three. The first two are excellent. The last, perhaps an experiment that did not come off. But McInnes\'s work is highly acclaimed; and rightly so. This musical is the novelist\'s ultimate nightmare - to see the fruits of one\'s mind being turned into a glitzy, badly-acted, soporific one-dimensional apology of a film that says it captures the spirit of 1950s London, and does nothing of the sort.<br /><br />Thank goodness Colin McInnes wasn\'t alive to witness it.',
       b'I really love the sexy action and sci-fi films of the sixties and its because of the actress\'s that appeared in them. They found the sexiest women to be in these films and it didn\'t matter if they could act (Remember "Candy"?). The reason I was disappointed by this film was because it wasn\'t nostalgic enough. The story here has a European sci-fi film called "Dragonfly" being made and the director is fired. So the producers decide to let a young aspiring filmmaker (Jeremy Davies) to complete the picture. They\'re is one real beautiful woman in the film who plays Dragonfly but she\'s barely in it. Film is written and directed by Roman Coppola who uses some of his fathers exploits from his early days and puts it into the script. I wish the film could have been an homage to those early films. They could have lots of cameos by actors who appeared in them. There is one actor in this film who was popular from the sixties and its John Phillip Law (Barbarella). Gerard Depardieu, Giancarlo Giannini and Dean Stockwell appear as well. I guess I\'m going to have to continue waiting for a director to make a good homage to the films of the sixties. If any are reading this, "Make it as sexy as you can"! I\'ll be waiting!',
       b'Sure, this one isn\'t really a blockbuster, nor does it target such a position. "Dieter" is the first name of a quite popular German musician, who is either loved or hated for his kind of acting and thats exactly what this movie is about. It is based on the autobiography "Dieter Bohlen" wrote a few years ago but isn\'t meant to be accurate on that. The movie is filled with some sexual offensive content (at least for American standard) which is either amusing (not for the other "actors" of course) or dumb - it depends on your individual kind of humor or on you being a "Bohlen"-Fan or not. Technically speaking there isn\'t much to criticize. Speaking of me I find this movie to be an OK-movie.'],
      dtype=object)

دعنا أيضًا نطبع أول 10 ملصقات.

train_labels[:10]

array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0])

بناء النموذج

يتم إنشاء الشبكة العصبية عن طريق تكديس الطبقات - وهذا يتطلب ثلاثة قرارات معمارية رئيسية:

• كيف تمثل النص؟
• كم عدد الطبقات لاستخدامها في النموذج؟
• عدد الوحدات خفية لاستخدامها في كل طبقة؟

في هذا المثال ، تتكون بيانات الإدخال من جمل. التسميات المطلوب توقعها هي إما 0 أو 1.

تتمثل إحدى طرق تمثيل النص في تحويل الجمل إلى متجهات للزفاف. يمكننا استخدام دمج نص تم تدريبه مسبقًا كطبقة أولى ، والتي سيكون لها ميزتان:

• لا داعي للقلق بشأن المعالجة المسبقة للنص ،
• يمكننا الاستفادة من نقل التعلم.

على سبيل المثال هذا سوف نستخدم نموذج من TensorFlow المحور تسمى جوجل / nnlm-أون-dim50 / 2 .

هناك نموذجان آخران يجب اختبارهما من أجل هذا البرنامج التعليمي:

• جوجل / nnlm-أون-dim50 مع التطبيع / 2 - نفس جوجل / nnlm-أون-dim50 / 2 ، ولكن مع التطبيع نص إضافي لإزالة علامات الترقيم. يمكن أن يساعد هذا في الحصول على تغطية أفضل لحفلات الزفاف داخل المفردات للرموز المميزة على نص الإدخال الخاص بك.
• جوجل / nnlm-أون-dim128 مع التطبيع / 2 - نموذج أكبر مع بعدا تضمين 128 بدلا من أصغر 50.

لنقم أولاً بإنشاء طبقة Keras تستخدم نموذج TensorFlow Hub لتضمين الجمل وتجربتها على بعض أمثلة الإدخال. علما بأن الشكل الناتج من التضمينات أنتجت هو متوقع: (num_examples, embedding_dimension) .

model = "https://tfhub.dev/google/nnlm-en-dim50/2"
hub_layer = hub.KerasLayer(model, input_shape=[], dtype=tf.string, trainable=True)
hub_layer(train_examples[:3])

<tf.Tensor: shape=(3, 50), dtype=float32, numpy=
array([[ 0.5423194 , -0.01190171,  0.06337537,  0.0686297 , -0.16776839,
        -0.10581177,  0.168653  , -0.04998823, -0.31148052,  0.07910344,
         0.15442258,  0.01488661,  0.03930155,  0.19772716, -0.12215477,
        -0.04120982, -0.27041087, -0.21922147,  0.26517656, -0.80739075,
         0.25833526, -0.31004202,  0.2868321 ,  0.19433866, -0.29036498,
         0.0386285 , -0.78444123, -0.04793238,  0.41102988, -0.36388886,
        -0.58034706,  0.30269453,  0.36308962, -0.15227163, -0.4439151 ,
         0.19462997,  0.19528405,  0.05666233,  0.2890704 , -0.28468323,
        -0.00531206,  0.0571938 , -0.3201319 , -0.04418665, -0.08550781,
        -0.55847436, -0.2333639 , -0.20782956, -0.03543065, -0.17533456],
       [ 0.56338924, -0.12339553, -0.10862677,  0.7753425 , -0.07667087,
        -0.15752274,  0.01872334, -0.08169781, -0.3521876 ,  0.46373403,
        -0.08492758,  0.07166861, -0.00670818,  0.12686071, -0.19326551,
        -0.5262643 , -0.32958236,  0.14394784,  0.09043556, -0.54175544,
         0.02468163, -0.15456744,  0.68333143,  0.09068333, -0.45327246,
         0.23180094, -0.8615696 ,  0.3448039 ,  0.12838459, -0.58759046,
        -0.40712303,  0.23061076,  0.48426905, -0.2712814 , -0.5380918 ,
         0.47016335,  0.2257274 , -0.00830665,  0.28462422, -0.30498496,
         0.04400366,  0.25025868,  0.14867125,  0.4071703 , -0.15422425,
        -0.06878027, -0.40825695, -0.31492147,  0.09283663, -0.20183429],
       [ 0.7456156 ,  0.21256858,  0.1440033 ,  0.52338624,  0.11032254,
         0.00902788, -0.36678016, -0.08938274, -0.24165548,  0.33384597,
        -0.111946  , -0.01460045, -0.00716449,  0.19562715,  0.00685217,
        -0.24886714, -0.42796353,  0.1862    , -0.05241097, -0.664625  ,
         0.13449019, -0.22205493,  0.08633009,  0.43685383,  0.2972681 ,
         0.36140728, -0.71968895,  0.05291242, -0.1431612 , -0.15733941,
        -0.15056324, -0.05988007, -0.08178931, -0.15569413, -0.09303784,
        -0.18971168,  0.0762079 , -0.02541647, -0.27134502, -0.3392682 ,
        -0.10296471, -0.27275252, -0.34078008,  0.20083308, -0.26644838,
         0.00655449, -0.05141485, -0.04261916, -0.4541363 ,  0.20023566]],
      dtype=float32)>

لنقم الآن ببناء النموذج الكامل:

model = tf.keras.Sequential()
model.add(hub_layer)
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Dense(1))

model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 keras_layer (KerasLayer)    (None, 50)                48190600  
                                                                 
 dense (Dense)               (None, 16)                816       
                                                                 
 dense_1 (Dense)             (None, 1)                 17        
                                                                 
=================================================================
Total params: 48,191,433
Trainable params: 48,191,433
Non-trainable params: 0
_________________________________________________________________

يتم تكديس الطبقات بالتسلسل لبناء المصنف:

1. الطبقة الأولى هي طبقة TensorFlow Hub. تستخدم هذه الطبقة نموذجًا محفوظًا تم تدريبه مسبقًا لتعيين جملة في متجه التضمين الخاص بها. النموذج الذي نحن نستخدم ( جوجل / nnlm-أون-dim50 / 2 ) يقسم الجملة إلى الرموز، يضمن كل رمز ثم يجمع بين التضمين. الأبعاد الناتجة: (num_examples, embedding_dimension) .
2. يتم إيصاله هذا متجه الانتاج ذات طول ثابت من خلال مرتبطة ارتباطا كاملا ( Dense طبقة) مع 16 وحدة المخفية.
3. الطبقة الأخيرة متصلة بكثافة مع عقدة خرج واحدة. هذه المخرجات logits: log-الاحتمالات للفئة الحقيقية ، وفقًا للنموذج.

الوحدات المخفية

يحتوي النموذج أعلاه على طبقتين وسيطة أو "مخفية" بين المدخلات والمخرجات. عدد النواتج (الوحدات أو العقد أو الخلايا العصبية) هو بُعد المساحة التمثيلية للطبقة. بمعنى آخر ، مقدار الحرية المسموح به للشبكة عند تعلم تمثيل داخلي.

إذا كان النموذج يحتوي على المزيد من الوحدات المخفية (مساحة تمثيل ذات أبعاد أعلى) ، و / أو طبقات أكثر ، فيمكن للشبكة أن تتعلم تمثيلات أكثر تعقيدًا. ومع ذلك ، فإنه يجعل الشبكة أكثر تكلفة من الناحية الحسابية وقد يؤدي إلى تعلم أنماط غير مرغوب فيها - أنماط تعمل على تحسين الأداء على بيانات التدريب ولكن ليس على بيانات الاختبار. وهذا ما يسمى overfitting، وسوف نستكشف في وقت لاحق.

وظيفة الخسارة والمحسن

يحتاج النموذج إلى وظيفة خسارة ومحسن للتدريب. منذ هذه مشكلة تصنيف الثنائية ونموذج مخرجات احتمال (طبقة وحدة واحدة مع تفعيل السيني)، ونحن سوف تستخدم binary_crossentropy وظيفة الخسارة.

ليس هذا هو الخيار الوحيد لفقدان وظيفة، هل يمكن، على سبيل المثال، اختيار mean_squared_error . ولكن، عموما، binary_crossentropy هو أفضل للتعامل مع الاحتمالات، فهو يقيس "المسافة" بين التوزيعات الاحتمالية، أو في حالتنا، بين توزيع الحقائق على الأرض والتنبؤات.

في وقت لاحق ، عندما نستكشف مشاكل الانحدار (على سبيل المثال ، للتنبؤ بسعر المنزل) ، سنرى كيفية استخدام دالة خسارة أخرى تسمى متوسط ​​الخطأ التربيعي.

الآن ، قم بتكوين النموذج لاستخدام مُحسِّن ووظيفة خسارة:

model.compile(optimizer='adam',
              loss=tf.losses.BinaryCrossentropy(from_logits=True),
              metrics=[tf.metrics.BinaryAccuracy(threshold=0.0, name='accuracy')])

إنشاء مجموعة التحقق من الصحة

عند التدريب ، نريد التحقق من دقة النموذج في البيانات التي لم يرها من قبل. إنشاء مجموعة التحقق من خلال وضع بصرف النظر 10،000 أمثلة من بيانات التدريب الأصلي. (لماذا لا تستخدم مجموعة الاختبار الآن؟ هدفنا هو تطوير وضبط نموذجنا باستخدام بيانات التدريب فقط ، ثم استخدام بيانات الاختبار مرة واحدة فقط لتقييم دقتنا).

x_val = train_examples[:10000]
partial_x_train = train_examples[10000:]

y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]

تدريب النموذج

قم بتدريب النموذج لمدة 40 حقبة على دفعات صغيرة من 512 عينة. هذا هو 40 تكرارات على جميع العينات في x_train و y_train التنسورات. أثناء التدريب ، راقب فقد النموذج ودقته على 10000 عينة من مجموعة التحقق من الصحة:

history = model.fit(partial_x_train,
                    partial_y_train,
                    epochs=40,
                    batch_size=512,
                    validation_data=(x_val, y_val),
                    verbose=1)

Epoch 1/40
30/30 [==============================] - 2s 34ms/step - loss: 0.6667 - accuracy: 0.6060 - val_loss: 0.6192 - val_accuracy: 0.7195
Epoch 2/40
30/30 [==============================] - 1s 28ms/step - loss: 0.5609 - accuracy: 0.7770 - val_loss: 0.5155 - val_accuracy: 0.7882
Epoch 3/40
30/30 [==============================] - 1s 29ms/step - loss: 0.4309 - accuracy: 0.8489 - val_loss: 0.4135 - val_accuracy: 0.8364
Epoch 4/40
30/30 [==============================] - 1s 28ms/step - loss: 0.3154 - accuracy: 0.8937 - val_loss: 0.3515 - val_accuracy: 0.8583
Epoch 5/40
30/30 [==============================] - 1s 29ms/step - loss: 0.2345 - accuracy: 0.9227 - val_loss: 0.3256 - val_accuracy: 0.8639
Epoch 6/40
30/30 [==============================] - 1s 28ms/step - loss: 0.1773 - accuracy: 0.9457 - val_loss: 0.3104 - val_accuracy: 0.8702
Epoch 7/40
30/30 [==============================] - 1s 29ms/step - loss: 0.1331 - accuracy: 0.9645 - val_loss: 0.3024 - val_accuracy: 0.8741
Epoch 8/40
30/30 [==============================] - 1s 28ms/step - loss: 0.0984 - accuracy: 0.9777 - val_loss: 0.3061 - val_accuracy: 0.8758
Epoch 9/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0707 - accuracy: 0.9869 - val_loss: 0.3136 - val_accuracy: 0.8745
Epoch 10/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0501 - accuracy: 0.9919 - val_loss: 0.3305 - val_accuracy: 0.8743
Epoch 11/40
30/30 [==============================] - 1s 28ms/step - loss: 0.0351 - accuracy: 0.9960 - val_loss: 0.3434 - val_accuracy: 0.8726
Epoch 12/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0247 - accuracy: 0.9984 - val_loss: 0.3568 - val_accuracy: 0.8722
Epoch 13/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0178 - accuracy: 0.9993 - val_loss: 0.3711 - val_accuracy: 0.8700
Epoch 14/40
30/30 [==============================] - 1s 30ms/step - loss: 0.0134 - accuracy: 0.9996 - val_loss: 0.3839 - val_accuracy: 0.8711
Epoch 15/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0103 - accuracy: 0.9998 - val_loss: 0.3968 - val_accuracy: 0.8701
Epoch 16/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0080 - accuracy: 0.9998 - val_loss: 0.4104 - val_accuracy: 0.8702
Epoch 17/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0063 - accuracy: 0.9999 - val_loss: 0.4199 - val_accuracy: 0.8694
Epoch 18/40
30/30 [==============================] - 1s 28ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.4305 - val_accuracy: 0.8691
Epoch 19/40
30/30 [==============================] - 1s 28ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.4403 - val_accuracy: 0.8688
Epoch 20/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.4493 - val_accuracy: 0.8687
Epoch 21/40
30/30 [==============================] - 1s 30ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.4580 - val_accuracy: 0.8682
Epoch 22/40
30/30 [==============================] - 1s 30ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4659 - val_accuracy: 0.8682
Epoch 23/40
30/30 [==============================] - 1s 31ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.4743 - val_accuracy: 0.8680
Epoch 24/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4808 - val_accuracy: 0.8678
Epoch 25/40
30/30 [==============================] - 1s 30ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.4879 - val_accuracy: 0.8669
Epoch 26/40
30/30 [==============================] - 1s 30ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4943 - val_accuracy: 0.8667
Epoch 27/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.5003 - val_accuracy: 0.8672
Epoch 28/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.5064 - val_accuracy: 0.8665
Epoch 29/40
30/30 [==============================] - 1s 29ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.5120 - val_accuracy: 0.8668
Epoch 30/40
30/30 [==============================] - 1s 30ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.5174 - val_accuracy: 0.8671
Epoch 31/40
30/30 [==============================] - 1s 30ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.5230 - val_accuracy: 0.8664
Epoch 32/40
30/30 [==============================] - 1s 29ms/step - loss: 9.2117e-04 - accuracy: 1.0000 - val_loss: 0.5281 - val_accuracy: 0.8663
Epoch 33/40
30/30 [==============================] - 1s 29ms/step - loss: 8.4693e-04 - accuracy: 1.0000 - val_loss: 0.5332 - val_accuracy: 0.8659
Epoch 34/40
30/30 [==============================] - 1s 30ms/step - loss: 7.8501e-04 - accuracy: 1.0000 - val_loss: 0.5376 - val_accuracy: 0.8666
Epoch 35/40
30/30 [==============================] - 1s 29ms/step - loss: 7.2613e-04 - accuracy: 1.0000 - val_loss: 0.5424 - val_accuracy: 0.8657
Epoch 36/40
30/30 [==============================] - 1s 29ms/step - loss: 6.7541e-04 - accuracy: 1.0000 - val_loss: 0.5468 - val_accuracy: 0.8659
Epoch 37/40
30/30 [==============================] - 1s 29ms/step - loss: 6.2841e-04 - accuracy: 1.0000 - val_loss: 0.5510 - val_accuracy: 0.8658
Epoch 38/40
30/30 [==============================] - 1s 29ms/step - loss: 5.8661e-04 - accuracy: 1.0000 - val_loss: 0.5553 - val_accuracy: 0.8656
Epoch 39/40
30/30 [==============================] - 1s 29ms/step - loss: 5.4869e-04 - accuracy: 1.0000 - val_loss: 0.5595 - val_accuracy: 0.8658
Epoch 40/40
30/30 [==============================] - 1s 30ms/step - loss: 5.1370e-04 - accuracy: 1.0000 - val_loss: 0.5635 - val_accuracy: 0.8659

قم بتقييم النموذج

ودعونا نرى كيف يعمل النموذج. سيتم إرجاع قيمتين. الخسارة (رقم يمثل خطأنا ، والقيم الأقل هي الأفضل) والدقة.

results = model.evaluate(test_examples, test_labels)

print(results)

782/782 [==============================] - 2s 3ms/step - loss: 0.6272 - accuracy: 0.8484
[0.6272369027137756, 0.848360002040863]

هذا النهج الساذج إلى حد ما يحقق دقة تبلغ حوالي 87٪. مع الأساليب الأكثر تقدمًا ، يجب أن يقترب النموذج من 95٪.

قم بإنشاء رسم بياني للدقة والخسارة بمرور الوقت

model.fit() ترجع History الكائن الذي يحتوي على القاموس مع كل ما حدث أثناء التدريب:

history_dict = history.history
history_dict.keys()

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])

هناك أربعة إدخالات: واحد لكل مقياس يتم مراقبته أثناء التدريب والتحقق من الصحة. يمكننا استخدام هذه لتخطيط فقدان التدريب والتحقق من الصحة للمقارنة ، بالإضافة إلى دقة التدريب والتحقق من الصحة:

acc = history_dict['accuracy']
val_acc = history_dict['val_accuracy']
loss = history_dict['loss']
val_loss = history_dict['val_loss']

epochs = range(1, len(acc) + 1)

# "bo" is for "blue dot"
plt.plot(epochs, loss, 'bo', label='Training loss')
# b is for "solid blue line"
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

plt.clf()   # clear figure

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

plt.show()

في هذه المؤامرة ، تمثل النقاط خسارة التدريب ودقته ، والخطوط الصلبة هي فقدان التحقق من الصحة والدقة.

لاحظ فقدان التدريب تتناقص مع كل عصر والتدريب وزيادة دقة مع كل عصر. هذا متوقع عند استخدام تحسين النسب المتدرج - يجب أن يقلل الكمية المرغوبة في كل تكرار.

هذا ليس هو الحال بالنسبة لفقدان التحقق من الصحة والدقة - يبدو أنهما يصلان إلى الذروة بعد حوالي عشرين حقبة. هذا مثال على التخصيص الزائد: يعمل النموذج بشكل أفضل على بيانات التدريب مقارنةً بالبيانات التي لم يسبق لها مثيل من قبل. بعد هذه النقطة، ونموذج الإفراط في المثلى ويتعلم التمثيل محددة لبيانات التدريب التي لا نعمم على بيانات الاختبار.

بالنسبة لهذه الحالة بالذات ، يمكننا منع فرط التجهيز ببساطة عن طريق إيقاف التدريب بعد عشرين حقبة أو نحو ذلك. لاحقًا ، سترى كيفية القيام بذلك تلقائيًا من خلال رد الاتصال.

# MIT License
#
# Copyright (c) 2017 François Chollet
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.