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Film İncelemeleri ile Metin Sınıflandırma

Koleksiyonlar ile düzeninizi koruyun İçeriği tercihlerinize göre kaydedin ve kategorilere ayırın.

TensorFlow.org'da görüntüleyin Google Colab'da çalıştırın GitHub'da görüntüle Not defterini indir TF Hub modellerine bakın

Yorum metni kullanarak pozitif veya negatif olarak bu dizüstü sınıflandırır film değerlendirmeleri. Bu ikili -veya iki sınıf-sınıflandırma, makine öğrenme sorununun önemli ve yaygın olarak uygulanabilir türden bir örnektir.

Biz kullanacağız IMDB veri kümesini 50.000 film incelemeleri metnini içerir Internet Movie Database . Bunlar, eğitim için 25.000 inceleme ve test için 25.000 incelemeye bölünmüştür. Eğitim ve test setleri pozitif ve negatif değerlendirmeleri eşit sayıda içerirler, yani dengeli edilir.

Bu defter kullandığı tf.keras TensorFlow içinde inşa ve tren modellerine, üst düzey bir API ve TensorFlow Hub , transfer öğrenme için bir kütüphane ve bir platform. Daha gelişmiş bir metin sınıflandırma öğretici kullanmak için tf.keras , bkz MLCC Metin Sınıflandırma Kılavuzu .

Daha fazla model

İşte size metin gömme oluşturmak için kullanabileceği daha etkileyici veya ölçülebilir modellerini bulabilirsiniz.


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

IMDB veri setini indirin

IMDB veri kümesi mevcuttur TensorFlow veri setleri . Aşağıdaki kod, IMDB veri kümesini makinenize (veya ortak çalışma zamanına) indirir:

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()`.

Verileri keşfedin

Verilerin biçimini anlamak için bir dakikanızı ayıralım. Her örnek, film incelemesini ve buna karşılık gelen bir etiketi temsil eden bir cümledir. Cümle hiçbir şekilde ön işleme tabi tutulmaz. Etiket, 0 veya 1 tamsayı değeridir; burada 0, olumsuz bir incelemedir ve 1, olumlu bir incelemedir.

print("Training entries: {}, test entries: {}".format(len(train_examples), len(test_examples)))
Training entries: 25000, test entries: 25000

İlk 10 örneği yazdıralım.

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.'],

İlk 10 etiketi de yazdıralım.

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

Modeli oluşturun

Sinir ağı, katmanları istifleyerek oluşturulur; bu, üç ana mimari karar gerektirir:

  • Metin nasıl temsil edilir?
  • Modelde kaç katman kullanılacak?
  • Her katman için kaç gizli birimler kullanılır?

Bu örnekte, giriş verileri cümlelerden oluşmaktadır. Tahmin edilecek etiketler ya 0 ya da 1'dir.

Metni temsil etmenin bir yolu, cümleleri gömme vektörlerine dönüştürmektir. İlk katman olarak iki avantajı olacak şekilde önceden eğitilmiş bir metin yerleştirme kullanabiliriz:

  • metin ön işleme konusunda endişelenmemize gerek yok,
  • transfer öğrenmeden yararlanabiliriz.

Bu örnek için biz bir model kullanacağız TensorFlow Hub adı verilen google / nnlm-en-dim50 / 2 .

Bu eğitim için test edilecek iki model daha var:

Önce cümleleri gömmek için TensorFlow Hub modelini kullanan bir Keras katmanı oluşturalım ve bunu birkaç giriş örneğinde deneyelim. Not üretilen tespitlerinin çıkış şekli beklenen olduğu: (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)
<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]],

Şimdi tam modeli oluşturalım:

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

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

Sınıflandırıcıyı oluşturmak için katmanlar sırayla istiflenir:

  1. İlk katman bir TensorFlow Hub katmanıdır. Bu katman, bir cümleyi gömme vektörüne eşlemek için önceden eğitilmiş bir Kaydedilmiş Model kullanır. Biz (kullandığınızı modeli google / nnlm-en-dim50 / 2 ) dizgeciklerine cümleyi böler, her simge gömer ve ardından gömme birleştirir. Elde edilen boyutları: (num_examples, embedding_dimension) .
  2. Bu sabit uzunluklu çıkış vektörü, bir tam-bağlı (boyunca yöneltilen Dense 16 gizli birimleri ile) tabaka.
  3. Son katman, tek bir çıkış düğümü ile yoğun bir şekilde bağlantılıdır. Bu logitleri verir: modele göre gerçek sınıfın log-oranları.

Gizli birimler

Yukarıdaki model, giriş ve çıkış arasında iki ara veya "gizli" katmana sahiptir. Çıktıların sayısı (birimler, düğümler veya nöronlar), katmanın temsili alanının boyutudur. Başka bir deyişle, dahili bir temsili öğrenirken ağın izin verdiği özgürlük miktarı.

Bir modelin daha fazla gizli birimi (daha yüksek boyutlu bir temsil alanı) ve/veya daha fazla katmanı varsa, ağ daha karmaşık temsilleri öğrenebilir. Bununla birlikte, ağı hesaplama açısından daha pahalı hale getirir ve istenmeyen kalıpların – eğitim verilerinde performansı artıran ancak test verilerinde olmayan kalıpların – öğrenilmesine yol açabilir. Bu aşırı oturma denir ve daha sonra bunu inceleyeceğiz.

Kayıp fonksiyonu ve optimize edici

Bir model, eğitim için bir kayıp işlevine ve bir optimize ediciye ihtiyaç duyar. Bu ikili bir sınıflandırma sorunu ve bir olasılık çıkışları modeli (bir sigmoid aktivasyonu ile bir tek ünite katmanı) olduğu için, kullanacağız binary_crossentropy kayıp fonksiyonunu.

Bu bir kayıp fonksiyonu için tek seçenek değil, örneğin, seçebilirsiniz mean_squared_error . Fakat genel olarak, binary_crossentropy çığır gerçeği dağıtım ve tahminler arasında, olasılıklar-it olasılık dağılımları arasında "mesafe" ölçer veya bizim durumumuzda ile başa çıkmak için daha iyidir.

Daha sonra, regresyon problemlerini araştırırken (örneğin, bir evin fiyatını tahmin etmek için), ortalama kare hatası adı verilen başka bir kayıp fonksiyonunun nasıl kullanılacağını göreceğiz.

Şimdi, modeli bir optimize edici ve bir kayıp işlevi kullanacak şekilde yapılandırın:

              metrics=[tf.metrics.BinaryAccuracy(threshold=0.0, name='accuracy')])

Bir doğrulama seti oluşturun

Eğitim sırasında, modelin daha önce görmediği veriler üzerindeki doğruluğunu kontrol etmek istiyoruz. Orijinal eğitim verileri dışında 10.000 örnekler ayarlayarak bir doğrulama kümesi oluşturun. (Neden şimdi test setini kullanmıyorsunuz? Amacımız, modelimizi yalnızca eğitim verilerini kullanarak geliştirmek ve ayarlamak, ardından doğruluğumuzu değerlendirmek için test verilerini yalnızca bir kez kullanmaktır).

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

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

Modeli eğit

Modeli, 512 örnekten oluşan mini gruplar halinde 40 dönem için eğitin. Bu tüm numunelerin üzerinde 40 iterasyon olan x_train ve y_train tensörlerle. Eğitim sırasında, doğrulama setindeki 10.000 örnekte modelin kaybını ve doğruluğunu izleyin:

history = model.fit(partial_x_train,
                    validation_data=(x_val, y_val),
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

Modeli değerlendirin

Ve modelin nasıl performans gösterdiğini görelim. İki değer döndürülür. Kayıp (hatamızı temsil eden bir sayı, daha düşük değerler daha iyidir) ve doğruluk.

results = model.evaluate(test_examples, test_labels)

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

Bu oldukça naif yaklaşım, yaklaşık %87'lik bir doğruluk sağlar. Daha gelişmiş yaklaşımlarla model %95'e yaklaşmalıdır.

Zaman içinde doğruluk ve kayıp grafiği oluşturun

model.fit() bir döner History eğitimi sırasında olanlardan sonra bir sözlük içerir nesnesi:

history_dict = history.history
dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])

Dört giriş vardır: eğitim ve doğrulama sırasında izlenen her metrik için bir tane. Bunları, karşılaştırma için eğitim ve doğrulama kaybının yanı sıra eğitim ve doğrulama doğruluğunu çizmek için kullanabiliriz:

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.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')



Bu çizimde, noktalar eğitim kaybını ve doğruluğunu temsil eder ve düz çizgiler doğrulama kaybı ve doğruluğunu gösterir.

Eğitim kaybı her döneme ve her çağın ile eğitim doğruluk artar azalır dikkat edin. Gradyan iniş optimizasyonu kullanılırken bu beklenir; her yinelemede istenen miktarı en aza indirmelidir.

Doğrulama kaybı ve doğruluğu için durum böyle değil - yaklaşık yirmi çağdan sonra zirveye ulaşıyor gibi görünüyorlar. Bu, fazla uydurmaya bir örnektir: model, eğitim verilerinde daha önce hiç görmediği verilerde olduğundan daha iyi performans gösterir. Bu noktadan sonra, model aşırı optimize eder ve test verilerine genelleme yoktur eğitim verileri öğrenir gösterimleri özgü.

Bu özel durum için, eğitimi yirmi ya da daha fazla çağdan sonra basitçe durdurarak fazla takmayı önleyebiliriz. Daha sonra, bunu bir geri arama ile otomatik olarak nasıl yapacağınızı göreceksiniz.

# 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.