 Lihat di TensorFlow.org |  Jalankan di Google Colab |  Lihat di GitHub |  Unduh buku catatan |  Lihat model TF Hub |
Ini review film mengklasifikasikan notebook sebagai positif atau negatif menggunakan teks review. Ini adalah contoh dari biner -atau dua kelas-klasifikasi, jenis yang penting dan banyak diterapkan mesin belajar masalah.
Kami akan menggunakan dataset IMDB yang berisi teks dari 50.000 ulasan film dari Internet Movie Database . Ini dibagi menjadi 25.000 ulasan untuk pelatihan dan 25.000 ulasan untuk pengujian. Pelatihan dan pengujian set seimbang, yang berarti mengandung jumlah yang sama dari ulasan positif dan negatif.
Notebook ini menggunakan tf.keras , tingkat tinggi API untuk membangun dan melatih model dalam TensorFlow, dan TensorFlow Hub , perpustakaan dan platform untuk belajar transfer. Untuk teks yang lebih maju klasifikasi tutorial menggunakan tf.keras , melihat MLCC Teks Klasifikasi Panduan .
Lebih banyak model
Di sini Anda dapat menemukan model yang lebih ekspresif atau performant yang dapat Anda gunakan untuk menghasilkan embedding teks.
Mempersiapkan
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
Unduh kumpulan data IMDB
The IMDB dataset tersedia pada dataset TensorFlow . Kode berikut mendownload set data IMDB ke mesin Anda (atau runtime 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()`.
Jelajahi datanya
Mari luangkan waktu sejenak untuk memahami format data. Setiap contoh adalah kalimat yang mewakili ulasan film dan label yang sesuai. Kalimat itu tidak diproses sebelumnya. Label adalah nilai bilangan bulat dari 0 atau 1, di mana 0 adalah ulasan negatif, dan 1 adalah ulasan positif.
print("Training entries: {}, test entries: {}".format(len(train_examples), len(test_examples)))
Training entries: 25000, test entries: 25000
Mari kita cetak 10 contoh pertama.
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)
Mari kita juga mencetak 10 label pertama.
train_labels[:10]
array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0])
Bangun modelnya
Jaringan saraf dibuat dengan menumpuk lapisan—ini membutuhkan tiga keputusan arsitektur utama:
- Bagaimana cara merepresentasikan teks?
- Berapa banyak lapisan yang akan digunakan dalam model?
- Berapa banyak unit tersembunyi untuk digunakan untuk setiap lapisan?
Dalam contoh ini, data input terdiri dari kalimat. Label yang akan diprediksi adalah 0 atau 1.
Salah satu cara untuk merepresentasikan teks adalah dengan mengubah kalimat menjadi vektor embeddings. Kita dapat menggunakan penyematan teks yang telah dilatih sebelumnya sebagai lapisan pertama, yang akan memiliki dua keuntungan:
- kita tidak perlu khawatir tentang pra-pemrosesan teks,
- kita bisa mendapatkan keuntungan dari transfer belajar.
Untuk contoh ini kita akan menggunakan model dari TensorFlow Hub disebut google / nnlm-en-dim50 / 2 .
Ada dua model lain untuk diuji demi tutorial ini:
- google / nnlm-en-dim50-dengan-normalisasi / 2 - sama seperti google / nnlm-en-dim50 / 2 , tetapi dengan normalisasi teks tambahan untuk menghapus tanda baca. Ini dapat membantu untuk mendapatkan cakupan yang lebih baik dari penyematan kosakata untuk token pada teks masukan Anda.
- google / nnlm-en-dim128-dengan-normalisasi / 2 - Sebuah model yang lebih besar dengan dimensi embedding dari 128 bukannya lebih kecil 50.
Pertama-tama mari kita buat layer Keras yang menggunakan model TensorFlow Hub untuk menyematkan kalimat, dan mencobanya pada beberapa contoh input. Perhatikan bahwa bentuk output dari embeddings dihasilkan adalah diharapkan: (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)>
Sekarang mari kita buat model lengkapnya:
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
_________________________________________________________________
Lapisan ditumpuk secara berurutan untuk membangun pengklasifikasi:
- Lapisan pertama adalah lapisan TensorFlow Hub. Lapisan ini menggunakan Model Tersimpan yang telah dilatih sebelumnya untuk memetakan kalimat ke dalam vektor penyematannya. Model yang kami gunakan ( google / nnlm-en-dim50 / 2 ) membagi kalimat menjadi token, sematan setiap token dan kemudian menggabungkan embedding. Dimensi yang dihasilkan adalah:
(num_examples, embedding_dimension). - Vektor output tetap-panjang ini disalurkan melalui sepenuhnya-terhubung (
Dense) layer dengan 16 unit tersembunyi. - Lapisan terakhir terhubung erat dengan simpul keluaran tunggal. Ini menghasilkan logit: log-peluang dari kelas sebenarnya, menurut model.
Unit tersembunyi
Model di atas memiliki dua lapisan perantara atau "tersembunyi", antara input dan output. Jumlah output (unit, node, atau neuron) adalah dimensi ruang representasional untuk lapisan. Dengan kata lain, jumlah kebebasan yang diperbolehkan jaringan saat mempelajari representasi internal.
Jika sebuah model memiliki lebih banyak unit tersembunyi (ruang representasi berdimensi lebih tinggi), dan/atau lebih banyak lapisan, maka jaringan dapat mempelajari representasi yang lebih kompleks. Namun, ini membuat jaringan lebih mahal secara komputasi dan dapat menyebabkan mempelajari pola yang tidak diinginkan—pola yang meningkatkan kinerja pada data pelatihan tetapi tidak pada data pengujian. Ini disebut overfitting, dan kami akan menjelajahi nanti.
Fungsi kerugian dan pengoptimal
Sebuah model membutuhkan fungsi kerugian dan pengoptimal untuk pelatihan. Karena ini adalah masalah klasifikasi biner dan model output probabilitas (satu unit lapisan dengan aktivasi sigmoid), kita akan menggunakan binary_crossentropy fungsi kerugian.
Ini bukan satu-satunya pilihan untuk fungsi kerugian, Anda bisa, misalnya, memilih mean_squared_error . Tapi, secara umum, binary_crossentropy lebih baik untuk berurusan dengan probabilitas-itu mengukur "jarak" antara distribusi probabilitas, atau dalam kasus kami, antara distribusi tanah-kebenaran dan prediksi.
Kemudian, ketika kita menjelajahi masalah regresi (misalnya, untuk memprediksi harga rumah), kita akan melihat bagaimana menggunakan fungsi kerugian lain yang disebut mean squared error.
Sekarang, konfigurasikan model untuk menggunakan pengoptimal dan fungsi kerugian:
model.compile(optimizer='adam',
loss=tf.losses.BinaryCrossentropy(from_logits=True),
metrics=[tf.metrics.BinaryAccuracy(threshold=0.0, name='accuracy')])
Buat set validasi
Saat pelatihan, kami ingin memeriksa keakuratan model pada data yang belum pernah dilihat sebelumnya. Buat satu set validasi dengan menetapkan selain 10.000 contoh dari data pelatihan asli. (Mengapa tidak menggunakan set pengujian sekarang? Tujuan kami adalah mengembangkan dan menyempurnakan model kami hanya dengan menggunakan data pelatihan, kemudian menggunakan data uji sekali saja untuk mengevaluasi akurasi kami).
x_val = train_examples[:10000]
partial_x_train = train_examples[10000:]
y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
Latih modelnya
Latih model untuk 40 epoch dalam batch mini 512 sampel. Ini adalah 40 iterasi atas semua sampel dalam x_train dan y_train tensor. Saat pelatihan, pantau kehilangan dan akurasi model pada 10.000 sampel dari set validasi:
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
Evaluasi modelnya
Dan mari kita lihat bagaimana kinerja modelnya. Dua nilai akan dikembalikan. Loss (angka yang mewakili kesalahan kami, nilai yang lebih rendah lebih baik), dan akurasi.
results = model.evaluate(test_examples, test_labels)
print(results)
782/782 [==============================] - 2s 3ms/step - loss: 0.6272 - accuracy: 0.8484 [0.6272369027137756, 0.848360002040863]
Pendekatan yang cukup naif ini mencapai akurasi sekitar 87%. Dengan pendekatan yang lebih maju, model harus mendekati 95%.
Buat grafik akurasi dan kerugian dari waktu ke waktu
model.fit() mengembalikan History obyek yang berisi kamus dengan segala sesuatu yang terjadi selama pelatihan:
history_dict = history.history
history_dict.keys()
dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
Ada empat entri: satu untuk setiap metrik yang dipantau selama pelatihan dan validasi. Kita dapat menggunakan ini untuk memplot kerugian pelatihan dan validasi untuk perbandingan, serta akurasi pelatihan dan validasi:
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()
Dalam plot ini, titik-titik mewakili kehilangan dan akurasi pelatihan, dan garis padat adalah kehilangan dan akurasi validasi.
Perhatikan hilangnya pelatihan menurun dengan setiap zaman dan akurasi pelatihan meningkat dengan setiap zaman. Ini diharapkan saat menggunakan optimasi penurunan gradien—ini harus meminimalkan kuantitas yang diinginkan pada setiap iterasi.
Ini tidak terjadi pada kehilangan validasi dan akurasi—mereka tampaknya mencapai puncaknya setelah sekitar dua puluh epoch. Ini adalah contoh overfitting: model berperforma lebih baik pada data pelatihan daripada pada data yang belum pernah dilihat sebelumnya. Setelah titik ini, model over-Mengoptimalkan dan belajar representasi khusus untuk data pelatihan yang tidak menggeneralisasi untuk data uji.
Untuk kasus khusus ini, kita dapat mencegah overfitting hanya dengan menghentikan pelatihan setelah dua puluh atau lebih epoch. Nanti, Anda akan melihat cara melakukannya secara otomatis dengan panggilan balik.
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# Copyright (c) 2017 François Chollet
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