 在 TensorFlow.org 上檢視
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 在 Google Colab 中執行
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 在 GitHub 上檢視
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 下載筆記本
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 查看 TF Hub 模型
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這個筆記本使用評論文本將電影評論分類為正面或負面。這是二元（或雙類別）分類的範例，二元分類是一種重要且廣泛適用的機器學習問題。
我們將使用 IMDB 資料集，其中包含來自 IMDb (網際網路電影資料庫) 的 50,000 則電影評論文本。這些評論分為 25,000 則用於訓練，25,000 則用於測試。訓練集和測試集是平衡的，表示它們包含相等數量的正面和負面評論。
這個筆記本使用 tf.keras (用於在 TensorFlow 中建構和訓練模型的高階 API) 以及 TensorFlow Hub (用於遷移學習的程式庫和平台)。如需使用 tf.keras 的更進階文本分類教學課程，請參閱 MLCC 文本分類指南。
更多模型
您可以在這裡找到更多具表現力或高效能的模型，可用於產生文本嵌入。
設定
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")
2023-12-08 12:29:56.398917: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2023-12-08 12:29:56.398969: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2023-12-08 12:29:56.400437: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Version: 2.15.0 Eager mode: True Hub version: 0.15.0 GPU is NOT AVAILABLE 2023-12-08 12:29:59.897456: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:274] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
下載 IMDB 資料集
IMDB 資料集可在 TensorFlow Datasets 上取得。以下程式碼會將 IMDB 資料集下載到您的機器 (或 Colab 執行階段)
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)
探索資料
讓我們花點時間瞭解資料的格式。每個範例都是代表電影評論的句子和對應的標籤。句子未以任何方式預先處理。標籤是 0 或 1 的整數值，其中 0 是負面評論，1 是正面評論。
print("Training entries: {}, test entries: {}".format(len(train_examples), len(test_examples)))
Training entries: 25000, test entries: 25000
讓我們印出前 10 個範例。
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)
我們也來印出前 10 個標籤。
train_labels[:10]
array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0])
建構模型
神經網路是透過堆疊層級建立的，這需要三個主要架構決策
- 如何表示文本？
- 模型中要使用多少層級？
- 每個層級要使用多少個隱藏單元？
在本範例中，輸入資料由句子組成。要預測的標籤是 0 或 1。
表示文本的一種方式是將句子轉換為嵌入向量。我們可以將預先訓練的文本嵌入用作第一層，這將有兩個優點
- 我們不必擔心文本預先處理，
- 我們可以從遷移學習中受益。
在本範例中，我們將使用來自 TensorFlow Hub 的模型，名為 google/nnlm-en-dim50/2。
為了本教學課程，還有另外兩個模型可供測試
- google/nnlm-en-dim50-with-normalization/2 - 與 google/nnlm-en-dim50/2 相同，但額外進行文本正規化以移除標點符號。這有助於更佳涵蓋輸入文本中詞元的詞彙內嵌入。
- google/nnlm-en-dim128-with-normalization/2 - 一個更大的模型，嵌入維度為 128，而不是較小的 50。
我們先建立一個 Keras 層級，使用 TensorFlow Hub 模型來嵌入句子，並在幾個輸入範例上試用。請注意，產生的嵌入的輸出形狀是預期的：(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([[ 0.5423195 , -0.0119017 , 0.06337538, 0.06862972, -0.16776837,
-0.10581174, 0.16865303, -0.04998824, -0.31148055, 0.07910346,
0.15442263, 0.01488662, 0.03930153, 0.19772711, -0.12215476,
-0.04120981, -0.2704109 , -0.21922152, 0.26517662, -0.80739075,
0.25833532, -0.3100421 , 0.28683215, 0.1943387 , -0.29036492,
0.03862849, -0.7844411 , -0.0479324 , 0.4110299 , -0.36388892,
-0.58034706, 0.30269456, 0.3630897 , -0.15227164, -0.44391504,
0.19462997, 0.19528408, 0.05666234, 0.2890704 , -0.28468323,
-0.00531206, 0.0571938 , -0.3201318 , -0.04418665, -0.08550783,
-0.55847436, -0.23336391, -0.20782952, -0.03543064, -0.17533456],
[ 0.56338924, -0.12339553, -0.10862679, 0.7753425 , -0.07667089,
-0.15752277, 0.01872335, -0.08169781, -0.3521876 , 0.4637341 ,
-0.08492756, 0.07166859, -0.00670817, 0.12686075, -0.19326553,
-0.52626437, -0.3295823 , 0.14394785, 0.09043556, -0.5417555 ,
0.02468163, -0.15456742, 0.68333143, 0.09068331, -0.45327246,
0.23180096, -0.8615696 , 0.34480393, 0.12838456, -0.58759046,
-0.4071231 , 0.23061076, 0.48426893, -0.27128142, -0.5380916 ,
0.47016326, 0.22572741, -0.00830663, 0.2846242 , -0.304985 ,
0.04400365, 0.25025874, 0.14867121, 0.40717036, -0.15422426,
-0.06878027, -0.40825695, -0.3149215 , 0.09283665, -0.20183425],
[ 0.7456154 , 0.21256861, 0.14400336, 0.5233862 , 0.11032254,
0.00902788, -0.3667802 , -0.08938274, -0.24165542, 0.33384594,
-0.11194605, -0.01460047, -0.0071645 , 0.19562712, 0.00685216,
-0.24886718, -0.42796347, 0.18620004, -0.05241098, -0.66462487,
0.13449019, -0.22205497, 0.08633006, 0.43685386, 0.2972681 ,
0.36140734, -0.7196889 , 0.05291241, -0.14316116, -0.1573394 ,
-0.15056328, -0.05988009, -0.08178931, -0.15569411, -0.09303783,
-0.18971172, 0.07620788, -0.02541647, -0.27134508, -0.3392682 ,
-0.10296468, -0.27275252, -0.34078008, 0.20083304, -0.26644835,
0.00655449, -0.05141488, -0.04261917, -0.45413622, 0.20023568]],
dtype=float32)>
現在我們來建構完整模型
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: 48191433 (183.84 MB)
Trainable params: 48191433 (183.84 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
層級會依序堆疊以建構分類器
- 第一層是 TensorFlow Hub 層級。此層級使用預先訓練的 Saved Model 將句子對應到其嵌入向量。我們正在使用的模型 (google/nnlm-en-dim50/2) 會將句子分割成詞元、嵌入每個詞元，然後組合嵌入。產生的維度為：
(num_examples, embedding_dimension)。 - 此固定長度輸出向量會透過具有 16 個隱藏單元的全連接 (
Dense) 層級傳輸。 - 最後一層與單一輸出節點密集連接。這會輸出 logits：根據模型，真實類別的對數機率。
隱藏單元
上述模型在輸入和輸出之間有兩個中間或「隱藏」層級。輸出的數量 (單元、節點或神經元) 是層級表示空間的維度。換句話說，網路在學習內部表示時允許的自由度。
如果模型具有更多隱藏單元 (更高維度的表示空間)，及/或更多層級，則網路可以學習更複雜的表示。但是，這會使網路在計算上更昂貴，並可能導致學習到不需要的模式，即改善訓練資料效能，但不會改善測試資料效能的模式。這稱為過度擬合，我們稍後將探索。
損失函數和最佳化工具
模型需要損失函數和最佳化工具才能進行訓練。由於這是二元分類問題，且模型輸出機率 (具有 Sigmoid 啟動的單元層級)，因此我們將使用 binary_crossentropy 損失函數。
這不是損失函數的唯一選擇，例如，您可以選擇 mean_squared_error。但是，一般來說，binary_crossentropy 更適合處理機率，它會測量機率分佈之間的「距離」，或在我們的案例中，測量真實分佈與預測之間的「距離」。
稍後，當我們探索迴歸問題 (例如，預測房屋價格) 時，我們將瞭解如何使用另一個稱為均方誤差的損失函數。
現在，設定模型以使用最佳化工具和損失函數
model.compile(optimizer='adam',
loss=tf.losses.BinaryCrossentropy(from_logits=True),
metrics=[tf.metrics.BinaryAccuracy(threshold=0.0, name='accuracy')])
建立驗證集
訓練時，我們想要檢查模型在之前未見過的資料上的準確度。透過從原始訓練資料中撥出 10,000 個範例，建立驗證集。(為何現在不使用測試集？我們的目標是僅使用訓練資料來開發和調整模型，然後僅使用測試資料一次來評估我們的準確度)。
x_val = train_examples[:10000]
partial_x_train = train_examples[10000:]
y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
訓練模型
以 512 個樣本的小批次訓練模型 40 個週期。這是 x_train 和 y_train 張量中所有樣本的 40 次迭代。訓練時，監控模型在驗證集中 10,000 個樣本上的損失和準確度
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 [==============================] - 22s 710ms/step - loss: 0.6627 - accuracy: 0.6301 - val_loss: 0.6155 - val_accuracy: 0.7314 Epoch 2/40 30/30 [==============================] - 21s 710ms/step - loss: 0.5568 - accuracy: 0.7823 - val_loss: 0.5170 - val_accuracy: 0.7844 Epoch 3/40 30/30 [==============================] - 21s 712ms/step - loss: 0.4290 - accuracy: 0.8513 - val_loss: 0.4133 - val_accuracy: 0.8382 Epoch 4/40 30/30 [==============================] - 21s 708ms/step - loss: 0.3133 - accuracy: 0.8981 - val_loss: 0.3504 - val_accuracy: 0.8575 Epoch 5/40 30/30 [==============================] - 21s 713ms/step - loss: 0.2312 - accuracy: 0.9267 - val_loss: 0.3194 - val_accuracy: 0.8673 Epoch 6/40 30/30 [==============================] - 21s 707ms/step - loss: 0.1734 - accuracy: 0.9482 - val_loss: 0.3065 - val_accuracy: 0.8712 Epoch 7/40 30/30 [==============================] - 21s 709ms/step - loss: 0.1288 - accuracy: 0.9655 - val_loss: 0.3014 - val_accuracy: 0.8740 Epoch 8/40 30/30 [==============================] - 21s 707ms/step - loss: 0.0946 - accuracy: 0.9792 - val_loss: 0.3034 - val_accuracy: 0.8759 Epoch 9/40 30/30 [==============================] - 21s 708ms/step - loss: 0.0691 - accuracy: 0.9877 - val_loss: 0.3098 - val_accuracy: 0.8754 Epoch 10/40 30/30 [==============================] - 21s 710ms/step - loss: 0.0501 - accuracy: 0.9938 - val_loss: 0.3199 - val_accuracy: 0.8747 Epoch 11/40 30/30 [==============================] - 21s 708ms/step - loss: 0.0367 - accuracy: 0.9969 - val_loss: 0.3313 - val_accuracy: 0.8751 Epoch 12/40 30/30 [==============================] - 21s 707ms/step - loss: 0.0268 - accuracy: 0.9986 - val_loss: 0.3430 - val_accuracy: 0.8734 Epoch 13/40 30/30 [==============================] - 21s 710ms/step - loss: 0.0202 - accuracy: 0.9992 - val_loss: 0.3553 - val_accuracy: 0.8714 Epoch 14/40 30/30 [==============================] - 21s 704ms/step - loss: 0.0154 - accuracy: 0.9995 - val_loss: 0.3682 - val_accuracy: 0.8723 Epoch 15/40 30/30 [==============================] - 21s 710ms/step - loss: 0.0121 - accuracy: 0.9999 - val_loss: 0.3787 - val_accuracy: 0.8717 Epoch 16/40 30/30 [==============================] - 21s 710ms/step - loss: 0.0097 - accuracy: 0.9999 - val_loss: 0.3881 - val_accuracy: 0.8699 Epoch 17/40 30/30 [==============================] - 21s 708ms/step - loss: 0.0079 - accuracy: 0.9999 - val_loss: 0.3984 - val_accuracy: 0.8697 Epoch 18/40 30/30 [==============================] - 21s 708ms/step - loss: 0.0066 - accuracy: 0.9999 - val_loss: 0.4079 - val_accuracy: 0.8700 Epoch 19/40 30/30 [==============================] - 21s 708ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.4158 - val_accuracy: 0.8692 Epoch 20/40 30/30 [==============================] - 21s 707ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.4252 - val_accuracy: 0.8692 Epoch 21/40 30/30 [==============================] - 21s 709ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.4321 - val_accuracy: 0.8687 Epoch 22/40 30/30 [==============================] - 21s 706ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.4393 - val_accuracy: 0.8685 Epoch 23/40 30/30 [==============================] - 21s 708ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.4461 - val_accuracy: 0.8682 Epoch 24/40 30/30 [==============================] - 21s 708ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4538 - val_accuracy: 0.8681 Epoch 25/40 30/30 [==============================] - 21s 712ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.4592 - val_accuracy: 0.8668 Epoch 26/40 30/30 [==============================] - 21s 715ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4659 - val_accuracy: 0.8671 Epoch 27/40 30/30 [==============================] - 21s 716ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4713 - val_accuracy: 0.8668 Epoch 28/40 30/30 [==============================] - 21s 711ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.4767 - val_accuracy: 0.8662 Epoch 29/40 30/30 [==============================] - 21s 714ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4828 - val_accuracy: 0.8663 Epoch 30/40 30/30 [==============================] - 21s 711ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.4876 - val_accuracy: 0.8659 Epoch 31/40 30/30 [==============================] - 21s 714ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.4926 - val_accuracy: 0.8658 Epoch 32/40 30/30 [==============================] - 21s 711ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.4975 - val_accuracy: 0.8659 Epoch 33/40 30/30 [==============================] - 21s 711ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.5021 - val_accuracy: 0.8658 Epoch 34/40 30/30 [==============================] - 21s 710ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.5066 - val_accuracy: 0.8655 Epoch 35/40 30/30 [==============================] - 21s 711ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.5112 - val_accuracy: 0.8654 Epoch 36/40 30/30 [==============================] - 21s 709ms/step - loss: 9.3126e-04 - accuracy: 1.0000 - val_loss: 0.5151 - val_accuracy: 0.8654 Epoch 37/40 30/30 [==============================] - 21s 711ms/step - loss: 8.7043e-04 - accuracy: 1.0000 - val_loss: 0.5191 - val_accuracy: 0.8654 Epoch 38/40 30/30 [==============================] - 21s 717ms/step - loss: 8.1289e-04 - accuracy: 1.0000 - val_loss: 0.5236 - val_accuracy: 0.8646 Epoch 39/40 30/30 [==============================] - 21s 717ms/step - loss: 7.6191e-04 - accuracy: 1.0000 - val_loss: 0.5278 - val_accuracy: 0.8649 Epoch 40/40 30/30 [==============================] - 21s 717ms/step - loss: 7.1471e-04 - accuracy: 1.0000 - val_loss: 0.5310 - val_accuracy: 0.8648
評估模型
讓我們看看模型的效能如何。將傳回兩個值。損失 (代表我們誤差的數字，值越低越好) 和準確度。
results = model.evaluate(test_examples, test_labels)
print(results)
782/782 [==============================] - 143s 183ms/step - loss: 0.5944 - accuracy: 0.8462 [0.5943577885627747, 0.8462399840354919]
這種相當簡單的方法達到約 87% 的準確度。使用更進階的方法，模型應更接近 95%。
建立準確度和損失隨時間變化的圖表
model.fit() 會傳回 History 物件，其中包含在訓練期間發生的所有事件的字典
history_dict = history.history
history_dict.keys()
dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
有四個條目：每個條目對應到訓練和驗證期間監控的指標。我們可以利用這些指標繪製訓練和驗證損失以進行比較，以及訓練和驗證準確度
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()
在此圖表中，點代表訓練損失和準確度，實線代表驗證損失和準確度。
請注意，訓練損失會隨著每個週期減少，而訓練準確度會隨著每個週期增加。這是使用梯度下降最佳化時的預期情況，它應在每次迭代時最小化所需的量。
驗證損失和準確度並非如此，它們似乎在大約 20 個週期後達到峰值。這是過度擬合的範例：模型在訓練資料上的效能優於在之前未見過的資料上的效能。在此之後，模型會過度最佳化並學習特定於訓練資料的表示，而這些表示無法泛化到測試資料。
對於此特定案例，我們可以透過在 20 個週期左右後停止訓練來防止過度擬合。稍後，您將瞭解如何使用回呼自動執行此操作。
# MIT License
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# Copyright (c) 2017 François Chollet # IGNORE_COPYRIGHT: cleared by OSS licensing
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