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This notebook classifies movie reviews as positive or negative using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem.
We'll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are balanced, meaning they contain an equal number of positive and negative reviews.
This notebook uses tf.keras, a high-level API to build and train models in TensorFlow, and TensorFlow Hub, a library and platform for transfer learning. For a more advanced text classification tutorial using tf.keras, see the MLCC Text Classification Guide.
More models
Here you can find more expressive or performant models that you could use to generate the text embedding.
Setup
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
Download the IMDB dataset
The IMDB dataset is available on TensorFlow datasets. The following code downloads the IMDB dataset to your machine (or the colab runtime):
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)
Explore the data
Let's take a moment to understand the format of the data. Each example is a sentence representing the movie review and a corresponding label. The sentence is not preprocessed in any way. The label is an integer value of either 0 or 1, where 0 is a negative review, and 1 is a positive review.
print("Training entries: {}, test entries: {}".format(len(train_examples), len(test_examples)))
Training entries: 25000, test entries: 25000
Let's print first 10 examples.
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)
Let's also print the first 10 labels.
train_labels[:10]
array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0])
Build the model
The neural network is created by stacking layers—this requires three main architectural decisions:
- How to represent the text?
- How many layers to use in the model?
- How many hidden units to use for each layer?
In this example, the input data consists of sentences. The labels to predict are either 0 or 1.
One way to represent the text is to convert sentences into embeddings vectors. We can use a pre-trained text embedding as the first layer, which will have two advantages:
- we don't have to worry about text preprocessing,
- we can benefit from transfer learning.
For this example we will use a model from TensorFlow Hub called google/nnlm-en-dim50/2.
There are two other models to test for the sake of this tutorial:
- google/nnlm-en-dim50-with-normalization/2 - same as google/nnlm-en-dim50/2, but with additional text normalization to remove punctuation. This can help to get better coverage of in-vocabulary embeddings for tokens on your input text.
- google/nnlm-en-dim128-with-normalization/2 - A larger model with an embedding dimension of 128 instead of the smaller 50.
Let's first create a Keras layer that uses a TensorFlow Hub model to embed the sentences, and try it out on a couple of input examples. Note that the output shape of the produced embeddings is a expected: (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)>
Let's now build the full model:
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)
_________________________________________________________________
The layers are stacked sequentially to build the classifier:
- The first layer is a TensorFlow Hub layer. This layer uses a pre-trained Saved Model to map a sentence into its embedding vector. The model that we are using (google/nnlm-en-dim50/2) splits the sentence into tokens, embeds each token and then combines the embedding. The resulting dimensions are:
(num_examples, embedding_dimension). - This fixed-length output vector is piped through a fully-connected (
Dense) layer with 16 hidden units. - The last layer is densely connected with a single output node. This outputs logits: the log-odds of the true class, according to the model.
Hidden units
The above model has two intermediate or "hidden" layers, between the input and output. The number of outputs (units, nodes, or neurons) is the dimension of the representational space for the layer. In other words, the amount of freedom the network is allowed when learning an internal representation.
If a model has more hidden units (a higher-dimensional representation space), and/or more layers, then the network can learn more complex representations. However, it makes the network more computationally expensive and may lead to learning unwanted patterns—patterns that improve performance on training data but not on the test data. This is called overfitting, and we'll explore it later.
Loss function and optimizer
A model needs a loss function and an optimizer for training. Since this is a binary classification problem and the model outputs a probability (a single-unit layer with a sigmoid activation), we'll use the binary_crossentropy loss function.
This isn't the only choice for a loss function, you could, for instance, choose mean_squared_error. But, generally, binary_crossentropy is better for dealing with probabilities—it measures the "distance" between probability distributions, or in our case, between the ground-truth distribution and the predictions.
Later, when we are exploring regression problems (say, to predict the price of a house), we will see how to use another loss function called mean squared error.
Now, configure the model to use an optimizer and a loss function:
model.compile(optimizer='adam',
loss=tf.losses.BinaryCrossentropy(from_logits=True),
metrics=[tf.metrics.BinaryAccuracy(threshold=0.0, name='accuracy')])
Create a validation set
When training, we want to check the accuracy of the model on data it hasn't seen before. Create a validation set by setting apart 10,000 examples from the original training data. (Why not use the testing set now? Our goal is to develop and tune our model using only the training data, then use the test data just once to evaluate our accuracy).
x_val = train_examples[:10000]
partial_x_train = train_examples[10000:]
y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
Train the model
Train the model for 40 epochs in mini-batches of 512 samples. This is 40 iterations over all samples in the x_train and y_train tensors. While training, monitor the model's loss and accuracy on the 10,000 samples from the validation set:
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
Evaluate the model
And let's see how the model performs. Two values will be returned. Loss (a number which represents our error, lower values are better), and accuracy.
results = model.evaluate(test_examples, test_labels)
print(results)
782/782 [==============================] - 143s 183ms/step - loss: 0.5944 - accuracy: 0.8462 [0.5943577885627747, 0.8462399840354919]
This fairly naive approach achieves an accuracy of about 87%. With more advanced approaches, the model should get closer to 95%.
Create a graph of accuracy and loss over time
model.fit() returns a History object that contains a dictionary with everything that happened during training:
history_dict = history.history
history_dict.keys()
dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
There are four entries: one for each monitored metric during training and validation. We can use these to plot the training and validation loss for comparison, as well as the training and validation accuracy:
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()
In this plot, the dots represent the training loss and accuracy, and the solid lines are the validation loss and accuracy.
Notice the training loss decreases with each epoch and the training accuracy increases with each epoch. This is expected when using a gradient descent optimization—it should minimize the desired quantity on every iteration.
This isn't the case for the validation loss and accuracy—they seem to peak after about twenty epochs. This is an example of overfitting: the model performs better on the training data than it does on data it has never seen before. After this point, the model over-optimizes and learns representations specific to the training data that do not generalize to test data.
For this particular case, we could prevent overfitting by simply stopping the training after twenty or so epochs. Later, you'll see how to do this automatically with a callback.
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