Keras LSTM 用于 IMDB 情感分类

这是一个如何使用 DeepExplainer 解释 Keras LSTM 模型的简单示例。

# This model training code is directly from:
# https://github.com/keras-team/keras/blob/master/examples/imdb_lstm.py

"""Trains an LSTM model on the IMDB sentiment classification task.
The dataset is actually too small for LSTM to be of any advantage
compared to simpler, much faster methods such as TF-IDF + LogReg.
# Notes
- RNNs are tricky. Choice of batch size is important,
choice of loss and optimizer is critical, etc.
Some configurations won't converge.
- LSTM loss decrease patterns during training can be quite different
from what you see with CNNs/MLPs/etc.
"""

from keras.datasets import imdb
from keras.layers import LSTM, Dense, Embedding
from keras.models import Sequential
from keras.preprocessing import sequence

max_features = 20000
maxlen = 80  # cut texts after this number of words (among top max_features most common words)
batch_size = 32

print("Loading data...")
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), "train sequences")
print(len(x_test), "test sequences")

print("Pad sequences (samples x time)")
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print("x_train shape:", x_train.shape)
print("x_test shape:", x_test.shape)

print("Build model...")
model = Sequential()
model.add(Embedding(max_features, 128))
model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation="sigmoid"))

# try using different optimizers and different optimizer configs
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])

print("Train...")
model.fit(x_train, y_train, batch_size=batch_size, epochs=15, validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("Test score:", score)
print("Test accuracy:", acc)

Using TensorFlow backend.

Loading data...
25000 train sequences
25000 test sequences
Pad sequences (samples x time)
x_train shape: (25000, 80)
x_test shape: (25000, 80)
Build model...
Train...
Train on 25000 samples, validate on 25000 samples
Epoch 1/15
25000/25000 [==============================] - 113s 5ms/step - loss: 0.4577 - acc: 0.7825 - val_loss: 0.3970 - val_acc: 0.8246
Epoch 2/15
25000/25000 [==============================] - 110s 4ms/step - loss: 0.3048 - acc: 0.8752 - val_loss: 0.3794 - val_acc: 0.8330
Epoch 3/15
25000/25000 [==============================] - 109s 4ms/step - loss: 0.2210 - acc: 0.9129 - val_loss: 0.4197 - val_acc: 0.8300
Epoch 4/15
25000/25000 [==============================] - 113s 5ms/step - loss: 0.1557 - acc: 0.9433 - val_loss: 0.4687 - val_acc: 0.8279
Epoch 5/15
25000/25000 [==============================] - 114s 5ms/step - loss: 0.1057 - acc: 0.9615 - val_loss: 0.6095 - val_acc: 0.8240
Epoch 6/15
25000/25000 [==============================] - 136s 5ms/step - loss: 0.0790 - acc: 0.9720 - val_loss: 0.7360 - val_acc: 0.8177
Epoch 7/15
25000/25000 [==============================] - 127s 5ms/step - loss: 0.0755 - acc: 0.9746 - val_loss: 0.6201 - val_acc: 0.8180
Epoch 8/15
25000/25000 [==============================] - 121s 5ms/step - loss: 0.0436 - acc: 0.9854 - val_loss: 0.8128 - val_acc: 0.8169
Epoch 9/15
25000/25000 [==============================] - 110s 4ms/step - loss: 0.0312 - acc: 0.9895 - val_loss: 0.9553 - val_acc: 0.8145
Epoch 10/15
25000/25000 [==============================] - 114s 5ms/step - loss: 0.0283 - acc: 0.9909 - val_loss: 0.9576 - val_acc: 0.8126
Epoch 11/15
25000/25000 [==============================] - 108s 4ms/step - loss: 0.0172 - acc: 0.9949 - val_loss: 0.9107 - val_acc: 0.8117
Epoch 12/15
25000/25000 [==============================] - 108s 4ms/step - loss: 0.0156 - acc: 0.9954 - val_loss: 0.9634 - val_acc: 0.8096
Epoch 13/15
25000/25000 [==============================] - 110s 4ms/step - loss: 0.0119 - acc: 0.9962 - val_loss: 1.0733 - val_acc: 0.8123
Epoch 14/15
25000/25000 [==============================] - 113s 5ms/step - loss: 0.0117 - acc: 0.9964 - val_loss: 1.1165 - val_acc: 0.8106
Epoch 15/15
25000/25000 [==============================] - 111s 4ms/step - loss: 0.0107 - acc: 0.9970 - val_loss: 1.0867 - val_acc: 0.8091
25000/25000 [==============================] - 17s 688us/step
Test score: 1.0867270610725879
Test accuracy: 0.80912

使用 DeepExplainer 解释模型并可视化首次预测

import shap

# we use the first 100 training examples as our background dataset to integrate over
explainer = shap.DeepExplainer(model, x_train[:100])

# explain the first 10 predictions
# explaining each prediction requires 2 * background dataset size runs
shap_values = explainer.shap_values(x_test[:10])

# init the JS visualization code
shap.initjs()

# transform the indexes to words
import numpy as np

words = imdb.get_word_index()
num2word = {}
for w in words.keys():
    num2word[words[w]] = w
x_test_words = np.stack([np.array([num2word.get(x, "NONE") for x in x_test[i]]) for i in range(10)])

# plot the explanation of the first prediction
# Note the model is "multi-output" because it is rank-2 but only has one column
shap.force_plot(explainer.expected_value[0], shap_values[0][0], x_test_words[0])

可视化已省略，Javascript 库未加载！
你是否在本 notebook 中运行了 `initjs()`？如果此 notebook 来自其他用户，您还必须信任此 notebook（文件 -> 信任 notebook）。如果您在 github 上查看此 notebook，则 Javascript 已被剥离以确保安全。如果您正在使用 JupyterLab，则此错误是因为尚未编写 JupyterLab 扩展。

请注意，每个样本都是一个 IMDB 评论文本文档，表示为单词序列。这意味着“特征 0”是评论中的第一个词，对于不同的评论将有所不同。这意味着调用 summary_plot 将按文本中位置组合所有单词的重要性。这可能不是您想要的特征重要性的全局度量（这就是为什么我们在此处没有调用 summary_plot）。如果您确实想要单词重要性的全局摘要，您可以分解特征归因值并按单词分组。