完整源代码
from tensorflow.keras.datasets import reuters import numpy as np from tensorflow.keras import models from tensorflow.keras import layers import matplotlib.pyplot as plt class MultiClassifier: def __init__(self, num_words, epochs): self.num_words = num_words self.epochs = epochs self.model = None self.eval = False if epochs == 20 else True def load_data(self): return reuters.load_data(num_words=self.num_words) def get_text(self, data): word_id_index = reuters.get_word_index() id_word_index = dict([(id, value) for (value, id) in word_id_index.items()]) return ' '.join([id_word_index.get(i - 3, '?') for i in data]) def vectorize_sequences(self, sequences, dimension=10000): results = np.zeros((len(sequences), dimension)) for i,sequence in enumerate(sequences): results[i, sequence] = 1. return results def to_one_hot(self, labels, dimension=46): results = np.zeros((len(labels), dimension)) for i,label in enumerate(labels): results[i, label] = 1 return results def plt_loss(self, history): plt.clf() loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(1, len(loss) + 1) plt.plot(epochs, loss, 'bo', label='Training loss') plt.plot(epochs, val_loss, 'b', label='Validation loss') plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend() plt.show() def plt_accuracy(self, history): plt.clf() acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] epochs = range(1, len(acc) + 1) plt.plot(epochs, acc, 'bo', label='Training accuracy') plt.plot(epochs, val_acc, 'b', label='Validation accuracy') plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.legend() plt.show() def evaluate(self): results = self.model.evaluate(self.x_test, self.one_hot_test_labels) print('evaluate test data:') print(results) def train(self): (train_data, train_labels), (test_data, test_labels) = self.load_data() print(len(train_data)) print(len(test_data)) print(train_data[0]) print(train_labels[0]) print(self.get_text(train_data[0])) self.x_train = x_train = self.vectorize_sequences(train_data) self.x_test = x_test = self.vectorize_sequences(test_data) self.one_hot_train_labels = one_hot_train_labels = self.to_one_hot(train_labels) self.one_hot_test_labels = one_hot_test_labels = self.to_one_hot(test_labels) model = self.model = models.Sequential() model.add(layers.Dense(64, activation='relu', input_shape=(10000,))) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(46, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics='accuracy') x_val = x_train[:1000] partial_x_train = x_train[1000:] y_val = one_hot_train_labels[:1000] partial_y_train = one_hot_train_labels[1000:] history = model.fit(partial_x_train, partial_y_train, epochs=self.epochs, batch_size=512, validation_data=(x_val, y_val)) if self.eval: self.evaluate() print(self.model.predict(x_test)) else: self.plt_loss(history) self.plt_accuracy(history) classifier = MultiClassifier(num_words=10000, epochs=20) # classifier = MultiClassifier(num_words=10000, epochs=9) classifier.train()