基于路透社新闻报道主题分类任务，训练和评估一个简单的MLP（多层感知机）。
MNIST
cifar-10-batches-py
nietzsche.txt
reuters.npz
fra-eng
'''Trains and evaluate a simple MLP on the Reuters newswire topic classification task. 基于路透社新闻报道主题分类任务，训练和评估一个简单的MLP（多层感知机）。 MLP（Multi-Layer Perceptron），即多层感知器，是一种前向结构的人工神经网络，映射一组输入向量到一组输出向量。 MLP可以被看做是一个有向图，由多个节点层组成，每一层全连接到下一层。 ''' from __future__ import print_function import numpy as np import keras from keras.datasets import reuters from keras.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.preprocessing.text import Tokenizer max_words = 1000 batch_size = 32 epochs = 5 print('Loading data...') (x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_words, test_split=0.2) print(len(x_train), 'train sequences') print(len(x_test), 'test sequences') num_classes = np.max(y_train) + 1 print(num_classes, 'classes') print('Vectorizing sequence data...') tokenizer = Tokenizer(num_words=max_words) x_train = tokenizer.sequences_to_matrix(x_train, mode='binary') x_test = tokenizer.sequences_to_matrix(x_test, mode='binary') print('x_train shape:', x_train.shape) print('x_test shape:', x_test.shape) print('Convert class vector to binary class matrix ' '(for use with categorical_crossentropy)') y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) print('y_train shape:', y_train.shape) print('y_test shape:', y_test.shape) print('Building model...') model = Sequential() model.add(Dense(512, input_shape=(max_words,))) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_split=0.1) score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1) print('Test score:', score[0]) print('Test accuracy:', score[1])C:\ProgramData\Anaconda3\python.exe E:/keras-master/examples/reuters_mlp.py Using TensorFlow backend. Loading data... 8982 train sequences 2246 test sequences 46 classes Vectorizing sequence data... x_train shape: (8982, 1000) x_test shape: (2246, 1000) Convert class vector to binary class matrix (for use with categorical_crossentropy) y_train shape: (8982, 46) y_test shape: (2246, 46) Building model... Train on 8083 samples, validate on 899 samples Epoch 1/5 32/8083 [..............................] - ETA: 4:13 - loss: 4.0334 - acc: 0.0000e+00 128/8083 [..............................] - ETA: 1:05 - loss: 3.7674 - acc: 0.0938 256/8083 [..............................] - ETA: 34s - loss: 3.4732 - acc: 0.2344 352/8083 [>.............................] - ETA: 26s - loss: 3.2942 - acc: 0.3011 448/8083 [>.............................] - ETA: 21s - loss: 3.1269 - acc: 0.3326 544/8083 [=>............................] - ETA: 18s - loss: 2.9496 - acc: 0.3603 640/8083 [=>............................] - ETA: 16s - loss: 2.8190 - acc: 0.3812 736/8083 [=>............................] - ETA: 14s - loss: 2.7076 - acc: 0.4103 832/8083 [==>...........................] - ETA: 13s - loss: 2.6083 - acc: 0.4291 928/8083 [==>...........................] - ETA: 12s - loss: 2.5429 - acc: 0.4407 1024/8083 [==>...........................] - ETA: 11s - loss: 2.4777 - acc: 0.4512 1120/8083 [===>..........................] - 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ETA: 0s - loss: 0.3199 - acc: 0.9195 7872/8083 [============================>.] - ETA: 0s - loss: 0.3242 - acc: 0.9183 7968/8083 [============================>.] - ETA: 0s - loss: 0.3248 - acc: 0.9180 8064/8083 [============================>.] - ETA: 0s - loss: 0.3243 - acc: 0.9183 8083/8083 [==============================] - 5s 664us/step - loss: 0.3254 - acc: 0.9181 - val_loss: 0.9151 - val_acc: 0.7987 32/2246 [..............................] - ETA: 0s 384/2246 [====>.........................] - ETA: 0s 704/2246 [========>.....................] - ETA: 0s 1056/2246 [=============>................] - ETA: 0s 1408/2246 [=================>............] - ETA: 0s 1728/2246 [======================>.......] - ETA: 0s 2080/2246 [==========================>...] - ETA: 0s 2246/2246 [==============================] - 0s 152us/step Test score: 0.8895948799401654 Test accuracy: 0.7929652715939448 Process finished with exit code 0Keras详细介绍
中文：http://keras-cn.readthedocs.io/en/latest/
实例下载
https://github.com/keras-team/keras
https://github.com/keras-team/keras/tree/master/examples
452205574，共享文件夹。
model、安装库文件等。
