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）。
Keras使用的数据集下载（包含以下数据集）
MNIST
cifar-10-batches-py
imdb.npz、imdb_word_index.json
nietzsche.txt
reuters.npz
fra-eng
代码注释
'''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])
代码执行
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 0
Keras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
完整项目下载
方便没积分童鞋，请加企鹅452205574，共享文件夹。
包括：代码、数据集合（图片）、已生成model、安装库文件等。
