这篇文章将为大家详细讲解有关Python实现Keras搭建神经网络训练分类模型的方法，小编觉得挺实用的，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。
注释讲解版：
# Classifier example
import numpy as np
# for reproducibility
np.random.seed(1337)
# from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import RMSprop
# 程序中用到的数据是经典的手写体识别mnist数据集
# download the mnist to the path if it is the first time to be called
# X shape (60,000 28x28), y
# (X_train, y_train), (X_test, y_test) = mnist.load_data()
# 下载minst.npz：
# 链接: https://pan.baidu.com/s/1b2ppKDOdzDJxivgmyOoQsA
# 提取码: y5ir
# 将下载好的minst.npz放到当前目录下
path='./mnist.npz'
f = np.load(path)
X_train, y_train = f['x_train'], f['y_train']
X_test, y_test = f['x_test'], f['y_test']
f.close()
# data pre-processing
# 数据预处理
# normalize
# X shape (60,000 28x28)，表示输入数据 X 是个三维的数据
# 可以理解为 60000行数据，每一行是一张28 x 28 的灰度图片
# X_train.reshape(X_train.shape[0], -1)表示：只保留第一维，其余的纬度，不管多少纬度，重新排列为一维
# 参数-1就是不知道行数或者列数多少的情况下使用的参数
# 所以先确定除了参数-1之外的其他参数，然后通过(总参数的计算) / (确定除了参数-1之外的其他参数) = 该位置应该是多少的参数
# 这里用-1是偷懒的做法,等同于 28*28
# reshape后的数据是：共60000行，每一行是784个数据点（feature）
# 输入的 x 变成 60,000*784 的数据，然后除以 255 进行标准化
# 因为每个像素都是在 0 到 255 之间的，标准化之后就变成了 0 到 1 之间
X_train = X_train.reshape(X_train.shape[0], -1) / 255
X_test = X_test.reshape(X_test.shape[0], -1) / 255
# 分类标签编码
# 将y转化为one-hot vector
y_train = np_utils.to_categorical(y_train, num_classes = 10)
y_test = np_utils.to_categorical(y_test, num_classes = 10)
# Another way to build your neural net
# 建立神经网络
# 应用了2层的神经网络，前一层的激活函数用的是relu，后一层的激活函数用的是softmax
#32是输出的维数
model = Sequential([
Dense(32, input_dim=784),
Activation('relu'),
Dense(10),
Activation('softmax')
])
# Another way to define your optimizer
# 优化函数
# 优化算法用的是RMSprop
rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
# We add metrics to get more results you want to see
# 不自己定义，直接用内置的优化器也行，optimizer='rmsprop'
#激活模型：接下来用 model.compile 激励神经网络
model.compile(
optimizer=rmsprop,
loss='categorical_crossentropy',
metrics=['accuracy']
)
print('Training------------')
# Another way to train the model
# 训练模型
# 上一个程序是用train_on_batch 一批一批的训练 X_train, Y_train
# 默认的返回值是 cost，每100步输出一下结果
# 输出的样式与上一个程序的有所不同，感觉用model.fit()更清晰明了
# 上一个程序是Python实现Keras搭建神经网络训练回归模型：
# https://blog.csdn.net/weixin_45798684/article/details/106503685
model.fit(X_train, y_train, nb_epoch=2, batch_size=32)
print('\nTesting------------')
# Evaluate the model with the metrics we defined earlier
# 测试
loss, accuracy = model.evaluate(X_test, y_test)
print('test loss:', loss)
print('test accuracy:', accuracy)运行结果：
Using TensorFlow backend.
Training------------
Epoch 1/2
32/60000 [..............................] - ETA: 5:03 - loss: 2.4464 - accuracy: 0.0625
864/60000 [..............................] - ETA: 14s - loss: 1.8023 - accuracy: 0.4850
1696/60000 [..............................] - ETA: 9s - loss: 1.5119 - accuracy: 0.6002
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60000/60000 [==============================] - 5s 87us/step - loss: 0.3435 - accuracy: 0.9046
Epoch 2/2
32/60000 [..............................] - ETA: 11s - loss: 0.0655 - accuracy: 1.0000
736/60000 [..............................] - ETA: 4s - loss: 0.2135 - accuracy: 0.9389
1408/60000 [..............................] - ETA: 4s - loss: 0.2217 - accuracy: 0.9361
1984/60000 [..............................] - ETA: 4s - loss: 0.2316 - accuracy: 0.9390
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59360/60000 [============================>.] - ETA: 0s - loss: 0.1947 - accuracy: 0.9440
60000/60000 [==============================] - 5s 76us/step - loss: 0.1946 - accuracy: 0.9440
Testing------------
32/10000 [..............................] - ETA: 15s
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10000/10000 [==============================] - 0s 47us/step
test loss: 0.17407772153392434
test accuracy: 0.9513000249862671
补充知识：Keras 搭建简单神经网络：顺序模型+回归问题
多层全连接神经网络
每层神经元个数、神经网络层数、激活函数等可自由修改
使用不同的损失函数可适用于其他任务，比如：分类问题
这是Keras搭建神经网络模型最基础的方法之一，Keras还有其他进阶的方法，官网给出了一些基本使用方法：Keras官网
# 这里搭建了一个4层全连接神经网络（不算输入层），传入函数以及函数内部的参数均可自由修改
def ann(X, y):
'''
X: 输入的训练集数据
y: 训练集对应的标签
'''
'''初始化模型'''
# 首先定义了一个顺序模型作为框架，然后往这个框架里面添加网络层
# 这是最基础搭建神经网络的方法之一
model = Sequential()
'''开始添加网络层'''
# Dense表示全连接层，第一层需要我们提供输入的维度 input_shape
# Activation表示每层的激活函数，可以传入预定义的激活函数，也可以传入符合接口规则的其他高级激活函数
model.add(Dense(64, input_shape=(X.shape[1],)))
model.add(Activation('sigmoid'))
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(256))
model.add(Activation('tanh'))
model.add(Dense(32))
model.add(Activation('tanh'))
# 输出层，输出的维度大小由具体任务而定
# 这里是一维输出的回归问题
model.add(Dense(1))
model.add(Activation('linear'))
'''模型编译'''
# optimizer表示优化器（可自由选择），loss表示使用哪一种
model.compile(optimizer='rmsprop', loss='mean_squared_error')
# 自定义学习率，也可以使用原始的基础学习率
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=10,
verbose=0, mode='auto', min_delta=0.001,
cooldown=0, min_lr=0)
'''模型训练'''
# 这里的模型也可以先从函数返回后，再进行训练
# epochs表示训练的轮数，batch_size表示每次训练的样本数量（小批量学习），validation_split表示用作验证集的训练数据的比例
# callbacks表示回调函数的集合，用于模型训练时查看模型的内在状态和统计数据，相应的回调函数方法会在各自的阶段被调用
# verbose表示输出的详细程度，值越大输出越详细
model.fit(X, y, epochs=100,
batch_size=50, validation_split=0.0,
callbacks=[reduce_lr],
verbose=0)
# 打印模型结构
print(model.summary())
return model下图是此模型的结构图，其中下划线后面的数字是根据调用次数而定
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