Python实现Keras搭建神经网络训练分类模型教程

我就废话不多说了，大家还是直接看代码吧~

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 
 2432/60000 [>.............................] - ETA: 7s - loss: 1.3151 - accuracy: 0.6637
 3200/60000 [>.............................] - ETA: 6s - loss: 1.1663 - accuracy: 0.7056
 3968/60000 [>.............................] - ETA: 5s - loss: 1.0533 - accuracy: 0.7344
 4704/60000 [=>............................] - ETA: 5s - loss: 0.9696 - accuracy: 0.7564
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 6112/60000 [==>...........................] - ETA: 5s - loss: 0.8692 - accuracy: 0.7804
 6784/60000 [==>...........................] - ETA: 4s - loss: 0.8225 - accuracy: 0.7933
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47808/60000 [======================>.......] - ETA: 1s - loss: 0.3744 - accuracy: 0.8963
48416/60000 [=======================>......] - ETA: 1s - loss: 0.3732 - accuracy: 0.8966
48928/60000 [=======================>......] - ETA: 0s - loss: 0.3714 - accuracy: 0.8971
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50688/60000 [========================>.....] - ETA: 0s - loss: 0.3669 - accuracy: 0.8983
51264/60000 [========================>.....] - ETA: 0s - loss: 0.3654 - accuracy: 0.8988
51872/60000 [========================>.....] - ETA: 0s - loss: 0.3636 - accuracy: 0.8992
52608/60000 [=========================>....] - ETA: 0s - loss: 0.3618 - accuracy: 0.8997
53376/60000 [=========================>....] - ETA: 0s - loss: 0.3599 - accuracy: 0.9003
54048/60000 [==========================>...] - ETA: 0s - loss: 0.3583 - accuracy: 0.9006
54560/60000 [==========================>...] - ETA: 0s - loss: 0.3568 - accuracy: 0.9010
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56064/60000 [===========================>..] - ETA: 0s - loss: 0.3526 - accuracy: 0.9021
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57376/60000 [===========================>..] - ETA: 0s - loss: 0.3499 - accuracy: 0.9029
58112/60000 [============================>.] - ETA: 0s - loss: 0.3482 - accuracy: 0.9033
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59584/60000 [============================>.] - ETA: 0s - loss: 0.3444 - accuracy: 0.9043
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
 2432/60000 [>.............................] - ETA: 4s - loss: 0.2280 - accuracy: 0.9379
 3040/60000 [>.............................] - ETA: 4s - loss: 0.2374 - accuracy: 0.9368
 3808/60000 [>.............................] - ETA: 4s - loss: 0.2251 - accuracy: 0.9386
 4576/60000 [=>............................] - ETA: 4s - loss: 0.2225 - accuracy: 0.9379
 5216/60000 [=>............................] - ETA: 4s - loss: 0.2208 - accuracy: 0.9377
 5920/60000 [=>............................] - ETA: 4s - loss: 0.2173 - accuracy: 0.9383
 6656/60000 [==>...........................] - ETA: 4s - loss: 0.2217 - accuracy: 0.9370
 7392/60000 [==>...........................] - ETA: 4s - loss: 0.2224 - accuracy: 0.9360
 8096/60000 [===>..........................] - ETA: 4s - loss: 0.2234 - accuracy: 0.9363
 8800/60000 [===>..........................] - ETA: 3s - loss: 0.2235 - accuracy: 0.9358
 9408/60000 [===>..........................] - ETA: 3s - loss: 0.2196 - accuracy: 0.9365
10016/60000 [====>.........................] - ETA: 3s - loss: 0.2207 - accuracy: 0.9363
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55168/60000 [==========================>...] - ETA: 0s - loss: 0.1962 - accuracy: 0.9436
55776/60000 [==========================>...] - ETA: 0s - loss: 0.1959 - accuracy: 0.9437
56448/60000 [===========================>..] - ETA: 0s - loss: 0.1965 - accuracy: 0.9437
57152/60000 [===========================>..] - ETA: 0s - loss: 0.1958 - accuracy: 0.9439
57824/60000 [===========================>..] - ETA: 0s - loss: 0.1956 - accuracy: 0.9438
58560/60000 [============================>.] - ETA: 0s - loss: 0.1951 - accuracy: 0.9440
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
 1248/10000 [==>...........................] - ETA: 0s 
 2656/10000 [======>.......................] - ETA: 0s
 4064/10000 [===========>..................] - ETA: 0s
 5216/10000 [==============>...............] - ETA: 0s
 6464/10000 [==================>...........] - ETA: 0s
 7744/10000 [======================>.......] - ETA: 0s
 9056/10000 [==========================>...] - ETA: 0s
 9984/10000 [============================>.] - ETA: 0s
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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