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 5408/60000 [=>............................] - ETA: 5s - loss: 0.9162 - accuracy: 0.7681 6112/60000 [==>...........................] - ETA: 5s - loss: 0.8692 - accuracy: 0.7804 6784/60000 [==>...........................] - ETA: 4s - loss: 0.8225 - accuracy: 0.7933 7424/60000 [==>...........................] - ETA: 4s - loss: 0.7871 - accuracy: 0.8021 8128/60000 [===>..........................] - 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accuracy: 0.9016 56064/60000 [===========================>..] - ETA: 0s - loss: 0.3526 - accuracy: 0.9021 56736/60000 [===========================>..] - ETA: 0s - loss: 0.3514 - accuracy: 0.9026 57376/60000 [===========================>..] - ETA: 0s - loss: 0.3499 - accuracy: 0.9029 58112/60000 [============================>.] - ETA: 0s - loss: 0.3482 - accuracy: 0.9033 58880/60000 [============================>.] - ETA: 0s - loss: 0.3459 - accuracy: 0.9039 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 [>.............................] - 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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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