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神经网络之MNIST数据集和CIFAR-10数据集训练_mnist cifar-10
作者：知新_RL | 2024-04-01 21:52:17
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mnist cifar-10
1.构建前馈神经网络训练MNIST
环境：pycharm + win10 + conda3 + python3.6
首先创建一个神经网络类NeuralNetwork：
import numpy as np #定义一个神经网络类 class NeuralNetwork: #初始化参数 def __init__(self,layers,alpha=0.1): self.Weight = [] self.layers = layers self.alpha = alpha #遍历从第一层到最后两层之前停止 for i in np.arange(0,len(layers) - 2): #构造MxN权重矩阵,节点相连，增加一个偏置项，正态化数据：除以当前层节点数平方根，得稳定方差 weight = np.random.randn(layers[i]+1,layers[i+1]+1) self.Weight.append(weight/np.sqrt(layers[i])) #最后两层输入连接需要一个偏置项，输入不需要 weight = np.random.randn(layers[-2] + 1, layers[-1]) self.Weight.append(weight/np.sqrt(layers[-2])) #返回网络结构信息 def __repr__(self): return "NeuralNetwork:{}".format("-".join(str(l) for l in self.layers)) #定义sigmoid激活函数 def sigmoid(self,x): return 1.0/(1+np.exp(-x)) #sigmoid导数 def sigmoid_deriv(self,x): return x * (1-x) #定义fit函数训练神经网络:X训练数据、y是X中单个数据对应的类别标签、epochs训练网络次数、displayUpdate显示更新间隔 def fit(self,X,y,epochs = 1000, displayUpdate=100): #插入一列偏置项特征矩阵 X = np.c_[X,np.ones(X.shape[0])] for epoch in np.arange(0,epochs): for (x,target) in zip(X,y): self.fit_partial(x,target) if epoch == 0 or (epoch +1) % displayUpdate ==0: loss = self.calculate_loss(X,y) print("epoch={}, loss={:.6f}".format(epoch+1,loss)) def fit_partial(self,x,y): #初始化列表，存储数据点x通过前向传播网络每层输出激活数据 A = [np.atleast_2d(x)] #前向传播 for layer in np.arange(0,len(self.Weight)): #点积计算 net = A[layer].dot(self.Weight[layer]) out = self.sigmoid(net) A.append(out) #反向传播,计算误差 error = A[-1] - y D = [error*self.sigmoid_deriv(A[-1])] #通过学习率更新权重矩阵 for layer in np.arange(len(A) - 2, 0, -1): delta = D[-1].dot(self.Weight[layer].T) delta = delta * self.sigmoid_deriv(A[layer]) D.append(delta) #更新权重 D = D[::-1] for layer in np.arange(0,len(self.Weight)): self.Weight[layer] += -self.alpha * A[layer].T.dot(D[layer]) #预测 def predict(self, X, addBias=True): p = np.atleast_2d(X) #判断偏置项是否存在 if addBias: p = np.c_[p, np.ones((p.shape[0]))] for layer in np.arange(0, len(self.Weight)): p = self.sigmoid(np.dot(p, self.Weight[layer])) return p #损失函数 def calculate_loss(self, X, targets): targets = np.atleast_2d(targets) predictions = self.predict(X, addBias=False) loss = 0.5 * np.sum((predictions - targets) ** 2) return loss
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通过sklearn下载手写字体数据集，并进行训练,先安装sklearn包：
conda install scikit-learn
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训练代码：
from neural_network import NeuralNetwork from sklearn.preprocessing import LabelBinarizer from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from sklearn import datasets import matplotlib.pyplot as plt import numpy as np def mnist_train(): # 下载 print("loading MNIST dataset...") digits = datasets.load_digits() data = digits.data.astype("float") data = (data - data.min()) / (data.max() - data.min()) print("samples:{}, dimension:{}".format(data.shape[0], data.shape[1])) # 训练/测试集 (trainX, testX, trainY, testY) = train_test_split(data, digits.target, test_size=0.25) # 将标签转化为向量 trainY = LabelBinarizer().fit_transform(trainY) testY = LabelBinarizer().fit_transform(testY) # 训练网络 print("training network...") nn = NeuralNetwork([trainX.shape[1], 32, 16, 10]) print("{}".format(nn)) nn.fit(trainX, trainY, epochs=1000) # 评估网络 print("evaluating network...") predictions = nn.predict(testX) predictions = predictions.argmax(axis=1) print(classification_report(testY.argmax(axis=1), predictions)) if __name__ =="__main__": mnist_train()
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结果：
loading MNIST dataset... samples:1797, dimension:64 training network... NeuralNetwork:64-32-16-10 epoch=1, loss=605.801722 epoch=100, loss=7.885670 epoch=200, loss=4.572620 epoch=300, loss=3.601540 epoch=400, loss=2.981110 epoch=500, loss=2.804141 epoch=600, loss=2.730295 epoch=700, loss=2.684942 epoch=800, loss=2.654072 epoch=900, loss=2.631717 epoch=1000, loss=2.614806 evaluating network... precision recall f1-score support 0 1.00 1.00 1.00 46 1 0.93 0.97 0.95 39 2 1.00 1.00 1.00 52 3 0.93 0.98 0.96 44 4 0.98 0.98 0.98 52 5 1.00 0.90 0.95 51 6 1.00 0.98 0.99 54 7 1.00 1.00 1.00 41 8 0.90 0.88 0.89 42 9 0.91 1.00 0.95 29 micro avg 0.97 0.97 0.97 450 macro avg 0.97 0.97 0.97 450 weighted avg 0.97 0.97 0.97 450
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2.Keras训练MNIST
环境:
- Ubuntu16.04
- Pycharm
- conda3
- python3.6
- tensorflow 1.8.0
- tensorflow-gpu 1.8.0
- scikit-learn 0.21.3
- numpy 1.13.3
- cudnn 7.0.5
- cudatoolkit 8.0
- matplotlib 2.2.2
训练代码：
from keras.models import Sequential from keras.layers.core import Dense from keras.optimizers import SGD from sklearn.preprocessing import LabelBinarizer from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from sklearn import datasets import matplotlib.pyplot as plt import numpy as np def mnist_train(): # 下载mnist数据集，大小有55M print("loading MNIST dataset...") dataset = datasets.fetch_mldata("MNIST Original") #数据缩放到0至1区间 data = dataset.data.astype("float") / 255.0 # 分割训练/测试集 (trainX, testX, trainY, testY) = train_test_split(data, dataset.target, test_size=0.25) # 将标签转化为向量 lb = LabelBinarizer() trainY = lb.fit_transform(trainY) testY = lb.fit_transform(testY) #定义一个 784-256-128-10网络结构 model = Sequential() model.add(Dense(256,input_shape=(784,), activation="sigmoid")) model.add(Dense(128, activation="sigmoid")) model.add(Dense(10,activation="softmax")) # 使用SGD优化器训练网络 print("training network...") sgd = SGD(0.01) model.compile(loss="categorical_crossentropy", optimizer=sgd,metrics=["accuracy"]) H = model.fit(trainX, trainY, validation_data=(testX, testY),epochs=100,batch_size=128) # 评估网络 print("evaluating network...") predictions = model.predict(testX, batch_size=128) print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1), target_names=[str(x) for x in lb.classes_])) #显示训练结果 plt.style.use("ggplot") plt.figure() plt.plot(np.arange(0,100), H.history["loss"], label="train_loss") plt.plot(np.arange(0,100), H.history["val_loss"], label="val_loss") plt.plot(np.arange(0,100), H.history["acc"], label="train_acc") plt.plot(np.arange(0,100), H.history["val_acc"], label="val_acc") plt.title("Training Loss and Accuracy") plt.xlabel("Epoch") plt.ylabel("Loss/Accuracy") plt.legend() plt.savefig("./mnist_keras.png") if __name__ =="__main__": mnist_train()
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运行一下报错: DeprecationWarning: Function mldata_filename is deprecated; mldata_filename was deprecated in version 0.20 and will be removed in version 0.22. Please use fetch_openml.
解决办法:
手动下载MNIST数据集:
https://github.com/amplab/datascience-sp14/raw/master/lab7/mldata/mnist-original.mat
将下载后的mnist-original.mat文件放入/home/ubuntu/scikit_learn_data/mldata路径下
结果：
Using TensorFlow backend. loading MNIST dataset... training network... Train on 52500 samples, validate on 17500 samples Epoch 1/100 128/52500 [..............................] - ETA: 11:43 - loss: 2.4822 - acc: 0.1016 2688/52500 [>.............................] - ETA: 32s - loss: 2.4141 - acc: 0.0911 4992/52500 [=>............................] - ETA: 17s - loss: 2.3748 - acc: 0.0972 7168/52500 [===>..........................] - ETA: 11s - loss: 2.3545 - acc: 0.1018 9088/52500 [====>.........................] - ETA: 9s - loss: 2.3433 - acc: 0.1039 11264/52500 [=====>........................] - ETA: 7s - loss: 2.3349 - acc: 0.1014 13440/52500 [======>.......................] - ETA: 5s - loss: 2.3283 - acc: 0.1026 15360/52500 [=======>......................] - ETA: 5s - loss: 2.3239 - acc: 0.1054 17280/52500 [========>.....................] - ETA: 4s - loss: 2.3199 - acc: 0.1080 ... Epoch 100/100 128/52500 [..............................] - ETA: 2s - loss: 0.2071 - acc: 0.9531 2048/52500 [>.............................] - ETA: 1s - loss: 0.2768 - acc: 0.9268 3968/52500 [=>............................] - ETA: 1s - loss: 0.2854 - acc: 0.9214 5888/52500 [==>...........................] - ETA: 1s - loss: 0.2810 - acc: 0.9222 7808/52500 [===>..........................] - ETA: 1s - loss: 0.2792 - acc: 0.9214 9984/52500 [====>.........................] - ETA: 1s - loss: 0.2813 - acc: 0.9206 12032/52500 [=====>........................] - ETA: 1s - loss: 0.2812 - acc: 0.9199 14208/52500 [=======>......................] - ETA: 1s - loss: 0.2797 - acc: 0.9205 16256/52500 [========>.....................] - ETA: 0s - loss: 0.2803 - acc: 0.9202 18432/52500 [=========>....................] - ETA: 0s - loss: 0.2766 - acc: 0.9211 20480/52500 [==========>...................] - ETA: 0s - loss: 0.2761 - acc: 0.9203 22272/52500 [===========>..................] - ETA: 0s - loss: 0.2754 - acc: 0.9205 24320/52500 [============>.................] - ETA: 0s - loss: 0.2769 - acc: 0.9201 26496/52500 [==============>...............] - ETA: 0s - loss: 0.2761 - acc: 0.9204 28672/52500 [===============>..............] - ETA: 0s - loss: 0.2733 - acc: 0.9210 30720/52500 [================>.............] - ETA: 0s - loss: 0.2721 - acc: 0.9215 32640/52500 [=================>............] - ETA: 0s - loss: 0.2720 - acc: 0.9216 34560/52500 [==================>...........] - ETA: 0s - loss: 0.2715 - acc: 0.9213 36480/52500 [===================>..........] - ETA: 0s - loss: 0.2714 - acc: 0.9210 38528/52500 [=====================>........] - ETA: 0s - loss: 0.2723 - acc: 0.9208 40576/52500 [======================>.......] - ETA: 0s - loss: 0.2720 - acc: 0.9205 42624/52500 [=======================>......] - ETA: 0s - loss: 0.2730 - acc: 0.9205 44416/52500 [========================>.....] - ETA: 0s - loss: 0.2728 - acc: 0.9206 46336/52500 [=========================>....] - ETA: 0s - loss: 0.2733 - acc: 0.9204 48256/52500 [==========================>...] - ETA: 0s - loss: 0.2727 - acc: 0.9203 50304/52500 [===========================>..] - ETA: 0s - loss: 0.2725 - acc: 0.9203 52352/52500 [============================>.] - ETA: 0s - loss: 0.2733 - acc: 0.9201 52500/52500 [==============================] - 2s 30us/step - loss: 0.2735 - acc: 0.9201 - val_loss: 0.2829 - val_acc: 0.9186 evaluating network... precision recall f1-score support 0.0 0.94 0.96 0.95 1722 1.0 0.95 0.97 0.96 2046 2.0 0.91 0.89 0.90 1682 3.0 0.92 0.89 0.90 1814 4.0 0.92 0.93 0.93 1734 5.0 0.87 0.88 0.87 1504 6.0 0.94 0.96 0.95 1705 7.0 0.93 0.93 0.93 1816 8.0 0.90 0.88 0.89 1753 9.0 0.89 0.89 0.89 1724 accuracy 0.92 17500 macro avg 0.92 0.92 0.92 17500 weighted avg 0.92 0.92 0.92 17500 Process finished with exit code 0
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训练图如下:
3.Keras训练CIFAR-10
环境:
- Ubuntu16.04
- Pycharm
- conda3
- python3.6
- tensorflow 1.8.0
- tensorflow-gpu 1.8.0
- scikit-learn 0.21.3
- numpy 1.13.3
- cudnn 7.0.5
- cudatoolkit 8.0
- matplotlib 2.2.2
cifar-10数据集有60000张图片，大小为32x32的RGB图，每一张图的数据量有32x32x3 = 3072。一共有10个类，每个类别有6000张图像。50000张图像用来训练，10000张用来测试。
训练代码：
from keras.models import Sequential from keras.layers.core import Dense from keras.optimizers import SGD from keras.datasets import cifar10 from sklearn.preprocessing import LabelBinarizer from sklearn.metrics import classification_report from sklearn import datasets import matplotlib.pyplot as plt import numpy as np def mnist_train(): # 下载cifar10数据集，大小有170M print("loading CIFAR10 dataset...") ((trainX,trainY),(testX,testY)) = cifar10.load_data() #数据缩放到0至1区间 trainX = trainX.astype("float") / 255.0 testX = testX.astype("float") / 255.0 #创建矩阵 trainX = trainX.reshape((trainX.shape[0], 3072)) testX = testX.reshape((testX.shape[0],3072)) # 将标签转化为向量 lb = LabelBinarizer() trainY = lb.fit_transform(trainY) testY = lb.fit_transform(testY) #类别标签 labelNames = ["airplane","automobile","bird","cat","deer", "dog","frog","horse","ship","truck"] #定义一个 3072-1024-512-10网络结构 model = Sequential() model.add(Dense(1024,input_shape=(3072,), activation="relu")) model.add(Dense(512, activation="relu")) model.add(Dense(10,activation="softmax")) # 使用SGD优化器训练网络 print("training network...") sgd = SGD(0.01) model.compile(loss="categorical_crossentropy", optimizer=sgd,metrics=["accuracy"]) H = model.fit(trainX, trainY, validation_data=(testX, testY),epochs=100,batch_size=128) # 评估网络 print("evaluating network...") predictions = model.predict(testX, batch_size=128) print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1), target_names= labelNames)) #显示训练结果 plt.style.use("ggplot") plt.figure() plt.plot(np.arange(0,100), H.history["loss"], label="train_loss") plt.plot(np.arange(0,100), H.history["val_loss"], label="val_loss") plt.plot(np.arange(0,100), H.history["acc"], label="train_acc") plt.plot(np.arange(0,100), H.history["val_acc"], label="val_acc") plt.title("Training Loss and Accuracy") plt.xlabel("Epoch") plt.ylabel("Loss/Accuracy") plt.legend() plt.savefig("./cifar10_keras.png") if __name__ =="__main__": mnist_train()
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结果：
Using TensorFlow backend. loading CIFAR10 dataset... Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz 8192/170498071 [..............................] - ETA: 3:51:38 24576/170498071 [..............................] - ETA: 2:34:56 40960/170498071 [..............................] - ETA: 2:19:34 57344/170498071 [..............................] - ETA: 2:01:56 73728/170498071 [..............................] - ETA: 1:52:03 90112/170498071 [..............................] - ETA: 1:45:49 ... 170401792/170498071 [============================>.] - ETA: 2s 170418176/170498071 [============================>.] - ETA: 2s 170434560/170498071 [============================>.] - ETA: 1s 170450944/170498071 [============================>.] - ETA: 1s 170467328/170498071 [============================>.] - ETA: 0s 170483712/170498071 [============================>.] - ETA: 0s 170500096/170498071 [==============================] - 4585s 27us/step training network... Train on 50000 samples, validate on 10000 samples Epoch 1/100 128/50000 [..............................] - ETA: 7:31 - loss: 2.3651 - acc: 0.0781 1664/50000 [..............................] - ETA: 35s - loss: 2.2779 - acc: 0.1436 3072/50000 [>.............................] - ETA: 19s - loss: 2.2363 - acc: 0.1735 4480/50000 [=>............................] - ETA: 13s - loss: 2.2142 - acc: 0.1833 5888/50000 [==>...........................] - ETA: 10s - loss: 2.1908 - acc: 0.1967 ... 47872/50000 [===========================>..] - ETA: 0s - loss: 0.6568 - acc: 0.7823 49280/50000 [============================>.] - ETA: 0s - loss: 0.6572 - acc: 0.7820 50000/50000 [==============================] - 2s 42us/step - loss: 0.6585 - acc: 0.7816 - val_loss: 1.4283 - val_acc: 0.5421 Epoch 100/100 128/50000 [..............................] - ETA: 2s - loss: 0.6698 - acc: 0.7578 1536/50000 [..............................] - ETA: 1s - loss: 0.6496 - acc: 0.7819 2944/50000 [>.............................] - ETA: 1s - loss: 0.6353 - acc: 0.7860 4352/50000 [=>............................] - ETA: 1s - loss: 0.6302 - acc: 0.7891 5632/50000 [==>...........................] - ETA: 1s - loss: 0.6478 - acc: 0.7814 7168/50000 [===>..........................] - ETA: 1s - loss: 0.6438 - acc: 0.7824 8704/50000 [====>.........................] - ETA: 1s - loss: 0.6528 - acc: 0.7792 10112/50000 [=====>........................] - ETA: 1s - loss: 0.6492 - acc: 0.7797 11520/50000 [=====>........................] - ETA: 1s - loss: 0.6623 - acc: 0.7752 12928/50000 [======>.......................] - ETA: 1s - loss: 0.6574 - acc: 0.7767 14336/50000 [=======>......................] - ETA: 1s - loss: 0.6549 - acc: 0.7785 15744/50000 [========>.....................] - ETA: 1s - loss: 0.6556 - acc: 0.7781 17024/50000 [=========>....................] - ETA: 1s - loss: 0.6519 - acc: 0.7803 18560/50000 [==========>...................] - ETA: 1s - loss: 0.6531 - acc: 0.7798 19968/50000 [==========>...................] - ETA: 1s - loss: 0.6532 - acc: 0.7799 21248/50000 [===========>..................] - ETA: 1s - loss: 0.6536 - acc: 0.7804 22528/50000 [============>.................] - ETA: 1s - loss: 0.6548 - acc: 0.7805 23936/50000 [=============>................] - ETA: 0s - loss: 0.6534 - acc: 0.7809 25344/50000 [==============>...............] - ETA: 0s - loss: 0.6505 - acc: 0.7819 26752/50000 [===============>..............] - ETA: 0s - loss: 0.6499 - acc: 0.7820 28160/50000 [===============>..............] - ETA: 0s - loss: 0.6499 - acc: 0.7823 29696/50000 [================>.............] - ETA: 0s - loss: 0.6495 - acc: 0.7821 31104/50000 [=================>............] - ETA: 0s - loss: 0.6498 - acc: 0.7818 32640/50000 [==================>...........] - ETA: 0s - loss: 0.6544 - acc: 0.7801 34048/50000 [===================>..........] - ETA: 0s - loss: 0.6537 - acc: 0.7803 35456/50000 [====================>.........] - ETA: 0s - loss: 0.6542 - acc: 0.7806 36864/50000 [=====================>........] - ETA: 0s - loss: 0.6533 - acc: 0.7805 38144/50000 [=====================>........] - ETA: 0s - loss: 0.6528 - acc: 0.7810 39552/50000 [======================>.......] - ETA: 0s - loss: 0.6508 - acc: 0.7819 40960/50000 [=======================>......] - ETA: 0s - loss: 0.6543 - acc: 0.7805 42368/50000 [========================>.....] - ETA: 0s - loss: 0.6537 - acc: 0.7805 43776/50000 [=========================>....] - ETA: 0s - loss: 0.6526 - acc: 0.7812 45056/50000 [==========================>...] - ETA: 0s - loss: 0.6521 - acc: 0.7815 46464/50000 [==========================>...] - ETA: 0s - loss: 0.6522 - acc: 0.7815 47872/50000 [===========================>..] - ETA: 0s - loss: 0.6522 - acc: 0.7815 49280/50000 [============================>.] - ETA: 0s - loss: 0.6530 - acc: 0.7811 50000/50000 [==============================] - 2s 42us/step - loss: 0.6532 - acc: 0.7810 - val_loss: 1.4354 - val_acc: 0.5334 evaluating network... precision recall f1-score support airplane 0.64 0.53 0.58 1000 automobile 0.61 0.69 0.64 1000 bird 0.55 0.36 0.43 1000 cat 0.41 0.33 0.37 1000 deer 0.58 0.37 0.46 1000 dog 0.58 0.32 0.41 1000 frog 0.60 0.65 0.63 1000 horse 0.45 0.76 0.57 1000 ship 0.46 0.84 0.59 1000 truck 0.62 0.48 0.54 1000 accuracy 0.53 10000 macro avg 0.55 0.53 0.52 10000 weighted avg 0.55 0.53 0.52 10000 Process finished with exit code 0
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结果图：
标准前馈神经网络在训练CIFAR-10数据集时，验证损失函数值增大，出现了过拟合现象，通过改变学习率, 网络节点数和深度等优化超参数， 结果并没有多大改善，需要通过卷积神经网来极大地改善准确率。
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