实战六：手把手教你用TensorFlow进行手写数字识别
本文共 20951 字，大约阅读时间需要 69 分钟。
手把手教你用TensorFlow进行手写数字识别
目录
手写体数字MNIST数据集介绍
MNIST Softmax网络介绍
实战MNIST Softmax网络
MNIST CNN网络介绍
实战MNIST CNN网络
一、MNIST数据集介绍
1.MNIST数据集
MNIST是一套手写体数字的图像数据集，包括60000个训练样例和10000个测试样例，由纽约大学的Yann LeCun等人维护。
more info:
2.MNIST手写体数字介绍
MNIST图像数据集使用形如[28,28]的二阶数组来表示每个手写体数字，数组中的每个元素对应一个像素点，即每张图像大小固定为28x28像素
MNIST数据集中的图像都是256阶灰度图，即灰度值0表示白色（背景），255表示黑色（前景），使用取值为[0,255]的uint8数据了诶下表示图像。为了加速训练，我们需要做数据规范化，将灰度值缩放为[0,1]的float32数据类型
由于每张图像的尺寸都是28x28像素，为了方便连续存储，我们可以将形如[28,28]的二阶数组“摊平”成形如[784,]的一阶数组，可以表示256256…*256 = 256^784张不同的图像。
但这些图像并非每一张都代表有效的手写体数字，其中绝大部分都是如下的噪声图:
3.下载和读取MNIST数据集
a.使用tf.contrib.learn模块加载MNIST数据集
一个曾广泛使用的方法，但如今已经被废弃的（deprecated）方法：
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data# 导入数据mnist = input_data.read_data_sets("./data/") WARNING:tensorflow:From:5: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.Instructions for updating:Please use alternatives such as official/mnist/dataset.py from tensorflow/models.WARNING:tensorflow:From D:\software\Anaconda\workplace\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.Instructions for updating:Please write your own downloading logic.WARNING:tensorflow:From D:\software\Anaconda\workplace\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.Instructions for updating:Please use tf.data to implement this functionality.Extracting ./data/train-images-idx3-ubyte.gzWARNING:tensorflow:From D:\software\Anaconda\workplace\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.Instructions for updating:Please use tf.data to implement this functionality.Extracting ./data/train-labels-idx1-ubyte.gzExtracting ./data/t10k-images-idx3-ubyte.gzExtracting ./data/t10k-labels-idx1-ubyte.gzWARNING:tensorflow:From D:\software\Anaconda\workplace\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.Instructions for updating:Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
因为，tf.contrib.learn整个模型均已被废弃了
b.使用Keras加载MNIST数据集
tf.kera.datasets.mnist.load_data(path=‘mnist.npz’)
from keras.datasets import mnistpath = "D:\\mnist data\mnist.npz"(x_train,y_train),(x_test,y_test) = mnist.load_data(path)
Using TensorFlow backend.
# 查看mnist data的维度print(x_train.shape,y_train.shape)print(x_test.shape,y_test.shape)
(60000, 28, 28) (60000,)(10000, 28, 28) (10000,)
# 查看数据print(x_train[0])print(y_train[0])
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 3 18 18 18 126 136 175 26 166 255 247 127 0 0 0 0] [ 0 0 0 0 0 0 0 0 30 36 94 154 170 253 253 253 253 253 225 172 253 242 195 64 0 0 0 0] [ 0 0 0 0 0 0 0 49 238 253 253 253 253 253 253 253 253 251 93 82 82 56 39 0 0 0 0 0] [ 0 0 0 0 0 0 0 18 219 253 253 253 253 253 198 182 247 241 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 80 156 107 253 253 205 11 0 43 154 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 14 1 154 253 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 139 253 190 2 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 11 190 253 70 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 35 241 225 160 108 1 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 81 240 253 253 119 25 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 186 253 253 150 27 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 93 252 253 187 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249 253 249 64 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 130 183 253 253 207 2 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 39 148 229 253 253 253 250 182 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 24 114 221 253 253 253 253 201 78 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 23 66 213 253 253 253 253 198 81 2 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 18 171 219 253 253 253 253 195 80 9 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 55 172 226 253 253 253 253 244 133 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 136 253 253 253 212 135 132 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]5
4.MNIST数据集 样例可视化
import matplotlib.pyplot as pltfig = plt.figure()for i in range(15): plt.subplot(3,5,i+1) # 绘制前15个手写体数字，以三行5列子图形式展示 plt.tight_layout # 自动适配子图尺寸 plt.imshow(x_train[i],cmap="Greys") # 使用灰色显示像素灰度值 plt.title("Label:{}".format(y_train[i])) # 设置标签为子图标题 plt.xticks([]) # 删除x轴标记 plt.yticks([]) # 删除y轴标记 二、MNIST Softmax网络介绍
1.前置知识
a.感知机模型
b.神经网络
c.线性不可分
d.激活函数（Activation Function）
e.全连接层（fully connected layers，FC）
2.MNIST Softmax网络
将表示手写数字的形如[784]的一维向量作为输入；中间定义2层512个神经元隐藏层，具备一定的模型复杂度，足以识别手写体数字；最后定义1层10个神经元的全连接层，用于输出10个不同类别的“概率”。
三、实战MNIST Softmax网络
1.加载MNIST数据集
from keras.datasets import mnistpath = "D:\\mnist data\mnist.npz"(x_train,y_train),(x_test,y_test) = mnist.load_data(path)
print(x_train.shape,type(x_train))print(y_train.shape,type(y_train))print(x_test.shape,type(x_test))print(y_test.shape,type(y_test))
(60000, 28, 28)(60000,) (10000, 28, 28) (10000,)
2.数据规范化
# 将图像本身从[28,28]转换为[784,]X_train = x_train.reshape(60000,784)X_test = x_test.reshape(10000,784)print(X_train.shape,type(X_train))print(X_test.shape,type(X_test))
(60000, 784)(10000, 784)
# 将数据类型转换为float32，然后将其归一化处理X_train = X_train.astype("float32")X_test = X_test.astype("float32")# 数据归一化X_train /= 255X_test /= 255 # 查看数据print(X_train[0])print(y_train[0])
3.统计训练数据中各标签的数量
import numpy as npimport matplotlib.pyplot as pltlabel,count = np.unique(y_train,return_counts=True)print(label,count)
[0 1 2 3 4 5 6 7 8 9] [5923 6742 5958 6131 5842 5421 5918 6265 5851 5949]
fig = plt.figure()plt.bar(label,count,width=0.7,align="center")plt.title("Label Distribution")plt.xlabel("Label")plt.ylabel("Count")plt.xticks(label)plt.ylim(0,7500)for a,b in zip(label,count): plt.text(a,b,"%d" %b,ha="center",va="bottom",fontsize=10) plt.show() 
4.one-hot 编码
a.几种编码方式的对比
| Binary | Gray code | One-hot |
|---|---|---|
| 000 | 000 | 00000001 |
| 001 | 001 | 00000010 |
| 010 | 011 | 00000100 |
| 011 | 010 | 00001000 |
| 100 | 110 | 00010000 |
| 101 | 111 | 00100000 |
| 110 | 101 | 01000000 |
| 111 | 100 | 10000000 |
b.对标签数据进行one-hot编码
from keras.utils import np_utilsn_classes = 10print("Shape before one-hot encoding:",y_train.shape)Y_train = np_utils.to_categorical(y_train,n_classes)Y_test = np_utils.to_categorical(y_test,n_classes)print("Shape after one-hot encoding:",Y_train.shape) Shape before one-hot encoding: (60000,)Shape after one-hot encoding: (60000, 10)
# 具体查看一下数据print(y_train[0])print(Y_train[0])
5[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
5.使用Keras sequential model来实现神经网络
a.定义模型 softmax网络层
from keras.models import Sequentialfrom keras.layers.core import Dense,Activationmodel = Sequential()model.add(Dense(512,input_shape=(784,)))model.add(Activation("relu"))model.add(Dense(512))model.add(Activation("relu"))model.add(Dense(10))model.add(Activation("softmax")) WARNING:tensorflow:From D:\software\Anaconda\workplace\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.Instructions for updating:Colocations handled automatically by placer.
b.编译模型
compile(optimizer, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None)
# loss:categorical_crossentropy 损失器采用交叉熵损失函数# metrics:accuracy 衡量指标采用准确率# optimizer:adam 优化器采用adam,结合AdaGrad和RMSProp两种优化算法的优点model.compile(loss="categorical_crossentropy",metrics=["accuracy"],optimizer="adam")
c.训练模型，并将指标保存到history中
fit(x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None)
history = model.fit(X_train, Y_train, batch_size=128, epochs=5, verbose=2, validation_data=(X_test,Y_test))
WARNING:tensorflow:From D:\software\Anaconda\workplace\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.Instructions for updating:Use tf.cast instead.Train on 60000 samples, validate on 10000 samplesEpoch 1/5 - 5s - loss: 0.2158 - acc: 0.9371 - val_loss: 0.1152 - val_acc: 0.9637Epoch 2/5 - 5s - loss: 0.0815 - acc: 0.9745 - val_loss: 0.0856 - val_acc: 0.9727Epoch 3/5 - 6s - loss: 0.0505 - acc: 0.9841 - val_loss: 0.0762 - val_acc: 0.9758Epoch 4/5 - 6s - loss: 0.0361 - acc: 0.9882 - val_loss: 0.0672 - val_acc: 0.9782Epoch 5/5 - 5s - loss: 0.0263 - acc: 0.9916 - val_loss: 0.0772 - val_acc: 0.9772
d.可视化指标
fig = plt.figure()plt.subplot(2,1,1)plt.plot(history.history["acc"])plt.plot(history.history["val_acc"])plt.title("Model Accuracy")plt.xlabel("epoch")plt.ylabel("accuracy")plt.legend(["train","test"],loc="lower right")plt.subplot(2,1,2)plt.plot(history.history["loss"])plt.plot(history.history["val_loss"])plt.title("Model loss")plt.xlabel("epoch")plt.ylabel("loss")plt.legend(["train","test"],loc="upper right")plt.tight_layout()plt.show() 
e.保存模型
You can use model.save(filepath) to save a Keras model into a single HDF5 file which will contain:
- the architecture of the model, allowing to re-create the model
- the weights of the model
- the training configuration (loss, optimizer)
- the state of the optimizer, allowing to resume training exactly where you left off.
You can then use keras.models.load_model(filepath) to reinstantiate your model. load_model will also take care of compiling the model using the saved training configuration (unless the model was never compiled in the first place).
import osimport tensorflow.gfile as gfilesave_dir = "./model/"if gfile.Exists(save_dir): gfile.DeleteRecursively(save_dir)gfile.MakeDirs(save_dir)model_name = "keras_mnist.h5"model_path = os.path.join(save_dir,model_name)model.save(model_path)print("Saved trained model at %s" % model_path) Saved trained model at ./model/keras_mnist.h5
f.加载模型
from keras.models import load_modelmnist_model = load_model(model_path)
g.统计模型在测试集上的分类结果
loss_and_metrics = mnist_model.evaluate(X_test,Y_test,verbose=2)print("Test Loss:{}".format(loss_and_metrics[0]))print("Test Accuracy:{}%".format(loss_and_metrics[1]*100))predicted_classes = mnist_model.predict_classes(X_test)correct_indices = np.nonzero(predicted_classes == y_test)[0]incorrect_indices = np.nonzero(predicted_classes != y_test)[0]print("Classified correctly count: {}".format(len(correct_indices)))print("Classified incorrectly count: {}".format(len(incorrect_indices))) Test Loss:0.07715968866588664Test Accuracy:97.72%Classified correctly count: 9772Classified incorrectly count: 228
四、MNIST CNN网络介绍
1.CNN简介
2.卷积
3.卷积层(Convolutional Layer,conv)
4.池化层(Pooling)
5.Dropout层
6.Flatten
五、实战MNIST CNN网络
MNIST CNN示意图
1.加载MNIST数据集
from keras.datasets import mnistpath = "D:\mnist data\mnist.npz"(x_train,y_train),(x_test,y_test) = mnist.load_data(path)print(x_train.shape,type(x_train))print(y_train.shape,type(y_train))print(x_test.shape,type(x_test))print(y_test.shape,type(y_test))
(60000, 28, 28)(60000,) (10000, 28, 28) (10000,)
2.数据处理：规范化
channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).
It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be channels_last.
from keras import backend as Kimg_rows,img_cols = 28,28if K.image_data_format() == "channels_first": x_train = x_train.reshape(x_train.shape[0],1,img_rows,img_cols) x_test = x_test.reshape(x_test.shape[0],1,img_rows,img_cols) input_shape = (1,img_rows,img_cols)else: x_train = x_train.reshape(x_train.shape[0],img_rows,img_cols,1) x_test = x_test.reshape(x_test.shape[0],img_rows,img_cols,1) input_shape = (img_rows,img_cols,1) print(x_train.shape,type(x_train))print(x_test.shape,type(x_test))
(60000, 28, 28, 1)(10000, 28, 28, 1)
# 将数据类型转换为float32X_train = x_train.astype("float32")X_test = x_test.astype("float32")# 数据归一化X_train /= 255X_test /= 255 3.统计训练数据中各标签数量
import numpy as npimport matplotlib.pyplot as pltlabel,count = np.unique(y_train,return_counts=True)print(label,count)
[0 1 2 3 4 5 6 7 8 9] [5923 6742 5958 6131 5842 5421 5918 6265 5851 5949]
fig = plt.figure()plt.bar(label,count,width=0.7,align="center")plt.title("Label Distribution")plt.xlabel("Label")plt.ylabel("Count")plt.xticks(label)plt.ylim(0,7500)for a,b in zip(label,count): plt.text(a,b,"%d" %b,ha="center",va="bottom",fontsize=10)plt.show() 
4.数据处理:one-hot 编码
from keras.utils import np_utilsn_classes = 10print("Shape before one-hot encoding:",y_train.shape)Y_train = np_utils.to_categorical(y_train,n_classes)Y_test = np_utils.to_categorical(y_test,n_classes)print("Shape after one-hot encoding:",Y_train.shape) Shape before one-hot encoding: (60000,)Shape after one-hot encoding: (60000, 10)
print(y_train[0])print(Y_train[0])
5[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
5.MNIST CNN网络实战
a.Keras sequential model定义MNIST CNN网络
from keras.models import Sequentialfrom keras.layers import Dense,Dropout,Flattenfrom keras.layers import Conv2D,MaxPooling2Dmodel = Sequential()# Feature Extraction# 第一层卷积，32个3*3的卷积核，激活函数使用relumodel.add(Conv2D(filters=32,kernel_size=(3,3),activation="relu",input_shape=input_shape))# 第二层卷积，64个3*3的卷积核，激活函数使用relumodel.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu"))# 最大池化层，池化窗口为2*2model.add(MaxPooling2D(pool_size=(2,2)))# Dropout 25%的输入神经元model.add(Dropout(0.25))# 将Pooled feature map摊平后输入全连接网络model.add(Flatten())# Classification# 全联接层model.add(Dense(128,activation="relu"))# Dropout 50%的输入神经元model.add(Dropout(0.5))# 使用softmax激活函数做多分类，输出各数字的概率model.add(Dense(n_classes,activation="softmax"))
WARNING:tensorflow:From D:\software\Anaconda\workplace\lib\site-packages\keras\backend\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.Instructions for updating:Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
# 查看MNIST CNN模型网络结构model.summary()
_________________________________________________________________Layer (type) Output Shape Param # =================================================================conv2d_1 (Conv2D) (None, 26, 26, 32) 320 _________________________________________________________________conv2d_2 (Conv2D) (None, 24, 24, 64) 18496 _________________________________________________________________max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64) 0 _________________________________________________________________dropout_1 (Dropout) (None, 12, 12, 64) 0 _________________________________________________________________flatten_1 (Flatten) (None, 9216) 0 _________________________________________________________________dense_4 (Dense) (None, 128) 1179776 _________________________________________________________________dropout_2 (Dropout) (None, 128) 0 _________________________________________________________________dense_5 (Dense) (None, 10) 1290 =================================================================Total params: 1,199,882Trainable params: 1,199,882Non-trainable params: 0_________________________________________________________________
b.编译模型
compile(optimizer, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
c.训练模型，并指标保存到history中
fit(x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None)
history = model.fit(X_train, Y_train, batch_size=128, epochs=5, verbose=2, validation_data=(X_test,Y_test))
Train on 60000 samples, validate on 10000 samplesEpoch 1/5 - 63s - loss: 0.2449 - acc: 0.9256 - val_loss: 0.0553 - val_acc: 0.9823Epoch 2/5 - 63s - loss: 0.0841 - acc: 0.9742 - val_loss: 0.0340 - val_acc: 0.9881Epoch 3/5 - 63s - loss: 0.0644 - acc: 0.9811 - val_loss: 0.0312 - val_acc: 0.9889Epoch 4/5 - 68s - loss: 0.0522 - acc: 0.9838 - val_loss: 0.0316 - val_acc: 0.9907Epoch 5/5 - 64s - loss: 0.0440 - acc: 0.9864 - val_loss: 0.0300 - val_acc: 0.9902
# 将history可视化fig = plt.figure()plt.subplot(2,1,1)plt.plot(history.history["acc"])plt.plot(history.history["val_acc"])plt.title("Model Accuracy")plt.xlabel("epoch")plt.ylabel("accuracy")plt.legend(["train","test"],loc="lower right")plt.subplot(2,1,2)plt.plot(history.history["loss"])plt.plot(history.history["val_loss"])plt.title("Model Loss")plt.xlabel("loss")plt.ylabel("epoch")plt.legend(["train","test"],loc="upper right")plt.tight_layout()plt.show() 
d.保存模型
You can use model.save(filepath) to save a Keras model into a single HDF5 file which will contain:
- the architecture of the model, allowing to re-create the model
- the weights of the model
- the training configuration (loss, optimizer)
- the state of the optimizer, allowing to resume training exactly where you left off.
You can then use keras.models.load_model(filepath) to reinstantiate your model. load_model will also take care of compiling the model using the saved training configuration (unless the model was never compiled in the first place).
import osimport tensorflow.gfile as gfilesave_dir = "./model/"if gfile.Exists(save_dir): gfile.DeleteRecursively(save_dir)gfile.MakeDirs(save_dir)model_name = "keras_mnist.h5"model_path = os.path.join(save_dir,model_name)model.save(model_path)print("Saved trained model at %s"% model_path) Saved trained model at ./model/keras_mnist.h5
e.加载模型
from keras.models import load_modelmnist_model = load_model(model_path)
f.统计模型在测试集上的分类结果
loss_and_metrics = mnist_model.evaluate(X_test,Y_test,verbose=2)print("Test Loss:{}".format(loss_and_metrics[0]))print("Test Accuracy:{}%".format(loss_and_metrics[1]*100))predicted_classes = mnist_model.predict_classes(X_test)correct_indices = np.nonzero(predicted_classes == y_test)[0]incorrect_indices = np.nonzero(predicted_classes != y_test)[0]print("Classified correctly count:{}".format(len(correct_indices)))print("Classified incorrectly count:{}".format(len(incorrect_indices))) Test Loss:0.02999471065380276Test Accuracy:99.02%Classified correctly count:9902Classified incorrectly count:98
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