1、MNIST数据集介绍
MNIST数据集是一个非常著名的手写数字数据集，是机器学习领域中最受欢迎的数据集之一。由该数据集构成的图片都是28x28的灰度图像。它总共有70000张图片，它由60000个训练图像和10000个测试图像组成。MNIST数据集已经经过预处理，可以直接拿来训练模型。
数据集中的图片显示了从0到9的手写数字，标签标识了图片显示的数字是多少。
2.获取训练集和测试
Python使用Keras深度学习框架基于卷积神经网络模型识别MNIST手写字体，模型在测试集上的预测准确率是99%
## 60000张 训练图片，每张图片28*28
## 10000张 测试图片，每张图片28*28
def prepareData(self):
(xTrain, yTrain), (xTest, yTest) = keras.datasets.mnist.load_data()
xTrain = xTrain / 255.0 # 缩放 numpy.ndarray,(60000, 28, 28)
xTest = xTest / 255.0 # 缩放， 范围[0,1]
xTrain = np.expand_dims(xTrain, -1) # (60000, 28, 28) -> (60000, 28, 28, 1)
xTest = np.expand_dims(xTest, -1) # (10000, 28, 28) -> (10000, 28, 28, 1)
print("xTrain shape", xTrain.shape)
yTrain = keras.utils.to_categorical(yTrain, num_classes=10) # 将整型的类别标签转为onehot编码
yTest = keras.utils.to_categorical(yTest, num_classes=10) # 10个数字，num_classes为10
print(type(yTrain))
return xTrain, yTrain, xTest, yTest
返回训练集X，y ； 测试X，y
3、创建卷积神经网络模型
def createModel(self):
inputShape = (28, 28, 1) # (60000, 28, 28, 1)，60000个样本
# 激活函数：sigmoid，relu，tanh
# 卷积层可以处理二维数据，矩阵
# 全连接层只能处理一维数据，向量
model = keras.Sequential(
[
keras.Input(shape=inputShape),
layers.Conv2D(32, kernel_size=(3, 3), activation='relu'), # 卷积层：卷积函数：32 过滤器个数
layers.MaxPooling2D(pool_size=(2, 2)), # 最大池化层
layers.Conv2D(32, kernel_size=(3, 3), activation='relu'), # 卷积层：卷积核尺寸，一般是3*3，或者5*5
layers.MaxPooling2D(pool_size=(2, 2)), # 最大池化层
layers.Flatten(), # 将输入层的数据压成一维数据，
layers.Dropout(0.5), # 深度学习网络训练过程中，按照一定的概率丢弃神经网络单元，防止过拟合，默认0.5，丢弃50%的神经元
# Softmax 函数能够将一个K维实值向量归一化，所以它主要被用于多分类任务
# Sigmoid 能够将一个实数归一化，因此它一般用于二分类任务。
# 当 Softmax 的维数 K=2 时，Softmax 会退化为 Sigmoid 函数
layers.Dense(10, activation="softmax") # Dense代表全连接网络，输出维度10，激活函数softmax
]
)
model.summary() # 输出模型各层的参数状况
return model
4、训练模型
def trainModel(self, model:keras.Sequential, xTrain:numpy.array, yTrain:numpy.array):
# 指定损失函数
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(xTrain,
yTrain,
batch_size=128, # 每次梯度更新的样本数，默认32
epochs=15, # 训练模型迭代次数
validation_split=0.1) # 验证集的比例
return model
5、测试模型
def testModel(self, model:keras.Sequential, xTest, yTest):
score = model.evaluate(xTest, yTest,
verbose=0) # 日志展示，0-不输出；1-显示进度条；2-每个epochs输出1行记录
print("Loss：", score[0]) # 损失
print("Accuracy：", score[1]) # 准确率
6、完整代码
# -*- coding: utf-8 -*-
import numpy
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
# Keras Mnist 模型
class KrMnistModel(object):
## 60000张 训练图片，每张图片28*28
## 10000张 测试图片，每张图片28*28
def prepareData(self):
(xTrain, yTrain), (xTest, yTest) = keras.datasets.mnist.load_data()
xTrain = xTrain / 255.0 # 缩放 numpy.ndarray,(60000, 28, 28)
xTest = xTest / 255.0 # 缩放， 范围[0,1]
xTrain = np.expand_dims(xTrain, -1) # (60000, 28, 28) -> (60000, 28, 28, 1)
xTest = np.expand_dims(xTest, -1) # (10000, 28, 28) -> (10000, 28, 28, 1)
print("xTrain shape", xTrain.shape)
yTrain = keras.utils.to_categorical(yTrain, num_classes=10) # 将整型的类别标签转为onehot编码
yTest = keras.utils.to_categorical(yTest, num_classes=10) # 10个数字，num_classes为10
print(type(yTrain))
return xTrain, yTrain, xTest, yTest
def createModel(self):
inputShape = (28, 28, 1) # (60000, 28, 28, 1)，60000个样本
# 激活函数：sigmoid，relu，tanh
# 卷积层可以处理二维数据，矩阵
# 全连接层只能处理一维数据，向量
model = keras.Sequential(
[
keras.Input(shape=inputShape),
layers.Conv2D(32, kernel_size=(3, 3), activation='relu'), # 卷积层：卷积函数：32 过滤器个数
layers.MaxPooling2D(pool_size=(2, 2)), # 最大池化层
layers.Conv2D(32, kernel_size=(3, 3), activation='relu'), # 卷积层：卷积核尺寸，一般是3*3，或者5*5
layers.MaxPooling2D(pool_size=(2, 2)), # 最大池化层
layers.Flatten(), # 将输入层的数据压成一维数据，
layers.Dropout(0.5), # 深度学习网络训练过程中，按照一定的概率丢弃神经网络单元，防止过拟合，默认0.5，丢弃50%的神经元
# Softmax 函数能够将一个K维实值向量归一化，所以它主要被用于多分类任务
# Sigmoid 能够将一个实数归一化，因此它一般用于二分类任务。
# 当 Softmax 的维数 K=2 时，Softmax 会退化为 Sigmoid 函数
layers.Dense(10, activation="softmax") # Dense代表全连接网络，输出维度10，激活函数softmax
]
)
model.summary() # 输出模型各层的参数状况
return model
def trainModel(self, model:keras.Sequential, xTrain:numpy.array, yTrain:numpy.array):
# 指定损失函数
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(xTrain,
yTrain,
batch_size=128, # 每次梯度更新的样本数，默认32
epochs=15, # 训练模型迭代次数
validation_split=0.1) # 验证集的比例
return model
def testModel(self, model:keras.Sequential, xTest, yTest):
score = model.evaluate(xTest, yTest,
verbose=0) # 日志展示，0-不输出；1-显示进度条；2-每个epochs输出1行记录
print("Loss：", score[0]) # 损失
print("Accuracy：", score[1]) # 准确率
# main function
if __name__ == '__main__':
krObj = KrMnistModel()
print("准备数据……")
xTrain, yTrain, xTest, yTest = krObj.prepareData()
print("创建模型……")
model = krObj.createModel()
print("训练模型……")
model = krObj.trainModel(model, xTrain, yTrain)
print("测试模型……")
krObj.testModel(model, xTest, yTest)
7、运行结果
"C:\Program Files\Python37\python.exe" D:/MyProj/TfDLProj/krModel/KrMnistModel.py
2023-04-09 20:22:06.754283: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
准备数据……
xTrain shape (60000, 28, 28, 1)
<class 'numpy.ndarray'>
创建模型……
2023-04-09 20:22:13.968864: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2023-04-09 20:22:13.978588: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2023-04-09 20:22:14.046314: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1650 Ti computeCapability: 7.5
coreClock: 1.485GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 178.84GiB/s
2023-04-09 20:22:14.047261: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2023-04-09 20:22:14.097692: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2023-04-09 20:22:14.097992: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2023-04-09 20:22:14.122078: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2023-04-09 20:22:14.129733: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2023-04-09 20:22:14.183585: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2023-04-09 20:22:14.199841: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2023-04-09 20:22:14.204166: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-04-09 20:22:14.204704: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2023-04-09 20:22:14.206580: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-09 20:22:14.208133: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1650 Ti computeCapability: 7.5
coreClock: 1.485GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 178.84GiB/s
2023-04-09 20:22:14.208710: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2023-04-09 20:22:14.208996: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2023-04-09 20:22:14.209275: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2023-04-09 20:22:14.209560: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2023-04-09 20:22:14.209838: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2023-04-09 20:22:14.210123: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2023-04-09 20:22:14.210397: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2023-04-09 20:22:14.210673: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-04-09 20:22:14.210985: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2023-04-09 20:22:15.198078: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-04-09 20:22:15.198413: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2023-04-09 20:22:15.198590: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
2023-04-09 20:22:15.199449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2903 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
2023-04-09 20:22:15.201051: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 11, 11, 32) 9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 800) 0
_________________________________________________________________
dropout (Dropout) (None, 800) 0
_________________________________________________________________
dense (Dense) (None, 10) 8010
=================================================================
Total params: 17,578
Trainable params: 17,578
Non-trainable params: 0
_________________________________________________________________
训练模型……
2023-04-09 20:22:15.442757: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
Epoch 1/15
2023-04-09 20:22:15.784489: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2023-04-09 20:22:16.640072: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2023-04-09 20:22:16.653720: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-04-09 20:22:18.352832: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0
2023-04-09 20:22:18.470222: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0
422/422 [==============================] - 7s 8ms/step - loss: 0.9618 - accuracy: 0.6814 - val_loss: 0.0942 - val_accuracy: 0.9747
Epoch 2/15
422/422 [==============================] - 3s 6ms/step - loss: 0.1555 - accuracy: 0.9518 - val_loss: 0.0662 - val_accuracy: 0.9832
Epoch 3/15
422/422 [==============================] - 3s 6ms/step - loss: 0.1119 - accuracy: 0.9658 - val_loss: 0.0522 - val_accuracy: 0.9853
Epoch 4/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0925 - accuracy: 0.9707 - val_loss: 0.0461 - val_accuracy: 0.9878
Epoch 5/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0804 - accuracy: 0.9739 - val_loss: 0.0444 - val_accuracy: 0.9878
Epoch 6/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0752 - accuracy: 0.9766 - val_loss: 0.0426 - val_accuracy: 0.9873
Epoch 7/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0723 - accuracy: 0.9783 - val_loss: 0.0379 - val_accuracy: 0.9890
Epoch 8/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0677 - accuracy: 0.9791 - val_loss: 0.0375 - val_accuracy: 0.9887
Epoch 9/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0633 - accuracy: 0.9798 - val_loss: 0.0333 - val_accuracy: 0.9907
Epoch 10/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0606 - accuracy: 0.9804 - val_loss: 0.0323 - val_accuracy: 0.9902
Epoch 11/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0571 - accuracy: 0.9826 - val_loss: 0.0348 - val_accuracy: 0.9905
Epoch 12/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0555 - accuracy: 0.9825 - val_loss: 0.0327 - val_accuracy: 0.9903
Epoch 13/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0520 - accuracy: 0.9841 - val_loss: 0.0326 - val_accuracy: 0.9905
Epoch 14/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0506 - accuracy: 0.9836 - val_loss: 0.0328 - val_accuracy: 0.9910
Epoch 15/15
422/422 [==============================] - 3s 6ms/step - loss: 0.0533 - accuracy: 0.9834 - val_loss: 0.0316 - val_accuracy: 0.9910
测试模型……
Loss： 0.029247775673866272
Accuracy： 0.9900000095367432
Process finished with exit code 0
Accuracy： 0.9900000095367432
正确率：99%