使用Keras 搭建卷积神经网络实现手写字体识别

最新推荐文章于 2023-04-04 15:00:00 发布

Mr_UncleDrew

最新推荐文章于 2023-04-04 15:00:00 发布

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分类专栏：机器学习文章标签：手写字体识别 keras

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本文介绍了一种基于卷积神经网络（CNN）的手写数字识别模型，该模型使用Keras框架实现，针对MNIST数据集进行训练。通过三层卷积层、池化层和Dropout层的组合，以及全连接层的加入，模型最终达到了较高的识别准确率。

import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D

def load_data():
path = “data/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()
return (x_train, y_train), (x_test, y_test)
def train_y(y):
y_ohe = np.zeros(10)
y_ohe[y] = 1
return y_ohe

‘’‘读入数据’’’
(X_train, y_train), (X_test, y_test) = load_data()
print(X_train[0].shape)
print(y_train[0])

X_train = X_train.reshape(X_train.shape[0],28,28,1).astype(‘float32’)
X_test = X_test.reshape(X_test.shape[0],28,28,1).astype(‘float32’)
X_train /= 255
X_test /=255

‘’‘Onehot编码’’’
y_train_ohe = np.array([train_y(y_train[i]) for i in range(len(y_train))])
y_test_ohe = np.array([train_y(y_test[i]) for i in range(len(y_test))])

‘’‘搭建卷积神经网络’’’
model = Sequential()
model.add(Conv2D(filters=64, kernel_size=(3,3),strides=(1,1), padding=‘same’, input_shape=(28,28,1),activation=“relu”))#添加卷积层
model.add(MaxPooling2D(pool_size=(2,2)))#添加池化层
model.add(Dropout(0.5))#设置Dropout层

model.add(Conv2D(filters=128, kernel_size=(3,3),strides=(1,1), padding=‘same’, input_shape=(28,28,1),activation=“relu”))#添加卷积层
model.add(MaxPooling2D(pool_size=(2,2)))#添加池化层
model.add(Dropout(0.5))#设置Dropout层

model.add(Conv2D(filters=256, kernel_size=(3,3),strides=(1,1), padding=‘same’, input_shape=(28,28,1),activation=“relu”))#添加卷积层
model.add(MaxPooling2D(pool_size=(2,2)))#添加池化层
model.add(Dropout(0.5))#设置Dropout层

model.add(Flatten())#将当前节点展平

‘’‘构造全连接神经网络层’’’
model.add(Dense(128,activation=‘relu’))
model.add(Dense(64,activation=‘relu’))
model.add(Dense(32,activation=‘relu’))
model.add(Dense(10,activation=‘softmax’))

‘’‘定义损失函数，分类问题一般使用交叉熵’’’
model.compile(loss=‘categorical_crossentropy’, optimizer=‘adagrad’, metrics=[‘accuracy’])

‘’‘批量放入数据，进行训练’’’
model.fit(X_train,y_train_ohe, validation_data=(X_test, y_test_ohe),epochs=20,batch_size=128)

‘’‘测试集评价模型准确度’’’
scores = model.evaluate(X_test,y_test_ohe,verbose=0)

print(scores)