keras图像识别
案例背景数据预处理普通神经网络要求建立模型模型评估
CNN要求模型建立（两个卷积层）模型建立（一个卷积层）
案例背景
数据集 caltech101 中给出了经过转化的 102 种物体图像数据 （ 128*128 像 素 ）， 共 9144 个样例，相应的类标签在 caltech101_labels给出。在此基础上将原始数据划分为训练集（80%） 和测试集（20%）。（如果计算机性能有限，可以从 102 种物体中任意 抽取 10~20 种作为数据集）
数据预处理
导入库
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import SGD
from sklearn.model_selection import train_test_split
读取数据
labels=pd.read_csv('caltech101_labels.csv')
data=pd.read_csv('caltech101.csv')
删除无用列
data.drop(data.columns[0],axis=1,inplace=True)
划分训练集测试集
x_train,x_test,y_train,y_test = train_test_split(data,labels,test_size = 0.2,random_state = 1)
标签处理
num_classes=102
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
普通神经网络
要求
建立具有两个隐藏层以及 Dropout 层的神经网络模型对图像数据进行分类，并对模型性能进行评价。
建立模型
model = Sequential()
model.add(Dense(4092, activation='relu', input_shape=(16384,)))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(102, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
model.summary()
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=128, epochs=30,
verbose=1, validation_data=(x_test, y_test))
模型评估
score = model.evaluate(x_train, y_train, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
训练集：0.59 测试集：0.41
CNN
要求
分别建立具有 1 个和 2 个卷积层的 CNN 模型对图像数据进行分类，并对模型性能进行评价，重点考察卷积核数量对模型性能的影响。
模型建立（两个卷积层）
导入库
from sklearn.datasets import fetch_mldata
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Conv2D, MaxPooling2D,Flatten
from keras.optimizers import SGD
挑选一小部分数据，共36类
data1=data.iloc[:5000,:]
label1=labels.iloc[:5000,:]
训练数据预处理
img_rows, img_cols = 128, 128
X_train,X_test,y_train,y_test = train_test_split(data1,label1,test_size = 0.2,random_state = 1)
X_train = np.array(X_train).reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = np.array(X_test).reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
# convert class vectors to binary class matrices
num_classes=len(label1.iloc[:,0].value_counts().index) #len(label1.iloc[:,0].unique())
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
搭建模型框架
model=Sequential()
model.add(Conv2D(16,
kernel_size=(3,3),
activation='relu',
input_shape=input_shape,
padding='same'
)
)
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(16,
kernel_size=(3,3),
activation='relu',
padding='same'
)
)
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(32*32*16,activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(num_classes,activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,metrics=['accuracy'])
model.fit(X_train, y_train, batch_size=128,epochs=5,
verbose=1,validation_data=(X_test, y_test))
模型评估
测试集
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
Test loss: 0.3388944187760353 Test accuracy: 0.91 2. 训练集
score = model.evaluate(X_train, y_train, verbose=0)
print('Train loss:', score[0])
print('Train accuracy:', score[1])
Train loss: 1.37 Train accuracy: 0.655
模型建立（一个卷积层）
搭建一个卷积层容易报错
搭建模型框架
model=Sequential()
model.add(Conv2D(16,
kernel_size=(3,3),
activation='relu',
input_shape=input_shape,
padding='same'
)
)
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(16*16*16,activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(num_classes,activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
model.fit(X_train, y_train, batch_size=128,epochs=5, verbose=1,validation_data=(X_test, y_test))
模型评估
测试集
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
Test loss: 1,。47 Test accuracy: 0.629 2. 训练集
score = model.evaluate(X_train, y_train, verbose=0)
print('Train loss:', score[0])
print('Train accuracy:', score[1])
Train loss: 1.192 Train accuracy: 0.6905
