练习——形状识别（是方还是圆） Keras

数据连接：形状识别：是方还是圆，本次练习使用的是简单的CNN（之后又时间再试其他网络模型），这个任务是二分类，Dense应该为1，但是我用了to_categorical，后面Dense必须为2。注意使用pd.read_csv读取文件，函数默认将数据第一行作为标题，这点很重要。在保存预测结果时，使用pd.DataFrame，具体的看前面Keras学习。本次使用代码如下：

import numpy as np import pandas as pd import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense,Activation,Conv2D from keras.layers import MaxPool2D,Flatten,Dropout,ZeroPadding2D,BatchNormalization fromkeras.utils import np_utils from keras import metrics import keras from keras.models import save_model,load_model from keras.models import Model from keras.callbacks import ModelCheckpoint import os from keras.utils import plot_model from keras.optimizers import SGD,Adamdf=pd.read_csv("data/train.csv") data=https://www.it610.com/article/df.as_matrix() df=Nonenp.random.shuffle(data) x_train=data[0:,1:-1] x_train=x_train.reshape(data.shape[0],40,40,1).astype("float32")#将x_train变为data.shape[0]=60000个28*28,1通道的矩阵 x_train=x_train/255.0 y_train=np_utils.to_categorical(data[0:,-1],2).astype("float32")df=pd.read_csv("data/test.csv") data=https://www.it610.com/article/df.as_matrix() df=None#np.random.shuffle(data)x_test=data[:,1:] x_test=x_test.reshape(data.shape[0],40,40,1).astype("float32") x_test=x_test/255.0# print(x_train.shape) # print(y_train.shape)batch_size=32 n_filters=32 pool_size=(2,2)cnn_net=Sequential() cnn_net.add(Conv2D(32,kernel_size=(3,3),strides=(1,1),input_shape=(40,40,1))) cnn_net.add(Activation('relu'))cnn_net.add(BatchNormalization(epsilon=1e-6,axis=1)) cnn_net.add(MaxPool2D(pool_size=pool_size))cnn_net.add(ZeroPadding2D((1,1))) cnn_net.add(Conv2D(48,kernel_size=(3,3))) cnn_net.add(Activation('relu'))cnn_net.add(BatchNormalization(epsilon=1e-6,axis=1)) cnn_net.add(MaxPool2D(pool_size=pool_size))cnn_net.add(ZeroPadding2D((1,1))) cnn_net.add(Conv2D(64,kernel_size=(2,2))) cnn_net.add(Activation('relu'))cnn_net.add(BatchNormalization(epsilon=1e-6,axis=1)) cnn_net.add(MaxPool2D(pool_size=pool_size))cnn_net.add(Dropout(0.25)) #Flatten层用来将输入“压平”，即把多维的输入一维化，常用在从卷积层到全连接层的过渡。 cnn_net.add(Flatten()) cnn_net.add(Dense(512))#3168 cnn_net.add(Activation('relu')) cnn_net.add(Dense(2)) cnn_net.add(Activation('softmax')) #summary查看网络结构 cnn_net.summary() sgd =SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True) cnn_net.compile(loss='categorical_crossentropy',optimizer=sgd, metrics=['acc'])hist=cnn_net.fit(x_train,y_train,batch_size=batch_size,epochs=50,verbose=1,validation_split=0.2)#50 cnn_net.save("cnn_net_1.h5") print("test predict------") result = cnn_net.predict(x_test,batch_size=64,verbose=0)result = cnn_net.predict_classes(x_test) name=['y'] title=list(range(4000,7550)) result=pd.DataFrame(data=https://www.it610.com/article/result,columns=name) result.index=title #result_test=pd.DataFrame(columns=name,data=result) result.to_csv('result_1.csv',index=True)# plt.plot(hist.history['loss']) # plt.plot(hist.history['val_loss']) # plt.title('model loss') # plt.ylabel('loss') # plt.xlabel('epoch') # plt.legend(['train', 'test'], loc='upper left') # plt.savefig("loss_1.png") # plt.clf() # plt.plot(hist.history['acc']) # plt.plot(hist.history['val_acc']) # plt.title('model acc') # plt.ylabel('acc') # plt.xlabel('epoch') # plt.legend(['train', 'test'], loc='upper left') # plt.savefig("acc_1.png")

形状识别2：方圆之外:
数据下载：形状识别2：方圆之外，这个表面上看似二分类，但是预测时是有3类，用Dense（1）二分类，结果只有一个概率，不好判断，这个时候用to_categorical编码，用Dense（2），结果会有2个概率，这个时候比较好判断，但运行结果不是很理想，F1只有0.7879，应该很最后预测的阈值有关，有待改进。当前训练代码如下：

import numpy as np import pandas as pd import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense,Activation,Conv2D from keras.layers import MaxPool2D,Flatten,Dropout,ZeroPadding2D,BatchNormalization fromkeras.utils import np_utils from keras import metrics import keras from keras.models import save_model,load_model from keras.models import Model from keras.callbacks import ModelCheckpoint import os from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import SGD,Adam from keras.utils import plot_model from keras.applications import VGG16 from keras.applications.vgg16 import preprocess_input import cv2 from PIL import Image from keras.preprocessing import imagedf=pd.read_csv("crPlus/data/train.csv") data=https://www.it610.com/article/df.as_matrix() df=None np.random.shuffle(data) x_train=data[:,1:-1] x_train=x_train.reshape(data.shape[0],40,40,1).astype("float32") x_train=x_train/255.0 y_train=np_utils.to_categorical(data[:,-1],2).astype("float32")#### df=pd.read_csv("crPlus/data/test.csv") # data=https://www.it610.com/article/df.as_matrix() # df=None # x_test=data[:,1:] # x_test=x_test.reshape(data.shape[0],40,40,1).astype("float32") # x_test=x_test/255.0# print(x_train.shape) # print(y_train.shape)batch_size=32 n_filters=32 pool_size=(2,2)cnn_net=Sequential() cnn_net.add(Conv2D(32,kernel_size=(3,3),strides=(1,1),input_shape=(40,40,1))) cnn_net.add(Activation('relu'))cnn_net.add(BatchNormalization(epsilon=1e-6,axis=1)) cnn_net.add(MaxPool2D(pool_size=pool_size))cnn_net.add(ZeroPadding2D((1,1))) cnn_net.add(Conv2D(48,kernel_size=(3,3))) cnn_net.add(Activation('relu'))cnn_net.add(BatchNormalization(epsilon=1e-6,axis=1)) cnn_net.add(MaxPool2D(pool_size=pool_size))cnn_net.add(ZeroPadding2D((1,1))) cnn_net.add(Conv2D(64,kernel_size=(2,2))) cnn_net.add(Activation('relu'))cnn_net.add(BatchNormalization(epsilon=1e-6,axis=1)) cnn_net.add(MaxPool2D(pool_size=pool_size))cnn_net.add(Dropout(0.25)) #Flatten层用来将输入“压平”，即把多维的输入一维化，常用在从卷积层到全连接层的过渡。 cnn_net.add(Flatten()) cnn_net.add(Dense(512))#3168 cnn_net.add(Activation('relu')) cnn_net.add(Dense(2)) cnn_net.add(Activation('softmax'))#sigmoid summary查看网络结构cnn_net.summary() sgd =SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)##0.0001 cnn_net.compile(loss='categorical_crossentropy',optimizer=sgd, metrics=['acc']) batch_size=32 hist=cnn_net.fit(x_train,y_train,batch_size=batch_size,epochs=55,verbose=1,validation_split=0.2)#50 cnn_net.save("crPlus/cnn_net_1.h5")plt.plot(hist.history['loss']) plt.plot(hist.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.savefig("crPlus/loss.png") plt.clf() plt.plot(hist.history['acc']) plt.plot(hist.history['val_acc']) plt.title('model acc') plt.ylabel('acc') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.savefig("crPlus/acc.png")

预测代码：

df=pd.read_csv("crPlus/data/test.csv") data=https://www.it610.com/article/df.as_matrix() df=None x_test=data[:,1:] x_test=x_test.reshape(data.shape[0],40,40,1).astype("float32") x_test=x_test/255.0cnn_net=load_model("crPlus/cnn_net_1.h5") print("test predict------") result = cnn_net.predict(x_test,batch_size=64, verbose=0)pre=[] for i in result: if i[0]>0.999: pre.append(0) elif i[1]>0.999: pre.append(1) else: pre.append(2) pre=np.array(pre) name=['y'] res=pd.DataFrame(data=https://www.it610.com/article/pre,columns=name) res.to_csv('crPlus/result_pre.csv',index=True)

未to_categorical编码代码：

df=pd.read_csv("crPlus/data/train.csv") data=https://www.it610.com/article/df.as_matrix() df=None np.random.shuffle(data) x_train=data[:,1:-1] x_train=x_train.reshape(data.shape[0],40,40,1).astype("float32") x_train=x_train/255.0 #y_train=np_utils.to_categorical(data[:,-1],2).astype("float32")### y_train=data[:,-1]batch_size=32 n_filters=32 pool_size=(2,2)cnn_net=Sequential() cnn_net.add(Conv2D(32,kernel_size=(3,3),strides=(1,1),input_shape=(40,40,1))) cnn_net.add(Activation('relu'))cnn_net.add(BatchNormalization(epsilon=1e-6,axis=1)) cnn_net.add(MaxPool2D(pool_size=pool_size))cnn_net.add(ZeroPadding2D((1,1))) cnn_net.add(Conv2D(48,kernel_size=(3,3))) cnn_net.add(Activation('relu'))cnn_net.add(BatchNormalization(epsilon=1e-6,axis=1)) cnn_net.add(MaxPool2D(pool_size=pool_size))cnn_net.add(ZeroPadding2D((1,1))) cnn_net.add(Conv2D(64,kernel_size=(2,2))) cnn_net.add(Activation('relu'))cnn_net.add(BatchNormalization(epsilon=1e-6,axis=1)) cnn_net.add(MaxPool2D(pool_size=pool_size))cnn_net.add(Dropout(0.25)) #Flatten层用来将输入“压平”，即把多维的输入一维化，常用在从卷积层到全连接层的过渡。 cnn_net.add(Flatten()) cnn_net.add(Dense(512))#3168 cnn_net.add(Activation('relu')) cnn_net.add(Dense(1))# cnn_net.add(Activation('sigmoid'))#softmax #summary查看网络结构cnn_net.summary() sgd =SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)##0.0001 cnn_net.compile(loss='binary_crossentropy',optimizer=sgd, metrics=['acc'])#categorical_crossentropy batch_size=32 hist=cnn_net.fit(x_train,y_train,batch_size=batch_size,epochs=60,verbose=1,validation_split=0.2)#50 cnn_net.save("crPlus/cnn_net_bin.h5")plt.plot(hist.history['loss']) plt.plot(hist.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.savefig("crPlus/loss_bin.png") plt.clf() plt.plot(hist.history['acc']) plt.plot(hist.history['val_acc']) plt.title('model acc') plt.ylabel('acc') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.savefig("crPlus/acc_bin.png")

【练习——形状识别（是方还是圆）】