Keras多層感知器識別手寫數字

2021-08-28 18:55:20 字數 3078 閱讀 6783

from keras.utils import np_utils


import numpy as np


np.random.seed(10);


from keras.datasets import mnist


(x_train_image,y_train_label),(x_test_image,y_test_label)=mnist.load_data();


# 特徵值進行reshape


x_train=x_train_image.reshape(60000,784).astype('float32');


x_test=x_test_image.reshape(10000,784).astype('float32');


# 將特徵值進行標準化


x_train_normalize=x_train/255;


x_test_normalize=x_test/255;


# 將標籤進行onehot編碼


y_train_onehot=np_utils.to_categorical(y_train_label);


y_test_onehot=np_utils.to_categorical(y_test_label);


# 建立模型


from keras.models import sequential


from keras.layers import dense


from keras.layers import dropout


model=sequential();


model.add(dense(units=1000,input_dim=784,kernel_initializer='normal',activation='relu'));


model.add(dropout(0.5));


model.add(dense(units=10,kernel_initializer='normal',activation='softmax'));


# 建立模型的摘要


print(model.summary());


# 建立訓練


model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy']);


# train_history=model.fit(x=x_train_normalize,y=y_train_onehot,validation_split=0,epochs=10,batch_size=200,verbose=1,validation_data=[x_test_normalize,y_test_onehot],shuffle=true);


train_history=model.fit(x=x_train_normalize,y=y_train_onehot,validation_split=0.2,epochs=10,batch_size=200,verbose=1,shuffle=true);


# 建立show_train_history顯示過程


import matplotlib.pyplot as plt


def show_train_history(train_history,train,validation):


plt.plot(train_history.history[train]);


plt.plot(train_history.history[validation]);


plt.title("train history");


plt.ylabel(train);


plt.xlabel('epoch');


plt.legend(['train','validation'],loc='upper left');


plt.show();


# show_train_history(train_history,'acc','val_acc');


# 測試資料評估模型準確率


scores=model.evaluate(x_test_normalize,y_test_onehot);


print("accuracy=",scores[1]);


# 進行**


prediction=model.predict_classes(x_test)


# 視覺化


def plot_images_labels_prediction(images,labels,prediction,idx,num=10):


#     設定顯示圖形大小


fig=plt.gcf();


fig.set_size_inches(12,14);


#     如果顯示項數引數大於25，就設定為25


if num>25:


num=25;


for i in range(0,num):


ax=plt.subplot(5,5,i+1);


ax.imshow(images[idx],cmap='binary');


title="label="+str(labels[idx]);


if len(prediction)>0:


title+=",predict="+str(prediction[idx]);


ax.set_title(title,fontsize=10);


#         設定不顯示刻度


ax.set_xticks();


ax.set_yticks();


idx+=1;


plt.show();


plot_images_labels_prediction(x_test_image,y_test_label,prediction,idx=340);


# 利用pandas建立混淆矩陣


import pandas as pd


error_metric=pd.crosstab(y_test_label,prediction,rownames=['label'],colnames=['predict']);


print(error_metric);


df=pd.dataframe();


print(df[(df.label==5)&(df.predict==3)]);

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