baseline_v2_changeModel（cnn）:score:267.2897

# 2021.05.08
# lightgbm 模型更换成CNN 模型
# 本文原创 望赞鼓励，转载请说明出处.

仍存疑问：
dense层的作用，需要几层
如何来构造CNN

import os
import gc
import math

import pandas as pd
import numpy as np

import lightgbm as lgb
#import xgboost as xgb
from catboost import CatBoostRegressor
from sklearn.linear_model import SGDRegressor, LinearRegression, Ridge
from sklearn.preprocessing import MinMaxScaler


from sklearn.model_selection import StratifiedKFold, KFold
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder

from tqdm import tqdm
import matplotlib.pyplot as plt
import time
import warnings
warnings.filterwarnings('ignore')

train = pd.read_csv('train.csv')
test=pd.read_csv('testA.csv')
train.head()

	id	heartbeat_signals	label
0	0	0.9912297987616655,0.9435330436439665,0.764677...	0.0
1	1	0.9714822034884503,0.9289687459588268,0.572932...	0.0
2	2	1.0,0.9591487564065292,0.7013782792997189,0.23...	2.0
3	3	0.9757952826275774,0.9340884687738161,0.659636...	0.0
4	4	0.0,0.055816398940721094,0.26129357194994196,0...	2.0

def reduce_mem_usage(df):
    start_mem = df.memory_usage().sum() / 1024**2 
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
    
    for col in df.columns:
        col_type = df[col].dtype
        
        if col_type != object:
            c_min = df[col].min()
            c_max = df[col].max()
            if str(col_type)[:3] == 'int':
                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                    df[col] = df[col].astype(np.int8)
                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                    df[col] = df[col].astype(np.int16)
                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                    df[col] = df[col].astype(np.int32)
                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                    df[col] = df[col].astype(np.int64)  
            else:
                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                    df[col] = df[col].astype(np.float16)
                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                    df[col] = df[col].astype(np.float32)
                else:
                    df[col] = df[col].astype(np.float64)
        else:
            df[col] = df[col].astype('category')

    end_mem = df.memory_usage().sum() / 1024**2 
    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))
    
    return df

# 简单预处理
train_list = []

for items in train.values:
    train_list.append([items[0]] + [float(i) for i in items[1].split(',')] + [items[2]])

train = pd.DataFrame(np.array(train_list))
train.columns = ['id'] + ['s_'+str(i) for i in range(len(train_list[0])-2)] + ['label']
train = reduce_mem_usage(train)

test_list=[]
for items in test.values:
    test_list.append([items[0]] + [float(i) for i in items[1].split(',')])

test = pd.DataFrame(np.array(test_list))
test.columns = ['id'] + ['s_'+str(i) for i in range(len(test_list[0])-1)]
test = reduce_mem_usage(test)

Memory usage of dataframe is 157.93 MB
Memory usage after optimization is: 39.67 MB
Decreased by 74.9%
Memory usage of dataframe is 31.43 MB
Memory usage after optimization is: 7.90 MB
Decreased by 74.9%

test

	id	s_0	s_1	s_2	s_3	s_4	s_5	s_6	s_7	s_8	...	s_195	s_196	s_197	s_198	s_199	s_200	s_201	s_202	s_203	s_204
0	100000.0	0.991699	1.000000	0.631836	0.136230	0.041412	0.102722	0.120850	0.123413	0.107910	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
1	100001.0	0.607422	0.541504	0.340576	0.000000	0.090698	0.164917	0.195068	0.168823	0.198853	...	0.389893	0.386963	0.367188	0.364014	0.360596	0.357178	0.350586	0.350586	0.350586	0.36377
2	100002.0	0.975098	0.670898	0.686523	0.708496	0.718750	0.716797	0.720703	0.701660	0.596680	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
3	100003.0	0.995605	0.916992	0.520996	0.000000	0.221802	0.404053	0.490479	0.527344	0.518066	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
4	100004.0	1.000000	0.888184	0.745605	0.531738	0.380371	0.224609	0.091125	0.057648	0.003914	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
19995	119995.0	1.000000	0.833008	0.634277	0.639160	0.624023	0.598145	0.613770	0.624023	0.628906	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
19996	119996.0	1.000000	0.826172	0.452148	0.082214	0.000000	0.137085	0.201050	0.165649	0.158081	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
19997	119997.0	0.951660	0.916504	0.667480	0.352051	0.255371	0.197388	0.173584	0.141968	0.134521	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
19998	119998.0	0.927734	0.677246	0.242920	0.055359	0.102112	0.072266	0.021011	0.038300	0.048553	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
19999	119999.0	0.665527	0.526855	0.516602	0.376465	0.489258	0.480713	0.459229	0.482910	0.469971	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000

20000 rows × 206 columns

 #删除表中的某一行或者某一列更明智的方法是使用drop，它不改变原有的df中的数据，而是返回另一个dataframe来存放删除后的数据。
#就是新开一个表 
#drop函数默认删除行，列需要加axis = 1
x_train = train.drop(['id','label'], axis=1)
y_train = train['label']
x_test=test.drop(['id'], axis=1)

x_train

	s_0	s_1	s_2	s_3	s_4	s_5	s_6	s_7	s_8	s_9	...	s_195	s_196	s_197	s_198	s_199	s_200	s_201	s_202	s_203	s_204
0	0.991211	0.943359	0.764648	0.618652	0.379639	0.190796	0.040222	0.026001	0.031708	0.065552	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.971680	0.929199	0.572754	0.178467	0.122986	0.132324	0.094421	0.089600	0.030487	0.040497	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	1.000000	0.958984	0.701172	0.231812	0.000000	0.080688	0.128418	0.187500	0.280762	0.328369	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.975586	0.934082	0.659668	0.249878	0.237061	0.281494	0.249878	0.249878	0.241455	0.230713	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.000000	0.055817	0.261230	0.359863	0.433105	0.453613	0.499023	0.542969	0.616699	0.676758	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
99995	1.000000	0.677734	0.222412	0.257080	0.204712	0.054657	0.026154	0.118164	0.244873	0.328857	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
99996	0.926758	0.906250	0.637207	0.415039	0.374756	0.382568	0.358887	0.341309	0.336426	0.317139	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
99997	0.925781	0.587402	0.633301	0.632324	0.639160	0.614258	0.599121	0.517578	0.403809	0.253174	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
99998	1.000000	0.994629	0.829590	0.458252	0.264160	0.240234	0.213745	0.189331	0.203857	0.210815	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
99999	0.925781	0.916504	0.404297	0.000000	0.262939	0.385498	0.361084	0.332764	0.339844	0.350586	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

100000 rows × 205 columns

x_test

	s_0	s_1	s_2	s_3	s_4	s_5	s_6	s_7	s_8	s_9	...	s_195	s_196	s_197	s_198	s_199	s_200	s_201	s_202	s_203	s_204
0	0.991699	1.000000	0.631836	0.136230	0.041412	0.102722	0.120850	0.123413	0.107910	0.110535	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
1	0.607422	0.541504	0.340576	0.000000	0.090698	0.164917	0.195068	0.168823	0.198853	0.153564	...	0.389893	0.386963	0.367188	0.364014	0.360596	0.357178	0.350586	0.350586	0.350586	0.36377
2	0.975098	0.670898	0.686523	0.708496	0.718750	0.716797	0.720703	0.701660	0.596680	0.487061	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
3	0.995605	0.916992	0.520996	0.000000	0.221802	0.404053	0.490479	0.527344	0.518066	0.545410	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
4	1.000000	0.888184	0.745605	0.531738	0.380371	0.224609	0.091125	0.057648	0.003914	0.007820	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
19995	1.000000	0.833008	0.634277	0.639160	0.624023	0.598145	0.613770	0.624023	0.628906	0.624023	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
19996	1.000000	0.826172	0.452148	0.082214	0.000000	0.137085	0.201050	0.165649	0.158081	0.165649	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
19997	0.951660	0.916504	0.667480	0.352051	0.255371	0.197388	0.173584	0.141968	0.134521	0.127075	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
19998	0.927734	0.677246	0.242920	0.055359	0.102112	0.072266	0.021011	0.038300	0.048553	0.017532	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
19999	0.665527	0.526855	0.516602	0.376465	0.489258	0.480713	0.459229	0.482910	0.469971	0.399170	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000

20000 rows × 205 columns

y_train

0        0.0
1        0.0
2        2.0
3        0.0
4        2.0
        ... 
99995    0.0
99996    2.0
99997    3.0
99998    2.0
99999    0.0
Name: label, Length: 100000, dtype: float16

# from keras.utils.np_utils import to_categorical
# y_train = to_categorical(y_train)

# y_train

# loss函数
def abs_sum(y_pre,y_tru):
    y_pre=np.array(y_pre)
    y_tru=np.array(y_tru)
    loss=sum(sum(abs(y_pre-y_tru)))
    return loss

from keras.models import Sequential
from keras.layers import Dense # for fully connected layers dense will be used
from keras.layers import Conv1D, MaxPooling1D, Flatten
from keras.optimizers import Adam

# avoid overfitting by normalizing the samples
from keras.layers.normalization import BatchNormalization

# cnn
def build_model():
    model = Sequential()
    

    #过滤器=神经元总数中的单位
    #Padding='相同'，零填充，在输入数据周围添加零像素
    model.add(Conv1D(filters = 64, kernel_size = 6, activation='relu', padding = 'same', input_shape = (205, 1))) #we pass individual values hence not 100000,187,1
    #：（none,205,64）
    
    # Normalization to avoid overfitting
    # 大概就是保持数据的敏感 ，加在全连接函数和激励函数之间：https://www.bilibili.com/video/BV1Lx411j7GT?from=search&seid=5048435414489430319
    model.add(BatchNormalization())
    # #：（none,205,64）#只是把数据重初始化一下，也就是重新分布了一下，为了使得这些数在下面的激励函数中区分的更大
    
    # Pooling 
    model.add(MaxPooling1D(pool_size=(3), strides = (2), padding = 'same'))
    # ：（none,103,64）# 因为strides是2 一下跳两步 padding=“same" 所以变化前特征总数不变 那么输出就是187/2=94

    model.add(Conv1D(filters = 64, kernel_size = 6, activation='relu', padding = 'same'))# （none, 103, 64）
    model.add(BatchNormalization())                                                      #:（none, 103, 64）
    model.add(MaxPooling1D(pool_size=(3), strides = (2), padding = 'same'))              #:（none, 52, 64）

    model.add(Conv1D( filters = 64, kernel_size = 6, activation='relu', padding = 'same'))#:（none, 52, 64）
    model.add(BatchNormalization())                                                       #:（none, 52, 64）
    model.add(MaxPooling1D(pool_size=(3), strides = (2), padding = 'same'))               #:（none, 26, 64）

    # Flatten 
    model.add(Flatten())
    #:(none, 1664) 这一步就是把多维的数据铺平 26*64 = 1664

    # Fully connected layer
    # input layer
    #Dense（用来写输出层）
    #当输入序列的长度固定时，该值为其长度。如果要在该层后接Flatten层，然后接Dense层，则必须指定该参数，否则Dense层的输出维度无法自动推断。
    #units：大于0的整数，代表该层的输出维度。
    model.add(Dense(units = 64, activation='relu'))
    
    # Hidden Layer
    model.add(Dense(units = 64, activation='relu'))
    
    # Output Layer
    model.add(Dense(units = 4, activation='softmax'))

    # loss = 'categorical_crossentropy'
    model.compile(optimizer = 'Adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
    return model

model = build_model()
# This is for one sample, i.e. one row
model.summary()

Model: “sequential”
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d (Conv1D) (None, 205, 64) 448
_________________________________________________________________
batch_normalization (BatchNo (None, 205, 64) 256
_________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 103, 64) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 103, 64) 24640
_________________________________________________________________
batch_normalization_1 (Batch (None, 103, 64) 256
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 52, 64) 0
_________________________________________________________________
conv1d_2 (Conv1D) (None, 52, 64) 24640
_________________________________________________________________
batch_normalization_2 (Batch (None, 52, 64) 256
_________________________________________________________________
max_pooling1d_2 (MaxPooling1 (None, 26, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 1664) 0
_________________________________________________________________
dense (Dense) (None, 64) 106560
_________________________________________________________________
dense_1 (Dense) (None, 64) 4160
_________________________________________________________________
dense_2 (Dense) (None, 4) 260
=================================================================
Total params: 161,476
Trainable params: 161,092
Non-trainable params: 384
_________________________________________________________________

from keras.utils.np_utils import to_categorical

def cv_model(clf, train_x, train_y, test_x, clf_name):
    folds = 5
    seed = 2021
    
    #k-交叉验证KFold
    # n_split:要划分的折数
    #shuffle: 每次都进行shuffle，测试集中折数的总和就是训练集的个数
    #random_state:随机状态 总结：对于那些本质上是随机的过程，我们有必要控制随机的状态，这样才能重复的展现相同的结果。
    #如果，对随机状态不加控制，那么实验的结果就无法固定，而是随机的显现。比喻的说一下，也不知道准不准确。
    # 一个容器中放置一定量的沙子，每次用手去抓沙子的时候，抓取的结果会受到抓取的力度、一只手抓还是两只手抓、手是干的或湿的等诸多因素的影响（将影响因素定为A={a,b,c,d,e,f,……}）。
    #固定random_state后，每次抓取沙子时的影响因素将被固定为具体的某一组，这样每次抓取的沙子就具有了相同的状态。

    
    kf = KFold(n_splits=folds, shuffle=True, random_state=seed)
    #为预测做准备
    test = np.zeros((test_x.shape[0],4))
    test_x = test_x.iloc[:, :].values
    test_x = test_x.reshape(len(test_x),test_x.shape[1],1)    

    cv_scores = []
    

    onehot_encoder = OneHotEncoder(sparse=False)
    

    for i, (train_index, test_index) in enumerate(kf.split(x_train, y_train)):
        
        print('************************************ {} ************************************'.format(str(i+1)))
        
        x_kf_train, y_kf_train, x_kf_test, y_kf_test = x_train.iloc[train_index], y_train[train_index], x_train.iloc[test_index], y_train[test_index]

        
        if clf_name == "cnn":
            x_kf_train = x_kf_train.iloc[:, :].values
            x_kf_test = x_kf_test.iloc[:, :].values
            x_kf_train = x_kf_train.reshape(len(x_kf_train),x_kf_train.shape[1],1)
            x_kf_test = x_kf_test.reshape(len(x_kf_test),x_kf_test.shape[1],1)
            
            
            y_kf_train = to_categorical(y_kf_train)
            y_kf_test = to_categorical(y_kf_test)
            
            history = model.fit(x_kf_train,y_kf_train, epochs = 15, batch_size = 32, validation_data=(x_kf_test, y_kf_test))
            x_kf_test_pred = model.predict(x_kf_test)
            test_pred = model.predict(test_x)
            
    
      
            
        
        print("y_kf_test++++++:")  
        print(y_kf_test)

        print('预测的概率矩阵为test_pred：')
        print(test_pred)
        print("abs_sum++++++:")
        score=abs_sum(y_kf_test,  x_kf_test_pred)
        cv_scores.append(score)
        print("cv_scores+++++:")
        print(cv_scores)
        
#         test += test_pred
#         test=test/kf.n_splits
        # 将将要预测的带到

            


    return test_pred

# folds = 5
# seed = 2021
# kf = KFold(n_splits=folds, shuffle=True, random_state=seed)

# # test = np.zeros((X_text.shape[0],4))
# cv_scores = []

# onehot_encoder = OneHotEncoder(sparse=False)

# for i, (train_index, valid_index) in enumerate(kf.split(x_train, y_train)):
        
#         print('************************************ {} ************************************'.format(str(i+1)))
        
#         # .iloc 提取的某一行，i取值就是【1，folds】，
#         # 其实就是去了四块内容，训练集(trn_)的x 训练集的y 测试集(valid_)x 测试集y
#         trn_x, trn_y, val_x, val_y = x_train[train_index], y_train[train_index], x_train[valid_index], y_train[valid_index]
        
#         ohe = OneHotEncoder()
#         trn_y = ohe.fit_transform(trn_y.values.reshape(-1,1))
#         val_y = ohe.transform(val_y.values.reshape(-1,1))
#         trn_x =trn_x.values.reshape(len(trn_x),trn_x.values.shape[1],1)
#         val_x = val_x.values.reshape(len(val_x),val_x.values.shape[1],1)
        
#         history = model.fit(trn_x,trn_y, epochs = 15, batch_size = 32, validation_data=(val_x,  val_y))
#         model.evaluate(trn_x, trn_y)

def lgb_model(x_train, y_train, x_test):
    lgb_test = cv_model(lgb, x_train, y_train, x_test, "cnn")
    return lgb_test
lgb_test = lgb_model(x_train, y_train, x_test)

************************************ 1 ************************************
Epoch 1/15
2500/2500 [==============================] - 89s 35ms/step - loss: 0.1964 - accuracy: 0.9370 - val_loss: 0.1091 - val_accuracy: 0.9664
Epoch 2/15
2500/2500 [==============================] - 93s 37ms/step - loss: 0.0664 - accuracy: 0.9800 - val_loss: 0.0643 - val_accuracy: 0.9805
Epoch 3/15
2500/2500 [==============================] - 108s 43ms/step - loss: 0.0505 - accuracy: 0.9834 - val_loss: 0.0625 - val_accuracy: 0.9823
Epoch 4/15
2500/2500 [==============================] - 111s 44ms/step - loss: 0.0363 - accuracy: 0.9884 - val_loss: 0.0549 - val_accuracy: 0.9809
Epoch 5/15
2500/2500 [==============================] - 108s 43ms/step - loss: 0.0325 - accuracy: 0.9897 - val_loss: 0.0411 - val_accuracy: 0.9883
Epoch 6/15
2500/2500 [==============================] - 107s 43ms/step - loss: 0.0246 - accuracy: 0.9921 - val_loss: 0.0623 - val_accuracy: 0.9807
Epoch 7/15
2500/2500 [==============================] - 110s 44ms/step - loss: 0.0260 - accuracy: 0.9919 - val_loss: 0.0427 - val_accuracy: 0.9879
Epoch 8/15
2500/2500 [==============================] - 99s 40ms/step - loss: 0.0184 - accuracy: 0.9941 - val_loss: 0.0471 - val_accuracy: 0.9865
Epoch 9/15
2500/2500 [==============================] - 100s 40ms/step - loss: 0.0185 - accuracy: 0.9942 - val_loss: 0.0419 - val_accuracy: 0.9885
Epoch 10/15
2500/2500 [==============================] - 101s 40ms/step - loss: 0.0173 - accuracy: 0.9943 - val_loss: 0.0379 - val_accuracy: 0.9901
Epoch 11/15
2500/2500 [==============================] - 102s 41ms/step - loss: 0.0141 - accuracy: 0.9958 - val_loss: 0.0404 - val_accuracy: 0.9900
Epoch 12/15
2500/2500 [==============================] - 99s 40ms/step - loss: 0.0127 - accuracy: 0.9960 - val_loss: 0.0385 - val_accuracy: 0.9900
Epoch 13/15
2500/2500 [==============================] - 96s 39ms/step - loss: 0.0128 - accuracy: 0.9957 - val_loss: 0.0440 - val_accuracy: 0.9908
Epoch 14/15
2500/2500 [==============================] - 103s 41ms/step - loss: 0.0111 - accuracy: 0.9962 - val_loss: 0.0438 - val_accuracy: 0.9902
Epoch 15/15
2500/2500 [==============================] - 100s 40ms/step - loss: 0.0098 - accuracy: 0.9967 - val_loss: 0.0379 - val_accuracy: 0.9907
y_kf_test++++++:
[[0. 0. 1. 0.]
 [1. 0. 0. 0.]
 [0. 0. 1. 0.]
 ...
 [1. 0. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 1. 0.]]
预测的概率矩阵为test_pred：
[[9.99999881e-01 1.23368892e-07 6.21502979e-12 4.32745534e-10]
 [7.47138074e-06 1.64497091e-04 9.99590218e-01 2.37837623e-04]
 [1.05034046e-11 1.90382871e-16 1.06615223e-08 1.00000000e+00]
 ...
 [1.77340873e-03 1.38662233e-06 9.98224914e-01 2.29101516e-07]
 [9.99994159e-01 5.82347275e-06 2.35584338e-12 1.23620975e-10]
 [9.99747932e-01 1.21477584e-04 2.38122061e-06 1.28281055e-04]]
abs_sum++++++:
cv_scores+++++:
[473.23671531677246]
************************************ 2 ************************************
Epoch 1/15
2500/2500 [==============================] - 93s 37ms/step - loss: 0.0187 - accuracy: 0.9948 - val_loss: 0.0102 - val_accuracy: 0.9966
Epoch 2/15
2500/2500 [==============================] - 103s 41ms/step - loss: 0.0122 - accuracy: 0.9961 - val_loss: 0.0097 - val_accuracy: 0.9966
Epoch 3/15
2500/2500 [==============================] - 97s 39ms/step - loss: 0.0122 - accuracy: 0.9958 - val_loss: 0.0206 - val_accuracy: 0.9940
Epoch 4/15
2500/2500 [==============================] - 91s 37ms/step - loss: 0.0116 - accuracy: 0.9963 - val_loss: 0.0133 - val_accuracy: 0.9960
Epoch 5/15
2500/2500 [==============================] - 103s 41ms/step - loss: 0.0098 - accuracy: 0.9969 - val_loss: 0.0182 - val_accuracy: 0.9953
Epoch 6/15
2500/2500 [==============================] - 96s 39ms/step - loss: 0.0087 - accuracy: 0.9971 - val_loss: 0.0145 - val_accuracy: 0.9952
Epoch 7/15
2500/2500 [==============================] - 95s 38ms/step - loss: 0.0074 - accuracy: 0.9978 - val_loss: 0.0347 - val_accuracy: 0.9913
Epoch 8/15
2500/2500 [==============================] - 103s 41ms/step - loss: 0.0088 - accuracy: 0.9972 - val_loss: 0.0179 - val_accuracy: 0.9956
Epoch 9/15
2500/2500 [==============================] - 90s 36ms/step - loss: 0.0082 - accuracy: 0.9976 - val_loss: 0.0256 - val_accuracy: 0.9941
Epoch 10/15
2500/2500 [==============================] - 104s 41ms/step - loss: 0.0073 - accuracy: 0.9979 - val_loss: 0.0212 - val_accuracy: 0.9944
Epoch 11/15
2500/2500 [==============================] - 100s 40ms/step - loss: 0.0067 - accuracy: 0.9980 - val_loss: 0.0263 - val_accuracy: 0.9926
Epoch 12/15
2500/2500 [==============================] - 87s 35ms/step - loss: 0.0073 - accuracy: 0.9977 - val_loss: 0.0159 - val_accuracy: 0.9960
Epoch 13/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0069 - accuracy: 0.9981 - val_loss: 0.0376 - val_accuracy: 0.9902
Epoch 14/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0060 - accuracy: 0.9982 - val_loss: 0.0246 - val_accuracy: 0.9942
Epoch 15/15
2500/2500 [==============================] - 82s 33ms/step - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.0292 - val_accuracy: 0.9940
y_kf_test++++++:
[[0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 1.]
 ...
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
预测的概率矩阵为test_pred：
[[1.0000000e+00 3.1578247e-11 8.9162606e-17 9.0605463e-16]
 [7.5351809e-26 4.6311908e-30 1.0000000e+00 2.6168691e-38]
 [1.3659213e-14 1.1359105e-21 7.8721543e-11 1.0000000e+00]
 ...
 [1.6125210e-04 1.5620843e-05 9.9982315e-01 1.6944726e-10]
 [1.0000000e+00 6.7933081e-10 7.0405877e-13 2.2816355e-14]
 [9.9999905e-01 4.4103444e-07 9.4723184e-08 4.0850134e-07]]
abs_sum++++++:
cv_scores+++++:
[473.23671531677246, 290.0407085418701]
************************************ 3 ************************************
Epoch 1/15
2500/2500 [==============================] - 86s 34ms/step - loss: 0.0106 - accuracy: 0.9969 - val_loss: 0.0042 - val_accuracy: 0.9987
Epoch 2/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0067 - accuracy: 0.9981 - val_loss: 0.0051 - val_accuracy: 0.9985
Epoch 3/15
2500/2500 [==============================] - 88s 35ms/step - loss: 0.0068 - accuracy: 0.9979 - val_loss: 0.0072 - val_accuracy: 0.9981
Epoch 4/15
2500/2500 [==============================] - 85s 34ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.0107 - val_accuracy: 0.9962
Epoch 5/15
2500/2500 [==============================] - 84s 33ms/step - loss: 0.0068 - accuracy: 0.9980 - val_loss: 0.0065 - val_accuracy: 0.9977
Epoch 6/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0057 - accuracy: 0.9981 - val_loss: 0.0177 - val_accuracy: 0.9950
Epoch 7/15
2500/2500 [==============================] - 85s 34ms/step - loss: 0.0058 - accuracy: 0.9984 - val_loss: 0.0072 - val_accuracy: 0.9980
Epoch 8/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0056 - accuracy: 0.9984 - val_loss: 0.0077 - val_accuracy: 0.9977
Epoch 9/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0040 - accuracy: 0.9986 - val_loss: 0.0161 - val_accuracy: 0.9967
Epoch 10/15
2500/2500 [==============================] - 86s 34ms/step - loss: 0.0059 - accuracy: 0.9983 - val_loss: 0.0135 - val_accuracy: 0.9963
Epoch 11/15
2500/2500 [==============================] - 85s 34ms/step - loss: 0.0057 - accuracy: 0.9984 - val_loss: 0.0137 - val_accuracy: 0.9967
Epoch 12/15
2500/2500 [==============================] - 85s 34ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.0100 - val_accuracy: 0.9974
Epoch 13/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0052 - accuracy: 0.9985 - val_loss: 0.0145 - val_accuracy: 0.9966
Epoch 14/15
2500/2500 [==============================] - 85s 34ms/step - loss: 0.0044 - accuracy: 0.9987 - val_loss: 0.0199 - val_accuracy: 0.9956
Epoch 15/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0053 - accuracy: 0.9987 - val_loss: 0.0189 - val_accuracy: 0.9955
y_kf_test++++++:
[[1. 0. 0. 0.]
 [0. 0. 1. 0.]
 [1. 0. 0. 0.]
 ...
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [1. 0. 0. 0.]]
预测的概率矩阵为test_pred：
[[1.0000000e+00 3.4608899e-11 6.7611266e-16 1.6289031e-16]
 [3.2447400e-17 3.6295522e-13 1.0000000e+00 1.7152423e-38]
 [2.6966924e-25 2.5888265e-33 4.9505679e-23 1.0000000e+00]
 ...
 [2.8026802e-04 1.5653444e-05 9.9970406e-01 6.4556320e-09]
 [9.9999952e-01 4.2466991e-07 4.3031992e-13 6.4092606e-11]
 [9.9703240e-01 2.8589300e-03 1.0849427e-04 6.7704690e-08]]
abs_sum++++++:
cv_scores+++++:
[473.23671531677246, 290.0407085418701, 216.68724060058594]
************************************ 4 ************************************
Epoch 1/15
2500/2500 [==============================] - 82s 33ms/step - loss: 0.0065 - accuracy: 0.9981 - val_loss: 0.0026 - val_accuracy: 0.9990
Epoch 2/15
2500/2500 [==============================] - 85s 34ms/step - loss: 0.0050 - accuracy: 0.9986 - val_loss: 0.0045 - val_accuracy: 0.9987
Epoch 3/15
2500/2500 [==============================] - 84s 33ms/step - loss: 0.0044 - accuracy: 0.9987 - val_loss: 0.0028 - val_accuracy: 0.9989
Epoch 4/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.0108 - val_accuracy: 0.9976
Epoch 5/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.0116 - val_accuracy: 0.9972
Epoch 6/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 0.0087 - val_accuracy: 0.9975
Epoch 7/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0030 - accuracy: 0.9990 - val_loss: 0.0230 - val_accuracy: 0.9944
Epoch 8/15
2500/2500 [==============================] - 82s 33ms/step - loss: 0.0052 - accuracy: 0.9987 - val_loss: 0.0126 - val_accuracy: 0.9963
Epoch 9/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0049 - accuracy: 0.9987 - val_loss: 0.0222 - val_accuracy: 0.9937
Epoch 10/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 0.0134 - val_accuracy: 0.9966
Epoch 11/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.0124 - val_accuracy: 0.9967
Epoch 12/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0046 - accuracy: 0.9989 - val_loss: 0.0068 - val_accuracy: 0.9976
Epoch 13/15
2500/2500 [==============================] - 84s 33ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.0149 - val_accuracy: 0.9965
Epoch 14/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 0.0086 - val_accuracy: 0.9979
Epoch 15/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.0172 - val_accuracy: 0.9952
y_kf_test++++++:
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 ...
 [0. 0. 0. 1.]
 [1. 0. 0. 0.]
 [0. 1. 0. 0.]]
预测的概率矩阵为test_pred：
[[1.0000000e+00 1.0942575e-12 3.9219293e-17 9.0291727e-18]
 [1.8889039e-13 1.7051572e-07 9.9999988e-01 8.8278994e-23]
 [1.4202803e-20 5.8440978e-34 1.3529702e-17 1.0000000e+00]
 ...
 [3.3775454e-05 2.1584659e-04 9.9975032e-01 4.9212700e-12]
 [9.9991751e-01 6.5086162e-05 2.6754990e-06 1.4732053e-05]
 [9.9999952e-01 8.1493896e-09 4.8628596e-07 3.6478176e-09]]
abs_sum++++++:
cv_scores+++++:
[473.23671531677246, 290.0407085418701, 216.68724060058594, 255.52966451644897]
************************************ 5 ************************************
Epoch 1/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0068 - accuracy: 0.9983 - val_loss: 0.0020 - val_accuracy: 0.9994
Epoch 2/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0041 - accuracy: 0.9990 - val_loss: 0.0135 - val_accuracy: 0.9972
Epoch 3/15
2500/2500 [==============================] - 84s 33ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 0.0023 - val_accuracy: 0.9995
Epoch 4/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0044 - accuracy: 0.9987 - val_loss: 0.0044 - val_accuracy: 0.9987
Epoch 5/15
2500/2500 [==============================] - 84s 33ms/step - loss: 0.0037 - accuracy: 0.9990 - val_loss: 0.0044 - val_accuracy: 0.9989
Epoch 6/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0033 - accuracy: 0.9991 - val_loss: 0.0128 - val_accuracy: 0.9973
Epoch 7/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0042 - accuracy: 0.9989 - val_loss: 0.0045 - val_accuracy: 0.9988
Epoch 8/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0041 - accuracy: 0.9990 - val_loss: 0.0029 - val_accuracy: 0.9988
Epoch 9/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 0.0089 - val_accuracy: 0.9969
Epoch 10/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 0.0076 - val_accuracy: 0.9974
Epoch 11/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 0.0062 - val_accuracy: 0.9981
Epoch 12/15
2500/2500 [==============================] - 84s 33ms/step - loss: 0.0041 - accuracy: 0.9988 - val_loss: 0.0058 - val_accuracy: 0.9981
Epoch 13/15
2500/2500 [==============================] - 84s 34ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.0087 - val_accuracy: 0.9975
Epoch 14/15
2500/2500 [==============================] - 85s 34ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.0033 - val_accuracy: 0.9990
Epoch 15/15
2500/2500 [==============================] - 83s 33ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.0074 - val_accuracy: 0.9981
y_kf_test++++++:
[[0. 0. 1. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 ...
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]]
预测的概率矩阵为test_pred：
[[1.0000000e+00 2.9956503e-13 1.9854391e-16 2.4101917e-17]
 [7.2484188e-19 1.7757707e-14 1.0000000e+00 0.0000000e+00]
 [2.4454344e-29 0.0000000e+00 1.4663728e-33 1.0000000e+00]
 ...
 [8.1580965e-06 1.2090248e-04 9.9987090e-01 1.8680077e-12]
 [1.0000000e+00 4.4534781e-10 1.6605388e-13 7.2103205e-15]
 [1.0000000e+00 1.0435092e-11 6.3772593e-13 2.8722074e-14]]
abs_sum++++++:
cv_scores+++++:
[473.23671531677246, 290.0407085418701, 216.68724060058594, 255.52966451644897, 83.4570825099945]

temp=pd.DataFrame(lgb_test)
result=pd.read_csv('sample_submit.csv')
result['label_0']=temp[0]
result['label_1']=temp[1]
result['label_2']=temp[2]
result['label_3']=temp[3]
result.to_csv('submit_baseline_v2.3.csv',index=False)

submit_data=pd.read_csv('submit_baseline_v2.3.csv')

submit_data

	id	label_0	label_1	label_2	label_3
0	100000	1.000000e+00	2.995650e-13	1.985439e-16	2.410192e-17
1	100001	7.248419e-19	1.775771e-14	1.000000e+00	0.000000e+00
2	100002	2.445434e-29	0.000000e+00	1.466373e-33	1.000000e+00
3	100003	1.000000e+00	1.637765e-21	2.459309e-21	1.862687e-24
4	100004	1.000000e+00	3.080988e-10	6.932140e-15	5.327876e-19
...	...	...	...	...	...
19995	119995	9.999999e-01	6.929825e-08	2.932834e-08	4.951478e-10
19996	119996	1.000000e+00	4.902514e-08	2.717561e-08	6.707961e-12
19997	119997	8.158096e-06	1.209025e-04	9.998709e-01	1.868008e-12
19998	119998	1.000000e+00	4.453478e-10	1.660539e-13	7.210321e-15
19999	119999	1.000000e+00	1.043509e-11	6.377259e-13	2.872207e-14

20000 rows × 5 columns

for index,row in submit_data.iterrows():
    row_max = max(list(row)[1:])
    if row_max > 0.9:
        for i in range(1,5):
            if row[i]>0.9:
                submit_data.iloc[index,i] = 1
            else:
                submit_data.iloc[index,i] = 0
submit_data

	id	label_0	label_1	label_2	label_3
0	100000	1.0	0.0	0.0	0.0
1	100001	0.0	0.0	1.0	0.0
2	100002	0.0	0.0	0.0	1.0
3	100003	1.0	0.0	0.0	0.0
4	100004	1.0	0.0	0.0	0.0
...	...	...	...	...	...
19995	119995	1.0	0.0	0.0	0.0
19996	119996	1.0	0.0	0.0	0.0
19997	119997	0.0	0.0	1.0	0.0
19998	119998	1.0	0.0	0.0	0.0
19999	119999	1.0	0.0	0.0	0.0

20000 rows × 5 columns

submit_data.to_csv('submit_baseline_v2.3.1.csv',index=False)

标签：loss,15,baseline,val,step,v2,CNN,2500,accuracy
来源： https://blog.csdn.net/wistonty11/article/details/116520535