如何设计神经网络？

Question

我试图建立一个深度学习预测模型来预测一个人是否是 CKD 患者。你能告诉我吗？我如何为它设计一个神经网络？我应该在每一层中添加多少个神经元？或者 Keras 中还有其他方法可以这样做吗？数据集link：https://github.com/Samar-080301/Python_Project/blob/master/ckd_full.csv

import tensorflow as tf
from tensorflow import keras
import pandas as pd
from sklearn.model_selection import train_test_split
import os
from matplotlib import pyplot as plt
os.chdir(r'C:\Users\samar\OneDrive\desktop\projects\Chronic_Kidney_Disease')
os.getcwd()
x=pd.read_csv('ckd_full.csv')
y=x[['class']]
y['class']=y['class'].replace(to_replace=(r'ckd',r'notckd'), value=(1,0))
x=x.drop(columns=['class'])
x['rbc']=x['rbc'].replace(to_replace=(r'normal',r'abnormal'), value=(1,0))
x['pcc']=x['pcc'].replace(to_replace=(r'present',r'notpresent'), value=(1,0))
x['ba']=x['ba'].replace(to_replace=(r'present',r'notpresent'), value=(1,0))
x['pc']=x['pc'].replace(to_replace=(r'normal',r'abnormal'), value=(1,0))
x['htn']=x['htn'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['dm']=x['dm'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['cad']=x['cad'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['pe']=x['pe'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['ane']=x['ane'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['appet']=x['appet'].replace(to_replace=(r'good',r'poor'), value=(1,0))
x[x=="?"]=np.nan
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.01)
#begin the model
model=keras.models.Sequential()
model.add(keras.layers.Dense(128,input_dim = 24, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu)) # adding a layer with 128 nodes and relu activaation function
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu)) # adding a layer with 128 nodes and relu activaation function
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu)) # adding a layer with 128 nodes and relu activaation function
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu)) # adding a layer with 128 nodes and relu activaation function 
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu)) # adding a layer with 128 nodes and relu activaation function
model.add(tf.keras.layers.Dense(2,activation=tf.nn.softmax)) # adding a layer with 2 nodes and softmax activaation function
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # specifiying hyperparameters
model.fit(xtrain,ytrain,epochs=5) # load the model
model.save('Nephrologist') # save the model with a unique name
myModel=tf.keras.models.load_model('Nephrologist')  # make an object of the model
prediction=myModel.predict((xtest))



     C:\Users\samar\anaconda3\lib\site-packages\ipykernel_launcher.py:12: SettingWithCopyWarning: 
    A value is trying to be set on a copy of a slice from a DataFrame.
    Try using .loc[row_indexer,col_indexer] = value instead
    
    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
      if sys.path[0] == '':
Epoch 1/5
396/396 [==============================] - 0s 969us/sample - loss: nan - acc: 0.3561
Epoch 2/5
396/396 [==============================] - 0s 343us/sample - loss: nan - acc: 0.3763
Epoch 3/5
396/396 [==============================] - 0s 323us/sample - loss: nan - acc: 0.3763
Epoch 4/5
396/396 [==============================] - 0s 283us/sample - loss: nan - acc: 0.3763
Epoch 5/5
396/396 [==============================] - 0s 303us/sample - loss: nan - acc: 0.3763

Answer 1

这是我通过以下方式实现 100% 测试准确度的结构：

model=keras.models.Sequential()
model.add(keras.layers.Dense(200,input_dim = 24, activation=tf.nn.tanh))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # specifiying hyperparameters

xtrain_tensor = tf.convert_to_tensor(xtrain, dtype=tf.float32)
ytrain_tensor = tf.convert_to_tensor(ytrain, dtype=tf.float32)
model.fit(xtrain_tensor , ytrain_tensor , epochs=500, batch_size=128, validation_split = 0.15, shuffle=True, verbose=2) # load the model
results = model.evaluate(xtest, ytest, batch_size=128)

输出：

3/3 - 0s - loss: 0.2560 - accuracy: 0.9412 - val_loss: 0.2227 - val_accuracy: 0.9815
Epoch 500/500
3/3 - 0s - loss: 0.2225 - accuracy: 0.9673 - val_loss: 0.2224 - val_accuracy: 0.9815
1/1 [==============================] - 0s 0s/step - loss: 0.1871 - accuracy: 1.0000

最后一行表示模型在测试数据集上的评估。似乎它概括得很好:)

-------------------------------------------- ---- 下面的原始答案---------------------------------------- ---------- 我会先使用逻辑回归模型，看看您的数据集是否有任何预测价值。

model=keras.models.Sequential()
model.add(keras.layers.Dense(1,input_dim = 24, activation=tf.nn.sigmoid))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # specifiying hyperparameters
model.fit(xtrain,ytrain,epochs=100) # Might require more or less epoches. It depends on the amount of noise in your dataset.

如果你看到你得到一个令你满意的准确度分数，我会试一试并添加 1 或 2 个更密集的隐藏层，节点数在 10 到 40 之间。值得一提的是，我的建议完全基于我的经验。

我强烈（!!!!）建议将 y_label 转换为二进制值，其中 1 表示阳性 class（一条记录是 CKD 患者的记录），0 表示否定 class。让我知道它是否有效，如果无效，我也会尝试使用您的数据集。

Answer 2

显然你的数据预处理有问题你可以使用

df.fillna('ffill')

您还可以使用特征列来完成那些长任务示例：

CATEGORICAL_COLUMNS = ['columns','which have','categorical data','like sex']
NUMERIC_COLUMNS = ['columns which have','numeric data']
feature_column =[]
for items in CATEGORICAL_COLUMNS:
    feature_column.append( tf.feature_clolumns.categorical_columns_with_vocavulary_list(items, df[items].unique()))

for items in NUMERIC_COLUMNS:
    feature_column.append( tf.feature_clolumns.numeric_columns(items, df[items].unique()))

现在您可以使用这些特征列对您的模型进行预测，这将更准确更多可以在数据预处理中完成这里是官方文档，可以为您提供更多帮助：tensorflow Documentation on feature columns

如何设计神经网络？

How to Design the Neural Network?

classification

machine-learning

neural-network

deep-learning

keras