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？我怎样才能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 有没有其他方法可以做到这一点？数据集链接：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

这是我达到 100% 测试准确率的结构：

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.

如果您看到您收到的准确度分数令您满意，我会尝试添加 1 或 2 个更密集的隐藏层，其中包含 10 到 40 个节点。 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 表示正类（记录是 CKD 患者的记录），0 表示负类。 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，您的数据预处理似乎有问题 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可以帮助您更多的官方文档：tensorflow Documentation on feature columns