[英]Using a trained Keras model to make predictions on new csv data
所以我正在做一个项目,基本上我必须预测房价是高于还是低于其中位数价格,为此,我使用来自 Kaggle 的这个数据集( https://drive.google.com/文件/d/1GfvKA0qznNVknghV4botnNxyH-KvDOC/视图)。 1 表示“高于中位数”,0 表示“低于中位数”。 我编写了这段代码来训练神经网络并将其保存为 .h5 文件:
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense
import h5py
df = pd.read_csv('housepricedata.csv')
dataset = df.values
X = dataset[:,0:10]
Y = dataset[:,10]
min_max_scaler = preprocessing.MinMaxScaler()
X_scale = min_max_scaler.fit_transform(X)
X_train, X_val_and_test, Y_train, Y_val_and_test = train_test_split(X_scale, Y, test_size=0.3)
X_val, X_test, Y_val, Y_test = train_test_split(X_val_and_test, Y_val_and_test, test_size=0.5)
model = Sequential([
Dense(32, activation='relu', input_shape=(10,)),
Dense(32, activation='relu'),
Dense(1, activation='sigmoid'),
])
model.compile(optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy'])
hist = model.fit(X_train, Y_train,
batch_size=32, epochs=100,
validation_data=(X_val, Y_val))
model.save("house_price.h5")
运行后,成功将.h5文件保存到我的目录中。 我现在想要做的是使用我训练的模型对新的 .csv 文件进行预测,并确定每个文件是否高于或低于中值价格。 这是 VSCode 中 csv 文件的图像,我希望它对其进行预测: csv 文件图像如您所见,该文件不包含 1(中位数以上)或 0(中位数以下),因为这就是我想要的它来预测。 这是我编写的代码:
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense
from keras.models import load_model
import h5py
df = pd.read_csv('data.csv')
dataset = df.values
X = dataset[:,0:10]
Y = dataset[:,10]
min_max_scaler = preprocessing.MinMaxScaler()
X_scale = min_max_scaler.fit_transform(X)
X_train, X_val_and_test, Y_train, Y_val_and_test = train_test_split(X_scale, Y, test_size=0.3)
X_val, X_test, Y_val, Y_test = train_test_split(X_val_and_test, Y_val_and_test, test_size=0.5)
model = load_model("house_price.h5")
y_pred = model.predict(X_test)
print(y_pred)
它的输出是[[0.00101464]]
我不知道那是什么以及为什么即使 csv 文件有 4 行它也只返回一个值。 有谁知道我如何解决这个问题并能够为 csv 文件中的每一行预测 1 或 0? 谢谢你!
我明白你想要什么! 咱们试试吧 ! 这段代码对我有用
import tensorflow
model = tensorflow.keras.models.load_model("house_price.h5")
y_pred=model.predict(X_test)
您仍然无法访问以下站点 1: answer1 2: answer2
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('C:\\Users\\acer\\Downloads\\housepricedata.csv')
dataset.head()
X=dataset.iloc[:,0:10]
y=dataset.iloc[:,10]
X.head()
from sklearn.preprocessing import StandardScaler
obj=StandardScaler()
X=obj.fit_transform(X)
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split
(X,y,random_state=2020,test_size=0.25)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
classifier = Sequential()
# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation =
'relu', input_dim = 10))
# classifier.add(Dropout(p = 0.1))
# Adding the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation
= 'relu'))
# classifier.add(Dropout(p = 0.1))
# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation
= 'sigmoid'))
# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics
= ['accuracy'])
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
print(y_pred)
classifier.save("house_price.h5")
import tensorflow
model = tensorflow.keras.models.load_model("house_price.h5")
y_pred=model.predict(X_test)
y_pred = (y_pred > 0.5)
print(y_pred)
两个 y_pred 为我产生相同的输出
这里有一件事你不 y_pred 不包含 0 和 1 因为你使用 sigmoid 函数来确定概率的预测所以如果(y_pred>0.5)它的平均值是一
#True rep one
#false rep zero
#you can use replace function or map function of pandas to get convert true
into 1
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.