Question

我是ANN的新手，我想知道如何将新的数据集（可能是新的csv）传递到我训练有素的模型中？例如，我了解：

model.predict()

仅接受数组，并且这些数组必须具有相同的形状才能工作。那么，如何通过一个完整的新csv来生成预测？

这是我的代码，我知道可能会一团糟，但我正在努力。

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Prova_1.csv')
dataset = dataset[np.isfinite(dataset['ID'])]
X = dataset.iloc[:, 3:6].values
y = dataset.iloc[:, 6].values

# Encoding 
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 0] = labelencoder_X_1.fit_transform(X[:, 0])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [2])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]

# Splitting
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#ANN
import keras
from keras.models import Sequential
from keras.layers import Dense

# Initialising the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 27, kernel_initializer = 'uniform', activation = 'relu', input_dim = 55))

# Adding the second hidden layer
classifier.add(Dense(units = 27, kernel_initializer = 'uniform', activation = 'relu'))

# 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'])

# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 1, epochs = 100)

# Prediction
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)

Answer 1

您需要执行所有预处理操作来训练新数据的数据。因为模型训练了您的输入形状和输入特征。确保输入数据的输入形状与以前相同，除非它不起作用。

经过训练的人工神经网络做出新的预测-Keras

1 个答案: