input: Write a function to construct a neural network in Python that can perform basic regression using Keras. The function should load data from a CSV file, split it into features and labels, create a neural network with specified layers, compile and train the model, and finally return the evaluation score of the model on the test data.
  
  import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from keras.models import Sequential
  from keras.layers import Dense
  
  def perform_regression(filepath):
      # Load the data
      data = pd.read_csv(filepath)
      
      # Split the data into features and labels
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Create the neural network
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
      model.add(Dense(12, kernel_initializer='normal', activation='relu'))
      model.add(Dense(1, kernel_initializer='normal'))
      
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
      
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
      
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score
  
  assert perform_regression('data.csv')==0.05
  assert perform_regression('other_data.csv')==0.1
output: def perform_regression(filepath):
      data = pd.read_csv(filepath)
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
      model.add(Dense(12, kernel_initializer='normal', activation='relu'))
      model.add(Dense(1, kernel_initializer='normal'))
      model.compile(loss='mean_squared_error', optimizer='adam')
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
      score = model.evaluate(X_test, y_test, verbose=0)
      return score