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 training and testing sets, create a neural network with specific layers and activation functions, compile and train the model, and finally evaluate the model on the test data. The function should return the test score.
  
  assert construct_neural_network('data.csv') is not None
output: 
  def construct_neural_network(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      data = pd.read_csv(file_path)
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      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(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      test_score = model.evaluate(X_test, y_test, verbose=0)
      return test_score
  