input: Write a function to construct a neural network in Python using the Keras library that can perform basic regression on a given dataset. The function should load the data from a CSV file, split the data into training and testing sets, create a neural network model with specified layers, compile the model, train it, and finally evaluate the model's performance.
  assert perform_regression('data.csv')==0.12345
  assert perform_regression('another_data.csv')==0.54321
  assert perform_regression('empty.csv')==0.0
  assert perform_regression('no_label.csv')==0.0
  assert perform_regression('large_data.csv')==0.98765
output: def perform_regression(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      try:
          data = pd.read_csv(file_path)
          X = data.iloc[:, :-1].values
          y = data.iloc[:, -1].values
      except:
          return 0.0
  
      if X.size == 0 or y.size == 0:
          return 0.0
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      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)
  
      loss = model.evaluate(X_test, y_test, verbose=0)
      return round(loss, 5)