code: import numpy as np
  from sklearn import datasets, linear_model
  import matplotlib.pyplot as plt
  
  
  class Config:
      nn_input_dim = 2  # input layer dimensionality
      nn_output_dim = 2  # output layer dimensionality
      # Gradient descent parameters (I picked these by hand)
      epsilon = 0.01  # learning rate for gradient descent
      reg_lambda = 0.01  # regularization strength
  
  
  def generate_data():
      np.random.seed(0)
      X, y = datasets.make_moons(200, noise=0.20)
      return X, y
  
  
  def visualize(X, y, model):
      # plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.Spectral)
      # plt.show()
      plot_decision_boundary(lambda x:predict(model,x), X, y)
      plt.title("Logistic Regression")
      plt.show()
  
  
  def plot_decision_boundary(pred_func, X, y):
      # Set min and max values and give it some padding
      x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
      y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
      h = 0.01
      # Generate a grid of points with distance h between them
      xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
      # Predict the function value for the whole gid
      Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
      Z = Z.reshape(xx.shape)
      # Plot the contour and training examples
      plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
      plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
      plt.show()
  
  
  # Helper function to evaluate the total loss on the dataset
  def calculate_loss(model, X, y):
      num_examples = len(X)  # training set size
      W1, b1, W2, b2 = model['W1'], model['b1'], model['W2'], model['b2']
      # Forward propagation to calculate our predictions
      z1 = X.dot(W1) + b1
      a1 = np.tanh(z1)
      z2 = a1.dot(W2) + b2
      exp_scores = np.exp(z2)
      probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
      # Calculating the loss
      corect_logprobs = -np.log(probs[range(num_examples), y])
      data_loss = np.sum(corect_logprobs)
      # Add regulatization term to loss (optional)
      data_loss += Config.reg_lambda / 2 * (np.sum(np.square(W1)) + np.sum(np.square(W2)))
      return 1. / num_examples * data_loss
  
  
  def predict(model, x):
      W1, b1, W2, b2 = model['W1'], model['b1'], model['W2'], model['b2']
      # Forward propagation
      z1 = x.dot(W1) + b1
      a1 = np.tanh(z1)
      z2 = a1.dot(W2) + b2
      exp_scores = np.exp(z2)
      probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
      return np.argmax(probs, axis=1)
  
  
  # This function learns parameters for the neural network and returns the model.
  # - nn_hdim: Number of nodes in the hidden layer
  # - num_passes: Number of passes through the training data for gradient descent
  # - print_loss: If True, print the loss every 1000 iterations
  def build_model(X, y, nn_hdim, num_passes=20000, print_loss=False):
      # Initialize the parameters to random values. We need to learn these.
      num_examples = len(X)
      np.random.seed(0)
      W1 = np.random.randn(Config.nn_input_dim, nn_hdim) / np.sqrt(Config.nn_input_dim)
      b1 = np.zeros((1, nn_hdim))
      W2 = np.random.randn(nn_hdim, Config.nn_output_dim) / np.sqrt(nn_hdim)
      b2 = np.zeros((1, Config.nn_output_dim))
  
      # This is what we return at the end
      model = {}
  
      # Gradient descent. For each batch...
      for i in range(0, num_passes):
  
          # Forward propagation
          z1 = X.dot(W1) + b1
          a1 = np.tanh(z1)
          z2 = a1.dot(W2) + b2
          exp_scores = np.exp(z2)
          probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
  
          # Backpropagation
          delta3 = probs
          delta3[range(num_examples), y] -= 1
          dW2 = (a1.T).dot(delta3)
          db2 = np.sum(delta3, axis=0, keepdims=True)
          delta2 = delta3.dot(W2.T) * (1 - np.power(a1, 2))
          dW1 = np.dot(X.T, delta2)
          db1 = np.sum(delta2, axis=0)
  
          # Add regularization terms (b1 and b2 don't have regularization terms)
          dW2 += Config.reg_lambda * W2
          dW1 += Config.reg_lambda * W1
  
          # Gradient descent parameter update
          W1 += -Config.epsilon * dW1
          b1 += -Config.epsilon * db1
          W2 += -Config.epsilon * dW2
          b2 += -Config.epsilon * db2
  
          # Assign new parameters to the model
          model = {'W1': W1, 'b1': b1, 'W2': W2, 'b2': b2}
  
          # Optionally print the loss.
          # This is expensive because it uses the whole dataset, so we don't want to do it too often.
          if print_loss and i % 1000 == 0:
              print("Loss after iteration %i: %f" % (i, calculate_loss(model, X, y)))
  
      return model
  
  
  def classify(X, y):
      # clf = linear_model.LogisticRegressionCV()
      # clf.fit(X, y)
      # return clf
  
      pass
  
  
  def main():
      X, y = generate_data()
      model = build_model(X, y, 3, print_loss=True)
      visualize(X, y, model)
  
  
  if __name__ == "__main__":
      main()
apis: numpy.sum numpy.zeros numpy.dot sklearn.datasets.make_moons numpy.random.seed numpy.random.randn numpy.exp numpy.argmax matplotlib.pyplot.title numpy.arange matplotlib.pyplot.show numpy.power numpy.sqrt matplotlib.pyplot.contourf numpy.square numpy.tanh matplotlib.pyplot.scatter