非洲人的战斗-one_hidden_layer

def (X,Y):
  n_x = X.shape[0]
  n_h = 4
  n_y = Y.shape[0]

  return (n_x,n_h,n_y)

def initialize_parameters(n_x,n_h,n_y):
  W1 = np.random.randn(n_h,n_x) * 0.01
  b1 = np.zeros((n_h,1))
  W2 = np.random.randn(n_y.n_h) * 0.01
  b2 = np.zeros((n_y,1))

  return {'W1':W1, 'b1':b1, 'W2':W2, 'b2':b2}

def forward_propagation(X, parameters):
  W1 = parameters['W1']
  b1 = parameters['b1']
  W2 = parameters['W2']
  b2 = parameters['b2']
  Z1 = np.dot(W1,X) + b1
  A1 = np.tanh(Z1)
  Z2 = np.dot(W2,A1) + b2
  A2 = sigmoid(Z2)


  cache = {'Z1': Z1,
           'A1': A1,
           'Z2': Z2,
           'A2': A2}

  return A2, cache

def compute_cost(A2,Y,parameters):
  m = Y.shape[1]

  logprobs = np.multiply(np.log(A2),Y) + np.multiply(np.log(1-A2),1-Y)
  cost = -np.sum(logprobs) / m

  return cost

def backward_propagation(parameters,cache,X,Y):
  m = X.shape[1]

  W1 = parameters['W1']
  W2 = parameters['W2']
  A1 = cache['A1']
  A2 = cache['A2']


  dZ2 = A2 - Y
  dW2 = 1/m * np.dot(dZ2,A1.T)
  db2 = 1/m * np.sum(dZ2,axis=1,keepdims=True)
  dZ1 = np.dot(W2.T,dZ2) * (1-np.power(A1,2))
  dW1 = 1/m * np.dot(dZ1,X.T)
  db1 = 1/m * np.sum(dZ1,axis=1,keepdims=True)

  return {'dW1':dW1,
          'db1':db1,
          'dW2':dW2,
          'db2':db2}

def predict(parameters,X):
  A2,cache = forward_propagation(X,parameters)
  predictions = np.around(A2)

  return predictions