shuffle数据（permutation）

shuffle数据（permutation）

def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):    """    Creates a list of random minibatches from (X, Y)    Arguments:    X -- input data, of shape (input size, number of examples)    Y -- true "label" vector (1 for blue dot / 0 for red dot), of shape (1, number of examples)    mini_batch_size -- size of the mini-batches, integer    Returns:    mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)    """    np.random.seed(seed)            # To make your "random" minibatches the same as ours    m = X.shape[1]                  # number of training examples    mini_batches = []    # Step 1: Shuffle (X, Y)    permutation = list(np.random.permutation(m))    shuffled_X = X[:, permutation]    shuffled_Y = Y[:, permutation].reshape((1,m))    # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.    num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning    for k in range(0, num_complete_minibatches):        ### START CODE HERE ### (approx. 2 lines)        mini_batch_X = shuffled_X[:, k*mini_batch_size : (k+1)*mini_batch_size]        mini_batch_Y = shuffled_Y[:, k*mini_batch_size : (k+1)*mini_batch_size]        ### END CODE HERE ###        mini_batch = (mini_batch_X, mini_batch_Y)        mini_batches.append(mini_batch)    # Handling the end case (last mini-batch < mini_batch_size)    if m % mini_batch_size != 0:        ### START CODE HERE ### (approx. 2 lines)        mini_batch_X = shuffled_X[:, num_complete_minibatches*mini_batch_size-m-1 : -1]        mini_batch_Y = shuffled_Y[:, num_complete_minibatches*mini_batch_size-m-1 : -1]        ### END CODE HERE ###        mini_batch = (mini_batch_X, mini_batch_Y)        mini_batches.append(mini_batch)    return mini_batches