梯度下降

梯度下降

 

 

Mini-Batch Gradient Descent

1.打亂數據。  將輸入的數據x和結果y,進行同步的隨機打亂。

2.分區。 將打亂後的數據進行分區，每個含有mini_batch_size大小個數據。

3.按照分區後的數據，進行梯度下降算法。

 

# GRADED FUNCTION: random_mini_batches
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)           
    m = X.shape[1]                  
    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) 
    for k in range(0, num_complete_minibatches):
        ### START CODE HERE ### (approx. 2 lines)
        mini_batch_X = shuffled_X[:, mini_batch_size*k : mini_batch_size*(k+1)]
        mini_batch_Y = shuffled_Y[:, mini_batch_size*k : mini_batch_size*(k+1)]
        ### 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[:, mini_batch_size*num_complete_minibatches:m]
        mini_batch_Y = shuffled_Y[:, mini_batch_size*num_complete_minibatches:m]
        ### END CODE HERE ###
        mini_batch = (mini_batch_X, mini_batch_Y)
        mini_batches.append(mini_batch)
    
    return mini_batches