mini_batches.py

def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):

    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 partitioning
    for k in range(0, num_complete_minibatches):
        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]
        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:
        mini_batch_X = shuffled_X[:,m-mini_batch_size*num_complete_minibatches:m]
        mini_batch_Y = shuffled_Y[:,m-mini_batch_size*num_complete_minibatches:m]
        mini_batch = (mini_batch_X, mini_batch_Y)
        mini_batches.append(mini_batch)
    
    return mini_batches