你好，我在运行的时候一直遇到这样的问题
比如第二课，第一周的Regularization
train_X, train_Y, test_X, test_Y = load_2D_dataset()
哎困扰我很久了，一直没有找到原因

TypeError Traceback (most recent call last)
c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\matplotlib\colors.py in to_rgba(c, alpha)
131 try:
--> 132 rgba = _colors_full_map.cache[c, alpha]
133 except (KeyError, TypeError): # Not in cache, or unhashable.

TypeError: unhashable type: 'numpy.ndarray'

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\matplotlib\axes_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
3985 # must be acceptable as PathCollection facecolors
-> 3986 colors = mcolors.to_rgba_array(c)
3987 except ValueError:

c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\matplotlib\colors.py in to_rgba_array(c, alpha)
232 for i, cc in enumerate(c):
--> 233 result[i] = to_rgba(cc, alpha)
234 return result

c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\matplotlib\colors.py in to_rgba(c, alpha)
133 except (KeyError, TypeError): # Not in cache, or unhashable.
--> 134 rgba = _to_rgba_no_colorcycle(c, alpha)
135 try:

c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\matplotlib\colors.py in _to_rgba_no_colorcycle(c, alpha)
188 if len(c) not in [3, 4]:
--> 189 raise ValueError("RGBA sequence should have length 3 or 4")
190 if len(c) == 3 and alpha is None:

ValueError: RGBA sequence should have length 3 or 4

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
in ()
----> 1 train_X, train_Y, test_X, test_Y = load_2D_dataset()

~\Desktop\deeplearning.ai_JupyterNotebooks-master\deeplearning.ai_JupyterNotebooks-master\2_Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization\week1\2_Regularization\reg_utils.py in load_2D_dataset()
332 test_Y = data['yval'].T
333
--> 334 plt.scatter(train_X[0, :], train_X[1, :], c=train_Y, s=40, cmap=plt.cm.Spectral);
335
336 return train_X, train_Y, test_X, test_Y

c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\matplotlib\pyplot.py in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)
3376 vmin=vmin, vmax=vmax, alpha=alpha,
3377 linewidths=linewidths, verts=verts,
-> 3378 edgecolors=edgecolors, data=data, **kwargs)
3379 finally:
3380 ax._hold = washold

c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\matplotlib_init_.py in inner(ax, *args, **kwargs)
1715 warnings.warn(msg % (label_namer, func.name),
1716 RuntimeWarning, stacklevel=2)
-> 1717 return func(ax, *args, **kwargs)
1718 pre_doc = inner.doc
1719 if pre_doc is None:

c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\matplotlib\axes_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
3989 msg = ("c of shape {0} not acceptable as a color sequence "
3990 "for x with size {1}, y with size {2}")
-> 3991 raise ValueError(msg.format(c.shape, x.size, y.size))
3992 else:
3993 colors = None # use cmap, norm after collection is created

ValueError: c of shape (1, 211) not acceptable as a color sequence for x with size 211, y with size 211

此处应该是：
inter_area=max((xi1-xi2)*(yi1-yi2),))
因为存在两个box不相交的情况，简单取绝对值是错误的。

The assign of the first week of Lesson 4 5.2.3 Putting it together: Pooling backward
Change Part:

                        # Set dA_prev to be dA_prev + (the mask multiplied by the correct entry of dA) (≈1 line)
                        dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += np.multiply(mask, dA[i, h, w, c])
                        # Get the value a from dA (≈1 line)
                        da = dA[i, h, w, c]
                        # Distribute it to get the correct slice of dA_prev. i.e. Add the distributed value of da. (≈1 line)
                        dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += distribute_value(da, shape)

The Reason:
Pooling Layer 前向传播是一个slice对应一个scalar，所以在反向传播的时候应该是一个scalar对应一个slice。multiply mask and the scalar 再与对应slice相加能够刚好把error传递给最大值；distribute_value与对应slice相加，能够将error 平均分给slice中的每个值，最终实现精准传播误差的目的。
不知道自己有没有描述清楚，@marsggbo谢谢分享作业！！！

R.T.
想问一下您是如何下载下来所有文件的？
麻烦了。谢谢！

我用的np.power()可以得到准确结果。

应该改成

                   if mode == "max":
                        
                        # Use the corners and "c" to define the current slice from a_prev (≈1 line)
                        a_prev_slice = a_prev[vert_start:vert_end, horiz_start:horiz_end, c]
                        # Create the mask from a_prev_slice (≈1 line)
                        mask = create_mask_from_window(a_prev_slice)
                        # Set dA_prev to be dA_prev + (the mask multiplied by the correct entry of dA) (≈1 line)
                        dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += np.multiply(mask, dA[i, h, w, c])
                        
                    elif mode == "average":
                        
                        # Get the value a from dA (≈1 line)
                        da = dA[i, h, w, c]
                        # Define the shape of the filter as fxf (≈1 line)
                        shape = (f, f)
                        # Distribute it to get the correct slice of dA_prev. i.e. Add the distributed value of da. (≈1 line)
                        dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += distribute_value(da, shape)

s_corrected["dW" + str(l+1)] = s["dW" + str(l+1)]/(1 - pow(beta2, t))
s_corrected["db" + str(l+1)] = s["dW" + str(l+1)]/(1 - pow(beta2, t))

变量写错了,应该是db,而不是dW
感谢您分享的作业

第二章week1第三个编程作业-梯度检验，最后一个代码块中的
numerator = np.linalg.norm(gradapprox - grad)
应该改为
numerator = np.linalg.norm(grad- gradapprox)
这样最后的结果会和样例输出相同。

自查了好几遍代码没发现问题出在哪里，从前向传播开始计算结果与答案不一致，我错误的结果是
Z3 = [[ 1.4416984 -0.24909666 5.450499 -0.2618962 -0.20669907 1.3654671 ]
[ 1.4070846 -0.02573211 5.08928 -0.48669922 -0.40940708 1.2624859 ]]
cost = 4.6648693
导致后面算cost也是偏大。
求助，讨论一下