错误:
plt.title("Model with Zeros initialization")
axes = plt.gca()
axes.set_xlim([-1.5,1.5])
axes.set_ylim([-1.5,1.5])
plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)
错误发生在这里,报错如下:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/anaconda2/envs/py3/lib/python3.6/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)
~/anaconda2/envs/py3/lib/python3.6/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
4049 # must be acceptable as PathCollection facecolors
-> 4050 colors = mcolors.to_rgba_array(c)
4051 except ValueError:
~/anaconda2/envs/py3/lib/python3.6/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
~/anaconda2/envs/py3/lib/python3.6/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:
~/anaconda2/envs/py3/lib/python3.6/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)
input -7-287696ebcb66> in <module>()
3 axes.set_xlim([-1.5,1.5])
4 axes.set_ylim([-1.5,1.5])
----> 5 plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)
~/文档/DL课后作业/代码作业/第二课第一周编程作业/assignment1/init_utils.py in plot_decision_boundary(model, X, y)
215 plt.ylabel('x2')
216 plt.xlabel('x1')
--> 217 plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
218 plt.show()
219
~/anaconda2/envs/py3/lib/python3.6/site-packages/matplotlib/pyplot.py in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)
3355 vmin=vmin, vmax=vmax, alpha=alpha,
3356 linewidths=linewidths, verts=verts,
-> 3357 edgecolors=edgecolors, data=data, **kwargs)
3358 finally:
3359 ax._hold = washold
~/anaconda2/envs/py3/lib/python3.6/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1708 warnings.warn(msg % (label_namer, func.__name__),
1709 RuntimeWarning, stacklevel=2)
-> 1710 return func(ax, *args, **kwargs)
1711 pre_doc = inner.__doc__
1712 if pre_doc is None:
~/anaconda2/envs/py3/lib/python3.6/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
4053 msg = ("c of shape {0} not acceptable as a color sequence "
4054 "for x with size {1}, y with size {2}")
-> 4055 raise ValueError(msg.format(c.shape, x.size, y.size))
4056 else:
4057 colors = None # use cmap, norm after collection is created
ValueError: c of shape (1, 300) not acceptable as a color sequence for x with size 300, y with size 300
解决方案:
1.找到下载的课程代码文件夹第二课第一周
2.找到init_utils.py
文件
3.找到plot_decision_boundary()
函数
4.修改plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
为plt.scatter(X[0, :], X[1, :], c=y.reshape(X[0,:].shape), cmap=plt.cm.Spectral)
5.记得在Ipython notebook
中点击Kernel->Restart & Run All
重启内核,重新导包,否则无效
错误:
train_X, train_Y, test_X, test_Y = load_2D_dataset()
问题发生在load_2D_dataset()
函数上,同样是矩阵维度不匹配
报错信息主要看这一条:
~/文档/DL课后作业/代码作业/第二课第一周编程作业/assignment1/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
解决方案 :
1.找到下载的课程代码文件夹第二课第一周
2.找到reg_utils.py
文件
3.找到load_2D_dataset()
函数
4.修改plt.scatter(train_X[0, :], train_X[1, :], c=train_Y, s=40, cmap=plt.cm.Spectral);
为plt.scatter(train_X[0, :], train_X[1, :], c=np.squeeze(train_Y), s=40, cmap=plt.cm.Spectral);
5.记得在Ipython notebook
中点击Kernel->Restart & Run All
重启内核,重新导包,否则无效
错误:
plt.title("Model without regularization")
axes = plt.gca()
axes.set_xlim([-0.75,0.40])
axes.set_ylim([-0.75,0.65])
plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)
报错重点在这:
~/文档/DL课后作业/代码作业/第二课第一周编程作业/assignment1/reg_utils.py in plot_decision_boundary(model, X, y)
322 plt.ylabel('x2')
323 plt.xlabel('x1')
--> 324 plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
325 plt.show()
326
解决方案 :
1.找到下载的课程代码文件夹第二课第一周
2.找到reg_utils.py
文件
3.找到plot_decision_boundary(model, X, y)
函数
4.修改plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
为plt.scatter(X[0, :], X[1, :], c=y.reshape(X[0,:].shape), cmap=plt.cm.Spectral)
5.记得在Ipython notebook
中点击Kernel->Restart & Run All
重启内核,重新导包,否则无效
错误:
出现了同样的问题
# train 3-layer model
layers_dims = [train_X.shape[0], 5, 2, 1]
parameters = model(train_X, train_Y, layers_dims, optimizer = "gd")
# Predict
predictions = predict(train_X, train_Y, parameters)
# Plot decision boundary
plt.title("Model with Gradient Descent optimization")
axes = plt.gca()
axes.set_xlim([-1.5,2.5])
axes.set_ylim([-1,1.5])
plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)
这里报错重点是:
~/文档/DL课后作业/代码作业/第二课第二周编程作业/assignment2/opt_utils.py in plot_decision_boundary(model, X, y)
230 plt.ylabel('x2')
231 plt.xlabel('x1')
--> 232 plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
233 plt.show()
234
解决方案:
1.找到下载的课程代码文件夹第二课第二周
2.找到opt_utils.py
文件
3.找到plot_decision_boundary(model, X, y)
函数
4.修改plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
为plt.scatter(X[0, :], X[1, :], c=y.reshape(X[0,:].shape), cmap=plt.cm.Spectral)
5.记得在Ipython notebook
中点击Kernel->Restart & Run All
重启内核,重新导包,否则无效