我python2.7, 做吴恩达深度学习第2课第2周编程作业 Optimization Methods 时有2个坑：
- 第一坑 需将辅助文件
opt_utils.py 的 nitialize_parameters(layer_dims) 函数中的 2 改成 2.0 , 保存后再重启jupyter notebook.第二坑 需将辅助文件
opt_utils.py 的 plot_decision_boundary(model, X, y) 函数中的 c=y 改成 c=y[0], 保存后再重启jupyter notebook. 我的计算机环境原来是不用修改的，不知道咋回事，有天画图出毛病，得改这里．另外，不只是这个作业，其他作业关于画图的地方相应处均需修改．坑1需修改的代码:
def initialize_parameters(layer_dims):
"""
Arguments:
layer_dims -- python array (list) containing the dimensions of each layer in our network
Returns:
parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
W1 -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
b1 -- bias vector of shape (layer_dims[l], 1)
Wl -- weight matrix of shape (layer_dims[l-1], layer_dims[l])
bl -- bias vector of shape (1, layer_dims[l])
Tips:
- For example: the layer_dims for the "Planar Data classification model" would have been [2,2,1].
This means W1's shape was (2,2), b1 was (1,2), W2 was (2,1) and b2 was (1,1). Now you have to generalize it!
- In the for loop, use parameters['W' + str(l)] to access Wl, where l is the iterative integer.
"""
np.random.seed(3)
parameters = {}
L = len(layer_dims) # number of layers in the network
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1])* np.sqrt(2.0 / layer_dims[l-1]) # <------- 坑1在这, 原来是2, 我们改成2.0了
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
assert(parameters['W' + str(l)].shape == layer_dims[l], layer_dims[l-1])
assert(parameters['W' + str(l)].shape == layer_dims[l], 1)
return parameters
坑2需修改的代码:
def plot_decision_boundary(model, X, y):
# Set min and max values and give it some padding
x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
h = 0.01
# Generate a grid of points with distance h between them
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict the function value for the whole grid
Z = model(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the contour and training examples
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.ylabel('x2')
plt.xlabel('x1')
plt.scatter(X[0, :], X[1, :], c=y[0], cmap=plt.cm.Spectral) # <----坑2在这 c=y 改成 c=y[0]
plt.show()
