我正在尝试使用Keras绘制模型预测的决策边界。然而,生成的边界似乎不正确。
这是我的模型:
这是我的绘图函数(取自这里)。
“我得到了一个像这样的图:
这是我的模型:
def base():
model = Sequential()
model.add(Dense(5,activation = 'relu', input_dim = 2))
model.add(Dense(2,activation = 'relu'))
model.add(Dense(1,activation = 'sigmoid'))
model.compile(optimizer = optimizers.SGD(lr=0.0007, momentum=0.0, decay=0.0), loss = 'binary_crossentropy', metrics= ['accuracy'])
return model
model = base()
history = model.fit(train_X,train_Y, epochs = 10000, batch_size =64, verbose = 2)
这是我的绘图函数(取自这里)。
def plot_decision_boundary(X, y, model, steps=1000, cmap='Paired'):
"""
Function to plot the decision boundary and data points of a model.
Data points are colored based on their actual label.
"""
cmap = get_cmap(cmap)
# Define region of interest by data limits
xmin, xmax = X[:,0].min() - 1, X[:,0].max() + 1
ymin, ymax = X[:,1].min() - 1, X[:,1].max() + 1
steps = 1000
x_span = linspace(xmin, xmax, steps)
y_span = linspace(ymin, ymax, steps)
xx, yy = meshgrid(x_span, y_span)
# Make predictions across region of interest
labels = model.predict(c_[xx.ravel(), yy.ravel()])
# Plot decision boundary in region of interest
z = labels.reshape(xx.shape)
fig, ax = subplots()
ax.contourf(xx, yy, z, cmap=cmap, alpha=0.5)
# Get predicted labels on training data and plot
train_labels = model.predict(X)
ax.scatter(X[:,0], X[:,1], c=y.ravel(), cmap=cmap, lw=0)
return fig, ax
plot_decision_boundary(train_X,train_Y, model, cmap = 'RdBu')
“我得到了一个像这样的图:
显然,这是一个非常有缺陷的决策边界描绘(由于存在这么多边界,它并不具有信息性)。 有人能指出我的错误吗?”