# In[42]:
# 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)
# ### 5.2 - Mini-batch gradient descent with momentum
#
# Run the following code to see how the model does with momentum. Because this example is relatively simple, the gains from using momemtum are small; but for more complex problems you might see bigger gains.
# In[43]:
# train 3-layer model
layers_dims = [train_X.shape[0], 5, 2, 1]
parameters = model(train_X,
train_Y,
layers_dims,
beta=0.9,
optimizer="momentum")
# In[16]:
# 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)
# ### 5.2 - Mini-batch gradient descent with momentum
#
# Run the following code to see how the model does with momentum. Because this example is relatively simple, the gains from using momemtum are small; but for more complex problems you might see bigger gains.
# In[17]:
# train 3-layer model
layers_dims = [train_X.shape[0], 5, 2, 1]
parameters = model(train_X, train_Y, layers_dims, beta = 0.9, optimizer = "momentum")
# Predict
predictions = predict(train_X, train_Y, parameters)
plt.show()
return parameters
if __name__ == '__main__':
train_X, train_Y = opt_utils.load_dataset(is_plot=True)
layers_dims = [train_X.shape[0], 5, 2, 1]
# #使用普通的梯度下降
# parameters = model(train_X, train_Y, layers_dims, optimizer="gd",is_plot=True)
# #使用动量的梯度下降
# parameters = model(train_X, train_Y, layers_dims, beta=0.9,optimizer="momentum",is_plot=True)
#使用Adam优化的梯度下降
parameters = model(train_X,
train_Y,
layers_dims,
optimizer="adam",
is_plot=True)
#预测
preditions = opt_utils.predict(train_X, train_Y, parameters)
#绘制分类图
plt.title("Model")
axes = plt.gca()
axes.set_xlim([-1.5, 2.5])
axes.set_ylim([-1, 1.5])
opt_utils.plot_decision_boundary(
lambda x: opt_utils.predict_dec(parameters, x.T), train_X, train_Y)