Python plot_decision_boundaryの例

コード例 #1

ファイル: week 2 - Optimization_methods_v1b.py プロジェクト: Matin-Noohnezhad/codes

# 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")

コード例 #2

ファイル: Optimization_Methods.py プロジェクト: innoventurist/Improving-Deep-Neural-Networks

# train 3-layer model
layers_dims = [train_X.shape[0], 5, 2,
               1]  # contains the dimensions of each layer in the network
parameters = model(
    train_X, train_Y, layers_dims, optimizer="gd"
)  # configuration variable that is internal to the variable using gradient descent

# Predict
predictions = predict(train_X, train_Y, parameters)

# Plot decision boundary
plt.title("Model with Gradient Descent optimization")  # set title for the axes
axes = plt.gca()  # To get the current polar axes
axes.set_xlim([-1.5, 2.5])  # set x-axis limits
axes.set_ylim([-1, 1.5])  # set y-axis limits
plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X,
                       train_Y)  # Generates contour plot

### 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, might see bigger gains.

#

# 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")