CODEWITHSUNDEEP
matplotlibplotmachine-learningscikit-learnneural-network
Dave
Dave

Reputation: 564

How to appropriately plot the losses values acquired by (loss_curve_) from MLPClassifier

I use the following code to fit a model via MLPClassifier given my dataset:

tr_X, ts_X, tr_y, ts_y = train_test_split(X, y, train_size=.8)
model = MLPClassifier(hidden_layer_sizes=(32, 32),
              activation='relu',
              solver=adam,
              learning_rate='adaptive',
              early_stopping=True)

model.fit(tr_X, tr_y)
prd_r = model.predict(ts_X)
test_acc = accuracy_score(ts_y, prd_r) * 100.
loss_values = model.estimator.loss_curve_
print (loss_values)

As seen above, the loss value from each batch can be acquired by calling loss_curve_ to return a list of losses. I got this:

[0.69411586222116872, 0.6923803442491846, 0.66657293575365906, 0.43212054205535255, 0.23119813830216157, 0.15497928755966919, 0.11799652235604828, 0.095235784011297939, 0.079951427356068624, 0.069012741113626194, 0.061282868601098078, 0.054871864138797251, 0.049835046972801049, 0.046056362860260207, 0.042823979794540182, 0.040681220899240651, 0.038262366774481374, 0.036256840660697079, 0.034418333946277503, 0.033547227978657508, 0.03285581956914093, 0.031671266419493666, 0.030941451221456757]

I want to plot these results to represent the loss curve from this model. The problem is that I don't know what the x-axis and y-axis would be in this case. If I make y-axis to be these losses values, what should be the x-axis here to show the loss curve either decreasing or increasing?

Any hint or idea is appreciated.

Upvotes: 18

Views: 53300

Answers (2)

Jayhello
Jayhello

Reputation: 6602

let's give a demo about draw loss and accuracy according to it's iteration time.

import numpy as np
import matplotlib.pyplot as plt
def draw_result(lst_iter, lst_loss, lst_acc, title):
    plt.plot(lst_iter, lst_loss, '-b', label='loss')
    plt.plot(lst_iter, lst_acc, '-r', label='accuracy')

    plt.xlabel("n iteration")
    plt.legend(loc='upper left')
    plt.title(title)

    # save image
    plt.savefig(title+".png")  # should before show method

    # show
    plt.show()


def test_draw():
    # iteration num
    lst_iter = range(100)

    # loss of iteration
    lst_loss = [0.01 * i + 0.01 * i ** 2 for i in xrange(100)]
    # lst_loss = np.random.randn(1, 100).reshape((100, ))

    # accuracy of iteration
    lst_acc = [0.01 * i - 0.01 * i ** 2 for i in xrange(100)]
    # lst_acc = np.random.randn(1, 100).reshape((100, ))
    draw_result(lst_iter, lst_loss, lst_acc, "sgd_method")


if __name__ == '__main__':
    test_draw()

output as below:

enter image description here

Upvotes: 8

Hans Musgrave
Hans Musgrave

Reputation: 7121

The plot() command is overloaded and doesn't require an x-axis. If you just pass in loss_curve_, the default x-axis will be the respective indices in the list of the plotted y values. For example, if we run

import matplotlib.pyplot as plt

plt.plot(loss_values)
plt.show()

We then get the following chart:

enter image description here

Upvotes: 21

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