Spaces:
Sleeping
Sleeping
File size: 1,666 Bytes
f1c2119 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
import numpy as np
import matplotlib.pyplot as plt
import gradio as gr
def modified_huber_loss(y_true, y_pred):
z = y_pred * y_true
loss = -4 * z
loss[z >= -1] = (1 - z[z >= -1]) ** 2
loss[z >= 1.0] = 0
return loss
def plot_loss_func():
xmin, xmax = -4, 4
xx = np.linspace(xmin, xmax, 100)
lw = 2
plt.clf()
fig = plt.figure(figsize=(10, 10), dpi=100)
plt.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], color="gold", lw=lw, label="Zero-one loss")
plt.plot(xx, np.where(xx < 1, 1 - xx, 0), color="teal", lw=lw, label="Hinge loss")
plt.plot(xx, -np.minimum(xx, 0), color="yellowgreen", lw=lw, label="Perceptron loss")
plt.plot(xx, np.log2(1 + np.exp(-xx)), color="cornflowerblue", lw=lw, label="Log loss")
plt.plot(
xx,
np.where(xx < 1, 1 - xx, 0) ** 2,
color="orange",
lw=lw,
label="Squared hinge loss",
)
plt.plot(
xx,
modified_huber_loss(xx, 1),
color="darkorchid",
lw=lw,
linestyle="--",
label="Modified Huber loss",
)
plt.ylim((0, 8))
plt.legend(loc="upper right")
plt.xlabel(r"Decision function $f(x)$")
plt.ylabel("$L(y=1, f(x))$")
return fig
title = "SGD convex loss functions"
# def greet(name):
# return "Hello " + name + "!"
with gr.Blocks(title=title) as demo:
gr.Markdown(f"# {title}")
gr.Markdown(" **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_loss_functions.html#sphx-glr-auto-examples-linear-model-plot-sgd-loss-functions-py)**")
btn = gr.Button(value="SGD convex loss functions")
btn.click(plot_loss_func, outputs= gr.Plot() ) #
demo.launch()
|