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# Gradio Implementation: Lenix Carter
# License: BSD 3-Clause or CC-0
import warnings
import gradio as gr
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn import datasets
from sklearn.exceptions import ConvergenceWarning
matplotlib.use('agg')
# different learning rate schedules and momentum parameters
params = [
{
"solver": "sgd",
"learning_rate": "constant",
"momentum": 0,
"learning_rate_init": 0.2,
},
{
"solver": "sgd",
"learning_rate": "constant",
"momentum": 0.9,
"nesterovs_momentum": False,
"learning_rate_init": 0.2,
},
{
"solver": "sgd",
"learning_rate": "constant",
"momentum": 0.9,
"nesterovs_momentum": True,
"learning_rate_init": 0.2,
},
{
"solver": "sgd",
"learning_rate": "invscaling",
"momentum": 0,
"learning_rate_init": 0.2,
},
{
"solver": "sgd",
"learning_rate": "invscaling",
"momentum": 0.9,
"nesterovs_momentum": True,
"learning_rate_init": 0.2,
},
{
"solver": "sgd",
"learning_rate": "invscaling",
"momentum": 0.9,
"nesterovs_momentum": False,
"learning_rate_init": 0.2,
},
{"solver": "adam", "learning_rate_init": 0.01},
]
labels = [
"constant learning-rate",
"constant with momentum",
"constant with Nesterov's momentum",
"inv-scaling learning-rate",
"inv-scaling with momentum",
"inv-scaling with Nesterov's momentum",
"adam",
]
plot_args = [
{"c": "red", "linestyle": "-"},
{"c": "green", "linestyle": "-"},
{"c": "blue", "linestyle": "-"},
{"c": "red", "linestyle": "--"},
{"c": "green", "linestyle": "--"},
{"c": "blue", "linestyle": "--"},
{"c": "black", "linestyle": "-"},
]
# load / generate some toy datasets
iris = datasets.load_iris()
X_digits, y_digits = datasets.load_digits(return_X_y=True)
data_sets = [
(iris.data, iris.target),
(X_digits, y_digits),
datasets.make_circles(noise=0.2, factor=0.5, random_state=1),
datasets.make_moons(noise=0.3, random_state=0),
]
def run_mlp(dataset, models, clr_lr,
cwm_lr, cwm_mom,
nest_lr, nest_mom,
inv_lr,
iwm_lr, iwm_mom,
invN_lr, invN_mom,
adam_lr):
plt.clf()
new_params = [
{"learning_rate_init": clr_lr},
{"learning_rate_init": cwm_lr,
"momentum": cwm_mom},
{"learning_rate_init": nest_lr,
"momentum": nest_mom},
{"learning_rate_init": inv_lr},
{"learning_rate_init": iwm_lr,
"momentum": iwm_mom},
{"learning_rate_init": invN_lr,
"momentum": invN_mom},
{"learning_rate_init": adam_lr}
]
for (param, new_param) in zip(params, new_params):
param.update(new_param)
iris = datasets.load_iris()
X_digits, y_digits = datasets.load_digits(return_X_y=True)
data_sets = [
(iris.data, iris.target),
(X_digits, y_digits),
datasets.make_circles(noise=0.2, factor=0.5, random_state=1),
datasets.make_moons(noise=0.3, random_state=0),
]
name = ["Iris", "Digits", "Circles", "Moons"]
return plot_on_dataset(*data_sets[dataset], models, name[dataset])
def plot_on_dataset(X, y, models, name):
# for each dataset, plot learning for each learning strategy
print("\nlearning on dataset %s" % name)
X = MinMaxScaler().fit_transform(X)
mlps = []
if name == "Digits":
# digits is larger but converges fairly quickly
max_iter = 15
else:
max_iter = 400
for model in models:
label = labels[model]
param = params[model]
print("training: %s" % label)
mlp = MLPClassifier(random_state=0, max_iter=max_iter, **param)
# some parameter combinations will not converge as can be seen on the
# plots so they are ignored here
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", category=ConvergenceWarning, module="sklearn"
)
mlp.fit(X, y)
mlps.append(mlp)
print("Training set score: %f" % mlp.score(X, y))
print("Training set loss: %f" % mlp.loss_)
print(label)
plt.plot(mlp.loss_curve_, label=label, **plot_args[model])
plt.legend(loc="upper right")
return plt
def plot_example(dataset):
if dataset == 0: # Iris
fig = plt.figure()
iris = datasets.load_iris()
col_1 = iris.data[:, 0]
col_2 = iris.data[:, 1]
target = iris.target
plt.scatter(col_1, col_2, c=target)
plt.title("Sepal Width vs. Sepal Height")
return fig
if dataset == 1: # Digits
digits = datasets.load_digits()
images = digits.images[:16]
labels = digits.target[:16]
fig, axes = plt.subplots(4, 4)
for i, ax in enumerate(axes.flat):
ax.imshow(images[i], cmap='gray')
ax.set_title(f"Label: {labels[i]}")
ax.axis('off')
plt.suptitle("First 16 Handwritten Digits")
plt.tight_layout()
return fig
if dataset == 2: # Circles
circles = datasets.make_circles(noise=0.2, factor=0.5, random_state=1),
X = circles[0][0]
y = circles[0][1]
fig = plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y)
plt.title("Circles Toy Dataset")
return fig
if dataset == 3: # Moons
moons = datasets.make_moons(noise=0.3, random_state=0),
X = moons[0][0]
y = moons[0][1]
fig = plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y)
plt.title("Moons Toy Dataset")
return fig
title = "Compare Stochastic learning strategies for MLPClassifier"
with gr.Blocks() as demo:
gr.Markdown(f" # {title}")
gr.Markdown("""
This example demonstrates different stochastic learning strategies on the MLP Classifier. You may also tweak some parameters of the learning strategies.
This is based on the example [here](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_training_curves.html#sphx-glr-auto-examples-neural-networks-plot-mlp-training-curves-py)
""")
with gr.Tabs():
with gr.TabItem("Model and Data Selection"):
with gr.Row():
with gr.Column():
dataset = gr.Dropdown(["Iris", "Digits", "Circles", "Moons"],
value="Iris",
type="index")
example_plot = gr.Plot(label="Dataset")
models = gr.CheckboxGroup(["Constant Learning-Rate",
"Constant with Momentum",
"Constant with Nesterov's Momentum",
"Inverse Scaling Learning-Rate",
"Inverse Scaling with Momentum",
"Inverse Scaling with Nesterov's Momentum",
"Adam"],
label="Stochastic Learning Strategy",
type="index")
with gr.TabItem("Model Tuning"):
with gr.Accordion("Constant Learning-Rate", open=False):
clr_lr = gr.Slider(0.01, 1.00, .2, label="Learning Rate")
with gr.Accordion("Constant with Momentum", open=False):
cwm_lr = gr.Slider(0.01, 1.00, .2, label="Learning Rate")
cwm_mom = gr.Slider(0.01, 1.00, 0.9, label="Momentum")
with gr.Accordion("Constant with Nesterov's Momentum", open=False):
nest_lr = gr.Slider(0.01, 1.00, .2, label="Learning Rate")
nest_mom = gr.Slider(0.01, 1.00, 0.9, label="Momentum")
with gr.Accordion("Inverse Scaling Learning-Rate", open=False):
inv_lr = gr.Slider(0.01, 1.00, .2, label="Learning Rate")
with gr.Accordion("Inverse Scaling with Momentum", open=False):
iwm_lr = gr.Slider(0.01, 1.00, .2, label="Learning Rate")
iwm_mom = gr.Slider(0.01, 1.00, 0.9, label="Momentum")
with gr.Accordion("Inverse Scaling with Nesterov's Momentum", open=False):
invN_lr = gr.Slider(0.01, 1.00, .2, label="Learning Rate")
invN_mom = gr.Slider(0.01, 1.00, 0.9, label="Momentum")
with gr.Accordion("Adam", open=False):
adam_lr = gr.Slider(0.001, 1.00, 0.01, label="Learning Rate")
btn = gr.Button(label="Run")
stoch_graph = gr.Plot(label="Stochastic Learning Strategies")
btn.click(
fn=run_mlp,
inputs=[dataset, models,
clr_lr,
cwm_lr,
cwm_mom,
nest_lr,
nest_mom,
inv_lr,
iwm_lr,
iwm_mom,
invN_lr,
invN_mom,
adam_lr],
outputs=[stoch_graph]
)
dataset.change(
fn=plot_example,
inputs=[dataset],
outputs=[example_plot]
)
if __name__ == '__main__':
demo.launch()
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