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# -*- coding: utf-8 -*-
"""effect of eta and iterations on sgd.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Lso8y1XapdHGJOHnY0pL4ZeSGaa-6lOz
"""
# Commented out IPython magic to ensure Python compatibility.
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline
plt.rcParams['figure.figsize'] = (10, 5)
def generate_data():
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)
return X, y
def get_norm_eqn(X, y):
X_b = np.c_[np.ones((100, 1)), X]
theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)
y_norm = X_b.dot(theta_best)
return X_b, y_norm
def generate_sgd_plot(eta, n_iterations):
#initialize parameters
m = 100
theta = np.random.randn(2,1)
X, y = generate_data()
X_b, y_norm = get_norm_eqn(X, y)
# plot how the parameters change wrt normal line
# as the algorithm learns
plt.scatter(X,y, c='#7678ed', label="data points")
plt.axis([0, 2.0, 0, 14])
for iteration in range(n_iterations):
gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y)
theta = theta - eta * gradients
y_new = X_b.dot(theta)
plt.plot(X, y_new, color='#f18701', linestyle='dashed', linewidth=0.2)
plt.plot(X, y_norm, '#3d348b', label="Normal Eqation line")
plt.xlabel('X')
plt.ylabel('Y')
plt.legend(loc='best')
return plt
import gradio as gr
demo = gr.Blocks()
with demo:
gr.Markdown(
"""
# How learning rate and number of iterations affect SGD
Move sliders to change the values of eta and number of iterations to see how it affects the convergance rate of algorithm.
"""
)
inputs = [gr.Slider(0.02, 0.5, label="learning rate, eta"), gr.Slider(500, 1000, 200, label="number of iterations")]
output = gr.Plot()
btn = gr.Button("Run")
btn.click(fn=generate_sgd_plot, inputs=inputs, outputs=output)
demo.launch()
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