| import torch |
| import numpy as np |
| import matplotlib.pyplot as plt |
| import seaborn as sns |
| import time |
| from tqdm.notebook import tqdm_notebook |
| import plotly.express as px |
| import plotly.graph_objects as go |
| import gradio as gr |
|
|
|
|
|
|
| def get_error(data, a, b): |
| n = data.size()[0] |
| y_pred = a * data[:,0] + b |
| |
| return (1 / 2) * torch.mean((y_pred - data[:,-1]) ** 2).item() |
|
|
| def get_grads(data, a, b, alpha): |
| n = data.size()[0] |
| y_pred = a * data[:,0] + b |
| |
| grad_a = alpha * torch.mean((y_pred - data[:,-1])* data[:,0]).item() |
| |
| grad_b = alpha * torch.mean((y_pred - data[:,-1])* data[:,0]).item() |
| return (grad_a, grad_b) |
|
|
| def train(data, alpha=0.0001, epochs=500, test_data=[1,2,3]): |
| tensors = torch.from_numpy(data.astype(np.float64)) |
| print(f"dataset:{tensors}, size:{tensors.size()}") |
| a = 1 |
| b = 1 |
| errors = [] |
| updated_a = [] |
| updated_b = [] |
| for i in tqdm_notebook(range(int(epochs)), desc=f"Epoch: "): |
| errors.append(get_error(tensors, a, b)) |
| grad_a_temp, grad_b_temp = get_grads(tensors, a, b, alpha) |
| updated_a.append(a) |
| updated_b.append(b) |
| a -= grad_a_temp |
| b -= grad_b_temp |
| |
| |
| y_pred = a * tensors[:,0] + b |
| fig1 = px.scatter(x=tensors[:, 0], y=tensors[:, -1], template="plotly_dark") |
| fig2 = px.line(x=tensors[:, 0], y=y_pred, template="plotly_dark") |
| fig3 = go.Figure(data=fig1.data + fig2.data) |
| fig3.update_layout( |
| |
| xaxis_title="X", |
| yaxis_title="Y", |
| template="plotly_dark" |
| ) |
|
|
| fig4 = px.line(errors, template="plotly_dark") |
| fig4.update_traces(line_color='orange') |
| fig4.update_layout( |
| |
| xaxis_title="Epoch", |
| yaxis_title="Error", |
| showlegend=False |
| ) |
| y_pred_test = a * test_data.astype(np.float64) + b |
| return f"{round(updated_a[-1],3)} * X + {round(updated_b[-1],3)}",fig3, fig4, np.concatenate((test_data.astype(np.float64), y_pred_test), axis=1) |
|
|
| |
|
|
| input_elements = [ |
| gr.Numpy( |
| value=[[40,9],[30,8.5],[25,8],[20,7],[10,6],[5,8]], |
| datatype="number", |
| row_count=(6,"dynamic"), |
| col_count=(2, "fixed"), |
| label="Dataset", |
| interactive=True |
| ), |
| gr.Number(label="Learning Rate", value=0.0001, interactive=True), |
| gr.Number(label="Number of epochs", value=500, interactive=True), |
| gr.Numpy( |
| value=[[10],[20],[30]], |
| datatype="number", |
| row_count=3, |
| col_count=(1,"fixed"), |
| label="Test Dataset", |
| interactive=True |
| ) |
| ] |
|
|
| output_elements = [ |
| gr.Textbox(label="Generated Model", placeholder="Model Equation: a * X + b"), |
| gr.Plot(label="Best Fit Line"), |
| gr.Plot(label="Learning Curve"), |
| gr.Numpy(label="Model Predictions") |
| |
| ] |
|
|
| app = gr.Interface( |
| title="Linear Regression using Pytorch", |
| |
| fn=train, |
| inputs=input_elements, |
| outputs=output_elements, |
| allow_flagging="never" |
| ) |
| app.launch(debug=True) |
