Upload app.py

app.py

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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()
+