Machine Learning, Linear Regression, Gradient Descent, Python, Stochastic Gradient.
np.mean(b0_sgd[200:]) number_of_pass_dict[str(stepsize)] = number_of_pass Then plot the gradient vector size for each stepsize: import matplotlib.pyplot as plt fig, axes = plt.subplots(1,5, figsize=(20,5)) for i, (key, value) in enumerate(graident_vector_length_dict.items()): axes[i].plot(value)