53 lines
1.1 KiB
Python
53 lines
1.1 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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def softmax(v: np.ndarray) -> np.ndarray:
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v = v - np.max(v)
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exp_v = np.exp(v)
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return exp_v / np.sum(exp_v)
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def relu(x: np.ndarray, derivative=False) -> np.ndarray:
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if derivative:
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return x > 0
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return x * (x > 0)
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def normalize(v: np.ndarray) -> np.ndarray:
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return v / (np.linalg.norm(v) + 1e-8)
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def regularize(v: np.ndarray) -> np.ndarray:
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v_min = v.min(axis=0)
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v_max = v.max(axis=0)
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if v_min - v_max == 0:
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return v
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return (v - v_min) / (v_max - v_min)
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def dynamic_loss_plot_init(losses: list):
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plt.ion()
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fig, ax = plt.subplots()
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line, = ax.plot([0], losses, label="Loss")
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ax.set_xlabel("Epoch")
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ax.set_ylabel("Loss")
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ax.set_title("Training Loss")
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ax.legend()
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return ax, line
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def dynamic_loss_plot_update(ax, line, loss):
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line.set_xdata(range(len(loss)))
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line.set_ydata(loss)
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ax.relim()
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ax.autoscale_view()
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plt.draw()
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plt.pause(0.1)
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def dynamic_loss_plot_finish(ax, line):
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plt.ioff()
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plt.show()
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