init: first draft of autoencoder

utils.py

@@ -0,0 +1,50 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def softmax(v: np.ndarray) -> np.ndarray:
+    v = v - np.max(v)
+    exp_v = np.exp(v)
+    return exp_v / np.sum(exp_v)
+
+
+def relu(x: np.ndarray) -> np.ndarray:
+    return x * (x > 0)
+
+
+def normalize(v: np.ndarray) -> np.ndarray:
+    return v / (np.linalg.norm(v) + 1e-8)
+
+
+def regularize(v: np.ndarray) -> np.ndarray:
+    v_min = v.min(axis=0)
+    v_max = v.max(axis=0)
+    if v_min - v_max == 0:
+        return v
+    return (v - v_min) / (v_max - v_min)
+
+
+def dynamic_loss_plot_init():
+    plt.ion()
+    fig, ax = plt.subplots()
+    line, = ax.plot([], [], label="Loss")
+    ax.set_xlabel("Epoch")
+    ax.set_ylabel("Loss")
+    ax.set_title("Training Loss")
+    ax.legend()
+    return ax, line
+
+
+def dynamic_loss_plot_update(ax, line, loss):
+    line.set_xdata(range(len(loss)))
+    line.set_ydata(loss)
+    ax.relim()
+    ax.autoscale_view()
+    plt.draw()
+    plt.pause(0.1)
+
+
+def dynamic_loss_plot_finish(ax, line):
+    plt.ioff()
+    plt.show()