--- a
+++ b/scripts/plot_loss.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+
+"""Code to generate plots for Extended Data Fig. 3."""
+
+import argparse
+import os
+import matplotlib
+import matplotlib.pyplot as plt
+
+import echonet
+
+
+def main():
+    """Generate plots for Extended Data Fig. 3."""
+
+    # Select paths and hyperparameter to plot
+    parser = argparse.ArgumentParser()
+    parser.add_argument("dir", nargs="?", default="output")
+    parser.add_argument("fig", nargs="?", default=os.path.join("figure", "loss"))
+    parser.add_argument("--frames", type=int, default=32)
+    parser.add_argument("--period", type=int, default=2)
+    args = parser.parse_args()
+
+    # Set up figure
+    echonet.utils.latexify()
+    os.makedirs(args.fig, exist_ok=True)
+    fig = plt.figure(figsize=(7, 5))
+    gs = matplotlib.gridspec.GridSpec(ncols=3, nrows=2, figure=fig, width_ratios=[2.75, 2.75, 1.50])
+
+    # Plot EF loss curve
+    ax0 = fig.add_subplot(gs[0, 0])
+    ax1 = fig.add_subplot(gs[0, 1], sharey=ax0)
+    for pretrained in [True]:
+        for (model, color) in zip(["r2plus1d_18", "r3d_18", "mc3_18"], matplotlib.colors.TABLEAU_COLORS):
+            loss = load(os.path.join(args.dir, "video", "{}_{}_{}_{}".format(model, args.frames, args.period, "pretrained" if pretrained else "random"), "log.csv"))
+            ax0.plot(range(1, 1 + len(loss["train"])), loss["train"], "-" if pretrained else "--", color=color)
+            ax1.plot(range(1, 1 + len(loss["val"])), loss["val"], "-" if pretrained else "--", color=color)
+
+    plt.axis([0, max(len(loss["train"]), len(loss["val"])), 0, max(max(loss["train"]), max(loss["val"]))])
+    ax0.text(-0.25, 1.00, "(a)", transform=ax0.transAxes)
+    ax1.text(-0.25, 1.00, "(b)", transform=ax1.transAxes)
+    ax0.set_xlabel("Epochs")
+    ax1.set_xlabel("Epochs")
+    ax0.set_xticks([0, 15, 30, 45])
+    ax1.set_xticks([0, 15, 30, 45])
+    ax0.set_ylabel("Training MSE Loss")
+    ax1.set_ylabel("Validation MSE Loss")
+
+    # Plot segmentation loss curve
+    ax0 = fig.add_subplot(gs[1, 0])
+    ax1 = fig.add_subplot(gs[1, 1], sharey=ax0)
+    pretrained = False
+    for (model, color) in zip(["deeplabv3_resnet50"], list(matplotlib.colors.TABLEAU_COLORS)[3:]):
+        loss = load(os.path.join(args.dir, "segmentation", "{}_{}".format(model, "pretrained" if pretrained else "random"), "log.csv"))
+        ax0.plot(range(1, 1 + len(loss["train"])), loss["train"], "--", color=color)
+        ax1.plot(range(1, 1 + len(loss["val"])), loss["val"], "--", color=color)
+
+    ax0.text(-0.25, 1.00, "(c)", transform=ax0.transAxes)
+    ax1.text(-0.25, 1.00, "(d)", transform=ax1.transAxes)
+    ax0.set_ylim([0, 0.13])
+    ax0.set_xlabel("Epochs")
+    ax1.set_xlabel("Epochs")
+    ax0.set_xticks([0, 25, 50])
+    ax1.set_xticks([0, 25, 50])
+    ax0.set_ylabel("Training Cross Entropy Loss")
+    ax1.set_ylabel("Validation Cross Entropy Loss")
+
+    # Legend
+    ax = fig.add_subplot(gs[:, 2])
+    for (model, color) in zip(["EchoNet-Dynamic (EF)", "R3D", "MC3", "EchoNet-Dynamic (Seg)"], matplotlib.colors.TABLEAU_COLORS):
+        ax.plot([float("nan")], [float("nan")], "-", color=color, label=model)
+    ax.set_title("")
+    ax.axis("off")
+    ax.legend(loc="center")
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(args.fig, "loss.pdf"))
+    plt.savefig(os.path.join(args.fig, "loss.eps"))
+    plt.savefig(os.path.join(args.fig, "loss.png"))
+    plt.close(fig)
+
+
+def load(filename):
+    """Loads losses from specified file."""
+
+    losses = {"train": [], "val": []}
+    with open(filename, "r") as f:
+        for line in f:
+            line = line.split(",")
+            if len(line) < 4:
+                continue
+            epoch, split, loss, *_ = line
+            epoch = int(epoch)
+            loss = float(loss)
+            assert(split in ["train", "val"])
+            if epoch == len(losses[split]):
+                losses[split].append(loss)
+            elif epoch == len(losses[split]) - 1:
+                losses[split][-1] = loss
+            else:
+                raise ValueError("File has uninterpretable formatting.")
+    return losses
+
+
+if __name__ == "__main__":
+    main()