Update.

diff --git a/bit_mlp.py b/bit_mlp.py
index 90409f2..6f7f92e 100755 (executable)
--- a/bit_mlp.py
+++ b/bit_mlp.py
@@ -116,33 +116,40 @@ for linear_layer in errors.keys():
 
         ######################################################################
 
 
         ######################################################################
 

-        errors[linear_layer].append((nb_hidden, test_error))
+        errors[linear_layer].append(
+            (nb_hidden, test_error * 100, acc_train_loss / train_input.size(0))
+        )

 
 import matplotlib.pyplot as plt
 
 
 import matplotlib.pyplot as plt
 

-fig = plt.figure()
-fig.set_figheight(6)
-fig.set_figwidth(8)

 
 

-ax = fig.add_subplot(1, 1, 1)
+def save_fig(filename, ymax, ylabel, index):
+    fig = plt.figure()
+    fig.set_figheight(6)
+    fig.set_figwidth(8)

 
 

-ax.set_ylim(0, 1)
-ax.spines.right.set_visible(False)
-ax.spines.top.set_visible(False)
-ax.set_xscale("log")
-ax.set_xlabel("Nb hidden units")
-ax.set_ylabel("Test error (%)")
+    ax = fig.add_subplot(1, 1, 1)

 
 

-X = torch.tensor([x[0] for x in errors[nn.Linear]])
-Y = torch.tensor([x[1] for x in errors[nn.Linear]])
-ax.plot(X, Y, color="gray", label="nn.Linear")
+    ax.set_ylim(0, ymax)
+    ax.spines.right.set_visible(False)
+    ax.spines.top.set_visible(False)
+    ax.set_xscale("log")
+    ax.set_xlabel("Nb hidden units")
+    ax.set_ylabel(ylabel)

 
 

-X = torch.tensor([x[0] for x in errors[QLinear]])
-Y = torch.tensor([x[1] for x in errors[QLinear]])
-ax.plot(X, Y, color="red", label="QLinear")
+    X = torch.tensor([x[0] for x in errors[nn.Linear]])
+    Y = torch.tensor([x[index] for x in errors[nn.Linear]])
+    ax.plot(X, Y, color="gray", label="nn.Linear")

 
 

-ax.legend(frameon=False, loc=1)
+    X = torch.tensor([x[0] for x in errors[QLinear]])
+    Y = torch.tensor([x[index] for x in errors[QLinear]])
+    ax.plot(X, Y, color="red", label="QLinear")

 
 

-filename = f"bit_mlp.pdf"
-print(f"saving {filename}")
-fig.savefig(filename, bbox_inches="tight")
+    ax.legend(frameon=False, loc=1)
+
+    print(f"saving {filename}")
+    fig.savefig(filename, bbox_inches="tight")
+
+
+save_fig("bit_mlp_err.pdf", ymax=15, ylabel="Test error (%)", index=1)
+save_fig("bit_mlp_loss.pdf", ymax=1.25, ylabel="Train loss", index=2)

diff --git a/redshift.py b/redshift.py
new file mode 100755 (executable)
index 0000000..2ed1e52
--- /dev/null
+++ b/redshift.py
@@ -0,0 +1,67 @@
+#!/usr/bin/env python
+
+import math
+
+import torch, torchvision
+
+from torch import nn
+from torch.nn import functional as F
+
+torch.set_default_dtype(torch.float64)
+
+nb_hidden = 5
+hidden_dim = 100
+
+res = 256
+
+input = torch.cat(
+    [
+        torch.linspace(-1, 1, res)[None, :, None].expand(res, res, 1),
+        torch.linspace(-1, 1, res)[:, None, None].expand(res, res, 1),
+    ],
+    dim=-1,
+).reshape(-1, 2)
+
+
+class Angles(nn.Module):
+    def forward(self, x):
+        return x.clamp(min=-0.5, max=0.5)
+
+
+for activation in [nn.ReLU, nn.Tanh, nn.Softplus, Angles]:
+    for s in [1.0, 10.0]:
+        layers = [nn.Linear(2, hidden_dim), activation()]
+        for k in range(nb_hidden - 1):
+            layers += [nn.Linear(hidden_dim, hidden_dim), activation()]
+        layers += [nn.Linear(hidden_dim, 2)]
+        model = nn.Sequential(*layers)
+
+        with torch.no_grad():
+            for p in model.parameters():
+                p *= s
+
+        output = model(input)
+
+        img = (output[:, 1] - output[:, 0]).reshape(1, 1, res, res)
+
+        img = (img - img.mean()) / (1 * img.std())
+
+        img = img.clamp(min=-1, max=1)
+
+        img = torch.cat(
+            [
+                (1 + img).clamp(max=1),
+                (1 - img.abs()).clamp(min=0),
+                (1 - img).clamp(max=1),
+            ],
+            dim=1,
+        )
+
+        name_activation = {
+            nn.ReLU: "relu",
+            nn.Tanh: "tanh",
+            nn.Softplus: "softplus",
+            Angles: "angles",
+        }[activation]
+
+        torchvision.utils.save_image(img, f"result-{name_activation}-{s}.png")