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Refactor examples/glvq_iris.py

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This commit is contained in:
blackfly 2020-04-14 19:57:19 +02:00
parent a0f20a40f6
commit 63a25e7a38
1 changed files with 20 additions and 23 deletions
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43
examples/glvq_iris.py
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@ -42,13 +42,17 @@ model = Model()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01) optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = GLVQLoss(squashing='sigmoid_beta', beta=10) criterion = GLVQLoss(squashing='sigmoid_beta', beta=10)
x_in = torch.Tensor(x_train)
y_in = torch.Tensor(y_train)
# Training loop # Training loop
fig = plt.figure('Prototype Visualization') title = 'Prototype Visualization'
fig = plt.figure(title)
for epoch in range(70): for epoch in range(70):
# Compute loss. # Compute loss
distances, plabels = model(torch.tensor(x_train)) dis, plabels = model(x_in)
loss = criterion([distances, plabels], torch.tensor(y_train)) loss = criterion([dis, plabels], y_in)
print(f'Epoch: {epoch + 1:03d} Loss: {loss.item():02.02f}') print(f'Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f}')
# Take a gradient descent step # Take a gradient descent step
optimizer.zero_grad() optimizer.zero_grad()
@ -61,6 +65,9 @@ for epoch in range(70):
# Visualize the data and the prototypes # Visualize the data and the prototypes
ax = fig.gca() ax = fig.gca()
ax.cla() ax.cla()
ax.set_title(title)
ax.set_xlabel('Data dimension 1')
ax.set_ylabel('Data dimension 2')
cmap = 'viridis' cmap = 'viridis'
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor='k') ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor='k')
ax.scatter(protos[:, 0], ax.scatter(protos[:, 0],
@ -72,28 +79,17 @@ for epoch in range(70):
s=50) s=50)
# Paint decision regions # Paint decision regions
border = 1
resolution = 50
x = np.vstack((x_train, protos)) x = np.vstack((x_train, protos))
x_min, x_max = x[:, 0].min(), x[:, 0].max() x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
y_min, y_max = x[:, 1].min(), x[:, 1].max() y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
x_min, x_max = x_min - border, x_max + border xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
y_min, y_max = y_min - border, y_max + border np.arange(y_min, y_max, 1 / 50))
try:
xx, yy = np.meshgrid(np.arange(x_min, x_max, 1.0 / resolution),
np.arange(y_min, y_max, 1.0 / resolution))
except ValueError as ve:
print(ve)
raise ValueError(f'x_min: {x_min}, x_max: {x_max}. '
f'x_min - x_max is {x_max - x_min}.')
except MemoryError as me:
print(me)
raise ValueError('Too many points. ' 'Try reducing the resolution.')
mesh_input = np.c_[xx.ravel(), yy.ravel()] mesh_input = np.c_[xx.ravel(), yy.ravel()]
torch_input = torch.from_numpy(mesh_input) torch_input = torch.Tensor(mesh_input)
d = model(torch_input)[0] d = model(torch_input)[0]
y_pred = np.argmin(d.detach().numpy(), axis=1) y_pred = np.argmin(d.detach().numpy(),
axis=1) # assume one prototype per class
y_pred = y_pred.reshape(xx.shape) y_pred = y_pred.reshape(xx.shape)
# Plot voronoi regions # Plot voronoi regions
@ -101,4 +97,5 @@ for epoch in range(70):
ax.set_xlim(left=x_min + 0, right=x_max - 0) ax.set_xlim(left=x_min + 0, right=x_max - 0)
ax.set_ylim(bottom=y_min + 0, top=y_max - 0) ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
plt.pause(0.1) plt.pause(0.1)