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"""
A class to record training dynamics, including:
1. loss
2. uncertainty
3. position
4. velocity
5. acceleration
6. hard samples
7. training dynamics
8.
"""
import numpy as np
import umap
import matplotlib.pyplot as plt
def softmax(x):
return np.exp(x) / np.sum(np.exp(x))
def cross_entropy(data, y):
log_p = np.array([np.log(softmax(data[i])) for i in range(len(data))])
y_onehot = np.eye(len(np.unique(y)))[y]
loss = - np.sum(y_onehot * log_p, axis=1)
return loss
class TD:
def __init__(self, data_provider, projector) -> None:
self.data_provider = data_provider
self.projector = projector
def loss_dynamics(self, ):
EPOCH_START = self.data_provider.s
EPOCH_END = self.data_provider.e
EPOCH_PERIOD = self.data_provider.p
labels = self.data_provider.train_labels(EPOCH_START)
# epoch, num, 1
losses = None
for epoch in range(EPOCH_START, EPOCH_END+1, EPOCH_PERIOD):
representation = self.data_provider.train_representation(epoch)
pred = self.data_provider.get_pred(epoch, representation)
loss = cross_entropy(pred, labels)
if losses is None:
losses = np.expand_dims(loss, axis=0)
else:
losses = np.concatenate((losses, np.expand_dims(loss, axis=0)), axis=0)
losses = np.transpose(losses, [1,0])
return losses
def uncertainty_dynamics(self):
EPOCH_START = self.data_provider.s
EPOCH_END = self.data_provider.e
EPOCH_PERIOD = self.data_provider.p
labels = self.data_provider.train_labels(EPOCH_START)
# epoch, num, 1
uncertainties = None
for epoch in range(EPOCH_START, EPOCH_END+1, EPOCH_PERIOD):
representation = self.data_provider.train_representation(epoch)
pred = self.data_provider.get_pred(epoch, representation)
uncertainty = pred[np.arange(len(labels)), labels]
if uncertainties is None:
uncertainties = np.expand_dims(uncertainty, axis=0)
else:
uncertainties = np.concatenate((uncertainties, np.expand_dims(uncertainty, axis=0)), axis=0)
uncertainties = np.transpose(uncertainties, [1,0])
return uncertainties
def pred_dynamics(self):
EPOCH_START = self.data_provider.s
EPOCH_END = self.data_provider.e
EPOCH_PERIOD = self.data_provider.p
# epoch, num, 1
preds = None
for epoch in range(EPOCH_START, EPOCH_END+1, EPOCH_PERIOD):
representation = self.data_provider.train_representation(epoch)
pred = self.data_provider.get_pred(epoch, representation)
if preds is None:
preds = np.expand_dims(pred, axis=0)
else:
preds = np.concatenate((preds, np.expand_dims(pred, axis=0)), axis=0)
preds = np.transpose(preds, [1,0, 2])
return preds
def dloss_dt_dynamics(self, ):
return
def position_dynamics(self):
EPOCH_START = self.data_provider.s
EPOCH_END = self.data_provider.e
EPOCH_PERIOD = self.data_provider.p
# epoch, num, dims
embeddings = None
for epoch in range(EPOCH_START, EPOCH_END+1, EPOCH_PERIOD):
representation = self.data_provider.train_representation(epoch)
embedding = self.projector.batch_project(epoch, representation)
if embeddings is None:
embeddings = np.expand_dims(embedding, axis=0)
else:
embeddings = np.concatenate((embeddings, np.expand_dims(embedding, axis=0)), axis=0)
embeddings = np.transpose(embeddings, [1,0,2])
return embeddings
def velocity_dynamics(self,):
position_dynamics = self.position_dynamics()
return position_dynamics[:, 1:, :] - position_dynamics[:, :-1, :]
def acceleration_dynamics(self, ):
velocity_dynamics = self.velocity_dynamics()
return velocity_dynamics[:, 1:, :] - velocity_dynamics[:, :-1, :]
def show_ground_truth(self, trajectories, noise_idxs, save_path=None):
num = len(trajectories)
trajectories = trajectories.reshape(num, -1)
reducer = umap.UMAP()
embeddings = reducer.fit_transform(trajectories)
EPOCH_START = self.data_provider.s
labels = self.data_provider.train_labels(EPOCH_START)
plt.scatter(
embeddings[:, 0],
embeddings[:, 1],
s=.3,
c=labels,
cmap="tab10")
plt.scatter(
embeddings[:, 0][noise_idxs],
embeddings[:, 1][noise_idxs],
s=.4,
c='black')
if save_path is None:
plt.show()
else:
plt.savefig(save_path)
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