def class_avg_chainthaw(model,
nb_classes,
train,
val,
test,
batch_size,
loss,
epoch_size,
nb_epochs,
checkpoint_weight_path,
f1_init_weight_path,
patience=5,
initial_lr=0.001,
next_lr=0.0001,
seed=None,
verbose=True):
""" Finetunes given model using chain-thaw and evaluates using F1.
For a dataset with multiple classes, the model is trained once for
each class, relabeling those classes into a binary classification task.
The result is an average of all F1 scores for each class.
# Arguments:
model: Model to be finetuned.
nb_classes: Number of classes in the given dataset.
train: Training data, given as a tuple of (inputs, outputs)
val: Validation data, given as a tuple of (inputs, outputs)
test: Testing data, given as a tuple of (inputs, outputs)
batch_size: Batch size.
loss: Loss function to be used during training.
epoch_size: Number of samples in an epoch.
nb_epochs: Number of epochs.
checkpoint_weight_path: Filepath where weights will be checkpointed to
during training. This file will be rewritten by the function.
f1_init_weight_path: Filepath where weights will be saved to and
reloaded from before training each class. This ensures that
each class is trained independently. This file will be rewritten.
initial_lr: Initial learning rate. Will only be used for the first
training step (i.e. the softmax layer)
next_lr: Learning rate for every subsequent step.
seed: Random number generator seed.
verbose: Verbosity flag.
# Returns:
Averaged F1 score.
"""
# Unpack args
X_train, y_train = train
X_val, y_val = val
X_test, y_test = test
total_f1 = 0
nb_iter = nb_classes if nb_classes > 2 else 1
model.save_weights(f1_init_weight_path)
for i in range(nb_iter):
if verbose:
print('Iteration number {}/{}'.format(i + 1, nb_iter))
model.load_weights(f1_init_weight_path, by_name=False)
y_train_new, y_val_new, y_test_new = prepare_labels(
y_train, y_val, y_test, i, nb_classes)
train_gen, X_val_resamp, y_val_resamp = \
prepare_generators(X_train, y_train_new, X_val, y_val_new,
batch_size, epoch_size)
if verbose:
print("Training..")
callbacks = finetuning_callbacks(checkpoint_weight_path,
patience=patience)
# Train using chain-thaw
train_by_chain_thaw(model=model,
train_gen=train_gen,
val_data=(X_val_resamp, y_val_resamp),
loss=loss,
callbacks=callbacks,
epoch_size=epoch_size,
nb_epochs=nb_epochs,
checkpoint_weight_path=checkpoint_weight_path,
initial_lr=initial_lr,
next_lr=next_lr,
batch_size=batch_size,
verbose=verbose)
# Evaluate
y_pred_val = np.array(model.predict(X_val, batch_size=batch_size))
y_pred_test = np.array(model.predict(X_test, batch_size=batch_size))
f1_test, best_t = find_f1_threshold(y_val_new, y_pred_val, y_test_new,
y_pred_test)
if verbose:
print('f1_test: {}'.format(f1_test))
print('best_t: {}'.format(best_t))
total_f1 += f1_test
return total_f1 / nb_iter
def class_avg_chainthaw(model, nb_classes, train, val, test, batch_size,
loss, epoch_size, nb_epochs, checkpoint_weight_path,
f1_init_weight_path, patience=5,
initial_lr=0.001, next_lr=0.0001,
seed=None, verbose=True):
""" Finetunes given model using chain-thaw and evaluates using F1.
For a dataset with multiple classes, the model is trained once for
each class, relabeling those classes into a binary classification task.
The result is an average of all F1 scores for each class.
# Arguments:
model: Model to be finetuned.
nb_classes: Number of classes in the given dataset.
train: Training data, given as a tuple of (inputs, outputs)
val: Validation data, given as a tuple of (inputs, outputs)
test: Testing data, given as a tuple of (inputs, outputs)
batch_size: Batch size.
loss: Loss function to be used during training.
epoch_size: Number of samples in an epoch.
nb_epochs: Number of epochs.
checkpoint_weight_path: Filepath where weights will be checkpointed to
during training. This file will be rewritten by the function.
f1_init_weight_path: Filepath where weights will be saved to and
reloaded from before training each class. This ensures that
each class is trained independently. This file will be rewritten.
initial_lr: Initial learning rate. Will only be used for the first
training step (i.e. the softmax layer)
next_lr: Learning rate for every subsequent step.
seed: Random number generator seed.
verbose: Verbosity flag.
# Returns:
Averaged F1 score.
"""
# Unpack args
X_train, y_train = train
X_val, y_val = val
X_test, y_test = test
total_f1 = 0
nb_iter = nb_classes if nb_classes > 2 else 1
model.save_weights(f1_init_weight_path)
for i in range(nb_iter):
if verbose:
print('Iteration number {}/{}'.format(i + 1, nb_iter))
model.load_weights(f1_init_weight_path, by_name=False)
y_train_new, y_val_new, y_test_new = prepare_labels(y_train, y_val,
y_test, i, nb_classes)
train_gen, X_val_resamp, y_val_resamp = \
prepare_generators(X_train, y_train_new, X_val, y_val_new,
batch_size, epoch_size)
if verbose:
print("Training..")
callbacks = finetuning_callbacks(checkpoint_weight_path, patience=patience)
# Train using chain-thaw
train_by_chain_thaw(model=model, train_gen=train_gen,
val_data=(X_val_resamp, y_val_resamp),
loss=loss, callbacks=callbacks,
epoch_size=epoch_size, nb_epochs=nb_epochs,
checkpoint_weight_path=checkpoint_weight_path,
initial_lr=initial_lr, next_lr=next_lr,
batch_size=batch_size, verbose=verbose)
# Evaluate
y_pred_val = np.array(model.predict(X_val, batch_size=batch_size))
y_pred_test = np.array(model.predict(X_test, batch_size=batch_size))
f1_test, best_t = find_f1_threshold(y_val_new, y_pred_val,
y_test_new, y_pred_test)
if verbose:
print('f1_test: {}'.format(f1_test))
print('best_t: {}'.format(best_t))
total_f1 += f1_test
return total_f1 / nb_iter