def make_mlp(training_data, validation_data, test_data, W, args):
(X_train, y_train) = training_data
(X_val, y_val) = validation_data
(X_test, y_test) = test_data
(W, hb) = W
dense_params = {}
if W is not None and hb is not None:
dense_params['weights'] = (W, hb)
# define and initialize MLP model
mlp = Sequential([
Dense(5000, input_shape=(3 * 32 * 32,),
kernel_regularizer=regularizers.l2(args.mlp_l2),
kernel_initializer=glorot_uniform(seed=3333),
**dense_params),
BN(),
Activation('relu'),
Dropout(args.mlp_dropout, seed=4444),
Dense(10, kernel_initializer=glorot_uniform(seed=5555)),
Activation('softmax'),
])
mlp.compile(optimizer=MultiAdam(lr=0.001,
lr_multipliers={'dense_1': args.mlp_lrm[0],
'dense_2': args.mlp_lrm[1]}),
loss='categorical_crossentropy',
metrics=['accuracy'])
# train and evaluate classifier
with Stopwatch(verbose=True) as s:
early_stopping = EarlyStopping(monitor=args.mlp_val_metric, patience=12, verbose=2)
reduce_lr = ReduceLROnPlateau(monitor=args.mlp_val_metric, factor=0.2, verbose=2,
patience=6, min_lr=1e-5)
callbacks = [early_stopping, reduce_lr]
try:
mlp.fit(X_train, one_hot(y_train, n_classes=10),
epochs=args.mlp_epochs,
batch_size=args.mlp_batch_size,
shuffle=False,
validation_data=(X_val, one_hot(y_val, n_classes=10)),
callbacks=callbacks)
except KeyboardInterrupt:
pass
y_pred = mlp.predict(X_test)
y_pred = unhot(one_hot_decision_function(y_pred), n_classes=10)
print("Test accuracy: {:.4f}".format(accuracy_score(y_test, y_pred)))
# save predictions, targets, and fine-tuned weights
np.save(args.mlp_save_prefix + 'y_pred.npy', y_pred)
np.save(args.mlp_save_prefix + 'y_test.npy', y_test)
W_finetuned, _ = mlp.layers[0].get_weights()
np.save(args.mlp_save_prefix + 'W_finetuned.npy', W_finetuned)
patience=3,
min_lr=1e-5)
callbacks = [early_stopping, reduce_lr]
try:
mlp.fit(X_train,
one_hot(y_train, n_classes=10),
epochs=args.mlp_epochs,
batch_size=args.mlp_batch_size,
shuffle=False,
validation_data=(X_val, one_hot(y_val, n_classes=10)),
callbacks=callbacks)
except KeyboardInterrupt:
pass
y_pred = mlp.predict(X_test)
y_pred = unhot(one_hot_decision_function(y_pred), n_classes=10)
print "Test accuracy: {:.4f}".format(accuracy_score(y_test, y_pred))
# save predictions, targets, and fine-tuned weights
np.save(args.mlp_save_prefix + 'y_pred.npy', y_pred)
np.save(args.mlp_save_prefix + 'y_test.npy', y_test)
W_finetuned, _ = mlp.layers[0].get_weights()
np.save(args.mlp_save_prefix + 'W_finetuned.npy', W_finetuned)
def main():
# training settings
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# general