handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle, train_dataset.output_types, train_dataset.output_shapes)
features, labels = iterator.get_next()
features = tf.reshape(features, (-1, 227, 227, 3))
labels = tf.one_hot(labels, depth=1000)
train_iterator = train_dataset.make_initializable_iterator()
val_iterator = val_dataset.make_initializable_iterator()
###############################################################
if args.act == 'tanh':
act = Tanh()
elif args.act == 'relu':
act = Relu()
else:
assert(False)
###############################################################
weights_conv = './transfer/alexnet_weights.npy'
weights_fc = None
train_conv = weights_conv == None
train_fc = weights_fc == None
###############################################################
batch_size = tf.placeholder(tf.int32, shape=())
dropout_rate = tf.placeholder(tf.float32, shape=())
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=200)
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--alpha', type=float, default=1e-4)
parser.add_argument('--beta', type=float,
default=1e-4) #feedback weights, B, learning rate
parser.add_argument('--sigma', type=float,
default=0.1) #node pert standard deviation
parser.add_argument('--l2', type=float, default=0.)
parser.add_argument('--decay', type=float, default=1.)
parser.add_argument('--eps', type=float, default=1e-5)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--act', type=str, default='tanh')
parser.add_argument('--bias', type=float, default=0.1)
parser.add_argument('--gpu', type=int, default=1)
parser.add_argument('--dfa', type=int, default=1)
parser.add_argument('--feedbacklearning', type=int,
default=1) #Whether or not to learn feedback weights
parser.add_argument('--sparse', type=int, default=0)
parser.add_argument('--rank', type=int, default=0)
parser.add_argument('--init', type=str, default="sqrt_fan_in")
parser.add_argument('--opt', type=str, default="adam")
parser.add_argument('--N', type=int, default=50)
parser.add_argument('--save', type=int, default=0)
parser.add_argument('--name', type=str, default="cifar10_conv_np")
parser.add_argument('--load', type=str, default=None)
args = parser.parse_args()
if args.gpu >= 0:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
cifar10 = tf.keras.datasets.cifar10.load_data()
##############################################
EPOCHS = args.epochs
TRAIN_EXAMPLES = 50000
TEST_EXAMPLES = 10000
BATCH_SIZE = args.batch_size
if args.act == 'tanh':
act = Tanh()
elif args.act == 'relu':
act = Relu()
else:
assert (False)
train_fc = True
if args.load:
train_conv = False
else:
train_conv = True
weights_fc = None
weights_conv = args.load
#Setup the parameters
attrs = ['sigma', 'alpha', 'beta']
log_scale = [True, True, True]
ranges = [[-4, -1], [-6, -3], [-6, -3]]
params = []
isnan = []
train_accs = []
test_accs = []
#Here we run a bunch of times for different parameters...
for idx in range(args.N):
#Choose some random parameters...
param = set_random_hyperparameters(args, attrs, ranges, log_scale)
params.append(param)
if args.feedbacklearning == 0:
args.beta = 0
#Tell me the params....
print('Alpha, beta, sigma are: ', args.alpha, args.beta, args.sigma)
tf.set_random_seed(0)
tf.reset_default_graph()
batch_size = tf.placeholder(tf.int32, shape=())
dropout_rate = tf.placeholder(tf.float32, shape=())
learning_rate = tf.placeholder(tf.float32, shape=())
sigma = tf.placeholder(tf.float32, shape=(), name="Sigma")
X = tf.placeholder(tf.float32, [None, 32, 32, 3])
X = tf.map_fn(lambda frame: tf.image.per_image_standardization(frame),
X)
Y = tf.placeholder(tf.float32, [None, 10])
l0 = Convolution(input_sizes=[batch_size, 32, 32, 3],
filter_sizes=[5, 5, 3, 96],
num_classes=10,
init_filters=args.init,
strides=[1, 1, 1, 1],
padding="SAME",
alpha=learning_rate,
activation=act,
bias=args.bias,
last_layer=False,
name='conv1',
load=weights_conv,
train=train_conv)
l1 = MaxPool(size=[batch_size, 32, 32, 96],
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding="SAME")
#Add perturbation to activity to get output to train feedback weights with
l2p = NodePert(size=[batch_size, 16, 16, 96], sigma=sigma)
l2 = FeedbackConv(size=[batch_size, 16, 16, 96],
num_classes=10,
sparse=args.sparse,
rank=args.rank,
name='conv1_fb')
l3 = Convolution(input_sizes=[batch_size, 16, 16, 96],
filter_sizes=[5, 5, 96, 128],
num_classes=10,
init_filters=args.init,
strides=[1, 1, 1, 1],
padding="SAME",
alpha=learning_rate,
activation=act,
bias=args.bias,
last_layer=False,
name='conv2',
load=weights_conv,
train=train_conv)
l4 = MaxPool(size=[batch_size, 16, 16, 128],
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding="SAME")
l5p = NodePert(size=[batch_size, 8, 8, 128], sigma=sigma)
l5 = FeedbackConv(size=[batch_size, 8, 8, 128],
num_classes=10,
sparse=args.sparse,
rank=args.rank,
name='conv2_fb')
l6 = Convolution(input_sizes=[batch_size, 8, 8, 128],
filter_sizes=[5, 5, 128, 256],
num_classes=10,
init_filters=args.init,
strides=[1, 1, 1, 1],
padding="SAME",
alpha=learning_rate,
activation=act,
bias=args.bias,
last_layer=False,
name='conv3',
load=weights_conv,
train=train_conv)
l7 = MaxPool(size=[batch_size, 8, 8, 256],
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding="SAME")
l8p = NodePert(size=[batch_size, 4, 4, 256], sigma=sigma)
l8 = FeedbackConv(size=[batch_size, 4, 4, 256],
num_classes=10,
sparse=args.sparse,
rank=args.rank,
name='conv3_fb')
l9 = ConvToFullyConnected(shape=[4, 4, 256])
l10p = NodePert(size=[batch_size, 4 * 4 * 256], sigma=sigma)
l10 = FullyConnected(size=[4 * 4 * 256, 2048],
num_classes=10,
init_weights=args.init,
alpha=learning_rate,
activation=act,
bias=args.bias,
last_layer=False,
name='fc1',
load=weights_fc,
train=train_fc)
l11 = Dropout(rate=dropout_rate)
l12 = FeedbackFC(size=[4 * 4 * 256, 2048],
num_classes=10,
sparse=args.sparse,
rank=args.rank,
name='fc1_fb')
l13p = NodePert(size=[batch_size, 2048], sigma=sigma)
l13 = FullyConnected(size=[2048, 2048],
num_classes=10,
init_weights=args.init,
alpha=learning_rate,
activation=act,
bias=args.bias,
last_layer=False,
name='fc2',
load=weights_fc,
train=train_fc)
l14 = Dropout(rate=dropout_rate)
l15 = FeedbackFC(size=[2048, 2048],
num_classes=10,
sparse=args.sparse,
rank=args.rank,
name='fc2_fb')
l16 = FullyConnected(size=[2048, 10],
num_classes=10,
init_weights=args.init,
alpha=learning_rate,
activation=Linear(),
bias=args.bias,
last_layer=True,
name='fc3',
load=weights_fc,
train=train_fc)
##############################################
model = Model(layers=[
l0, l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12, l13, l14,
l15, l16
])
model_perturbed = Model(layers=[
l0, l1, l2p, l2, l3, l4, l5p, l5, l6, l7, l8p, l8, l9, l10p, l10,
l11, l12, l13p, l13, l14, l15, l16
])
predict = model.predict(X=X)
predict_perturbed = model_perturbed.predict(X=X)
#######
#Pairs of perturbations and feedback weights
#feedbackpairs = [[l2p, l2], [l5p, l5], [l8p, l8], [l10p, l12], [l13p, l15]]
#Test one at a time... this works, so it must be l10p, 12 pair that fails
feedbackpairs = [[l2p, l2], [l5p, l5], [l8p, l8], [l13p, l15]]
#Get noise, feedback matrices, and loss function and unperturbed loss function, to make update rule for feedback weights
loss = tf.reduce_sum(tf.pow(tf.nn.softmax(predict) - Y, 2), 1) / 2
loss_perturbed = tf.reduce_sum(
tf.pow(tf.nn.softmax(predict_perturbed) - Y, 2), 1) / 2
train_B = []
E = tf.nn.softmax(predict) - Y
for idx, (noise, feedback) in enumerate(feedbackpairs):
print(idx, batch_size, feedback.output_size)
xi = tf.reshape(noise.get_noise(),
(batch_size, feedback.output_size))
B = feedback.B
lambd = tf.matmul(
tf.diag(loss_perturbed - loss) / args.sigma / args.sigma, xi)
np_error = tf.matmul(E, B) - lambd
grad_B = tf.matmul(tf.transpose(E), np_error)
new_B = B.assign(B - args.beta * grad_B)
train_B.append(new_B)
#######
weights = model.get_weights()
if args.opt == "adam" or args.opt == "rms" or args.opt == "decay":
if args.dfa:
grads_and_vars = model.dfa_gvs(X=X, Y=Y)
else:
grads_and_vars = model.gvs(X=X, Y=Y)
if args.opt == "adam":
train = tf.train.AdamOptimizer(
learning_rate=learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=args.eps).apply_gradients(
grads_and_vars=grads_and_vars)
elif args.opt == "rms":
train = tf.train.RMSPropOptimizer(
learning_rate=learning_rate, decay=0.99,
epsilon=args.eps).apply_gradients(
grads_and_vars=grads_and_vars)
elif args.opt == "decay":
train = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate).apply_gradients(
grads_and_vars=grads_and_vars)
else:
assert (False)
else:
if args.dfa:
train = model.dfa(X=X, Y=Y)
else:
train = model.train(X=X, Y=Y)
correct = tf.equal(tf.argmax(predict, 1), tf.argmax(Y, 1))
total_correct = tf.reduce_sum(tf.cast(correct, tf.float32))
##############################################
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
(x_train, y_train), (x_test, y_test) = cifar10
x_train = x_train.reshape(TRAIN_EXAMPLES, 32, 32, 3)
y_train = keras.utils.to_categorical(y_train, 10)
x_test = x_test.reshape(TEST_EXAMPLES, 32, 32, 3)
y_test = keras.utils.to_categorical(y_test, 10)
##############################################
filename = args.name + '.results'
f = open(filename, "w")
f.write(filename + "\n")
f.write("total params: " + str(model.num_params()) + "\n")
f.close()
##############################################
for ii in range(EPOCHS):
if args.opt == 'decay' or args.opt == 'gd':
decay = np.power(args.decay, ii)
lr = args.alpha * decay
else:
lr = args.alpha
print(ii)
#############################
_count = 0
_total_correct = 0
#The training loop... here we add something to also update the feedback weights with the node pert
for jj in range(int(TRAIN_EXAMPLES / BATCH_SIZE)):
xs = x_train[jj * BATCH_SIZE:(jj + 1) * BATCH_SIZE]
ys = y_train[jj * BATCH_SIZE:(jj + 1) * BATCH_SIZE]
_correct, _ = sess.run(
[total_correct, train],
feed_dict={
sigma: 0.0,
batch_size: BATCH_SIZE,
dropout_rate: args.dropout,
learning_rate: lr,
X: xs,
Y: ys
})
#Add step to update B......
_ = sess.run(
[train_B],
feed_dict={
sigma: args.sigma,
batch_size: BATCH_SIZE,
dropout_rate: args.dropout,
learning_rate: lr,
X: xs,
Y: ys
})
_total_correct += _correct
_count += BATCH_SIZE
train_acc = 1.0 * _total_correct / _count
train_accs.append(train_acc)
#############################
_count = 0
_total_correct = 0
for jj in range(int(TEST_EXAMPLES / BATCH_SIZE)):
xs = x_test[jj * BATCH_SIZE:(jj + 1) * BATCH_SIZE]
ys = y_test[jj * BATCH_SIZE:(jj + 1) * BATCH_SIZE]
_correct = sess.run(total_correct,
feed_dict={
sigma: 0.0,
batch_size: BATCH_SIZE,
dropout_rate: 0.0,
learning_rate: 0.0,
X: xs,
Y: ys
})
_total_correct += _correct
_count += BATCH_SIZE
test_acc = 1.0 * _total_correct / _count
test_accs.append(test_acc)
isnan.append(None)
#try:
# trainer.train()
#except ValueError:
# print("Method fails to converge for these parameters")
# isnan[n,m] = 1
#Save results...
#############################
print("train acc: %f test acc: %f" % (train_acc, test_acc))
f = open(filename, "a")
f.write("train acc: %f test acc: %f\n" % (train_acc, test_acc))
f.close()
#Save params after each run
fn = "./cifar10_conv_np_hyperparam_search_varalpha_septsearch_2_dfa_%d_fblearning_%d.npz" % (
args.dfa, args.feedbacklearning)
to_save = {
'attr': attrs,
'params': params,
'train_accs': train_accs,
'test_accs': test_accs,
'isnan': isnan
}
pickle.dump(to_save, open(fn, "wb"))