def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_custom(
tr_x,
tr_y,
te_x,
te_y,
net_func=cifar10_sequential_cbn6d_wd,
optimizer=tf.train.RMSPropOptimizer,
optimizer_args={
'decay': 0.9,
'epsilon': 1e-8
},
n_epochs=125,
batch_size=64,
lr_decay_func=DivideAtRatesWithDecay(start=0.001,
divide_by=2,
at=[0.6, 0.8],
max_steps=125,
decay=1e-6),
weight_decay=0.0001)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_basic(
tr_x, tr_y, te_x, te_y, net_func=cifar10_sequential_cbn6d)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_basic(
tr_x, tr_y, te_x, te_y, net_func=cifar10_bn_inception_v1)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_basic(
tr_x, tr_y, te_x, te_y, net_func=cifar10_resnet_bottleneck_20)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_basic(tr_x,
tr_y,
te_x,
te_y,
net_func=partial(
cifar10_se_resnext_29,
cardinality=8,
group_width=16))
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_custom(
tr_x, tr_y, te_x, te_y,
net_func = partial(cifar10_sequential_clrn5d3_wd, drop_rate = 0.3),
optimizer = tf.train.AdamOptimizer,
optimizer_args = None,
n_epochs = 100,
batch_size = 128,
lr_decay_func = FixValue(value = 1e-4),
weight_decay = 0.0001
)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_custom(
tr_x,
tr_y,
te_x,
te_y,
net_func=partial(cifar10_sequential_c3d_selu_drop, drop_rate=0.5),
n_epochs=50,
batch_size=128,
lr_decay_func=ExponentialDecay(start=0.001, stop=0.0001, max_steps=50),
optimizer=tf.train.AdamOptimizer,
weight_decay=None,
augmentation=False)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_custom(
tr_x,
tr_y,
te_x,
te_y,
net_func=cifar10_inception_v3,
optimizer=tf.train.AdamOptimizer,
optimizer_args=None,
n_epochs=50,
batch_size=128,
aux_loss_weight=0.3,
label_smoothing=0.1,
lr_decay_func=ExponentialDecay(start=0.01, stop=0.001, max_steps=50),
weight_decay=None,
augmentation=False)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main(seed=42):
weight_decay = 0.0001
margin = 0.2
n_epochs = 50
batch_size = 128
_tr_x, _tr_y, _te_x, _te_y = load_mnist_data()
_tr_x, _te_x = global_mean_std(_tr_x, _te_x)
temp_path = os.path.join(temp_folder, "mnist_siamese_data.pkl")
if not os.path.exists(temp_path):
# tr_x, tr_y = generate_pair_indices(_tr_x, _tr_y, max_pos = 9000, max_neg = 9000, seed = seed)
tr_x, tr_y = generate_pair_indices2(_tr_x,
_tr_y,
max_pos=45000,
max_neg=45000,
seed=seed)
# te_x, te_y = generate_pair_indices(_te_x, _te_y, max_pos = 2250, max_neg = 2250, seed = seed)
te_x, te_y = generate_pair_indices2(_te_x,
_te_y,
max_pos=9000,
max_neg=9000,
seed=seed)
with open(temp_path, "wb") as f:
pickle.dump((tr_x, tr_y, te_x, te_y), f)
else:
with open(temp_path, "rb") as f:
tr_x, tr_y, te_x, te_y = pickle.load(f)
height = _tr_x.shape[1]
width = _tr_x.shape[2]
n_chans = _tr_x.shape[3]
n_classes = _tr_y.shape[1]
tf.reset_default_graph()
np.random.seed(seed)
tf.set_random_seed(seed)
x1 = tf.placeholder(tf.float32, [None, height, width, n_chans],
name="input1")
x2 = tf.placeholder(tf.float32, [None, height, width, n_chans],
name="input2")
label = tf.placeholder(tf.float32, [None, 1], name='label')
layers, variables = mnist_siamese(x1,
x2,
weight_decay=weight_decay,
seed=seed)
training = tuple_list_find(variables, "training")[1]
output = tuple_list_find(layers, "output")[1]
output1 = tuple_list_find(output[0], "output")[1]
output2 = tuple_list_find(output[1], "output")[1]
_loss = contrastive_loss(output1, output2, label, margin=margin)
if weight_decay is not None:
reg_ws = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
loss_fn = _loss + tf.reduce_sum(reg_ws)
else:
loss_fn = _loss
global_step = tf.Variable(0, trainable=False, name="global_step")
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=0.99,
use_nesterov=True).minimize(
loss_fn,
global_step=global_step)
# learning rate with exponential decay
lr_decay = ExponentialDecay(start=0.01, stop=0.001, max_steps=50)
# data augmentation
# transformer = RandomizedTransformer(
# transformer_class = Affine,
# params = [('shape', (height, width, n_chans)),
# ('scale', 1.0)],
# rand_params = [('r', [-3.0, 3.0]),
# ('tx', [-3.0, 3.0]),
# ('ty', [-3.0, 3.0]),
# ('reflect_y', [False, True])],
# mode = 'each',
# random_seed = seed)
transformer = None
session = tf.Session()
with session.as_default():
session.run(tf.global_variables_initializer())
for i in range(n_epochs):
lr = next(lr_decay)
# training via random batches
for (xb, yb) in random_batch_generator(batch_size,
tr_x,
tr_y,
seed=seed + i):
idx1 = xb[:, 0]
idx2 = xb[:, 1]
xb1 = _tr_x[idx1]
xb2 = _tr_x[idx2]
if transformer is not None:
xbtr1 = np.zeros_like(xb1)
for j in range(len(xb1)):
xbtr1[j] = transformer.transform(xb1[j])
xbtr2 = np.zeros_like(xb2)
for j in range(len(xb2)):
xbtr2[j] = transformer.transform(xb2[j])
else:
xbtr1 = xb1
xbtr2 = xb2
session.run(train_step,
feed_dict={
x1: xbtr1,
x2: xbtr2,
label: yb,
training: True,
learning_rate: lr
})
tr_loss = []
# evaluations on train set
for (xb, yb) in batch_generator(256, tr_x, tr_y, fixed_size=False):
idx1 = xb[:, 0]
idx2 = xb[:, 1]
xb1 = _tr_x[idx1]
xb2 = _tr_x[idx2]
loss = session.run(_loss,
feed_dict={
x1: xb1,
x2: xb2,
label: yb,
training: False
})
tr_loss.append(loss)
te_loss = []
# evaluations on test set
for (xb, yb) in batch_generator(256, te_x, te_y, fixed_size=False):
idx1 = xb[:, 0]
idx2 = xb[:, 1]
xb1 = _te_x[idx1]
xb2 = _te_x[idx2]
loss = session.run(_loss,
feed_dict={
x1: xb1,
x2: xb2,
label: yb,
training: False
})
te_loss.append(loss)
print("Epoch: ", i)
print("Learning rate: ", lr)
print("Train loss: " + str(np.mean(tr_loss)))
print("Test loss: " + str(np.mean(te_loss)))
var_path = os.path.join(temp_folder, "mnist_siamese_net.h5")
save_variables(session, var_path)
embeddings = []
labels = []
for xb in batch_generator(256, tr_x, y=None, fixed_size=False):
idx1 = xb[:, 0]
idx2 = xb[:, 1]
xb1 = _tr_x[idx1]
xb2 = _tr_x[idx2]
yb1 = _tr_y[idx1]
yb2 = _tr_y[idx2]
out1 = session.run(output1, feed_dict={x1: xb1, training: False})
out2 = session.run(output1, feed_dict={x1: xb2, training: False})
embeddings.append(out1)
embeddings.append(out2)
labels.append(yb1)
labels.append(yb2)
embeddings = np.concatenate(embeddings)
labels = np.concatenate(labels)
labels = np.argmax(labels, axis=1)
# generated by glasbey
palette = load_palette("palette50.txt", skip_first=True,
scale01=True) # skip white
palette = palette[0:n_classes]
#palette = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', '#ff00ff', '#990000', '#999900', '#009900', '#009999']
f = plt.figure()
for i in range(n_classes):
idx = labels == i
coords = embeddings[idx, :]
plt.plot(coords[:, 0], coords[:, 1], '.', color=palette[i])
plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
plt.show()
