def feature_extraction_siamese():
"""
This method restores a TensorFlow checkpoint file (.ckpt) and rebuilds inference
model with restored parameters. From then on you can basically use that model in
any way you want, for instance, feature extraction, finetuning or as a submodule
of a larger architecture. However, this method should extract features from a
specified layer and store them in data files such as '.h5', '.npy'/'.npz'
depending on your preference. You will use those files later in the assignment.
Args:
[optional]
Returns:
None
"""
########################
# PUT YOUR CODE HERE #
########################
tf.reset_default_graph()
classes = [
'plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship',
'truck'
]
tf.set_random_seed(42)
np.random.seed(42)
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
x_test, y_test = cifar10.test.images, cifar10.test.labels
y_test = np.argmax(y_test, axis=1)
input_data_dim = cifar10.test.images.shape[1]
n_classes = 10
cnn_siamese = Siamese()
x = tf.placeholder(tf.float32,
shape=(None, input_data_dim, input_data_dim, 3),
name="x1")
y = tf.placeholder(tf.float32, shape=(None, 1), name="y")
with tf.name_scope('train_cnn'):
infs1 = cnn_siamese.inference(x, reuse=None)
l2_out = cnn_siamese.l2_out
with tf.Session() as sess:
saver = tf.train.Saver()
saver.restore(sess, FLAGS.checkpoint_dir + '/cnn_model_siamese.ckpt')
l2_out_features = sess.run([l2_out], feed_dict={x: x_test})[0]
_plot_tsne("L2 out", l2_out_features, y_test)
_train_one_vs_all(l2_out_features, y_test, "L2 norm", classes)
def feature_extraction():
"""
This method restores a TensorFlow checkpoint file (.ckpt) and rebuilds inference
model with restored parameters. From then on you can basically use that model in
any way you want, for instance, feature extraction, finetuning or as a submodule
of a larger architecture. However, this method should extract features from a
specified layer and store them in data files such as '.h5', '.npy'/'.npz'
depending on your preference. You will use those files later in the assignment.
Args:
[optional]
Returns:
None
"""
########################
# PUT YOUR CODE HERE #
########################
print('doing feature extraction...')
tf.reset_default_graph()
sess = tf.Session()
cifar10, image_shape, num_classes = standard_cifar10_get(FLAGS)
if FLAGS.train_model == 'siamese':
# Construct siamese graph
# Placeholder variables
x = tf.placeholder(tf.float32,
shape=[None] + list(image_shape),
name='x1')
y = tf.placeholder(tf.float32, shape=(None), name='y')
is_training = tf.placeholder(dtype=tf.bool,
shape=(),
name='isTraining')
# CNN model
model = Siamese(is_training=is_training,
dropout_rate=FLAGS.dropout_rate,
save_stuff=FLAGS.save_stuff,
fc_reg_str=FLAGS.fc_reg_str)
# Get outputs of two siamese models, loss, train optimisation step
l2 = model.inference(x)
#fc2 = model.fc2
fc2 = l2
else:
# Construct linear convnet graph
x = tf.placeholder(tf.float32,
shape=[None] + list(image_shape),
name='x')
y = tf.placeholder(tf.int32, shape=(None, num_classes), name='y')
is_training = tf.placeholder(dtype=tf.bool,
shape=(),
name='isTraining')
model = ConvNet(is_training=is_training,
dropout_rate=FLAGS.dropout_rate)
_ = model.inference(x)
fc2 = model.fc2
# Initialise all variables
tf.initialize_all_variables().run(session=sess)
# Restore checkpoint
saver = tf.train.Saver()
saver.restore(sess, FLAGS.ckpt_path + FLAGS.ckpt_file)
# Get testing data for feed dict
x_data_test, y_data_test = \
cifar10.test.images[:FLAGS.test_size], cifar10.test.labels[:FLAGS.test_size]
# Get the test set features at flatten, fc1 and fc2 layers
flatten_features_test, fc1_features_test, fc2_features_test = \
sess.run([model.flatten, model.fc1, fc2],
{x : x_data_test, y : y_data_test, is_training : False})
# Get t-SNE manifold of these features
tsne = TSNE()
manifold = tsne.fit_transform(fc2_features_test)
# Save to disk for plotting later
indices = np.arange(FLAGS.test_size)
cPickle.dump((manifold, indices),
open('manifold' + FLAGS.train_model + '.dump', 'wb'))
# Get training data for feed dict
x_data_train, y_data_train = \
cifar10.train.images[:FLAGS.train_size_lm], cifar10.train.labels[:FLAGS.train_size_lm]
# Get train set features at flatten, fc1 and fc2 layers
flatten_features_train, fc1_features_train, fc2_features_train = \
sess.run([model.flatten, model.fc1, fc2],
{x : x_data_train, y : y_data_train, is_training : False})
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
features_list = [['flat', flatten_features_train, flatten_features_train],
['fc1 ', fc1_features_train, fc1_features_test],
['fc2 ', fc2_features_train, fc2_features_test]]
for (name, features_train, features_test) in features_list:
classif = OneVsRestClassifier(SVC(kernel='linear'))
classif.fit(features_train, y_data_train)
lm_test_predictions = classif.predict(features_test)
acc = np.mean(
np.argmax(y_data_test, 1) == np.argmax(lm_test_predictions, 1))
print(name, 'accuracy =', np.round(acc * 100, 2), '%')
def train_siamese():
"""
Performs training and evaluation of Siamese model.
First define your graph using class Siamese and its methods. Then define
necessary operations such as trainer (train_step in this case), savers
and summarizers. Finally, initialize your model within a tf.Session and
do the training.
---------------------------
How to evaluate your model:
---------------------------
On train set, it is fine to monitor loss over minibatches. On the other
hand, in order to evaluate on test set you will need to create a fixed
validation set using the data sampling function you implement for siamese
architecture. What you need to do is to iterate over all minibatches in
the validation set and calculate the average loss over all minibatches.
---------------------------------
How often to evaluate your model:
---------------------------------
- on training set every print_freq iterations
- on test set every eval_freq iterations
------------------------
Additional requirements:
------------------------
Also you are supposed to take snapshots of your model state (i.e. graph,
weights and etc.) every checkpoint_freq iterations. For this, you should
study TensorFlow's tf.train.Saver class. For more information, please
checkout:
[https://www.tensorflow.org/versions/r0.11/how_tos/variables/index.html]
"""
# Set the random seeds for reproducibility. DO NOT CHANGE.
tf.set_random_seed(42)
np.random.seed(42)
########################
# PUT YOUR CODE HERE #
########################
# Cifar10 stuff
cifar10, image_shape, num_classes = standard_cifar10_get(FLAGS)
# Placeholder variables
x1 = tf.placeholder(tf.float32,
shape=[None] + list(image_shape),
name='x1')
x2 = tf.placeholder(tf.float32,
shape=[None] + list(image_shape),
name='x2')
y = tf.placeholder(tf.float32, shape=(None), name='y')
is_training = tf.placeholder(dtype=tf.bool, shape=(), name='isTraining')
margin = tf.placeholder(tf.float32, shape=(), name='margin')
# CNN model
model = Siamese(is_training=is_training,
dropout_rate=FLAGS.dropout_rate,
save_stuff=FLAGS.save_stuff,
fc_reg_str=FLAGS.fc_reg_str)
# Get outputs of two siamese models, loss, train optimisation step
l2_out_1 = model.inference(x1)
l2_out_2 = model.inference(x2, reuse=True)
loss_no_reg, d2 = model.loss(l2_out_1, l2_out_2, y, margin)
reg_loss = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
loss_w_reg = loss_no_reg + reg_loss
accuracy = model.accuracy(d2, y, margin)
tf.scalar_summary('loss_incl_reg', loss_w_reg)
train_op = train_step(loss_w_reg)
validation_tuples = create_dataset(
cifar10.test,
num_tuples=FLAGS.siamese_vali_ntuples,
batch_size=FLAGS.batch_size,
fraction_same=FLAGS.siamese_fraction_same)
xv1, xv2, yv = np.vstack([i[0] for i in validation_tuples]),\
np.vstack([i[1] for i in validation_tuples]),\
np.hstack([i[2] for i in validation_tuples])
num_val_chunks = 10
assert (FLAGS.siamese_vali_ntuples % num_val_chunks) == 0
chunks = range(0, xv1.shape[0], int(xv1.shape[0] / num_val_chunks)) + \
[int(FLAGS.siamese_vali_ntuples * FLAGS.batch_size)]
# Function for getting feed dicts
def get_feed(c, train=True, chunk=None, chunks=None):
if train == 'train' or train == 't':
xd1, xd2, yd = \
c.train.next_batch(FLAGS.batch_size, FLAGS.siamese_fraction_same)
return {
x1: xd1,
x2: xd2,
y: yd,
is_training: True,
margin: FLAGS.siamese_margin
}
elif train == 'vali' or train == 'v' or train == 'validation':
if chunk is None:
return {
x1: xv1,
x2: xv2,
y: yv,
is_training: False,
margin: FLAGS.siamese_margin
}
else:
st, en = chunks[chunk], chunks[chunk + 1]
return {
x1: xv1[st:en],
x2: xv2[st:en],
y: yv[st:en],
is_training: False,
margin: FLAGS.siamese_margin
}
else:
pass
# TODO Implement test set feed dict siamese
# For saving checkpoints
saver = tf.train.Saver()
with tf.Session() as sess:
# Initialise all variables
tf.initialize_all_variables().run(session=sess)
# Merge all the summaries
merged = tf.merge_all_summaries()
if FLAGS.save_stuff:
train_writer = tf.train.SummaryWriter(FLAGS.log_dir + '/train',
sess.graph)
test_writer = tf.train.SummaryWriter(FLAGS.log_dir + '/test')
# Start training loops
for epoch in range(0, FLAGS.max_steps):
if epoch % 100 == 0:
# Print accuracy and loss on validation set
accuracies = []
losses = []
for i in range(num_val_chunks):
loss_val, acc = \
sess.run([loss_no_reg, accuracy],
get_feed(cifar10, 'vali', i, chunks))
accuracies.append(acc)
losses.append(loss_val)
# if FLAGS.save_stuff:
# test_writer.add_summary(summary, epoch)
print('\nEpoch', epoch, '\nValidation accuracy:',
np.mean(accuracies), '\nValidation loss :',
np.mean(losses))
if epoch % FLAGS.checkpoint_freq == 0:
# Save model checkpoint
if epoch > 0:
save_path = saver.save(sess, FLAGS.checkpoint_dir + \
'/epoch'+ str(epoch) + '.ckpt')
print("Model saved in file: %s" % save_path)
# Do training update
if FLAGS.save_stuff:
summary, _ = sess.run([merged, train_op],
feed_dict=get_feed(cifar10, 'train'))
train_writer.add_summary(summary, epoch)
else:
sess.run([train_op], feed_dict=get_feed(cifar10, 'train'))
# Print the final accuracy
summary, loss_val = \
sess.run([merged, loss_no_reg], get_feed(cifar10, 'vali'))
if FLAGS.save_stuff:
test_writer.add_summary(summary, epoch + 1)
print('\nFinal validation loss :', loss_val)
def train_siamese():
"""
Performs training and evaluation of Siamese model.
First define your graph using class Siamese and its methods. Then define
necessary operations such as trainer (train_step in this case), savers
and summarizers. Finally, initialize your model within a tf.Session and
do the training.
---------------------------
How to evaluate your model:
---------------------------
On train set, it is fine to monitor loss over minibatches. On the other
hand, in order to evaluate on test set you will need to create a fixed
validation set using the data sampling function you implement for siamese
architecture. What you need to do is to iterate over all minibatches in
the validation set and calculate the average loss over all minibatches.
---------------------------------
How often to evaluate your model:
---------------------------------
- on training set every print_freq iterations
- on test set every eval_freq iterations
------------------------
Additional requirements:
------------------------
Also you are supposed to take snapshots of your model state (i.e. graph,
weights and etc.) every checkpoint_freq iterations. For this, you should
study TensorFlow's tf.train.Saver class. For more information, please
checkout:
[https://www.tensorflow.org/versions/r0.11/how_tos/variables/index.html]
"""
# Set the random seeds for reproducibility. DO NOT CHANGE.
def _check_loss(data):
loss_val = 0.
for batch in data:
x1_data, x2_data, y_data = batch
[curr_loss] = sess.run([loss],
feed_dict={
x1: x1_data,
x2: x2_data,
y: y_data
})
loss_val += curr_loss
# test_wrt.add_summary(summary_test, it)
# print(len(data))
# print(loss_val)
return loss_val / len(data)
tf.set_random_seed(42)
np.random.seed(42)
cifar10 = get_cifar_10_siamese(FLAGS.data_dir, validation_size=5000)
val_data = create_dataset_siamese(cifar10.validation, num_tuples=500)
test_data = create_dataset_siamese(cifar10.test, num_tuples=500)
#### PARAMETERS
classes = [
'plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship',
'truck'
]
n_classes = len(classes)
input_data_dim = cifar10.test.images.shape[1]
#####
cnn_siamese = Siamese()
x1 = tf.placeholder(tf.float32, shape=[None, 32, 32, 3], name="x1")
x2 = tf.placeholder(tf.float32, shape=[None, 32, 32, 3], name="x2")
y = tf.placeholder(tf.float32, shape=[None], name="y")
with tf.name_scope('train_cnn'):
infs1 = cnn_siamese.inference(x1)
infs2 = cnn_siamese.inference(x2, reuse=True)
with tf.name_scope('cross-entropy-loss'):
loss = cnn_siamese.loss(infs1, infs2, y, 0.48)
merged = tf.merge_all_summaries()
opt_operation = train_step(loss)
with tf.Session() as sess:
saver = tf.train.Saver()
sess.run(tf.initialize_all_variables())
# test_loss = _check_loss(test_data)
# print("Initial Test Loss = {0:.3f}".format(test_loss))
#train_writer = tf.train.SummaryWriter(FLAGS.log_dir + "/train/", sess.graph)
#test_writer = tf.train.SummaryWriter(FLAGS.log_dir + "/test/", sess.graph)
val_losses = []
train_losses = []
for iteration in range(FLAGS.max_steps + 1):
x1_train, x2_train, y_train = cifar10.train.next_batch(
FLAGS.batch_size)
_ = sess.run([opt_operation],
feed_dict={
x1: x1_train,
x2: x2_train,
y: y_train
})
if iteration % FLAGS.print_freq == 0:
[train_loss] = sess.run([loss],
feed_dict={
x1: x1_train,
x2: x2_train,
y: y_train
})
train_losses.append(train_loss)
# train_writer.add_summary(summary_train, iteration)
print("Iteration {0:d}/{1:d}. Train Loss = {2:.6f}".format(
iteration, FLAGS.max_steps, train_loss))
if iteration % FLAGS.eval_freq == 0:
val_loss = _check_loss(val_data)
val_losses.append(val_loss)
# [test_acc, test_loss, summary_test] = sess.run([accuracy, loss, merged], feed_dict={x: x_test, y: y_test})
# test_writer.add_summary(summary_test, iteration)
print(
"Iteration {0:d}/{1:d}. Validation Loss = {2:.6f}".format(
iteration, FLAGS.max_steps, val_loss))
if iteration > 0 and iteration % FLAGS.checkpoint_freq == 0:
saver.save(sess,
FLAGS.checkpoint_dir + '/cnn_model_siamese.ckpt')
# test_loss = _check_loss(test_data)
# print("Final Test Loss = {0:.3f}".format(test_loss))
# train_writer.flush()
# test_writer.flush()
# train_writer.close()
# test_writer.close()
sess.close()
print("train_loss", train_losses)
print("val_loss", val_losses)
def train_siamese():
"""
Performs training and evaluation of Siamese model.
First define your graph using class Siamese and its methods. Then define
necessary operations such as trainer (train_step in this case), savers
and summarizers. Finally, initialize your model within a tf.Session and
do the training.
---------------------------
How to evaluate your model:
---------------------------
On train set, it is fine to monitor loss over minibatches. On the other
hand, in order to evaluate on test set you will need to create a fixed
validation set using the data sampling function you implement for siamese
architecture. What you need to do is to iterate over all minibatches in
the validation set and calculate the average loss over all minibatches.
---------------------------------
How often to evaluate your model:
---------------------------------
- on training set every print_freq iterations
- on test set every eval_freq iterations
------------------------
Additional requirements:
------------------------
Also you are supposed to take snapshots of your model state (i.e. graph,
weights and etc.) every checkpoint_freq iterations. For this, you should
study TensorFlow's tf.train.Saver class. For more information, please
checkout:
[https://www.tensorflow.org/versions/r0.11/how_tos/variables/index.html]
"""
# Set the random seeds for reproducibility. DO NOT CHANGE.
tf.set_random_seed(42)
np.random.seed(42)
########################
# PUT YOUR CODE HERE #
########################
weight_init_scale = 0.001
cifar10 = cifar10_siamese_utils.get_cifar10(validation_size=500)
cnet = Siamese()
#swriter = tf.train.SummaryWriter(FLAGS.log_dir + "/Siamese/")
x_anchor = tf.placeholder(tf.float32, [None, 32, 32, 3])
x_in = tf.placeholder(tf.float32, [None, 32, 32, 3])
y_true = tf.placeholder(tf.float32, [None])
with tf.variable_scope("Siamese", reuse=None):
filter1 = tf.get_variable("filter1",
initializer=tf.random_normal(
[5, 5, 3, 64],
stddev=weight_init_scale,
dtype=tf.float32))
filter2 = tf.get_variable("filter2",
initializer=tf.random_normal(
[5, 5, 64, 64],
stddev=weight_init_scale,
dtype=tf.float32))
W1 = tf.get_variable("W1",
initializer=tf.random_normal(
[4096, 384],
stddev=weight_init_scale,
dtype=tf.float32))
W2 = tf.get_variable("W2",
initializer=tf.random_normal(
[384, 192],
stddev=weight_init_scale,
dtype=tf.float32))
sess = tf.Session()
saver = tf.train.Saver()
#define things
logits_anchor, _f1, _f2, _fl = cnet.inference(x_anchor)
logits_in, _f1, _f2, _fl = cnet.inference(x_in)
loss = cnet.loss(logits_anchor, logits_in, y_true, 1.0)
opt_iter = train_step(loss)
sess.run(tf.initialize_all_variables())
#xbat, ybat = cifar10.train.next_batch(100)
#begin the training
with sess:
# loop
for i in range(FLAGS.max_steps + 1):
ancbat, xbat, ybat = cifar10.train.next_batch(FLAGS.batch_size)
sess.run(opt_iter,
feed_dict={
x_anchor: ancbat,
x_in: xbat,
y_true: ybat
})
if i % FLAGS.print_freq == 0:
ancbat, xbat, ybat = cifar10.validation.next_batch(100)
val_loss = sess.run([loss],
feed_dict={
x_anchor: ancbat,
x_in: xbat,
y_true: ybat
})
sys.stderr.write("iteration : " + str(i) +
", validation loss : " + str(val_loss) + "\n")
#swriter.add_summary(
# sess.run(tf.scalar_summary("loss", val_loss),
# feed_dict = {x_anchor: ancbat, x_in: xbat, y_true:ybat})
# ,i)
if i % FLAGS.checkpoint_freq == 0:
saver.save(
sess,
FLAGS.checkpoint_dir + "/Siamese/" + "checkpoint.ckpt")
lo, flatsave, fc1save, fc2save = sess.run(cnet.inference(x_in),
feed_dict={
x_in: xbat,
y_true: ybat,
x_anchor: ancbat
})
loa, flatsavea, fc1savea, fc2savea = sess.run(
cnet.inference(x_anchor),
feed_dict={
x_in: xbat,
y_true: ybat,
x_anchor: ancbat
})
np.save(FLAGS.checkpoint_dir + "/Siamese/other", lo)
np.save(FLAGS.checkpoint_dir + "/Siamese/anchor", loa)
"""
np.save(FLAGS.checkpoint_dir +"/Siamese/flatten", flatsave)
np.save(FLAGS.checkpoint_dir + "/Siamese/fc1", fc1save)
np.save(FLAGS.checkpoint_dir + "/Siamese/fc2", fc2save)
np.save(FLAGS.checkpoint_dir +"/Siamese/flattena", flatsavea)
np.save(FLAGS.checkpoint_dir + "/Siamese/fc1a", fc1savea)
np.save(FLAGS.checkpoint_dir + "/Siamese/fc2a", fc2savea)
"""
if i % FLAGS.eval_freq == 0:
ancbat, xbat, ybat = cifar10.test.next_batch(100)
sys.stderr.write("test loss:" + str(
sess.run(loss,
feed_dict={
x_anchor: ancbat,
x_in: xbat,
y_true: ybat
})) + "\n")
