for index in shuffle_indices:
x_shuffled.append(X[index])
y_shuffled.append(Y[index])
size_of_dataset = len(x_shuffled)
n_train = int(np.ceil(size_of_dataset * train_test_ratio))
n_test = int(np.ceil(size_of_dataset * (1 - train_test_ratio)))
return np.asarray(x_shuffled[0:n_train]), np.asarray(x_shuffled[n_train + 1:size_of_dataset]), np.asarray(y_shuffled[0:n_train]), np.asarray(y_shuffled[n_train + 1:size_of_dataset])
train_images, test_images, train_label, test_label = get_train_test_dataset(0.7)
model_path = './model_triplet/'
model = TripletLoss()
# Input and output tensor
img_placeholder = tf.placeholder(tf.float32, [None, 28, 28, 3], name='img')
net = model.conv_net(img_placeholder, reuse=False)
print(net)
# generate random index from test image corpus and display the image
idx = np.random.randint(0, len(test_images))
im = test_images[idx]
# show the test image
print('************Query Image**************')
#show_image(idx, test_images)
# Find k-nearest neighbor using cosine similarity
# compute vector representation for each training images and normalize those
def generate_db_normed_vectors():
saver = tf.train.Saver()
with tf.Session() as sess:
print("placeholder_shape", placeholder_shape)
# Setup Network
next_batch = dataset.get_triplets_batch
anchor_input = tf.placeholder(tf.float32,
placeholder_shape,
name='anchor_input')
positive_input = tf.placeholder(tf.float32,
placeholder_shape,
name='positive_input')
negative_input = tf.placeholder(tf.float32,
placeholder_shape,
name='negative_input')
margin = 0.5
anchor_output = model.conv_net(anchor_input, reuse=False)
positive_output = model.conv_net(positive_input, reuse=True)
negative_output = model.conv_net(negative_input, reuse=True)
loss = model.triplet_loss(anchor_output, positive_output, negative_output,
margin)
# Setup Optimizer
global_step = tf.Variable(0, trainable=False)
train_step = tf.train.MomentumOptimizer(FLAGS.learning_rate,
FLAGS.momentum,
use_nesterov=True).minimize(
loss, global_step=global_step)
# Start Training
saver = tf.train.Saver()
def train():
data_src = './ChallengeImages2/'
learning_rate = 0.01
train_iter = 250
batch_size = 128
momentum = 0.99
step = 50
# Setup Dataset
dataset = PreProcessing(data_src)
print(dataset.images_test)
print(dataset.labels_test)
model = TripletLoss()
height = 50
width = 150
dims = [height, width, 3]
placeholder_shape = [None] + dims
print("placeholder_shape", placeholder_shape)
# Setup Network
next_batch = dataset.get_triplets_batch
anchor_input = tf.placeholder(tf.float32,
placeholder_shape,
name='anchor_input')
positive_input = tf.placeholder(tf.float32,
placeholder_shape,
name='positive_input')
negative_input = tf.placeholder(tf.float32,
placeholder_shape,
name='negative_input')
margin = 0.5
# Will be of size N x 28
anchor_output = model.conv_net(anchor_input, reuse=tf.AUTO_REUSE)
positive_output = model.conv_net(positive_input, reuse=tf.AUTO_REUSE)
negative_output = model.conv_net(negative_input, reuse=tf.AUTO_REUSE)
print(tf.shape(anchor_output))
return
'''
compute the similarity between the positive_output and the negative_output
if similarity is < 0.25 that's great
'''
similarity_pos_neg = computeCosSimilarity(positive_output, negative_output)
similarity_pos_anchor = computeCosSimilarity(positive_output,
anchor_output)
loss = model.triplet_loss(anchor_output, positive_output, negative_output,
margin)
# Setup Optimizer
global_step = tf.Variable(0, trainable=False)
train_step = tf.train.MomentumOptimizer(learning_rate,
momentum,
use_nesterov=True).minimize(
loss, global_step=global_step)
# Start Training
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Setup Tensorboard
tf.summary.scalar('step', global_step)
tf.summary.scalar('loss', loss)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter('train.log', sess.graph)
# Train iter
for i in range(train_iter):
batch_anchor, batch_positive, batch_negative = next_batch(
batch_size)
_, l, summary_str = sess.run(
[train_step, loss, merged],
feed_dict={
anchor_input: batch_anchor,
positive_input: batch_positive,
negative_input: batch_negative
})
#pNAccuracy = computeAccuracyLess(0.25, similarity_p_n)
#pAAccuracy = computeAccuracyGreat(0.75, similarity_p_a)
writer.add_summary(summary_str, i)
print("#%d - Loss" % i, l)
#print("#%d - P and A Accuracy" % i, pAAccuracy)
#print("#%d - N and A Accuracy" % i, pNAccuracy)
if (i + 1) % step == 0:
saver.save(sess, "model_triplet/model.ckpt")
saver.save(sess, "model_triplet/model.ckpt")
print('Training completed successfully.')
return dataset