beta1=0.9,
beta2=0.999,
epsilon=1e-08,
use_locking=False).minimize(
triplet_loss, global_step=global_step)
#train_op = tf.train.MomentumOptimizer(learning_rate,0.9).minimize(cost,global_step=global_step)
sess.run(tf.initialize_all_variables())
ISOTIMEFORMAT = '%Y-%m-%d %X'
iter_show = 0
for epoch in range(n_epoch):
start_time = time.time()
iter_per_epoch = 2
for iter in xrange(iter_per_epoch):
street_batch, shop_batch, nopair_batch, _, _, _ = input.load_batchsize_images(
batch_size)
feed_dict = {
x_street: street_batch,
x_shop: shop_batch,
x_nopair: nopair_batch,
train_mode: True
}
#conv1, conv2, conv3, conv4, conv5, fc8, fc7, fc6, pool3 = sess.run([network.conv1, network.conv2, network.conv3, network.conv4, network.conv5, network.fc8, network.fc7, network.fc6, network.pool3], feed_dict=feed_dict)
_, err, d_pair, d_nopair, lr, train_summary = sess.run(
[
train_op, triplet_loss, dist_pair, dist_nopair, learning_rate,
merged
],
feed_dict=feed_dict)
iter_show += 1
train_writer.add_summary(train_summary, iter_show)
#loss = tf.reduce_mean(-tf.reduce_sum(true_out*tf.log(nin.prob),[1]))
#loss = tf.reduce_mean(tf.reduce_sum((nin.prob-true_out)**2,[1]))
#train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# train = tf.train.AdamOptimizer(1e-4).minimize(loss)
global_step = tf.Variable(0)
starter_learning_rate = 0.0001
learning_rate = tf.train.exponential_decay(starter_learning_rate,
global_step,
1000,
0.96,
staircase=True)
optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(
loss, global_step=global_step)
sess.run(tf.initialize_all_variables())
for i in xrange(10000):
x_batch, y_batch = input.load_batchsize_images(batch_size=64)
#cost = tf.reduce_sum((vgg.prob - true_out) ** 2)
batch_size = y_batch.shape[0]
#loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(nin.prob,true_out))
#print loss.eval(feed_dict={images: x_batch, true_out: y_batch, train_mode: True})
#optimizer = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
lr, prob, f, pool, cost, _ = sess.run(
[learning_rate, nin.prob, nin.final, nin.pool4, loss, optimizer],
feed_dict={
images: x_batch,
true_out: y_batch,
train_mode: True
})
#print "np.argsort:"
#print np.argsort(prob,axis=1).shape
index = np.argsort(prob, axis=1)[:, 49:50]
street_nin.print_params()
street_nin.print_layers()
shop_nin.print_params()
shop_nin.print_layers()
nopair_nin.print_params()
nopair_nin.print_layers()
print(' batch_size: %d' % batch_size)
iter_show = 0
for epoch in range(n_epoch):
start_time = time.time()
iter_per_epoch = 2
for iter in xrange(iter_per_epoch):
street_batch, shop_batch, nopair_batch, y_street_batch, y_shop_batch, y_nopair_batch = input.load_batchsize_images(
batch_size)
#print "street_batch.shape:"
#print street_batch.shape
#print "iter:"
#print iter
feed_dict = {
x_street: street_batch,
x_shop: shop_batch,
x_nopair: nopair_batch
}
feed_dict.update(
dict(street_nin.all_drop.items() + shop_nin.all_drop.items())
) # enable all dropout/dropconnect/denoising layers
err, d_pair, d_nopair, t_lr, _, train_summary = sess.run(
[
triplet_loss, dist_pair, dist_nopair, triplet_lr, train_op,