def main(arg=None):
affNIST_in, affNIST_out = affNIST.load_affNIST()
mnist = input_data.read_data_sets('/mnist')
print('affNIST min', np.min(affNIST_in[0]), np.max(affNIST_in[0]))
print(' MNIST min', np.min(mnist.train.images[0]),
np.max(mnist.train.images[0]))
trainIn, trainOut = util.skip_no_equal_neighbor(mnist.train.images,
mnist.train.labels)
validIn, validOut = util.skip_no_equal_neighbor(mnist.test.images,
mnist.test.labels)
affNIST_in, affNIST_out = util.skip_no_equal_neighbor(
affNIST_in, affNIST_out)
h = w = 28
if AFFIN: h = w = 40
X = tf.placeholder(tf.float32, [None, None, None, 1])
Y = tf.placeholder(tf.float32, [None])
D = tf.placeholder(tf.bool)
y_int = tf.cast(Y, tf.int32)
Y_ONE_HOT = tf.one_hot(y_int, 10)
x_resize = tf.image.resize_bilinear(X, [28, 28])
x_overlap = tf.clip_by_value(x_resize[0::2] + x_resize[1::2], 0, 1)
y_0 = Y_ONE_HOT[0::2]
y_1 = Y_ONE_HOT[1::2]
y_overlap = y_0 + y_1
y_overlap = tf.clip_by_value(y_overlap, 0, 1)
hyperthesis = baseCNN(x_overlap, D)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_overlap,
logits=hyperthesis))
optimizer = tf.train.AdamOptimizer(learning_rate)
gvs = optimizer.compute_gradients(loss)
capped_gvs = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gvs]
train_step = optimizer.apply_gradients(capped_gvs)
top_values, top_predict = tf.nn.top_k(hyperthesis, 2)
y_gt = tf.stack([y_int[0::2], y_int[1::2]], 1)
predict_sort = tf.py_func(np.sort, [top_predict], tf.int32)
y_gt_sort = tf.py_func(np.sort, [y_gt], tf.int32)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(predict_sort, y_gt_sort), tf.float32))
sess = tf.Session()
saver = tf.train.Saver()
if isNewTrain:
sess.run(tf.global_variables_initializer())
print('Initialized!')
else:
saver.restore(sess, modelName)
print("Model restored")
start_sec = time.time()
print(' train:%d, valid:%d, test:%d, REDUCE_DATA_COUNT_RATIO:%d' %
(len(mnist.train.images), len(affNIST_in), len(
mnist.test.images), REDUCE_DATA_COUNT_RATIO))
def feed_all(x, y, train=False, Pad=False):
m = (int)(len(y) / REDUCE_DATA_COUNT_RATIO)
iter = (int)((m - 1) / BATCH + 1)
acc_sum = np.zeros((1), np.float)
for i in range(iter):
start = i * BATCH
end = np.minimum(start + BATCH, m)
batch_x = x[start:end]
if Pad: batch_x = util.padding(batch_x)
else: batch_x = np.reshape(batch_x, [-1, h, w, 1])
feed = {X: batch_x, Y: y[start:end], D: train}
if train: _, ML, acc = sess.run([train_step, loss, accuracy], feed)
else: ML, acc = sess.run([loss, accuracy], feed)
acc_sum += acc / iter
return acc_sum, ML
for i in range(epoch):
train_accuracy, ML_tr = feed_all(trainIn,
trainOut,
train=True,
Pad=True)
if i < 10 or i % FREQ == 0:
valid_accuracy, ML_v = feed_all(validIn,
validOut,
train=False,
Pad=True)
test_accuracy, ML_te = feed_all(affNIST_in,
affNIST_out,
train=False,
Pad=False)
now = strftime("%H:%M:%S", localtime())
print(
'step %d/%d, accuracy train:%.3f valid:%.3f test:%.3f loss:(%.7f) %s'
% (i, epoch, train_accuracy, valid_accuracy, test_accuracy,
ML_tr, now))
this_sec = time.time()
if i == epoch - 1 or this_sec - start_sec > 60 * 5:
start_sec = this_sec
save_path = saver.save(sess, modelName)
print("Model Saved, time:%s, %s" % (now, save_path))
def main(arg=None):
affNIST_in, affNIST_out = affNIST.load_affNIST()
mnist = input_data.read_data_sets('/mnist')
print('affNIST min', np.min(affNIST_in[0]), np.max(affNIST_in[0]))
print(' MNIST min', np.min(mnist.train.images[0]),
np.max(mnist.train.images[0]))
h = w = 40
X = tf.placeholder(tf.float32, [None, None, None, 1])
Y = tf.placeholder(tf.float32, [None])
D = tf.placeholder(tf.bool)
y_int = tf.cast(Y, tf.int32)
Y_ONE_HOT = tf.one_hot(y_int, 10)
x_4d = tf.image.resize_bilinear(X, [28, 28])
hyperthesis = baseCNN(x_4d, D)
print(' hyperthesis', hyperthesis)
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_int,
logits=hyperthesis))
train_step = tf.train.AdamOptimizer(learning_rate, 0.9).minimize(loss)
#train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
predict = tf.cast(tf.argmax(hyperthesis, 1), tf.int32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predict, y_int), tf.float32))
with tf.Session() as sess:
saver = tf.train.Saver()
if isNewTrain:
tf.global_variables_initializer().run()
print('Initialized!')
else:
saver.restore(sess, modelName)
print("Model restored")
start_sec = time.time()
print(' train:%d, valid:%d, test:%d, REDUCE_DATA_COUNT_RATIO:%d' %
(len(mnist.train.images), len(affNIST_in), len(
mnist.test.images), REDUCE_DATA_COUNT_RATIO))
def feed_all(x, y, train=False, Pad=False):
m = (int)(len(y) / REDUCE_DATA_COUNT_RATIO)
iter = (int)((m - 1) / BATCH + 1)
acc_sum = np.zeros_like((1), np.float)
for i in range(iter):
start = i * BATCH
end = np.minimum(start + BATCH, m)
batch_x = x[start:end]
if Pad: batch_x = util.padding(batch_x)
else: batch_x = np.reshape(batch_x, [-1, h, w, 1])
feed = {X: batch_x, Y: y[start:end], D: train}
if train:
_, ML, acc = sess.run([train_step, loss, accuracy], feed)
else:
ML, acc = sess.run([loss, accuracy], feed)
acc_sum += acc / iter
return acc_sum, ML
for i in range(epoch):
train_accuracy, ML_tr = feed_all(mnist.train.images,
mnist.train.labels,
train=True,
Pad=True)
if i < 20 or i % FREQ == 0:
valid_accuracy, ML_v = feed_all(mnist.test.images,
mnist.test.labels,
train=False,
Pad=True)
test_accuracy, ML_te = feed_all(affNIST_in,
affNIST_out,
train=False,
Pad=False)
now = strftime("%H:%M:%S", localtime())
print(
'step %d/%d, accuracy train:%.3f valid:%.3f test:%.3f loss:(%.7f) %s'
% (i, epoch, train_accuracy, valid_accuracy, test_accuracy,
ML_tr, now))
this_sec = time.time()
if i == epoch - 1 or this_sec - start_sec > 60 * 5:
start_sec = this_sec
save_path = saver.save(sess, modelName)
print("Model Saved, time:%s, %s" % (now, save_path))
def main(arg=None):
affNIST_in, affNIST_out = affNIST.load_affNIST()
mnist = input_data.read_data_sets('/mnist')
print('affNIST min', np.min(affNIST_in[0]), np.max(affNIST_in[0]))
print(' MNIST min', np.min(mnist.train.images[0]),
np.max(mnist.train.images[0]))
trainIn, trainOut = util.skip_no_equal_neighbor(mnist.train.images,
mnist.train.labels)
validIn, validOut = util.skip_no_equal_neighbor(mnist.test.images,
mnist.test.labels)
affNIST_in, affNIST_out = util.skip_no_equal_neighbor(
affNIST_in, affNIST_out)
h = w = 28
if AFFIN: h = w = 40
X = tf.placeholder(tf.float32, [None, None, None, 1])
Y = tf.placeholder(tf.float32, [None])
y_int = tf.cast(Y, tf.int32)
Y_ONE_HOT = tf.one_hot(y_int, 10)
x_resize = tf.image.resize_bilinear(X, [28, 28])
x_overlap = tf.clip_by_value(x_resize[0::2] + x_resize[1::2], 0, 1)
y_0 = Y_ONE_HOT[0::2]
y_1 = Y_ONE_HOT[1::2]
y_overlap = y_0 + y_1
y_overlap = tf.clip_by_value(y_overlap, 0, 1)
DigitCaps = CapsuleLayer.capsnet_forward(x_overlap)
hyperthesis = tf.norm(DigitCaps, ord=2, axis=-1)
recon_x_0 = CapsuleLayer.reconstruct(DigitCaps, y_0)
recon_x_1 = CapsuleLayer.reconstruct(DigitCaps, y_1)
recon_x = tf.clip_by_value(recon_x_0 + recon_x_1, 0, 1)
margin_loss = CapsuleLayer.margin_loss(y_overlap, hyperthesis)
restruc_loss = tf.reduce_mean(
tf.reduce_sum(tf.square(x_overlap - recon_x), axis=[1, 2]))
loss = margin_loss
if RECONSTRUCT: loss += 5e-5 * restruc_loss
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
top_values, top_predict = tf.nn.top_k(hyperthesis, 2)
y_gt = tf.stack([y_int[0::2], y_int[1::2]], 1)
predict_sort = tf.py_func(np.sort, [top_predict], tf.int32)
y_gt_sort = tf.py_func(np.sort, [y_gt], tf.int32)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(predict_sort, y_gt_sort), tf.float32))
sess = tf.Session()
saver = tf.train.Saver()
if isNewTrain:
sess.run(tf.global_variables_initializer())
print('Initialized!')
else:
saver.restore(sess, modelName)
print("Model restored")
start_sec = time.time()
print(' train:%d, valid:%d, test:%d, REDUCE_DATA_COUNT_RATIO:%d' %
(len(mnist.train.images), len(affNIST_in), len(
mnist.test.images), REDUCE_DATA_COUNT_RATIO))
def feed_all(x, y, train=False, Pad=False):
m = (int)(len(y) / REDUCE_DATA_COUNT_RATIO)
iter = (int)((m - 1) / BATCH + 1)
acc_sum = np.zeros((1), np.float)
for i in range(iter):
start = i * BATCH
end = np.minimum(start + BATCH, m)
batch_x = x[start:end]
if Pad: batch_x = util.padding(batch_x)
else: batch_x = np.reshape(batch_x, [-1, h, w, 1])
feed = {X: batch_x, Y: y[start:end]}
#equalRatio = np.mean(np.equal(y[::2], y[1::2]))
#print (i,'equalRatio ',equalRatio )
if train:
_, ML, RL, acc = sess.run(
[train_step, margin_loss, restruc_loss, accuracy], feed)
else:
ML, RL, acc = sess.run([margin_loss, restruc_loss, accuracy],
feed)
acc_sum += acc / iter
return acc_sum, ML, RL
for i in range(epoch):
train_accuracy, ML_tr, RL_tr = feed_all(trainIn,
trainOut,
train=True,
Pad=True)
if i < 10 or i % FREQ == 0:
valid_accuracy, ML_v, RL_v = feed_all(validIn,
validOut,
train=False,
Pad=True)
test_accuracy, ML_te, RL_te = feed_all(affNIST_in,
affNIST_out,
train=False,
Pad=False)
now = strftime("%H:%M:%S", localtime())
print(
'step %d/%d, accuracy train:%.3f valid:%.3f test:%.3f loss:(%.7f, %.4f) %s'
% (i, epoch, train_accuracy, valid_accuracy, test_accuracy,
ML_tr, RL_tr, now))
this_sec = time.time()
if i == epoch - 0 or this_sec - start_sec > 60 * 5:
start_sec = this_sec
save_path = saver.save(sess, modelName)
print("Model Saved, time:%s, %s" % (now, save_path))
for i in range(10):
start = i
end = start + 2
batch_x = mnist.train.images[start:end]
batch_x = util.padding(batch_x)
batch_y = mnist.train.labels[start:end]
feed = {X: batch_x, Y: batch_y}
acc, x_overlap_in, recon_0, recon_1, ori_arr, y_gt_out, predict2 = sess.run(
[
accuracy, x_overlap, recon_x_0, recon_x_1, x_resize, y_gt,
top_predict
], feed)
print('ori_arr', ori_arr.shape)
print('recon_0', recon_0.shape)
print('y_gt_out', y_gt_out)
in_rgb = np.stack([x_overlap_in[0], x_overlap_in[0], x_overlap_in[0]],
2)
r = ori_arr[0]
g = ori_arr[1]
b = np.zeros_like(r)
ori_rgb = np.stack([r, g, b], 2)
r = recon_0[0]
g = recon_1[0]
recon_rgb = np.stack([r, g, b], 2)
dual_image = np.stack([in_rgb, ori_rgb, recon_rgb])
print('dual_image ', dual_image.shape)
recon_image = np.reshape(dual_image, [28 * 3, 28, 3])
util.save(recon_image, y_gt_out, './reconstruct/', predict2)
save_path = saver.save(sess, modelName)
def main(arg=None):
affNIST_in,affNIST_out = affNIST.load_affNIST()
mnist = input_data.read_data_sets('/mnist')
print ('affNIST min',np.min(affNIST_in[0]),np.max(affNIST_in[0]))
print (' MNIST min',np.min(mnist.train.images[0]),np.max(mnist.train.images[0]))
h = w = 28
if AFFINE: h = w = 40
X = tf.placeholder(tf.float32, [None, None,None,1])
Y = tf.placeholder(tf.float32, [None])
y_int = tf.cast(Y, tf.int32)
Y_ONE_HOT = tf.one_hot(y_int,10)
x_4d = tf.image.resize_bilinear(X, [28, 28])
DigitCaps = CapsuleLayer.capsnet_forward(x_4d)
hyperthesis = tf.norm(DigitCaps, ord=2, axis=-1)#(?, 10)
recon_x = CapsuleLayer.reconstruct(DigitCaps,Y_ONE_HOT)
margin_loss = CapsuleLayer.margin_loss(Y_ONE_HOT,hyperthesis)
restruc_loss = tf.reduce_mean(tf.reduce_sum(tf.square(x_4d-recon_x), axis=[1,2]))
loss = margin_loss
if RECONSTRUCT: loss += 5e-5 * restruc_loss
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
#train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
predict = tf.cast(tf.argmax(hyperthesis, 1),tf.int32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predict, y_int), tf.float32))
sess = tf.Session()
saver = tf.train.Saver()
if isNewTrain:
sess.run(tf.global_variables_initializer())
print('Initialized!')
else :
saver.restore(sess, modelName)
print("Model restored")
start_sec = time.time()
print (' train:%d, valid:%d, test:%d, REDUCE_DATA_COUNT_RATIO:%d' %( len(mnist.train.images),len(affNIST_in),len(mnist.test.images),REDUCE_DATA_COUNT_RATIO))
def feed_all(x, y, train=False, Pad=False):
m = (int)(len(y)/REDUCE_DATA_COUNT_RATIO)
iter = (int)((m-1)/BATCH+1)
acc_sum = np.zeros((1), np.float)
for i in range(iter):
start = i * BATCH
end = np.minimum(start + BATCH, m)
batch_x = x[start:end]
if Pad: batch_x = util.padding(batch_x)
else: batch_x = np.reshape(batch_x, [-1,h,w,1])
feed = {X:batch_x , Y: y[start:end]}
if train: _,ML,RL,acc = sess.run([train_step,margin_loss,restruc_loss,accuracy],feed)
else : ML,RL,acc = sess.run([margin_loss,restruc_loss,accuracy],feed)
acc_sum += acc/iter
return acc_sum,ML,RL
for i in range(epoch):
train_accuracy,ML_tr,RL_tr = feed_all(mnist.train.images, mnist.train.labels,train=True, Pad=True)
if i<10 or i % FREQ == 0:
valid_accuracy,ML_v,RL_v = feed_all(mnist.test.images, mnist.test.labels,train=False, Pad=True)
test_accuracy,ML_te,RL_te = feed_all(affNIST_in,affNIST_out,train=False,Pad=False)
now = strftime("%H:%M:%S", localtime())
print('step %d/%d, accuracy train:%.3f valid:%.3f test:%.3f loss:(%.7f, %.4f) %s' % (i,epoch, train_accuracy,valid_accuracy,test_accuracy,ML_tr,RL_tr,now))
this_sec = time.time()
if i==epoch-0 or this_sec - start_sec > 60 * 5 :
start_sec = this_sec
save_path = saver.save(sess, modelName)
print("Model Saved, time:%s, %s" %(now, save_path))
for i in range(10):
start = i
end = start + 1
batch_x = mnist.train.images[start:end]
batch_x = util.padding(batch_x)
batch_y = mnist.train.labels[start:end]
feed = {X:batch_x , Y: batch_y}
acc,recon_arr, ori_arr = sess.run([accuracy,recon_x,x_4d],feed)
dual_image = np.stack([ori_arr,recon_arr])
recon_image = np.reshape(dual_image,[28*2,28])
util.save(recon_image,batch_y,'./reconstruct/',i)
save_path = saver.save(sess, modelName)