Reference
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Data
images_train = mnist.train.images
labels_train = mnist.train.labels
images_test = mnist.test.images
labels_test = mnist.test.labels
import skimage.transform
# for train data
indices = np.random.choice(55000, 10000, replace = False)
images_train_0 = images_train[indices]
images_train_0_2d = np.reshape(images_train_0, (-1, 28, 28))
labels_train_0 = labels_train[indices]
#images_train_flip_2d = images_train_0_2d[:, :, ::-1]
#images_train_flip = np.reshape(images_train_0_2d[:, :, ::-1], (-1, 28*28))
#labels_train_flip = labels_train[indices]
#images_train_30_2d = []
#for i in range(len(images_train_0)):
# images_train_30_2d.append(skimage.transform.rotate(images_train_0_2d[i], 30))
#images_train_30 = np.reshape(images_train_30_2d, (-1, 28*28))
#labels_train_30 = labels_train[indices]
images_train_60_2d = []
for i in range(len(images_train_0)):
images_train_60_2d.append(skimage.transform.rotate(images_train_0_2d[i], 60))
images_train_60 = np.reshape(images_train_60_2d, (-1, 28*28))
labels_train_60 = labels_train[indices]
#images_train_90_2d = []
#for i in range(len(images_train_0)):
# images_train_90_2d.append(skimage.transform.rotate(images_train_0_2d[i], 90))
#images_train_90 = np.reshape(images_train_90_2d, (-1, 28*28))
#labels_train_90 = labels_train[indices]
#images_train_180_2d = []
#for i in range(len(images_train_0)):
# images_train_180_2d.append(skimage.transform.rotate(images_train_0_2d[i], 180))
#images_train_180 = np.reshape(images_train_180_2d, (-1, 28*28))
#labels_train_180 = labels_train[indices]
Sample Code
# Maximum Classifier Discrepancy Domain Adaptation
class MCD_DA():
def __init__(self):
pass
def weight_variable(self, name, shape):
initializer = tf.truncated_normal_initializer(mean = 0.0, stddev = 0.01, dtype = tf.float32)
return tf.get_variable(name, shape, initializer = initializer)
def bias_variable(self, name, shape):
initializer = tf.constant_initializer(value = 0.0, dtype = tf.float32)
return tf.get_variable(name, shape, initializer = initializer)
def alpha_variable(self, name):
initializer = tf.constant_initializer(value = 0.75, dtype = tf.float32)
return tf.get_variable(name, shape = (), initializer = initializer)
def generator(self, x, filter_size, n_filters_1, n_filters_2, n_units, keep_prob, reuse = False):
x_reshaped = tf.reshape(x, [-1, 28, 28, 1])
with tf.variable_scope('generator', reuse = reuse):
w_1 = self.weight_variable('w_1', [filter_size, filter_size, 1, n_filters_1])
b_1 = self.bias_variable('b_1', [n_filters_1])
# conv
conv = tf.nn.conv2d(x_reshaped, w_1, strides = [1, 2, 2, 1], padding = 'SAME') + b_1
# batch norm
#batch_mean, batch_var = tf.nn.moments(conv, [0, 1, 2])
#conv = (conv - batch_mean) / (tf.sqrt(batch_var) + 1e-10)
# relu
conv = tf.nn.relu(conv)
# max_pool
#conv = tf.nn.max_pool(conv, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
w_2 = self.weight_variable('w_2', [filter_size, filter_size, n_filters_1, n_filters_2])
b_2 = self.bias_variable('b_2', [n_filters_2])
# conv
conv = tf.nn.conv2d(conv, w_2, strides = [1, 2, 2, 1], padding = 'SAME') + b_2
# batch norm
#batch_mean, batch_var = tf.nn.moments(conv, [0, 1, 2])
#conv = (conv - batch_mean) / (tf.sqrt(batch_var) + 1e-10)
# relu
conv = tf.nn.relu(conv)
# max_pool
#conv = tf.nn.max_pool(conv, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
conv_flat = tf.reshape(conv, [-1, 7 * 7 * n_filters_2])
w_3 = self.weight_variable('w_3', [7 * 7 * n_filters_2, n_units])
b_3 = self.bias_variable('b_3', [n_units])
fc = tf.matmul(conv_flat, w_3) + b_3
# batch norm
#batch_mean, batch_var = tf.nn.moments(fc, [0])
#fc = (fc - batch_mean) / (tf.sqrt(batch_var) + 1e-10)
# dropout
#fc = tf.nn.dropout(fc, keep_prob)
# relu
fc = tf.nn.relu(fc)
# leaky relu
#fc = tf.maximum(0.2 * fc, fc)
feature = fc
return feature
def classifier_1(self, x, n_units_1, n_units_2, keep_prob, reuse = False):
with tf.variable_scope('classifier_1', reuse = reuse):
w_1 = self.weight_variable('w_1', [n_units_1, n_units_2])
b_1 = self.bias_variable('b_1', [n_units_2])
fc = tf.matmul(x, w_1) + b_1
# batch norm
batch_mean, batch_var = tf.nn.moments(fc, [0])
fc = (fc - batch_mean) / (tf.sqrt(batch_var) + 1e-10)
# relu
fc = tf.nn.relu(fc)
# dropout
#fc = tf.nn.dropout(fc, keep_prob)
w_2 = self.weight_variable('w_2', [n_units_2, 10])
b_2 = self.bias_variable('b_2', [10])
fc = tf.matmul(fc, w_2) + b_2
logits = fc
return logits
def classifier_2(self, x, n_units_1, n_units_2, keep_prob, reuse = False):
with tf.variable_scope('classifier_2', reuse = reuse):
w_1 = self.weight_variable('w_1', [n_units_1, n_units_2])
b_1 = self.bias_variable('b_1', [n_units_2])
fc = tf.matmul(x, w_1) + b_1
# batch norm
batch_mean, batch_var = tf.nn.moments(fc, [0])
fc = (fc - batch_mean) / (tf.sqrt(batch_var) + 1e-10)
# relu
fc = tf.nn.relu(fc)
# dropout
#fc = tf.nn.dropout(fc, keep_prob)
w_2 = self.weight_variable('w_2', [n_units_2, 10])
b_2 = self.bias_variable('b_2', [10])
fc = tf.matmul(fc, w_2) + b_2
logits = fc
return logits
def loss_cross_entropy(self, y, t):
cross_entropy = - tf.reduce_mean(tf.reduce_sum(t * tf.log(tf.clip_by_value(y, 1e-10, 1.0)), axis = 1))
return cross_entropy
def loss_discriminator(self, probs_1, probs_2):
return - tf.reduce_mean(tf.reduce_sum(tf.abs(probs_1 - probs_2), axis = 1))
def loss_generator(self, probs_1, probs_2):
return tf.reduce_mean(tf.reduce_sum(tf.abs(probs_1 - probs_2), axis = 1))
def loss_entropy(self, p):
entropy = - tf.reduce_mean(tf.reduce_sum(p * tf.log(tf.clip_by_value(p, 1e-10, 1.0)), axis = 1))
return entropy
def loss_mutual_information(self, p):
p_ave = tf.reduce_mean(p, axis = 0)
h_y = -tf.reduce_sum(p_ave * tf.log(p_ave + 1e-16))
h_y_x = - tf.reduce_mean(tf.reduce_sum(p * tf.log(tf.clip_by_value(p, 1e-10, 1.0)), axis = 1))
mutual_info = h_y - h_y_x
return -mutual_info
def accuracy(self, y, t):
correct_preds = tf.equal(tf.argmax(y, axis = 1), tf.argmax(t, axis = 1))
accuracy = tf.reduce_mean(tf.cast(correct_preds, tf.float32))
return accuracy
def training(self, loss, learning_rate, var_list):
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)
#optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate)
train_step = optimizer.minimize(loss, var_list = var_list)
return train_step
def training_clipped(self, loss, learning_rate, clip_norm, var_list):
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)
grads_and_vars = optimizer.compute_gradients(loss, var_list = var_list)
clipped_grads_and_vars = [(tf.clip_by_norm(grad, clip_norm = clip_norm), \
var) for grad, var in grads_and_vars]
train_step = optimizer.apply_gradients(clipped_grads_and_vars)
return train_step
def fit(self, images_train_1, labels_train_1, images_test_1, labels_test_1, \
images_train_t, labels_train_t, images_test_t, labels_test_t, \
filter_size, n_filters_1, n_filters_2, n_units_g, n_units_c, \
learning_rate, n_iter, batch_size, show_step, is_saving, model_path):
tf.reset_default_graph()
x_1 = tf.placeholder(shape = [None, 28 * 28], dtype = tf.float32)
y_1 = tf.placeholder(shape = [None, 10], dtype = tf.float32)
x_t = tf.placeholder(shape = [None, 28 * 28], dtype = tf.float32)
y_t = tf.placeholder(shape = [None, 10], dtype = tf.float32)
keep_prob = tf.placeholder(shape = (), dtype = tf.float32)
feat_1 = self.generator(x_1, filter_size, n_filters_1, n_filters_2, n_units_g, \
keep_prob, reuse = False)
feat_t = self.generator(x_t, filter_size, n_filters_1, n_filters_2, n_units_g, \
keep_prob, reuse = True)
logits_1_1 = self.classifier_1(feat_1, n_units_g, n_units_c, keep_prob, reuse = False)
probs_1_1 = tf.nn.softmax(logits_1_1)
loss_1_1 = self.loss_cross_entropy(probs_1_1, y_1)
logits_1_t = self.classifier_1(feat_t, n_units_g, n_units_c, keep_prob, reuse = True)
probs_1_t = tf.nn.softmax(logits_1_t)
loss_1_t = self.loss_cross_entropy(probs_1_t, y_t)
logits_2_1 = self.classifier_2(feat_1, n_units_g, n_units_c, keep_prob, reuse = False)
probs_2_1 = tf.nn.softmax(logits_2_1)
loss_2_1 = self.loss_cross_entropy(probs_2_1, y_1)
logits_2_t = self.classifier_2(feat_t, n_units_g, n_units_c, keep_prob, reuse = True)
probs_2_t = tf.nn.softmax(logits_2_t)
loss_2_t = self.loss_cross_entropy(probs_2_t, y_t)
loss_a = loss_1_1 + loss_2_1
loss_b = loss_a + self.loss_discriminator(probs_1_t, probs_2_t)
#loss_b = self.loss_discriminator(probs_1_t, probs_2_t)
loss_c = self.loss_generator(probs_1_t, probs_2_t)
var_list_g = tf.trainable_variables('generator')
var_list_c_1 = tf.trainable_variables('classifier_1')
var_list_c_2 = tf.trainable_variables('classifier_2')
var_list_a = var_list_g + var_list_c_1 + var_list_c_2
var_list_b = var_list_c_1 + var_list_c_2
var_list_c = var_list_g
# Without Gradient Clipping
train_step_a = self.training(loss_a, learning_rate, var_list_a)
train_step_b = self.training(loss_b, learning_rate, var_list_b)
train_step_c = self.training(loss_c, learning_rate, var_list_c)
acc_1_1 = self.accuracy(probs_1_1, y_1)
acc_1_t = self.accuracy(probs_1_t, y_t)
acc_2_1 = self.accuracy(probs_2_1, y_1)
acc_2_t = self.accuracy(probs_2_t, y_t)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
history_loss_train_1_1 = []
history_loss_train_1_t = []
history_loss_train_2_1 = []
history_loss_train_2_t = []
history_loss_test_1_1 = []
history_loss_test_1_t = []
history_loss_test_2_1 = []
history_loss_test_2_t = []
history_acc_train_1_1 = []
history_acc_train_1_t = []
history_acc_train_2_1 = []
history_acc_train_2_t = []
history_acc_test_1_1 = []
history_acc_test_1_t = []
history_acc_test_2_1 = []
history_acc_test_2_t = []
history_loss_train_a = []
history_loss_train_b = []
history_loss_train_c = []
history_loss_test_a = []
history_loss_test_b = []
history_loss_test_c = []
for i in range(n_iter):
# Train
# Step A
rand_index = np.random.choice(len(images_train_1), size = batch_size)
x_batch = images_train_1[rand_index]
y_batch = labels_train_1[rand_index]
feed_dict = {x_1: x_batch, y_1: y_batch, keep_prob: 1.0}
sess.run(train_step_a, feed_dict = feed_dict)
# Step B
rand_index = np.random.choice(len(images_train_1), size = batch_size)
x_batch_1 = images_train_1[rand_index]
y_batch_1 = labels_train_1[rand_index]
rand_index = np.random.choice(len(images_train_t), size = batch_size)
x_batch_t = images_train_t[rand_index]
feed_dict = {x_1: x_batch_1, y_1: y_batch_1, x_t: x_batch_t, keep_prob: 1.0}
sess.run(train_step_b, feed_dict = feed_dict)
# Step C
rand_index = np.random.choice(len(images_train_t), size = batch_size)
x_batch_t = images_train_t[rand_index]
feed_dict = {x_t: x_batch_t, keep_prob: 1.0}
sess.run(train_step_c, feed_dict = feed_dict)
# Checking
# Train data
rand_index = np.random.choice(len(images_train_1), size = batch_size)
x_batch_1 = images_train_1[rand_index]
y_batch_1 = labels_train_1[rand_index]
rand_index = np.random.choice(len(images_train_t), size = batch_size)
x_batch_t = images_train_t[rand_index]
y_batch_t = labels_train_t[rand_index]
feed_dict = {x_1: x_batch_1, y_1: y_batch_1, x_t: x_batch_t, y_t: y_batch_t, keep_prob: 1.0}
temp_loss_train_1_1 = sess.run(loss_1_1, feed_dict = feed_dict)
temp_loss_train_1_t = sess.run(loss_1_t, feed_dict = feed_dict)
temp_loss_train_2_1 = sess.run(loss_2_1, feed_dict = feed_dict)
temp_loss_train_2_t = sess.run(loss_2_t, feed_dict = feed_dict)
temp_acc_train_1_1 = sess.run(acc_1_1, feed_dict = feed_dict)
temp_acc_train_1_t = sess.run(acc_1_t, feed_dict = feed_dict)
temp_acc_train_2_1 = sess.run(acc_2_1, feed_dict = feed_dict)
temp_acc_train_2_t = sess.run(acc_2_t, feed_dict = feed_dict)
history_loss_train_1_1.append(temp_loss_train_1_1)
history_loss_train_1_t.append(temp_loss_train_1_t)
history_loss_train_2_1.append(temp_loss_train_2_1)
history_loss_train_2_t.append(temp_loss_train_2_t)
history_acc_train_1_1.append(temp_acc_train_1_1)
history_acc_train_1_t.append(temp_acc_train_1_t)
history_acc_train_2_1.append(temp_acc_train_2_1)
history_acc_train_2_t.append(temp_acc_train_2_t)
temp_loss_train_a = sess.run(loss_a, feed_dict = feed_dict)
temp_loss_train_b = sess.run(loss_b, feed_dict = feed_dict)
temp_loss_train_c = sess.run(loss_c, feed_dict = feed_dict)
history_loss_train_a.append(temp_loss_train_a)
history_loss_train_b.append(temp_loss_train_b)
history_loss_train_c.append(temp_loss_train_c)
if (i + 1) % show_step == 0:
print ('-' * 15)
print ('Iteration: ' + str(i + 1) + ' Loss_a: ' + str(temp_loss_train_a) + \
' Loss_b: ' + str(temp_loss_train_b) + ' Loss_c: ' + str(temp_loss_train_c))
#print ('Iteration: ' + str(i + 1))
# Test data
rand_index = np.random.choice(len(images_test_1), size = batch_size)
x_batch_1 = images_test_1[rand_index]
y_batch_1 = labels_test_1[rand_index]
rand_index = np.random.choice(len(images_test_t), size = batch_size)
x_batch_t = images_test_t[rand_index]
y_batch_t = labels_test_t[rand_index]
feed_dict = {x_1: x_batch_1, y_1: y_batch_1, x_t: x_batch_t, y_t: y_batch_t, keep_prob: 1.0}
temp_loss_test_1_1 = sess.run(loss_1_1, feed_dict = feed_dict)
temp_loss_test_1_t = sess.run(loss_1_t, feed_dict = feed_dict)
temp_loss_test_2_1 = sess.run(loss_2_1, feed_dict = feed_dict)
temp_loss_test_2_t = sess.run(loss_2_t, feed_dict = feed_dict)
temp_acc_test_1_1 = sess.run(acc_1_1, feed_dict = feed_dict)
temp_acc_test_1_t = sess.run(acc_1_t, feed_dict = feed_dict)
temp_acc_test_2_1 = sess.run(acc_2_1, feed_dict = feed_dict)
temp_acc_test_2_t = sess.run(acc_2_t, feed_dict = feed_dict)
history_loss_test_1_1.append(temp_loss_test_1_1)
history_loss_test_1_t.append(temp_loss_test_1_t)
history_loss_test_2_1.append(temp_loss_test_2_1)
history_loss_test_2_t.append(temp_loss_test_2_t)
history_acc_test_1_1.append(temp_acc_test_1_1)
history_acc_test_1_t.append(temp_acc_test_1_t)
history_acc_test_2_1.append(temp_acc_test_2_1)
history_acc_test_2_t.append(temp_acc_test_2_t)
temp_loss_test_a = sess.run(loss_a, feed_dict = feed_dict)
temp_loss_test_b = sess.run(loss_b, feed_dict = feed_dict)
temp_loss_test_c = sess.run(loss_c, feed_dict = feed_dict)
history_loss_test_a.append(temp_loss_test_a)
history_loss_test_b.append(temp_loss_test_b)
history_loss_test_c.append(temp_loss_test_c)
print ('-'* 15)
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter), history_loss_train_1_1, 'b-', label = 'Train')
ax1.plot(range(n_iter), history_loss_test_1_1, 'r--', label = 'Test')
ax1.set_title('Loss_1_1')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter), history_acc_train_1_1, 'b-', label = 'Train')
ax2.plot(range(n_iter), history_acc_test_1_1, 'r--', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_1_1')
ax2.legend(loc = 'lower right')
plt.show()
print ('-'* 15)
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter), history_loss_train_2_1, 'b-', label = 'Train')
ax1.plot(range(n_iter), history_loss_test_2_1, 'r--', label = 'Test')
ax1.set_title('Loss_2_1')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter), history_acc_train_2_1, 'b-', label = 'Train')
ax2.plot(range(n_iter), history_acc_test_2_1, 'r--', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_2_1')
ax2.legend(loc = 'lower right')
plt.show()
print ('-'* 15)
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter), history_loss_train_1_t, 'b-', label = 'Train')
ax1.plot(range(n_iter), history_loss_test_1_t, 'r--', label = 'Test')
ax1.set_title('Loss_1_t')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter), history_acc_train_1_t, 'b-', label = 'Train')
ax2.plot(range(n_iter), history_acc_test_1_t, 'r--', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_1_t')
ax2.legend(loc = 'lower right')
plt.show()
print ('-'* 15)
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter), history_loss_train_2_t, 'b-', label = 'Train')
ax1.plot(range(n_iter), history_loss_test_2_t, 'r--', label = 'Test')
ax1.set_title('Loss_2_t')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter), history_acc_train_2_t, 'b-', label = 'Train')
ax2.plot(range(n_iter), history_acc_test_2_t, 'r--', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_2_t')
ax2.legend(loc = 'lower right')
print ('-'* 15)
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter), history_loss_train_a, 'b-', label = 'Train_a')
ax1.plot(range(n_iter), history_loss_train_b, 'r-', label = 'Train_b')
ax1.plot(range(n_iter), history_loss_train_c, 'y-', label = 'Train_c')
ax1.set_title('Loss_train')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter), history_loss_test_a, 'b-', label = 'Test_a')
ax2.plot(range(n_iter), history_loss_test_b, 'r-', label = 'Test_b')
ax2.plot(range(n_iter), history_loss_test_c, 'y-', label = 'Test_c')
ax2.set_title('Loss_test')
ax2.legend(loc = 'upper right')
plt.show()
if is_saving:
model_path = saver.save(sess, model_path)
print ('done saving at ', model_path)
Parameters
filter_size = 3
n_filters_1 = 64
n_filters_2 = 64
n_units_g = 256
n_units_c = 128
n_units_d_1 = 64
n_units_d_2 = 64
learning_rate = 0.001
n_iter = 300
batch_size = 64
show_step = 100
model_path = 'datalab/model'
Output
images_train_1 = images_train_0
labels_train_1 = labels_train_0
images_test_1 = images_test_0
labels_test_1 = labels_test_0
images_train_t = images_train_60
labels_train_t = labels_train_60
images_test_t = images_test_60
labels_test_t = labels_test_60
is_saving = False
mcd_da.fit(images_train_1, labels_train_1, images_test_1, labels_test_1, \
images_train_t, labels_train_t, images_test_t, labels_test_t, \
filter_size, n_filters_1, n_filters_2, n_units_g, n_units_c, \
learning_rate, n_iter, batch_size, show_step, is_saving, model_path)
