Reference
Generalizing Across Domains via Cross-Gradient Training
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]
import skimage.transform
# for test data
indices = np.random.choice(10000, 10000, replace = False)
images_test_0 = images_test[indices]
images_test_0_2d = np.reshape(images_test_0, (-1, 28, 28))
labels_test_0 = labels_test[indices]
#images_test_flip_2d = images_test_0_2d[:, :, ::-1]
#images_test_flip = np.reshape(images_test_flip_2d, (-1, 28*28))
#labels_test_flip = labels_test[indices]
images_test_30_2d = []
for i in range(len(images_test_0)):
images_test_30_2d.append(skimage.transform.rotate(images_test_0_2d[i], 30))
images_test_30 = np.reshape(images_test_30_2d, (-1, 28*28))
labels_test_30 = labels_test[indices]
images_test_60_2d = []
for i in range(len(images_test_0)):
images_test_60_2d.append(skimage.transform.rotate(images_test_0_2d[i], 60))
images_test_60 = np.reshape(images_test_60_2d, (-1, 28*28))
labels_test_60 = labels_test[indices]
#images_test_90_2d = []
#for i in range(len(images_test_0)):
# images_test_90_2d.append(skimage.transform.rotate(images_test_0_2d[i], 90))
#images_test_90 = np.reshape(images_test_90_2d, (-1, 28*28))
#labels_test_90 = labels_test[indices]
#images_test_180_2d = []
#for i in range(len(images_test_0)):
# images_test_180_2d.append(skimage.transform.rotate(images_test_0_2d[i], 180))
#images_test_180 = np.reshape(images_test_180_2d, (-1, 28*28))
#labels_test_180 = labels_test[indices]
index = np.random.randint(1000)
# for train data
fig = plt.figure(figsize = (7, 5))
ax = fig.add_subplot(1, 6, 1)
ax.imshow(np.reshape(images_train_0[index], (28, 28)), cmap = 'gray')
ax.set_title('image_0')
ax.set_axis_off()
ax = fig.add_subplot(1, 6, 2)
ax.imshow(np.reshape(images_train_30[index], (28, 28)), cmap = 'gray')
ax.set_title('image_30')
ax.set_axis_off()
ax = fig.add_subplot(1, 6, 3)
ax.imshow(np.reshape(images_train_60[index], (28, 28)), cmap = 'gray')
ax.set_title('image_60')
ax.set_axis_off()
#ax = fig.add_subplot(1, 6, 4)
#ax.imshow(np.reshape(images_train_90[index], (28, 28)), cmap = 'gray')
#ax.set_title('image_90')
#ax.set_axis_off()
#ax = fig.add_subplot(1, 6, 5)
#ax.imshow(np.reshape(images_train_180[index], (28, 28)), cmap = 'gray')
#ax.set_title('image_180')
#ax.set_axis_off()
#ax = fig.add_subplot(1, 6, 6)
#ax.imshow(np.reshape(images_train_flip[index], (28, 28)), cmap = 'gray')
#ax.set_title('image_flip')
#ax.set_axis_off()
plt.show()
# for test data
fig = plt.figure(figsize = (7, 5))
ax = fig.add_subplot(1, 6, 1)
ax.imshow(np.reshape(images_test_0[index], (28, 28)), cmap = 'gray')
ax.set_title('image_0')
ax.set_axis_off()
ax = fig.add_subplot(1, 6, 2)
ax.imshow(np.reshape(images_test_30[index], (28, 28)), cmap = 'gray')
ax.set_title('image_30')
ax.set_axis_off()
ax = fig.add_subplot(1, 6, 3)
ax.imshow(np.reshape(images_test_60[index], (28, 28)), cmap = 'gray')
ax.set_title('image_60')
ax.set_axis_off()
#ax = fig.add_subplot(1, 6, 4)
#ax.imshow(np.reshape(images_test_90[index], (28, 28)), cmap = 'gray')
#ax.set_title('image_90')
#ax.set_axis_off()
#ax = fig.add_subplot(1, 6, 5)
#ax.imshow(np.reshape(images_test_180[index], (28, 28)), cmap = 'gray')
#ax.set_title('image_180')
#ax.set_axis_off()
#ax = fig.add_subplot(1, 6, 6)
#ax.imshow(np.reshape(images_test_flip[index], (28, 28)), cmap = 'gray')
#ax.set_title('image_flip')
#ax.set_axis_off()
plt.show()
Sample Code
# Cross-Gradient Training
class CrossGrad():
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_d(self, x, n_units_g, n_units_d, n_domains, keep_prob, reuse = False):
with tf.variable_scope('classifier_d', reuse = reuse):
w_1 = self.weight_variable('w_1', [n_units_g, n_units_d])
b_1 = self.bias_variable('b_1', [n_units_d])
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_d, n_domains])
b_2 = self.bias_variable('b_2', [n_domains])
fc = tf.matmul(fc, w_2) + b_2
logits = fc
return logits
def classifier_l(self, x, n_units_g, n_units_l, n_labels, keep_prob, reuse = False):
with tf.variable_scope('classifier_l', reuse = reuse):
w_1 = self.weight_variable('w_1', [n_units_g, n_units_l])
b_1 = self.bias_variable('b_1', [n_units_l])
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_l, n_labels])
b_2 = self.bias_variable('b_2', [n_labels])
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_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)
train_step = optimizer.minimize(loss, var_list = var_list)
return train_step
def training_2(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_2, labels_train_2, images_test_2, labels_test_2, \
images_train_3, labels_train_3, images_test_3, labels_test_3, \
filter_size, n_filters_1, n_filters_2, n_units_g, \
n_units_d, n_domains, n_units_l, n_labels, eps, alpha_d, alpha_l, \
learning_rate, n_iter_1, n_iter_2, batch_size, show_step_1, show_step_2, is_saving, model_path):
tf.reset_default_graph()
x = tf.placeholder(shape = [None, 28 * 28], dtype = tf.float32)
y = tf.placeholder(shape = [None, n_labels], dtype = tf.float32)
d = tf.placeholder(shape = [None, n_domains], dtype = tf.float32)
keep_prob = tf.placeholder(shape = (), dtype = tf.float32)
# for initialization
feat = self.generator(x, filter_size, n_filters_1, n_filters_2, n_units_g, \
keep_prob, reuse = False)
logits_d = self.classifier_d(feat, n_units_g, n_units_d, n_domains, keep_prob, reuse = False)
probs_d = tf.nn.softmax(logits_d)
loss_d = self.loss_cross_entropy(probs_d, d)
logits_l = self.classifier_l(feat, n_units_g, n_units_l, n_labels, keep_prob, reuse = False)
probs_l = tf.nn.softmax(logits_l)
loss_l = self.loss_cross_entropy(probs_l, y)
var_list_g = tf.trainable_variables('generator')
var_list_c_d = tf.trainable_variables('classifier_d')
var_list_c_l = tf.trainable_variables('classifier_l')
var_list_d = var_list_g + var_list_c_d
var_list_l = var_list_g + var_list_c_l
# Without Gradient Clipping
train_step_d = self.training(loss_d, learning_rate, var_list_d)
train_step_l = self.training(loss_l, learning_rate, var_list_l)
acc_d = self.accuracy(probs_d, d)
acc_l = self.accuracy(probs_l, y)
# for training
feat_1 = self.generator(x, filter_size, n_filters_1, n_filters_2, n_units_g, \
keep_prob, reuse = True)
logits_d_1 = self.classifier_d(feat_1, n_units_g, n_units_d, n_domains, keep_prob, reuse = True)
probs_d_1 = tf.nn.softmax(logits_d_1)
loss_d_1 = self.loss_cross_entropy(probs_d_1, d)
logits_l_1 = self.classifier_l(feat_1, n_units_g, n_units_l, n_labels, keep_prob, reuse = True)
probs_l_1 = tf.nn.softmax(logits_l_1)
loss_l_1 = self.loss_cross_entropy(probs_l_1, y)
grad_d = tf.stop_gradient(tf.gradients(loss_d_1, [x])[0])
grad_norm_d = grad_d / (tf.reshape(tf.sqrt(tf.reduce_sum(tf.pow(grad_d, 2), axis = 1)), [-1, 1]) + 1e-10)
x_d = x + eps * grad_norm_d
grad_l = tf.stop_gradient(tf.gradients(loss_l_1, [x])[0])
grad_norm_l = grad_l / (tf.reshape(tf.sqrt(tf.reduce_sum(tf.pow(grad_l, 2), axis = 1)), [-1, 1]) + 1e-10)
x_l = x + eps * grad_norm_l
feat_l = self.generator(x_l, filter_size, n_filters_1, n_filters_2, n_units_g, \
keep_prob, reuse = True)
feat_d = self.generator(x_d, filter_size, n_filters_1, n_filters_2, n_units_g, \
keep_prob, reuse = True)
logits_d_2 = self.classifier_d(feat_l, n_units_g, n_units_d, n_domains, keep_prob, reuse = True)
probs_d_2 = tf.nn.softmax(logits_d_2)
loss_d_2 = self.loss_cross_entropy(probs_d_2, d)
logits_l_2 = self.classifier_l(feat_d, n_units_g, n_units_l, n_labels, keep_prob, reuse = True)
probs_l_2 = tf.nn.softmax(logits_l_2)
loss_l_2 = self.loss_cross_entropy(probs_l_2, y)
loss_cg_d = (1.0 - alpha_d) * loss_d_1 + alpha_d * loss_d_2
loss_cg_l = (1.0 - alpha_l) * loss_l_1 + alpha_l * loss_l_2
#var_list_cg_d = var_list_g + var_list_c_d
#var_list_cg_l = var_list_g + var_list_c_l
# Without Gradient Clipping
train_step_cg_d = self.training_2(loss_cg_d, learning_rate, var_list_d)
train_step_cg_l = self.training_2(loss_cg_l, learning_rate, var_list_l)
#train_step_cg_d = self.training(loss_cg_d, learning_rate, var_list_cg_d)
#train_step_cg_l = self.training(loss_cg_l, learning_rate, var_list_cg_l)
acc_cg_d_1 = self.accuracy(probs_d_1, d)
acc_cg_d_2 = self.accuracy(probs_d_2, d)
acc_cg_l_1 = self.accuracy(probs_l_1, y)
acc_cg_l_2 = self.accuracy(probs_l_2, y)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
##########################################################################
print ('-' * 30)
print ('Initialization ')
print ('-' * 30)
history_loss_d_train = []
history_loss_d_test = []
history_acc_d_train = []
history_acc_d_test = []
history_loss_l_train = []
history_loss_l_test = []
history_acc_l_train = []
history_acc_l_test = []
history_loss_l_3_test = []
history_acc_l_3_test = []
for i in range(n_iter_1):
# Training data of 1 and 2
rand_index = np.random.choice(len(images_train_1), size = batch_size//2)
x_batch_1 = images_train_1[rand_index]
y_batch_1 = labels_train_1[rand_index]
d_1 = np.ones(shape = (batch_size//2), dtype = np.int32) * 0
d_1_one_hot = np.identity(2)[d_1]
rand_index = np.random.choice(len(images_train_2), size = batch_size//2)
x_batch_2 = images_train_2[rand_index]
y_batch_2 = labels_train_2[rand_index]
d_2 = np.ones(shape = (batch_size//2), dtype = np.int32) * 1
d_2_one_hot = np.identity(2)[d_2]
x_batch = np.concatenate((x_batch_1, x_batch_2), axis = 0)
y_batch = np.concatenate((y_batch_1, y_batch_2), axis = 0)
d_batch = np.concatenate((d_1_one_hot, d_2_one_hot), axis = 0).astype(np.float32)
perm = np.random.permutation(batch_size)
x_batch_p = x_batch[perm]
y_batch_p = y_batch[perm]
d_batch_p = d_batch[perm]
feed_dict = {x: x_batch_p, y: y_batch_p, d: d_batch_p, keep_prob: 1.0}
sess.run(train_step_d, feed_dict = feed_dict)
sess.run(train_step_l, feed_dict = feed_dict)
temp_loss_d = sess.run(loss_d, feed_dict = feed_dict)
temp_loss_l = sess.run(loss_l, feed_dict = feed_dict)
temp_acc_d = sess.run(acc_d, feed_dict = feed_dict)
temp_acc_l = sess.run(acc_l, feed_dict = feed_dict)
history_loss_d_train.append(temp_loss_d)
history_loss_l_train.append(temp_loss_l)
history_acc_d_train.append(temp_acc_d)
history_acc_l_train.append(temp_acc_l)
if (i + 1) % show_step_1 == 0:
print ('-' * 100)
print ('Iteration: ' + str(i + 1) + \
' Loss_d: ' + str(temp_loss_d) + ' Accuracy_d: ' + str(temp_acc_d) + \
' Loss_l: ' + str(temp_loss_l) + ' Accuracy_l: ' + str(temp_acc_l))
# Test data of 1 and 2
rand_index = np.random.choice(len(images_test_1), size = batch_size//2)
x_batch_1 = images_test_1[rand_index]
y_batch_1 = labels_test_1[rand_index]
d_1 = np.ones(shape = (batch_size//2), dtype = np.int32) * 0
d_1_one_hot = np.identity(2)[d_1]
rand_index = np.random.choice(len(images_test_2), size = batch_size//2)
x_batch_2 = images_test_2[rand_index]
y_batch_2 = labels_test_2[rand_index]
d_2 = np.ones(shape = (batch_size//2), dtype = np.int32) * 1
d_2_one_hot = np.identity(2)[d_2]
x_batch = np.concatenate((x_batch_1, x_batch_2), axis = 0)
y_batch = np.concatenate((y_batch_1, y_batch_2), axis = 0)
d_batch = np.concatenate((d_1_one_hot, d_2_one_hot), axis = 0).astype(np.float32)
perm = np.random.permutation(batch_size)
x_batch_p = x_batch[perm]
y_batch_p = y_batch[perm]
d_batch_p = d_batch[perm]
feed_dict = {x: x_batch_p, y: y_batch_p, d: d_batch_p, keep_prob: 1.0}
temp_loss_d = sess.run(loss_d, feed_dict = feed_dict)
temp_loss_l = sess.run(loss_l, feed_dict = feed_dict)
temp_acc_d = sess.run(acc_d, feed_dict = feed_dict)
temp_acc_l = sess.run(acc_l, feed_dict = feed_dict)
history_loss_d_test.append(temp_loss_d)
history_loss_l_test.append(temp_loss_l)
history_acc_d_test.append(temp_acc_d)
history_acc_l_test.append(temp_acc_l)
# Test data of 3
rand_index = np.random.choice(len(images_test_3), size = batch_size)
x_batch = images_test_3[rand_index]
y_batch = labels_test_3[rand_index]
feed_dict = {x: x_batch, y: y_batch, keep_prob: 1.0}
temp_loss_l = sess.run(loss_l, feed_dict = feed_dict)
temp_acc_l = sess.run(acc_l, feed_dict = feed_dict)
history_loss_l_3_test.append(temp_loss_l)
history_acc_l_3_test.append(temp_acc_l)
print ('-' * 100)
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter_1), history_loss_d_train, 'b-', label = 'Training')
ax1.plot(range(n_iter_1), history_loss_d_test, 'r-', label = 'Test')
ax1.set_title('Loss_d')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter_1), history_acc_d_train, 'b-', label = 'Training')
ax2.plot(range(n_iter_1), history_acc_d_test, 'r-', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_d')
ax2.legend(loc = 'lower right')
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter_1), history_loss_l_train, 'b-', label = 'Training')
ax1.plot(range(n_iter_1), history_loss_l_test, 'r-', label = 'Test')
ax1.set_title('Loss_l')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter_1), history_acc_l_train, 'b-', label = 'Training')
ax2.plot(range(n_iter_1), history_acc_l_test, 'r-', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_l')
ax2.legend(loc = 'lower right')
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter_1), history_loss_l_3_test, 'r-', label = 'Test')
ax1.set_title('Loss_l_3')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter_1), history_acc_l_3_test, 'r-', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_l_3')
ax2.legend(loc = 'lower right')
plt.show()
##########################################################################
print ('-' * 30)
print ('Training ')
print ('-' * 30)
history_loss_cg_d_train = []
history_loss_cg_d_test = []
history_acc_cg_d_1_train = []
history_acc_cg_d_1_test = []
history_acc_cg_d_2_train = []
history_acc_cg_d_2_test = []
history_loss_cg_l_train = []
history_loss_cg_l_test = []
history_acc_cg_l_1_train = []
history_acc_cg_l_1_test = []
history_acc_cg_l_2_train = []
history_acc_cg_l_2_test = []
history_loss_cg_l_3_test = []
history_acc_cg_l_3_test = []
for i in range(n_iter_2):
# Training data of 1 and 2
rand_index = np.random.choice(len(images_train_1), size = batch_size//2)
x_batch_1 = images_train_1[rand_index]
y_batch_1 = labels_train_1[rand_index]
d_1 = np.ones(shape = (batch_size//2), dtype = np.int32) * 0
d_1_one_hot = np.identity(2)[d_1]
rand_index = np.random.choice(len(images_train_2), size = batch_size//2)
x_batch_2 = images_train_2[rand_index]
y_batch_2 = labels_train_2[rand_index]
d_2 = np.ones(shape = (batch_size//2), dtype = np.int32) * 1
d_2_one_hot = np.identity(2)[d_2]
x_batch = np.concatenate((x_batch_1, x_batch_2), axis = 0)
y_batch = np.concatenate((y_batch_1, y_batch_2), axis = 0)
d_batch = np.concatenate((d_1_one_hot, d_2_one_hot), axis = 0).astype(np.float32)
perm = np.random.permutation(batch_size)
x_batch_p = x_batch[perm]
y_batch_p = y_batch[perm]
d_batch_p = d_batch[perm]
feed_dict = {x: x_batch_p, y: y_batch_p, d: d_batch_p, keep_prob: 1.0}
sess.run(train_step_cg_d, feed_dict = feed_dict)
sess.run(train_step_cg_l, feed_dict = feed_dict)
temp_loss_cg_d = sess.run(loss_cg_d, feed_dict = feed_dict)
temp_loss_cg_l = sess.run(loss_cg_l, feed_dict = feed_dict)
temp_acc_cg_d_1 = sess.run(acc_cg_d_1, feed_dict = feed_dict)
temp_acc_cg_l_1 = sess.run(acc_cg_l_1, feed_dict = feed_dict)
temp_acc_cg_d_2 = sess.run(acc_cg_d_2, feed_dict = feed_dict)
temp_acc_cg_l_2 = sess.run(acc_cg_l_2, feed_dict = feed_dict)
history_loss_cg_d_train.append(temp_loss_cg_d)
history_loss_cg_l_train.append(temp_loss_cg_l)
history_acc_cg_d_1_train.append(temp_acc_cg_d_1)
history_acc_cg_d_2_train.append(temp_acc_cg_d_2)
history_acc_cg_l_1_train.append(temp_acc_cg_l_1)
history_acc_cg_l_2_train.append(temp_acc_cg_l_2)
if (i + 1) % show_step_2 == 0:
print ('-' * 100)
#print ('Iteration: ' + str(i + 1) + \
# ' Loss_cg_d: ' + str(temp_loss_d) + ' Accuracy_cg_d: ' + str(temp_acc_d) + \
# ' Loss_cg_l: ' + str(temp_loss_l) + ' Accuracy_cg_l: ' + str(temp_acc_l))
print ('Iteration: ' + str(i + 1) + \
' Loss_cg_d: ' + str(temp_loss_cg_d) + ' Loss_cg_l: ' + str(temp_loss_cg_l))
# Test data of 1 and 2
rand_index = np.random.choice(len(images_test_1), size = batch_size//2)
x_batch_1 = images_test_1[rand_index]
y_batch_1 = labels_test_1[rand_index]
d_1 = np.ones(shape = (batch_size//2), dtype = np.int32) * 0
d_1_one_hot = np.identity(2)[d_1]
rand_index = np.random.choice(len(images_test_2), size = batch_size//2)
x_batch_2 = images_test_2[rand_index]
y_batch_2 = labels_test_2[rand_index]
d_2 = np.ones(shape = (batch_size//2), dtype = np.int32) * 1
d_2_one_hot = np.identity(2)[d_2]
x_batch = np.concatenate((x_batch_1, x_batch_2), axis = 0)
y_batch = np.concatenate((y_batch_1, y_batch_2), axis = 0)
d_batch = np.concatenate((d_1_one_hot, d_2_one_hot), axis = 0).astype(np.float32)
perm = np.random.permutation(batch_size)
x_batch_p = x_batch[perm]
y_batch_p = y_batch[perm]
d_batch_p = d_batch[perm]
feed_dict = {x: x_batch_p, y: y_batch_p, d: d_batch_p, keep_prob: 1.0}
temp_loss_cg_d = sess.run(loss_cg_d, feed_dict = feed_dict)
temp_loss_cg_l = sess.run(loss_cg_l, feed_dict = feed_dict)
temp_acc_cg_d_1 = sess.run(acc_cg_d_1, feed_dict = feed_dict)
temp_acc_cg_d_2 = sess.run(acc_cg_d_2, feed_dict = feed_dict)
temp_acc_cg_l_1 = sess.run(acc_cg_l_1, feed_dict = feed_dict)
temp_acc_cg_l_2 = sess.run(acc_cg_l_2, feed_dict = feed_dict)
history_loss_cg_d_test.append(temp_loss_cg_d)
history_loss_cg_l_test.append(temp_loss_cg_l)
history_acc_cg_d_1_test.append(temp_acc_cg_d_1)
history_acc_cg_d_2_test.append(temp_acc_cg_d_2)
history_acc_cg_l_1_test.append(temp_acc_cg_l_1)
history_acc_cg_l_2_test.append(temp_acc_cg_l_2)
# Test data of 3
rand_index = np.random.choice(len(images_test_3), size = batch_size)
x_batch = images_test_3[rand_index]
y_batch = labels_test_3[rand_index]
feed_dict = {x: x_batch, y: y_batch, keep_prob: 1.0}
temp_loss_l = sess.run(loss_l, feed_dict = feed_dict)
temp_acc_l = sess.run(acc_l, feed_dict = feed_dict)
history_loss_cg_l_3_test.append(temp_loss_l)
history_acc_cg_l_3_test.append(temp_acc_l)
print ('-' * 100)
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter_2), history_loss_cg_d_train, 'b-', label = 'Training')
ax1.plot(range(n_iter_2), history_loss_cg_d_test, 'r-', label = 'Test')
ax1.set_title('Loss_cg_d')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter_2), history_loss_cg_l_train, 'b-', label = 'Training')
ax2.plot(range(n_iter_2), history_loss_cg_l_test, 'r-', label = 'Test')
ax2.set_title('Loss_cg_l')
ax2.legend(loc = 'upper right')
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter_2), history_acc_cg_d_1_train, 'b-', label = 'Training')
ax1.plot(range(n_iter_2), history_acc_cg_d_1_test, 'r-', label = 'Test')
ax1.set_ylim(0.0, 1.0)
ax1.set_title('Accuracy_cg_d_1')
ax1.legend(loc = 'lower right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter_2), history_acc_cg_l_1_train, 'b-', label = 'Training')
ax2.plot(range(n_iter_2), history_acc_cg_l_1_test, 'r-', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_cg_l_1')
ax2.legend(loc = 'lower right')
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter_2), history_acc_cg_d_2_train, 'b-', label = 'Training')
ax1.plot(range(n_iter_2), history_acc_cg_d_2_test, 'r-', label = 'Test')
ax1.set_ylim(0.0, 1.0)
ax1.set_title('Accuracy_cg_d_2')
ax1.legend(loc = 'lower right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter_2), history_acc_cg_l_2_train, 'b-', label = 'Training')
ax2.plot(range(n_iter_2), history_acc_cg_l_2_test, 'r-', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_cg_l_2')
ax2.legend(loc = 'lower right')
fig = plt.figure(figsize = (10, 3))
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(range(n_iter_2), history_loss_cg_l_3_test, 'r-', label = 'Test')
ax1.set_title('Loss_cg_l_3')
ax1.legend(loc = 'upper right')
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(range(n_iter_2), history_acc_cg_l_3_test, 'r-', label = 'Test')
ax2.set_ylim(0.0, 1.0)
ax2.set_title('Accuracy_cg_l_3')
ax2.legend(loc = 'lower 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 = 128
n_units_d = 128
n_domains = 2
n_units_l = 128
n_labels = 10
eps = 1.0
alpha_d = 0.5
alpha_l = 0.5
learning_rate = 0.001
n_iter_1 = 300
n_iter_2 = 300
batch_size = 64
show_step_1 = 100
show_step_2 = 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_2 = images_train_60
labels_train_2 = labels_train_60
images_test_2 = images_test_60
labels_test_2 = labels_test_60
images_train_3 = images_train_30
labels_train_3 = labels_train_30
images_test_3 = images_test_30
labels_test_3 = labels_test_30
is_saving = False
cg.fit(images_train_1, labels_train_1, images_test_1, labels_test_1, \
images_train_2, labels_train_2, images_test_2, labels_test_2, \
images_train_3, labels_train_3, images_test_3, labels_test_3, \
filter_size, n_filters_1, n_filters_2, n_units_g, \
n_units_d, n_domains, n_units_l, n_labels, eps, alpha_d, alpha_l, \
learning_rate, n_iter_1, n_iter_2, batch_size, show_step_1, show_step_2, is_saving, model_path)