#####################################################################################
######## Loss Function
for kk in range(K):
if kk == 0:
rate_total = rate[kk]
else:
rate_total = rate_total + rate[kk]
loss = tf.reduce_mean(rate_total)
######### Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate)
training_op = optimizer.minimize(loss, name="training_op")
init = tf.global_variables_initializer()
saver = tf.train.Saver()
######################################################################
########### Validation Set
h_act_test, hR_act_test, hI_act_test = generate_batch_data(
test_size, M, K, Lp, LSF_UE, Mainlobe_UE, HalfBW_UE)
DNN_input_BS_Q_test = generate_batch_data_specializedB(h_act_test,Xp_r,Xp_i,noise_std_dl,K,\
A1,A2,A3,A4,b1,b2,b3,b4,Qmin,Qmax)
feed_dict_test = {
DNN_input_BS: DNN_input_BS_Q_test,
hR: hR_act_test,
hI: hI_act_test,
lay['noise_std']: noise_std_dl
}
rate_MRT, rate_ZF = generate_MRT_ZF_performance(h_act_test, noise_std_dl, P)
########### Load Final Data Set
AA = sio.loadmat('Data_test_K{}M{}Lp{}_{}HBW_withParams.mat'.format(
K, M, Lp, int(HalfBW_UE[0])))
h_act_test_Final = AA['h_act_test_Final']
hR_act_test_Final = AA['hR_act_test_Final']
hI_act_test_Final = AA['hI_act_test_Final']
csv_img, csv_ann = util.load_sample_from_csv(FLAGS.csv)
if csv_img is not None and len(csv_img) > 0:
images.extend(csv_img)
annotations.extend(csv_ann)
if len(images) == 0:
print('Samples is empty, exit()')
exit()
print('fine tuning images size: {}'.format(len(images)))
print('fine tuning annotations size: {}'.format(len(annotations)))
assert len(images) == len(annotations)
image_tensor, annotation_tensor = generate_batch_data(images,
annotations,
batch_size=batch_size)
if __name__ == "__main__":
is_training = tf.placeholder(tf.bool)
dsn_fuse, dsn1, dsn2, dsn3, dsn4, dsn5 = mobilenet_v2_style_hed(
image_tensor, is_training)
hed_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='hed')
cost = class_balanced_sigmoid_cross_entropy(dsn_fuse, annotation_tensor)
# cost = class_balanced_sigmoid_cross_entropy(dsn_fuse, annotation_tensor) \
# + class_balanced_sigmoid_cross_entropy(dsn1, annotation_tensor)\
# + class_balanced_sigmoid_cross_entropy(dsn2, annotation_tensor)\