def train():
parser = OptionParser()
parser.add_option("--train_inputDir", dest="train_inputDir", help="Input directory", metavar="DIRECTORY")
parser.add_option("--train_inputFile", dest="train_inputFile", help="Input file", metavar="FILE")
parser.add_option("--train_type", dest="train_type", help="Training type, 1|2|3|4.", metavar="VALUE", default=2)
parser.add_option("--particle_number", dest="train_number", help="Number of positive samples to train.", metavar="VALUE", default=-1)
parser.add_option("--mrc_number", dest="mrc_number", help="Number of mrc files to be trained.", metavar="VALUE", default=-1)
parser.add_option("--coordinate_symbol", dest="coordinate_symbol", help="The symbol of the coordinate file, like '_manualPick'", metavar="STRING")
parser.add_option("--particle_size", dest="particle_size", help="the size of the particle.", metavar="VALUE", default=-1)
parser.add_option("--validation_ratio", dest="validation_ratio", help="the ratio.", metavar="VALUE", default=0.1)
parser.add_option("--model_retrain", action="store_true", dest="model_retrain", help="train the model using the pre-trained model as parameters initialization .", default=False)
parser.add_option("--model_load_file", dest="model_load_file", help="pre-trained model", metavar="FILE")
parser.add_option("--model_save_dir", dest="model_save_dir", help="save the model to this directory", metavar="DIRECTORY", default="../trained_model")
parser.add_option("--model_save_file", dest="model_save_file", help="save the model to file", metavar="FILE")
(opt, args) = parser.parse_args()
# set the tensoflow seed
tf.set_random_seed(1234)
# set the numpy seed
np.random.seed(1234)
# define the input size of the model
model_input_size = [100, 64, 64, 1]
num_class = 2 # the number of the class
batch_size = model_input_size[0]
# define input parameters
train_type = int(opt.train_type)
train_inputDir = opt.train_inputDir
train_inputFile = opt.train_inputFile
train_number = float(opt.train_number)
mrc_number = int(opt.mrc_number)
coordinate_symbol = opt.coordinate_symbol
debug_dir = '../train_output' # output dir
particle_size = int(opt.particle_size)
validation_ratio = float(opt.validation_ratio)
# define the save model
model_retrain = opt.model_retrain
model_load_file = opt.model_load_file
model_save_dir = opt.model_save_dir
model_save_file = os.path.join(model_save_dir, opt.model_save_file)
if not os.access(model_save_dir, os.F_OK):
os.mkdir(model_save_dir)
if not os.access(debug_dir, os.F_OK):
os.mkdir(debug_dir)
# define the learning rate decay parameters
# more information about this, refer to function tf.train.exponential_decay()
learning_rate = 0.01
learning_rate_decay_factor = 0.95
# the value will be changed base on the train_size and batch size
learning_rate_decay_steps = 400
learning_rate_staircase = True
# momentum
momentum = 0.9
# load training dataset
dataLoader = DataLoader()
if train_type == 1:
# load train data from mrc file dir
train_number = int(train_number)
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_mrcFileDir(train_inputDir, particle_size, model_input_size, validation_ratio, coordinate_symbol, mrc_number, train_number)
elif train_type == 2:
# load train data from numpy data struct
train_number = int(train_number)
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_ExtractedDataFile(train_inputDir, train_inputFile, model_input_size, validation_ratio, train_number)
elif train_type == 3:
# load train data from relion .star file
train_number = int(train_number)
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_RelionStarFile(train_inputFile, particle_size, model_input_size, validation_ratio, train_number)
elif train_type == 4:
# load train data from prepicked results
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_PrePickedResults(train_inputDir, train_inputFile, particle_size, model_input_size, validation_ratio, train_number)
else:
print("ERROR: invalid value of train_type:", train_type)
display.show_particle(train_data, os.path.join(debug_dir, 'positive.png'))
# test whether train_data exist
try:
train_data
except NameError:
print("ERROR: in function load.loadInputTrainData.")
return None
else:
print("Load training data successfully!")
# shuffle the training data
train_data, train_label = shuffle_in_unison_inplace(train_data, train_label)
eval_data, eval_label = shuffle_in_unison_inplace(eval_data, eval_label)
train_size = train_data.shape[0]
eval_size = eval_data.shape[0]
# initalize the decay_steps based on train_size and batch size.
# change the learning rate each 2 epochs
learning_rate_decay_steps = 10*(train_size // batch_size)
# initialize the parameters of deepModel
deepModel = DeepModel(particle_size, model_input_size, num_class)
deepModel.init_learning_rate(learning_rate = learning_rate, learning_rate_decay_factor = learning_rate_decay_factor,
decay_steps = learning_rate_decay_steps, staircase = learning_rate_staircase)
deepModel.init_momentum(momentum = momentum)
# initialize the model
# define the computation procedure of optimizer, loss, lr, prediction, eval_prediction
deepModel.init_model_graph_train()
saver = tf.train.Saver(tf.all_variables())
start_time = time.time()
init = tf.initialize_all_variables()
with tf.Session(config=tf.ConfigProto(log_device_placement=False)) as sess:
# initialize all the parameters
sess.run(init)
max_epochs = 200 # the max number of epoch to train the model
best_eval_error_rate = 100
toleration_patience = 10
toleration_patience_flag = 0
eval_frequency = train_size // batch_size # the frequency to evaluate the evaluation dataset
for step in xrange(int(max_epochs * train_size) // batch_size):
# get the batch training data
offset = (step * batch_size) % (train_size - batch_size)
batch_data = train_data[offset:(offset+batch_size), ...]
batch_label = train_label[offset:(offset+batch_size)]
# online augmentation
#batch_data = DataLoader.preprocess_particle_online(batch_data)
loss_value, lr, train_prediction = deepModel.train_batch(batch_data, batch_label,sess)
# do the computation
if step % eval_frequency == 0:
stop_time = time.time() - start_time
start_time = time.time()
eval_prediction = deepModel.evaluation(eval_data, sess)
eval_error_rate = error_rate(eval_prediction, eval_label)
print('epoch: %.2f , %.2f ms' % (step * batch_size /train_size, 1000 * stop_time / eval_frequency))
print('train loss: %.6f,\t learning rate: %.6f' % (loss_value, lr))
print('train error: %.6f%%,\t valid error: %.6f%%' % (error_rate(train_prediction, batch_label), eval_error_rate))
if eval_error_rate < best_eval_error_rate:
best_eval_error_rate = eval_error_rate
toleration_patience = 10
else:
toleration_patience = toleration_patience - 1
if toleration_patience == 0:
saver.save(sess, model_save_file)
break
def train():
parser = OptionParser()
parser.add_option("--train_inputDir",
dest="train_inputDir",
help="Input directory",
metavar="DIRECTORY")
parser.add_option("--train_inputFile",
dest="train_inputFile",
help="Input file",
metavar="FILE")
parser.add_option("--train_type",
dest="train_type",
help="Training type, 1|2|3|4.",
metavar="VALUE",
default=2)
parser.add_option("--particle_number",
dest="train_number",
help="Number of positive samples to train.",
metavar="VALUE",
default=-1)
parser.add_option("--mrc_number",
dest="mrc_number",
help="Number of mrc files to be trained.",
metavar="VALUE",
default=-1)
parser.add_option(
"--coordinate_symbol",
dest="coordinate_symbol",
help="The symbol of the coordinate file, like '_manualPick'",
metavar="STRING")
parser.add_option("--particle_size",
dest="particle_size",
help="the size of the particle.",
metavar="VALUE",
default=-1)
parser.add_option("--validation_ratio",
dest="validation_ratio",
help="the ratio.",
metavar="VALUE",
default=0.1)
parser.add_option(
"--model_retrain",
action="store_true",
dest="model_retrain",
help=
"train the model using the pre-trained model as parameters initialization .",
default=False)
parser.add_option("--model_load_file",
dest="model_load_file",
help="pre-trained model",
metavar="FILE")
parser.add_option("--model_save_dir",
dest="model_save_dir",
help="save the model to this directory",
metavar="DIRECTORY",
default="../trained_model")
parser.add_option("--model_save_file",
dest="model_save_file",
help="save the model to file",
metavar="FILE")
parser.add_option("--pos_list",
dest="pos_list",
help="",
metavar="VALUE",
default="")
parser.add_option("--neg_list",
dest="neg_list",
help="",
metavar="VALUE",
default="")
parser.add_option("--mixup",
dest="mixup",
help="",
metavar="VALUE",
default="0")
(opt, args) = parser.parse_args()
model_input_size = [128, 64, 64, 1]
num_class = 2
batch_size = model_input_size[0]
# define input parameters
train_type = int(opt.train_type)
train_inputDir = opt.train_inputDir
train_inputFile = opt.train_inputFile
protein_number = len(train_inputFile.split(';'))
train_number = float(opt.train_number)
mrc_number = int(opt.mrc_number)
dropout_rate = 0.5
coordinate_symbol = opt.coordinate_symbol
debug_dir = '../train_output' # output dir
particle_size = int(opt.particle_size)
validation_ratio = float(opt.validation_ratio)
# define the save model
model_retrain = opt.model_retrain
model_load_file = opt.model_load_file
model_save_dir = opt.model_save_dir
model_save_file = os.path.join(model_save_dir, opt.model_save_file)
pos_list = opt.pos_list
neg_list = opt.neg_list
mixup = int(opt.mixup)
print("MIXUP=======", mixup)
if not os.access(model_save_dir, os.F_OK):
os.mkdir(model_save_dir)
if not os.access(debug_dir, os.F_OK):
os.mkdir(debug_dir)
dataLoader = DataLoader()
train_number = int(train_number)
if train_type == 1:
# load train data from mrc file dir
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_mrcFileDir(
train_inputDir, particle_size, model_input_size, validation_ratio,
coordinate_symbol, mrc_number, train_number)
elif train_type == 2:
# load train data from numpy data struct
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_ExtractedDataFile(
train_inputDir, train_inputFile, model_input_size,
validation_ratio, train_number)
elif train_type == 3:
# load train data from prepicked results
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_PrePickedResults(
train_inputDir, train_inputFile, particle_size, model_input_size,
validation_ratio, train_number)
elif train_type == 4:
# load train data from relion .star file
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_RelionStarFile(
train_inputFile, particle_size, model_input_size, validation_ratio,
train_number)
elif train_type == 5:
# load train data from class2d .star file
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_Class2dStarFile(
train_inputDir, train_inputFile, model_input_size,
validation_ratio, train_number)
elif train_type == 6:
left = 0
right = 50
get_partition = lambda x, y: (x + y) / 2
'''
# load train data from auto_filter_class .star file
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_AutoClass2dStarFile(
train_inputDir, train_inputFile, model_input_size, validation_ratio, train_number)
'''
else:
print("ERROR: invalid value of train_type:", train_type)
try:
train_type == 6 or train_data
except NameError:
print("Error: in function load.loadInputTrainData.")
return None
else:
print("Load training data successfully!")
idx = 0
good_enough = False
while True and not good_enough:
best_eval_error_rate = 100
all_error = []
finetune = False if train_type == 6 else False
dropout_rate = 0.5 if train_type == 6 else dropout_rate
deepModel = DeepModel(particle_size,
model_input_size,
num_class,
dropout_rate=dropout_rate,
finetune=finetune)
if train_type == 6:
deepModel.learning_rate = deepModel.learning_rate / 10.0
deepModel.decay_steps *= 2
if good_enough:
partition = partition + 1
else:
partition = get_partition(left, right)
print "PARTITOIN --->>>", partition
partition = 9
good_enough = True #Set this=True to run while for just once!!!
#train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_AutoClass2dStarFile(train_inputDir, train_inputFile, model_input_size, validation_ratio, train_number, partition)
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_AutoClass2dStarFile(
train_inputDir, train_inputFile, model_input_size,
validation_ratio, train_number, partition, pos_list, neg_list)
train_data, train_label = shuffle_in_unison_inplace(
train_data, train_label)
print("label_shape = ", np.array(train_label).shape)
'''
mix_data, mix_label = [], []
if mixup:
mixnum = len(train_data)
#for cnt in tqdm(range(mixnum)):
for cnt in range(mixnum):
#for cnt in range(mixnum):
L = np.random.beta(0.2, 0.2)
i1, i2 = np.random.randint(mixnum, size=2)
if train_data[i1].shape[1] == train_data[i2].shape[1]:
new_data = (1-L) * train_data[i1] + L * train_data[i2]
new_label = (1-L) * train_label[i1][1] + L * train_label[i2][1]
mix_data.append(new_data)
mix_label.append([1.0-new_label, new_label])
train_data = train_data + mix_data
train_label = train_label + mix_label
'''
print("label_shape = ", np.array(train_label).shape)
#eval_data, eval_label = shuffle_in_unison_inplace(eval_data, eval_label)
bs2train = {}
bs2eval = {}
for idx, t in enumerate(train_data):
if t.shape[1] not in bs2train.keys():
bs2train[t.shape[1]] = [idx]
else:
bs2train[t.shape[1]].append(idx)
for idx, t in enumerate(eval_data):
if t.shape[1] not in bs2eval.keys():
bs2eval[t.shape[1]] = [idx]
else:
bs2eval[t.shape[1]].append(idx)
train_size = len(train_data)
eval_size = len(eval_data)
print("train size=%d, eval_size=%d" % (train_size, eval_size))
print("batch_size=%d" % batch_size)
print("dropout=%.2f" % dropout_rate)
if train_size < 1000:
print("NOTE: no enough training data!\n<Failed>! ")
exit()
'''
if eval_size < model_input_size[0]: #TODO
tile_size = model_input_size[0] // eval_size + 1
eval_data = np.array(eval_data)
eval_data = np.tile(eval_data, [tile_size,1,1,1])
print ("tiled eval_data !!!!", tile_size)
'''
saver = tf.train.Saver(tf.all_variables(), max_to_keep=30)
start_time = time.time()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.26)
train_error = []
valid_error = []
eval_time = 0
with tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options, log_device_placement=False)) as sess:
tf.initialize_all_variables().run()
if model_load_file:
print model_load_file
saver.restore(sess, model_load_file)
max_epochs = 200
best_eval_error_rate = 100
toleration_patience = 10
toleration_patience_flag = 0
eval_frequency = train_size // batch_size
print("total_step=%d" %
(int(max_epochs * train_size) // batch_size))
#fout = open('trainingcurve%d_%s_test2_block1_lr0.1.txt'%(protein_number, deepModel.arch), 'w')
#fout = open('trainingcurve%d_%s_lr0.1.txt'%(protein_number, deepModel.arch), 'w')
#fout = open('trainingcurve%d_resnet.txt'%protein_number, 'w')
idx += 1
batch_type = bs2train.keys()
batch_type_number = len(batch_type)
po = {}
for k in range(batch_type_number):
po[k] = 0
batch_type_idx = 0
train_error_list = []
print(
"==================================================================="
)
#for step in xrange(int(max_epochs * train_size) // batch_size):
eval_prediction = deepModel.evaluation(eval_data,
sess,
label=eval_label)
eval_error_rate = error_rate(eval_prediction, eval_label)
eval_before_retrain = eval_error_rate
print('valid error before training: %.6f%%' % eval_error_rate)
print(
"==================================================================="
)
for epoch in range(int(max_epochs)):
start_time = time.time()
#for s in tqdm(range(eval_frequency)):
for s in range(eval_frequency):
step = epoch * eval_frequency + s
# get the batch training data
offset = (step * batch_size) % (train_size - batch_size)
batch_type_idx = (batch_type_idx + 1) % batch_type_number
batch = batch_type[batch_type_idx]
if po[batch_type_idx] + batch_size >= len(bs2train[batch]):
po[batch_type_idx] = 0
p = po[batch_type_idx]
idxs = bs2train[batch][p:(p + batch_size)]
batch_data = []
batch_label = []
for ix in idxs:
batch_data.append(train_data[ix])
batch_label.append(train_label[ix])
po[batch_type_idx] = po[batch_type_idx] + batch_size
#batch_data = train_data[offset:(offset+batch_size)]
#batch_label = train_label[offset:(offset+batch_size)]
'''
batch_data_shape = batch_data[0].shape
con = False
for bb in batch_data:
if bb.shape != batch_data_shape:
con = True
break
if con:
continue
'''
# online augmentation
#batch_data = DataLoader.preprocess_particle_online(batch_data)
loss_value, lr, train_prediction = deepModel.train_batch(
batch_data, batch_label, sess)
train_error_list.append(
error_rate(train_prediction, batch_label))
# do the computation
#if step % eval_frequency == 0:
#if step % 50 == 0:
#TODO:display after each epoch
stop_time = time.time() - start_time
eval_prediction = deepModel.evaluation(eval_data,
sess,
label=eval_label)
eval_error_rate = error_rate(eval_prediction, eval_label)
#best_eval_error_rate = min(best_eval_error_rate, eval_error_rate)
#print('>> epoch: %.2f , %.2f ms' % (step * batch_size /train_size, 1000 * stop_time / eval_frequency))
train_error_mean = np.mean(train_error_list)
print(
'>> epoch: %d, train loss: %.2f, lr: %.6f, toleration:%d, train error: %.2f%%, valid error: %.2f%%'
% (epoch, loss_value, lr, toleration_patience,
train_error_mean, eval_error_rate))
#print >>fout, step, train_error_mean, eval_error_rate
train_error.append(train_error_mean)
valid_error.append(eval_error_rate)
eval_time += 1
train_error_list = []
all_error.append(eval_error_rate)
if eval_error_rate < best_eval_error_rate:
best_eval_error_rate = eval_error_rate
toleration_patience = 10
saver.save(sess, model_save_file)
else:
if epoch > 50:
toleration_patience = toleration_patience - 1
if toleration_patience == 0:
break
good_enough = True
'''
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.title('Training curve')
plt.ylabel('Error(%)')
plt.xlabel('Epoch')
axes = plt.gca()
axes.set_ylim([0, 60])
plt.plot(range(eval_time), train_error, label='training')
plt.plot(range(eval_time), valid_error, label='validation')
plt.legend(loc='upper right')
plt.show()
#plt.savefig('pickercurve.png')
'''
print("Accuracy: before retrain: %.2f%%, after retrain: %.2f%%" %
(100.0 - eval_before_retrain, 100.0 - best_eval_error_rate))
print("Retrain <Successful>!")
def train():
parser = OptionParser()
parser.add_option("--train_inputDir",
dest="train_inputDir",
help="Input directory",
metavar="DIRECTORY")
parser.add_option("--train_inputFile",
dest="train_inputFile",
help="Input file",
metavar="FILE")
parser.add_option("--train_type",
dest="train_type",
help="Training type, 1|2|3|4.",
metavar="VALUE",
default=2)
parser.add_option("--particle_number",
dest="train_number",
help="Number of positive samples to train.",
metavar="VALUE",
default=-1)
parser.add_option("--mrc_number",
dest="mrc_number",
help="Number of mrc files to be trained.",
metavar="VALUE",
default=-1)
parser.add_option(
"--coordinate_symbol",
dest="coordinate_symbol",
help="The symbol of the coordinate file, like '_manualPick'",
metavar="STRING")
parser.add_option("--particle_size",
dest="particle_size",
help="the size of the particle.",
metavar="VALUE",
default=-1)
parser.add_option("--validation_ratio",
dest="validation_ratio",
help="the ratio.",
metavar="VALUE",
default=0.1)
parser.add_option(
"--model_retrain",
action="store_true",
dest="model_retrain",
help=
"train the model using the pre-trained model as parameters initialization .",
default=False)
parser.add_option("--model_load_file",
dest="model_load_file",
help="pre-trained model",
metavar="FILE")
parser.add_option("--model_save_dir",
dest="model_save_dir",
help="save the model to this directory",
metavar="DIRECTORY",
default="../trained_model")
parser.add_option("--model_save_file",
dest="model_save_file",
help="save the model to file",
metavar="FILE")
(opt, args) = parser.parse_args()
# set the tensoflow seed
tf.set_random_seed(1234)
# set the numpy seed
np.random.seed(1234)
# define the input size of the model
model_input_size = [100, 64, 64, 1]
num_class = 2 # the number of the class
batch_size = model_input_size[0]
# define input parameters
train_type = int(opt.train_type)
train_inputDir = opt.train_inputDir
train_inputFile = opt.train_inputFile
train_number = float(opt.train_number)
mrc_number = int(opt.mrc_number)
coordinate_symbol = opt.coordinate_symbol
debug_dir = '../train_output' # output dir
particle_size = int(opt.particle_size)
validation_ratio = float(opt.validation_ratio)
# define the save model
model_retrain = opt.model_retrain
model_load_file = opt.model_load_file
model_save_dir = opt.model_save_dir
model_save_file = os.path.join(model_save_dir, opt.model_save_file)
if not os.access(model_save_dir, os.F_OK):
os.mkdir(model_save_dir)
if not os.access(debug_dir, os.F_OK):
os.mkdir(debug_dir)
# define the learning rate decay parameters
# more information about this, refer to function tf.train.exponential_decay()
learning_rate = 0.01
learning_rate_decay_factor = 0.95
# the value will be changed base on the train_size and batch size
learning_rate_decay_steps = 400
learning_rate_staircase = True
# momentum
momentum = 0.9
# load training dataset
dataLoader = DataLoader()
if train_type == 1:
# load train data from mrc file dir
train_number = int(train_number)
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_mrcFileDir(
train_inputDir, particle_size, model_input_size, validation_ratio,
coordinate_symbol, mrc_number, train_number)
elif train_type == 2:
# load train data from numpy data struct
train_number = int(train_number)
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_ExtractedDataFile(
train_inputDir, train_inputFile, model_input_size,
validation_ratio, train_number)
elif train_type == 3:
# load train data from prepicked results
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_PrePickedResults(
train_inputDir, train_inputFile, particle_size, model_input_size,
validation_ratio, train_number)
elif train_type == 4:
# load train data from relion .star file
train_number = int(train_number)
train_data, train_label, eval_data, eval_label = dataLoader.load_trainData_From_RelionStarFile(
train_inputFile, particle_size, model_input_size, validation_ratio,
train_number)
else:
print("ERROR: invalid value of train_type:", train_type)
# display.show_particle(train_data, os.path.join(debug_dir, 'positive.png'))
# test whether train_data exist
try:
train_data
except NameError:
print("ERROR: in function load.loadInputTrainData.")
return None
else:
print("Load training data successfully!")
# shuffle the training data
train_data, train_label = shuffle_in_unison_inplace(
train_data, train_label)
eval_data, eval_label = shuffle_in_unison_inplace(eval_data, eval_label)
train_size = train_data.shape[0]
eval_size = eval_data.shape[0]
# initalize the decay_steps based on train_size and batch size.
# change the learning rate each 2 epochs
learning_rate_decay_steps = 10 * (train_size // batch_size)
# initialize the parameters of deepModel
deepModel = DeepModel(particle_size, model_input_size, num_class)
deepModel.init_learning_rate(
learning_rate=learning_rate,
learning_rate_decay_factor=learning_rate_decay_factor,
decay_steps=learning_rate_decay_steps,
staircase=learning_rate_staircase)
deepModel.init_momentum(momentum=momentum)
# initialize the model
# define the computation procedure of optimizer, loss, lr, prediction, eval_prediction
deepModel.init_model_graph_train()
saver = tf.train.Saver(tf.all_variables())
start_time = time.time()
init = tf.initialize_all_variables()
with tf.Session(config=tf.ConfigProto(log_device_placement=False)) as sess:
# initialize all the parameters
sess.run(init)
max_epochs = 200 # the max number of epoch to train the model
best_eval_error_rate = 100
toleration_patience = 10
toleration_patience_flag = 0
eval_frequency = train_size // batch_size # the frequency to evaluate the evaluation dataset
for step in xrange(int(max_epochs * train_size) // batch_size):
# get the batch training data
offset = (step * batch_size) % (train_size - batch_size)
batch_data = train_data[offset:(offset + batch_size), ...]
batch_label = train_label[offset:(offset + batch_size)]
# online augmentation
#batch_data = DataLoader.preprocess_particle_online(batch_data)
loss_value, lr, train_prediction = deepModel.train_batch(
batch_data, batch_label, sess)
# do the computation
if step % eval_frequency == 0:
stop_time = time.time() - start_time
start_time = time.time()
eval_prediction = deepModel.evaluation(eval_data, sess)
eval_error_rate = error_rate(eval_prediction, eval_label)
print('epoch: %.2f , %.2f ms' %
(step * batch_size / train_size,
1000 * stop_time / eval_frequency))
print('train loss: %.6f,\t learning rate: %.6f' %
(loss_value, lr))
print(
'train error: %.6f%%,\t valid error: %.6f%%' % (error_rate(
train_prediction, batch_label), eval_error_rate))
if eval_error_rate < best_eval_error_rate:
best_eval_error_rate = eval_error_rate
toleration_patience = 10
else:
toleration_patience = toleration_patience - 1
if toleration_patience == 0:
saver.save(sess, model_save_file)
break
