def loadData(self):
file = open(self.fileName, "r")
numLines = sum(1 for line in open(self.fileName))
#numLines = open(self.fileName,"r")
for i in range(0, numLines):
strLine = file.readline()
strLine = strLine.strip('\n')
feats = strLine.split(' ')
label = feats[len(feats) - 1]
feats = feats[0:len(feats) - 2]
dataObj = DataClass.Data(feats, label)
self.data.append(dataObj)
def train():
print('\n\n', 'training', '\n\n')
sess = tf.Session()
dequeueSize = 100
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
decay_step = 25
decay_rate = 0.96
learning_rate = tf.train.exponential_decay(starter_learning_rate,
global_step * dequeueSize,
decay_step * dequeueSize,
decay_rate,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
######################
# DATASET PARAMETERS #
######################
if os.path.exists(
'/media/asgharpn/daten2017-03/Bone_Machine_learning/Learning_dataset/projected_augmented_not_squared_yz_test_01_without_2mice_10'
):
print('\nusing full dataset\n')
dataBaseDir = '/media/asgharpn/daten2017-03/Bone_Machine_learning/Learning_dataset/projected_augmented_not_squared_yz_test_01_without_2mice_10/'
else:
raise NameError('Dataset can not be found')
trainHdf5 = dataBaseDir + 'bone_projected_train_set.hdf5'
validHdf5 = dataBaseDir + 'bone_projected_valid_set.hdf5'
cropSize = 733
batchSize = 50
stretchLow = 0.1 # stretch channels lower percentile
stretchHigh = 99.9 # stretch channels upper percentile
imSize_x = 733
imSize_z = 161
numClasses = 4
numChan = 1
data = dataClass.Data(trainHdf5, ['data', 'Index'], batchSize)
dataTest = dataClass.Data(validHdf5, ['data', 'Index'],
batchSize * 2) # larger batch size at test time
### define model
is_training = tf.placeholder(tf.bool, [],
name='is_training') # for batch norm
input = tf.placeholder('float32',
shape=[None, imSize_x, imSize_z, numChan],
name='input') # for batch norm
print(input.get_shape)
labels = tf.placeholder('float32', shape=[None, numClasses],
name='labels') # for batch norm
logits = BAAM(input, is_training)
predicted_y = tf.nn.softmax(logits, name='softmax')
acc = accuracy(predicted_y, labels)
cross_entropy = loss_logits(logits, labels)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(
cross_entropy, global_step=global_step)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(checkpoint_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(checkpoint_dir + '/test', sess.graph)
sess.run(tf.global_variables_initializer(), {is_training: True})
# training loop
for i in range(MAX_STEPS):
if i % 50 == 0: # Record execution stats
batch = dataTest.getBatch()
processedBatch = procIm.preProcessImages(batch['data'],
imSize_x,
imSize_z,
cropSize,
numChan,
rescale=False,
stretch=False,
means=None,
stds=None,
stretchLow=stretchLow,
stretchHigh=stretchHigh,
jitter=False,
randTransform=False)
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, cur_test_acc, cur_test_loss = sess.run(
[merged, acc, cross_entropy],
feed_dict={
is_training: False,
input: processedBatch,
labels: batch['Index']
},
options=run_options,
run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
test_writer.add_summary(summary, i)
print('Adding run metadata for', i)
print('Valid accuracy at step %s: %s, loss: %s' %
(i, cur_test_acc, cur_test_loss))
batch = data.getBatch()
processedBatch = procIm.preProcessImages(batch['data'],
imSize_x,
imSize_z,
cropSize,
numChan,
rescale=False,
stretch=False,
means=None,
stds=None,
stretchLow=stretchLow,
stretchHigh=stretchHigh,
jitter=False,
randTransform=False)
summary, _, cur_train_acc, cur_train_loss = sess.run(
[merged, train_step, acc, cross_entropy],
feed_dict={
is_training: True,
input: processedBatch,
labels: batch['Index']
})
train_writer.add_summary(summary, i)
print('Train accuracy at step %s: %s, loss: %s' %
(i, cur_train_acc, cur_train_loss))
if i % SAVE_INTERVAL == 0:
checkpoint_path = os.path.join(checkpoint_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=i)
train_writer.close()
test_writer.close()
