def __init__(self,sess,input_placer,gold_placer,learn_rate_placer,keep_prob,
window_size = 15,model_path = './model/version_15',
image_path = './data/neuron/train',
valid_flag = False,valid_num = 2048,
nn = FbUp(), predict_flag = True,
wd = 0.001, wdo = 0.001):
self.sess = sess
self.batch_generator = None
self.nn = nn
self.saver = None
self.predict_flag = predict_flag
self.fg_para_dict = dict()
self.valid_flag = valid_flag
self.window_size = window_size
if valid_flag:
self.batch_generator = ImageListBatchGenerator((window_size - 1)//2,image_path = image_path)
self.valid = self.next_batch(valid_num)
self.get_nn(input_placer,gold_placer,learn_rate_placer,
keep_prob,wd = wd, wdo = wdo, window_size = window_size)
print("Creation Complete\nIntialization")
init_op = tf.initialize_all_variables()
print("Start Intialization")
sess.run(init_op)
print("Intialization Done")
self.restore(model_path)
print("Saving")
def get_nn(self, input_placer, gold_placer, keep_prob, learn_rate_placer,
wd):
self.x = input_placer
self.y_ = gold_placer
self.l_rate = learn_rate_placer
self.keep_prob = keep_prob
self.para_dict = dict()
para_dict = FbUp().generate_flow(input_placer, keep_prob, wd=wd)
self.y_conv = para_dict['y_conv']
self.y_res = para_dict['y_res']
if self.train_flag:
self.y_conv_all = self.y_conv
self.y_conv_2x = dilation3D(self.y_conv_all)
self.y_res_all = self.y_res
self.shape = tf.shape(self.y_res)
width = self.shape[1]
self.y_conv = self.y_conv[:, 3, 3, 3, :]
self.y_res = self.y_res[:, 3, 3, 3]
cross_entropy_mean = -tf.reduce_mean(self.y_ * tf.log(self.y_conv))
tf.add_to_collection('losses', cross_entropy_mean)
cross_entropy = tf.add_n(tf.get_collection('losses'),
name='total_loss')
self.train_step = tf.train.AdamOptimizer(
self.l_rate).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(self.y_conv, 1),
tf.argmax(self.y_, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction,
"float"))
else:
self.y_conv_all = self.y_conv
self.y_conv_2x = dilation3D(self.y_conv_all)
self.y_res_all = self.y_res
self.y_res_2x = dilation3D(self.y_res_all)
self.para_dict['y_conv'] = self.y_conv_2x
self.para_dict['y_res'] = self.y_res_2x
self.para_dict['h_conv4'] = para_dict['h_conv4']
self.para_dict['h_pool2'] = para_dict['h_pool2']
self.para_dict['x'] = para_dict['x']
self.para_dict['h_conv3'] = para_dict['h_conv3']
self.para_dict['h_conv2'] = para_dict['h_conv2']
self.para_dict['h_conv_out'] = para_dict['h_conv_out']