def _import_project_parameters(self, input_file):
import input_manager
self.input_manager = input_manager.InputManager(input_file)
return self.input_manager.GetProjectParameters()
import tensorflow as tf
import numpy as np
import input_manager as input_manager
import cnnmodel as model
model_dir = "../model_output/wganset"
tb_dir = "../model_output/wganset/tensorboard"
total_steps =200000
check_interval = 100
batch_size = 64
datam = input_manager.InputManager("../data/wgan_set.pb2" , batch_size, 1 )
cnn = model.cnn_model( batch_size )
iterator = datam.iterator()
input_tensors = iterator.get_next()
global_step = tf.get_variable(
name="global_step" ,
shape = [] ,
dtype = tf.int64 ,
initializer = tf.zeros_initializer() ,
trainable = False ,
collections = [ tf.GraphKeys.GLOBAL_VARIABLES , tf.GraphKeys.GLOBAL_STEP]
)
import numpy as np
import model as model
import input_manager
z_dim = 100
mb_size = 32 # batch_size
total_steps = 10000
model_dir = "../model_output/ships"
tb_dir = "../model_output/ships/tensorboard"
CHECK_INTERVAL = 100
repeat = 1
tf.reset_default_graph()
datam = input_manager.InputManager("../data/tf_ships_norm.pb2", mb_size,
repeat * mb_size)
gan = model.GAN(1, 1, 1, 1)
iterator = datam.iterator()
input_test = iterator.get_next()
#global step
global_step = tf.get_variable(
name="global_step",
shape=[],
dtype=tf.int64,
initializer=tf.zeros_initializer(),
trainable=False,
collections=[tf.GraphKeys.GLOBAL_VARIABLES, tf.GraphKeys.GLOBAL_STEP])