def setup_tensorflow():
"""Restores a tensorflow session and returns it if successful
"""
net_model = NNModel()
tf_config = tf.ConfigProto(device_count={'GPU': config.should_use_gpu})
sess = tf.Session(config=tf_config)
# Add ops to save and restore all of the variables
saver = tf.train.Saver()
# Load the model checkpoint file
try:
tmp_file = config.tf_checkpoint_file
print("Loading model from config: {}".format(tmp_file))
except:
tmp_file = config.load(
'last_tf_model') #gets the cached last tf trained model
print "loading latest trained model: " + str(tmp_file)
# print("CAN'T FIND THE GOOD MODEL")
# sys.exit(-1)
# Try to restore a session
try:
saver.restore(sess, tmp_file)
except:
print("Error restoring TF model: {}".format(tmp_file))
# sys.exit(-1)
return sess, net_model
import sys, os
import matplotlib
import numpy as np
from matplotlib.pylab import *
matplotlib.use('Agg')
sys.path.append('..')
import tensorflow as tf
import config
from NeuralNet.convnetshared1 import NNModel
from NeuralNet.data_model import TrainingData
if __name__ == '__main__':
net_model = NNModel()
tf_config = tf.ConfigProto(device_count={'GPU': config.should_use_gpu})
sess = tf.Session(config=tf_config)
# Add ops to save and restore all of the variables
saver = tf.train.Saver()
# Load the model checkpoint file
try:
tmp_file = config.tf_checkpoint_file
print("Loading model from config: {}".format(tmp_file))
except:
tmp_file = config.load(
'last_tf_model') #gets the cached last tf trained model
print "loading latest trained model: " + str(tmp_file)
# print("CAN'T FIND THE GOOD MODEL")
# sys.exit(-1)