def __init__(self, sess, network_params):
self._sess = sess
self._params = network_params
self._device = self._params['device']
self._tf_sumry_wrtr = None
if network_params['write_summary']:
if 'summary_dir' in network_params:
summary_dir = network_params['summary_dir']
else:
summary_dir = None
self._tf_sumry_wrtr = TfSummaryWriter(tf_session=sess,
summary_dir=summary_dir)
with tf.device(self._device):
if self._params['write_summary']:
tf.global_variables_initializer().run()
self._mdn = MixtureDensityNetwork(
network_params, tf_sumry_wrtr=self._tf_sumry_wrtr)
self._mdn._init_model()
self._net_ops = self._mdn._ops
self._init_op = tf.global_variables_initializer()
self._saver = tf.train.Saver()
def __init__(self, sess, network_params):
self._sess = sess
self._params = network_params
self._device = self._params['device']
self._tf_sumry_wrtr = None
self._optimiser = network_params['optimiser']
self._data_configured = False
if network_params['write_summary']:
if 'summary_dir' in network_params:
summary_dir = network_params['summary_dir']
else:
summary_dir = None
self._tf_sumry_wrtr = TfSummaryWriter(tf_session=sess,summary_dir=summary_dir)
cuda_path = '/usr/local/cuda/extras/CUPTI/lib64'
curr_ld_path = os.environ["LD_LIBRARY_PATH"]
if not cuda_path in curr_ld_path.split(os.pathsep):
print "Enviroment variable LD_LIBRARY_PATH does not contain %s"%cuda_path
print "Please add it, else the program will crash!"
raw_input("Press Ctrl+C")
# os.environ["LD_LIBRARY_PATH"] = curr_ld_path + ':'+cuda_path
with tf.device(self._device):
self._net_ops = tf_model(dim_input=network_params['dim_input'],
dim_output=network_params['dim_output'],
loss_type='quadratic',
cnn_params=network_params['cnn_params'],
fc_params=network_params['fc_params'],
optimiser_params=network_params['optimiser'],
tf_sumry_wrtr=self._tf_sumry_wrtr)
self._init_op = tf.initialize_all_variables()
self._saver = tf.train.Saver()
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import tensorflow as tf
from aml_dl.utilities.tf_summary_writer import TfSummaryWriter
from tensorflow.examples.tutorials.mnist import input_data
FLAGS = None
sess = tf.InteractiveSession()
tf_sum_wrtr = TfSummaryWriter(tf_session=sess)
def train():
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir,
one_hot=True,
fake_data=FLAGS.fake_data)
# Create a multilayer model.
# Input placeholders
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, [None, 784], name='x-input')
y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
with tf.name_scope('input_reshape'):