python类no_op()的实例源码

model.py 文件源码 项目:tfplus 作者: renmengye 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def build_all(self, param_avg=False):
        """Build all nodes."""
        if self._has_built_all:
            raise Exception('Only call build_all or build_eval once.')
        self._has_built_all = True
        with tf.device(self.get_device_fn()):
            with tf.variable_scope(self.name):
                inp_var = self.build_input()
                output_var = self.build(inp_var)
                loss_var = self.build_loss(inp_var, output_var)
                train_step = self.build_optim(loss_var)
                if param_avg:
                    ema_op, avg_var = self.get_average_var()
                    self._avg_var = avg_var
                    with tf.control_dependencies([train_step, ema_op]):
                        train_step = tf.no_op(name='train_step')
                self.register_var('train_step', train_step)
        return self
model.py 文件源码 项目:Saliency_Detection_Convolutional_Autoencoder 作者: arthurmeyer 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def train(self, loss, global_step):
    """
    Return a training step for the tensorflow graph

    Args:
      loss                   : loss to do sgd on
      global_step            : which step are we at
    """

    opt = tf.train.AdamOptimizer(self.learning_rate)
    grads = opt.compute_gradients(loss)
    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

    variable_averages = tf.train.ExponentialMovingAverage(self.moving_avg_decay, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())

    with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
      train_op = tf.no_op(name='train')

    return train_op
model_deploy_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def testPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_clones=1, num_ps_tasks=1)

    self.assertDeviceEqual(deploy_config.clone_device(0),
                           '/job:worker')
    self.assertEqual(deploy_config.clone_scope(0), '')
    self.assertDeviceEqual(deploy_config.optimizer_device(),
                           '/job:worker/device:CPU:0')
    self.assertDeviceEqual(deploy_config.inputs_device(),
                           '/job:worker/device:CPU:0')
    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())
    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 70 收藏 0 点赞 0 评论 0
def testVariablesPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_ps_tasks=2)

    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())

    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:1/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:terngrad 作者: wenwei202 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def testPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_clones=1, num_ps_tasks=1)

    self.assertDeviceEqual(deploy_config.clone_device(0),
                           '/job:worker')
    self.assertEqual(deploy_config.clone_scope(0), '')
    self.assertDeviceEqual(deploy_config.optimizer_device(),
                           '/job:worker/device:CPU:0')
    self.assertDeviceEqual(deploy_config.inputs_device(),
                           '/job:worker/device:CPU:0')
    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())
    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:terngrad 作者: wenwei202 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def testVariablesPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_ps_tasks=2)

    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())

    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:1/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:YOLO2TensorFlow 作者: PaulChongPeng 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def testPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_clones=1, num_ps_tasks=1)

    self.assertDeviceEqual(deploy_config.clone_device(0),
                           '/job:worker')
    self.assertEqual(deploy_config.clone_scope(0), '')
    self.assertDeviceEqual(deploy_config.optimizer_device(),
                           '/job:worker/device:CPU:0')
    self.assertDeviceEqual(deploy_config.inputs_device(),
                           '/job:worker/device:CPU:0')
    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())
    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:YOLO2TensorFlow 作者: PaulChongPeng 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def testVariablesPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_ps_tasks=2)

    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())

    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:1/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
rhn_train.py 文件源码 项目:RecurrentHighwayNetworks 作者: julian121266 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def evaluate_mc(data_path, dataset, load_model, mc_steps, seed):
  """Evaluate the model on the given data using MC averaging."""
  ex.commands['print_config']()
  print("MC Evaluation of model:", load_model)
  assert mc_steps > 0
  reader, (train_data, valid_data, test_data, _) = get_data(data_path, dataset)

  config = get_config()
  val_config = deepcopy(config)
  test_config = deepcopy(config)
  test_config.batch_size = test_config.num_steps = 1
  with tf.Session() as session:
    initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
    with tf.variable_scope("model", reuse=None, initializer=initializer):
      _ = Model(is_training=True, config=config)
    with tf.variable_scope("model", reuse=True, initializer=initializer):
      _ = Model(is_training=False, config=val_config)
      mtest = Model(is_training=False, config=test_config)
    tf.initialize_all_variables()
    saver = tf.train.Saver()
    saver.restore(session, load_model)

    print("Testing on non-batched Test ...")
    test_perplexity = run_mc_epoch(seed, session, mtest, test_data, tf.no_op(), test_config, mc_steps, verbose=True)
    print("Full Test Perplexity: %.3f, Bits: %.3f" % (test_perplexity, np.log2(test_perplexity)))
cpm.py 文件源码 项目:human-pose-estimation-by-deep-learning 作者: HYPJUDY 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def train_op(self, total_loss, global_step):
        self._loss_summary(total_loss)

        optimizer = tf.train.AdamOptimizer()
        grads = optimizer.compute_gradients(total_loss)

        apply_gradient_op = optimizer.apply_gradients(grads, global_step=global_step)

        variable_averages = tf.train.ExponentialMovingAverage(
                self.moving_average_decay, global_step)
        variable_averages_op = variable_averages.apply(tf.trainable_variables())

        with tf.control_dependencies([apply_gradient_op, variable_averages_op]):
            train_op = tf.no_op(name = "train")

        return train_op
test.py 文件源码 项目:deep-attention-text-classifier-tf 作者: krayush07 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def run_test(session, test_obj, dict_obj):
    start_time = time.time()

    print("Starting test computation\n")
    test_loss = run_epoch(session, tf.no_op(), test_obj, dict_obj)

    curr_time = time.time()
    print('1 epoch run takes ' + str(((curr_time - start_time) / 60)) + ' minutes.')

# def main():
#     session, test_obj = init_test()
#     dict_obj = set_dict.Dictionary()
#     run_test(session, test_obj, dict_obj)
#
#
# if __name__ == "__main__":
#     main()
model_deploy_test.py 文件源码 项目:tensorflow_yolo2 作者: wenxichen 项目源码 文件源码 阅读 38 收藏 0 点赞 0 评论 0
def testPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_clones=1, num_ps_tasks=1)

    self.assertDeviceEqual(deploy_config.clone_device(0),
                           '/job:worker')
    self.assertEqual(deploy_config.clone_scope(0), '')
    self.assertDeviceEqual(deploy_config.optimizer_device(),
                           '/job:worker/device:CPU:0')
    self.assertDeviceEqual(deploy_config.inputs_device(),
                           '/job:worker/device:CPU:0')
    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())
    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:tensorflow_yolo2 作者: wenxichen 项目源码 文件源码 阅读 37 收藏 0 点赞 0 评论 0
def testVariablesPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_ps_tasks=2)

    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())

    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:1/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
learning_seg.py 文件源码 项目:tefla 作者: openAGI 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def _setup_model_loss(self, keep_moving_averages=False, num_classes=10):
        self.learning_rate = tf.placeholder(
            tf.float32, shape=[], name="learning_rate_placeholder")
        optimizer = self._optimizer(self.learning_rate, optname=self.cnf.get(
            'optname', 'momentum'), **self.cnf.get('opt_kwargs', {'decay': 0.9}))
        self.grads_and_vars, self.training_loss = self._process_towers_grads(
            optimizer, self.model, is_classification=self.classification, loss_type=self.loss_type)

        if self.clip_norm and not self.clip_by_global_norm:
            self.grads_and_vars = self._clip_grad_norms(
                self.grads_and_vars, max_norm=self.norm_threshold)
        apply_gradients_op = optimizer.apply_gradients(self.grads_and_vars)
        if keep_moving_averages:
            variables_averages_op = self._moving_averages_op()
            with tf.control_dependencies([apply_gradients_op, variables_averages_op]):
                self.train_op = tf.no_op(name='train_op')
        else:
            self.train_op = apply_gradients_op
train.py 文件源码 项目:chinese-char-rnn 作者: indiejoseph 项目源码 文件源码 阅读 40 收藏 0 点赞 0 评论 0
def run_epochs(sess, x, y, model, is_training=True):
  start = time.time()
  feed = {model.input_data: x, model.targets: y, model.is_training: is_training}

  if is_training:
    extra_op = model.train_op
  else:
    extra_op = tf.no_op()

  fetchs = {"loss": model.loss,
            "extra_op": extra_op}

  res = sess.run(fetchs, feed)
  end = time.time()

  return res, end - start
mnist.py 文件源码 项目:a3c 作者: siemanko 项目源码 文件源码 阅读 37 收藏 0 点赞 0 评论 0
def build_graph(reuse):
    with tf.variable_scope('model', reuse=reuse):
        x = tf.placeholder(tf.float32, shape=[None, 784])
        y_ = tf.placeholder(tf.float32, shape=[None, 10])
        keep_prob = tf.placeholder(tf.float32)

        y_conv = forward(x, keep_prob)

        cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))

        train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

        correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

        num_correct = tf.reduce_sum(tf.cast(correct_prediction, tf.float32))

        no_op = tf.no_op()

        return x, y_, keep_prob, train_step, num_correct, no_op
mnist.py 文件源码 项目:a3c 作者: siemanko 项目源码 文件源码 阅读 38 收藏 0 点赞 0 评论 0
def accuracy(session, graphs, data_iter, num_threads, train=False):
    num_total   = 0
    num_correct = 0

    def process_batch(batch_x, batch_y):
        nonlocal num_correct
        nonlocal num_total
        with graphs.lease() as g:
            input_placeholder, output_placeholder, keep_prob_placeholder, train_step_f, num_correct_f, no_op = g
            batch_num_correct, _ = session.run(
                [num_correct_f, train_step_f if train else no_op],
                {
                    input_placeholder:     batch_x,
                    output_placeholder:    batch_y,
                    keep_prob_placeholder: 0.5 if train else 1.0,
                })
            num_correct += batch_num_correct
            num_total   += len(batch_x)

    with BlockOnFullThreadPool(max_workers=num_threads, queue_size=num_threads // 2) as pool:
        for i, (batch_x, batch_y) in enumerate(data_iter):
            pool.submit(process_batch, batch_x, batch_y)
        pool.shutdown(wait=True)

    return float(num_correct) / float(num_total)
basic_session_run_hooks_test.py 文件源码 项目:lsdc 作者: febert 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def test_stop_based_on_last_step(self):
    h = basic_session_run_hooks.StopAtStepHook(last_step=10)
    with tf.Graph().as_default():
      global_step = tf.contrib.framework.get_or_create_global_step()
      no_op = tf.no_op()
      h.begin()
      with tf.Session() as sess:
        mon_sess = monitored_session._HookedSession(sess, [h])
        sess.run(tf.assign(global_step, 5))
        mon_sess.run(no_op)
        self.assertFalse(mon_sess.should_stop())
        sess.run(tf.assign(global_step, 9))
        mon_sess.run(no_op)
        self.assertFalse(mon_sess.should_stop())
        sess.run(tf.assign(global_step, 10))
        mon_sess.run(no_op)
        self.assertTrue(mon_sess.should_stop())
        sess.run(tf.assign(global_step, 11))
        mon_sess._should_stop = False
        mon_sess.run(no_op)
        self.assertTrue(mon_sess.should_stop())
topn.py 文件源码 项目:lsdc 作者: febert 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def insert(self, ids, scores):
    """Insert the ids and scores into the TopN."""
    with tf.control_dependencies(self.last_ops):
      scatter_op = tf.scatter_update(self.id_to_score, ids, scores)
      larger_scores = tf.greater(scores, self.sl_scores[0])

      def shortlist_insert():
        larger_ids = tf.boolean_mask(tf.to_int64(ids), larger_scores)
        larger_score_values = tf.boolean_mask(scores, larger_scores)
        shortlist_ids, new_ids, new_scores = self.ops.top_n_insert(
            self.sl_ids, self.sl_scores, larger_ids, larger_score_values)
        u1 = tf.scatter_update(self.sl_ids, shortlist_ids, new_ids)
        u2 = tf.scatter_update(self.sl_scores, shortlist_ids, new_scores)
        return tf.group(u1, u2)

      # We only need to insert into the shortlist if there are any
      # scores larger than the threshold.
      cond_op = tf.cond(
          tf.reduce_any(larger_scores), shortlist_insert, tf.no_op)
      with tf.control_dependencies([cond_op]):
        self.last_ops = [scatter_op, cond_op]
topn.py 文件源码 项目:lsdc 作者: febert 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def insert(self, ids, scores):
    """Insert the ids and scores into the TopN."""
    with tf.control_dependencies(self.last_ops):
      scatter_op = tf.scatter_update(self.id_to_score, ids, scores)
      larger_scores = tf.greater(scores, self.sl_scores[0])

      def shortlist_insert():
        larger_ids = tf.boolean_mask(tf.to_int64(ids), larger_scores)
        larger_score_values = tf.boolean_mask(scores, larger_scores)
        shortlist_ids, new_ids, new_scores = self.ops.top_n_insert(
            self.sl_ids, self.sl_scores, larger_ids, larger_score_values)
        u1 = tf.scatter_update(self.sl_ids, shortlist_ids, new_ids)
        u2 = tf.scatter_update(self.sl_scores, shortlist_ids, new_scores)
        return tf.group(u1, u2)

      # We only need to insert into the shortlist if there are any
      # scores larger than the threshold.
      cond_op = tf.cond(
          tf.reduce_any(larger_scores), shortlist_insert, tf.no_op)
      with tf.control_dependencies([cond_op]):
        self.last_ops = [scatter_op, cond_op]
model_deploy_test.py 文件源码 项目:segmentation-models 作者: desimone 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def testPS(self):
        deploy_config = model_deploy.DeploymentConfig(
            num_clones=1, num_ps_tasks=1)

        self.assertDeviceEqual(deploy_config.clone_device(0), '/job:worker')
        self.assertEqual(deploy_config.clone_scope(0), '')
        self.assertDeviceEqual(deploy_config.optimizer_device(),
                               '/job:worker/device:CPU:0')
        self.assertDeviceEqual(deploy_config.inputs_device(),
                               '/job:worker/device:CPU:0')
        with tf.device(deploy_config.variables_device()):
            a = tf.Variable(0)
            b = tf.Variable(0)
            c = tf.no_op()
            d = slim.variable(
                'a', [], caching_device=deploy_config.caching_device())
        self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
        self.assertDeviceEqual(a.device, a.value().device)
        self.assertDeviceEqual(b.device, '/job:ps/task:0/device:CPU:0')
        self.assertDeviceEqual(b.device, b.value().device)
        self.assertDeviceEqual(c.device, '')
        self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
        self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:segmentation-models 作者: desimone 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def testVariablesPS(self):
        deploy_config = model_deploy.DeploymentConfig(num_ps_tasks=2)

        with tf.device(deploy_config.variables_device()):
            a = tf.Variable(0)
            b = tf.Variable(0)
            c = tf.no_op()
            d = slim.variable(
                'a', [], caching_device=deploy_config.caching_device())

        self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
        self.assertDeviceEqual(a.device, a.value().device)
        self.assertDeviceEqual(b.device, '/job:ps/task:1/device:CPU:0')
        self.assertDeviceEqual(b.device, b.value().device)
        self.assertDeviceEqual(c.device, '')
        self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
        self.assertDeviceEqual(d.value().device, '')
model.py 文件源码 项目:tensorlm 作者: batzner 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def _sample_step(self, session, inputs, update_state=True):
        """Feeds batch inputs to the model and returns the batch output ids.

        Args:
            session (tf.Session): The TF session to run the operations in.
            inputs (np.ndarray): A batch of inputs. Must have the shape (batch_size, num_timesteps)
                and contain only integers. The batch size and number of timesteps are determined
                dynamically, so the shape of inputs can vary between calls of this function.
            update_state (bool): If True, the LSTM's memory state will be updated after feeding the
                batch inputs, so that the LSTM will use this state before the next feed of inputs.
                If this function gets called during training, make sure to call it between
                on_pause_training and will_resume_training. Thus, the training's memory state will
                be frozen before and unfrozen after this function call.

        Returns:
            np.ndarray: A batch of outputs with the same shape and data type as the inputs
                parameter.
        """
        # Feed the input
        feed_dict = {self._inputs: inputs}
        runs = [self._logits, self._update_state_op if update_state else tf.no_op()]

        # Get the output
        logits, _ = session.run(runs, feed_dict=feed_dict)
        return np.argmax(logits, axis=2)
model.py 文件源码 项目:CNN-LSTM-Caption-Generator 作者: mosessoh 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def run_epoch(self, session, train_op):
        total_steps = sum(1 for x in train_data_iterator(self.train_captions, self.train_caption_id2sentence, self.train_caption_id2image_id, self.train_image_id2feature, self.config))
        total_loss = []
        if not train_op:
            train_op = tf.no_op()
        start = time.time()

        for step, (sentences, images, targets) in enumerate(train_data_iterator(self.train_captions, self.train_caption_id2sentence, self.train_caption_id2image_id, self.train_image_id2feature, self.config)):

            feed = {self._sent_placeholder: sentences,
                    self._img_placeholder: images,
                    self._targets_placeholder: targets,
                    self._dropout_placeholder: self.config.keep_prob}
            loss, _ = session.run([self.loss, train_op], feed_dict=feed)
            total_loss.append(loss)

            if (step % 50) == 0:
                print '%d/%d: loss = %.2f time elapsed = %d' % (step, total_steps, np.mean(total_loss) , time.time() - start)

        print 'Total time: %ds' % (time.time() - start) 
        return total_loss
model_deploy_test.py 文件源码 项目:Embarrassingly-Parallel-Image-Classification 作者: Azure 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def testPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_clones=1, num_ps_tasks=1)

    self.assertDeviceEqual(deploy_config.clone_device(0),
                           '/job:worker')
    self.assertEqual(deploy_config.clone_scope(0), '')
    self.assertDeviceEqual(deploy_config.optimizer_device(),
                           '/job:worker/device:CPU:0')
    self.assertDeviceEqual(deploy_config.inputs_device(),
                           '/job:worker/device:CPU:0')
    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())
    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:Embarrassingly-Parallel-Image-Classification 作者: Azure 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def testVariablesPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_ps_tasks=2)

    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())

    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:1/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:the-neural-perspective 作者: GokuMohandas 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def testPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_clones=1, num_ps_tasks=1)

    self.assertDeviceEqual(deploy_config.clone_device(0),
                           '/job:worker')
    self.assertEqual(deploy_config.clone_scope(0), '')
    self.assertDeviceEqual(deploy_config.optimizer_device(),
                           '/job:worker/device:CPU:0')
    self.assertDeviceEqual(deploy_config.inputs_device(),
                           '/job:worker/device:CPU:0')
    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())
    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:the-neural-perspective 作者: GokuMohandas 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def testVariablesPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_ps_tasks=2)

    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())

    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:1/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:MobileNet 作者: Zehaos 项目源码 文件源码 阅读 37 收藏 0 点赞 0 评论 0
def testPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_clones=1, num_ps_tasks=1)

    self.assertDeviceEqual(deploy_config.clone_device(0),
                           '/job:worker')
    self.assertEqual(deploy_config.clone_scope(0), '')
    self.assertDeviceEqual(deploy_config.optimizer_device(),
                           '/job:worker/device:CPU:0')
    self.assertDeviceEqual(deploy_config.inputs_device(),
                           '/job:worker/device:CPU:0')
    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())
    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')
model_deploy_test.py 文件源码 项目:MobileNet 作者: Zehaos 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def testVariablesPS(self):
    deploy_config = model_deploy.DeploymentConfig(num_ps_tasks=2)

    with tf.device(deploy_config.variables_device()):
      a = tf.Variable(0)
      b = tf.Variable(0)
      c = tf.no_op()
      d = slim.variable('a', [],
                        caching_device=deploy_config.caching_device())

    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(a.device, a.value().device)
    self.assertDeviceEqual(b.device, '/job:ps/task:1/device:CPU:0')
    self.assertDeviceEqual(b.device, b.value().device)
    self.assertDeviceEqual(c.device, '')
    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
    self.assertDeviceEqual(d.value().device, '')


问题


面经


文章

微信
公众号

扫码关注公众号