Myeongjoon

   public class AdAccount : ValueObject
    {
        private AdAccount()
        {
        }

        public static AdAccount For(string accountString)
        {
            var account = new AdAccount();

Myeongjoon

rnn_cell = tf.contrib.rnn.BasicRNNCell(hidden_layer_size)
outputs, _ = tf.nn.dynamic_rnn(rnn_cell, _inputs, dtype=tf.float32)

Myeongjoon

# Merge all the summaries
merged = tf.summary.merge_all()

# Get a small test set
test_data = mnist.test.images[:batch_size].reshape((-1, time_steps, element_size))
test_label = mnist.test.labels[:batch_size]

with tf.Session() as sess:
    # Write summaries to LOG_DIR -- used by TensorBoard
    train_writer = tf.summary.FileWriter(LOG_DIR + '/train',

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# Weights for output layers
with tf.name_scope('linear_layer_weights') as scope:
    with tf.name_scope("W_linear"):
        Wl = tf.Variable(tf.truncated_normal([hidden_layer_size, num_classes],
                                             mean=0, stddev=.01))
        variable_summaries(Wl)
    with tf.name_scope("Bias_linear"):
        bl = tf.Variable(tf.truncated_normal([num_classes],
                                             mean=0, stddev=.01))
        variable_summaries(bl)

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# Weights and bias for input and hidden layer
with tf.name_scope('rnn_weights'):
    with tf.name_scope("W_x"):
        Wx = tf.Variable(tf.zeros([element_size, hidden_layer_size]))
        variable_summaries(Wx)
    with tf.name_scope("W_h"):
        Wh = tf.Variable(tf.zeros([hidden_layer_size, hidden_layer_size]))
        variable_summaries(Wh)
    with tf.name_scope("Bias"):
        b_rnn = tf.Variable(tf.zeros([hidden_layer_size]))

Myeongjoon

from __future__ import print_function
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

element_size = 28
time_steps = 28
num_classes = 10
batch_size = 128