tensorflow如何動(dòng)態(tài)獲取BatchSzie大小

import tensorflow as tf
import sys

with tf.variable_scope('ha'):
  a1 = tf.get_variable('a', shape=[], dtype=tf.int32)
  with tf.variable_scope('haha'):
    a2 = tf.get_variable('a', shape=[], dtype=tf.int32)
    with tf.variable_scope('hahaha'):
      a3 = tf.get_variable('a', shape=[], dtype=tf.int32)

with tf.variable_scope('ha', reuse=True):
  # 不會(huì)創(chuàng)建新的變量
  a4 = tf.get_variable('a', shape=[], dtype=tf.int32)
  
sum = a1 + a2 + a3 + a4

fts_s = tf.placeholder(tf.float32, shape=(None, 100), name='fts_s')
b = tf.zeros(shape=(tf.shape(fts_s)[0], tf.shape(fts_s)[1]))

concat = tf.concat(axis=1, values=[fts_s, b])

init_op = tf.global_variables_initializer()
with tf.Session() as sess:
  sess.run(init_op)
  for var in tf.global_variables():
    print var.name
  import numpy as np
  ft_sample = np.ones((10, 100))
  con_value = sess.run([concat], feed_dict={fts_s: ft_sample})
  print con_value[0].shape

lstm_cell_1 = [tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_SIZE),output_keep_prob=dropout_keep_prob) for _ in range(NUM_LAYERS)]
cell_1 = tf.nn.rnn_cell.MultiRNNCell(lstm_cell_1)
self.init_state_1 = cell_1.zero_state(self.batch_size,tf.float32)