tensorflowのエラーコマンドの解決方法について

DCGANで#Discriminatorを定義して、学習のためのコードを定義しました。そしたら下記エラーが出てしまいました。(より詳細なエラーは「全体コード」に記載しています)

ValueError: Variable d_hidden1_weight already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:

内容によるとVariable d_hidden1_weightはすでに存在しているため、d_output_from_given_data = trainable_inference(d_given_data_placeholder)

の格納は行えないということなのでしょうか？解決策がわからないためためご教授お願いします。

全体コード

import tensorflow as tf
import numpy

#Discriminator


INPUT_SIZE = 3072
HIDDEN_UNIT_SIZE = 64
TRAIN_DATA_SIZE = 10000


def d_inference(input, hidden1_weight, hidden1_bias, output_weight, output_bias):
    hidden1_output = tf.nn.relu(tf.matmul(input, hidden1_weight) + hidden1_bias)
    output = tf.sigmoid(tf.matmul(hidden1_output, output_weight) + output_bias)
    return output


def trainable_inference(input):
    hidden1_weight = tf.get_variable("d_hidden1_weight",[INPUT_SIZE, HIDDEN_UNIT_SIZE], initializer = tf.random_normal_initializer(0, 0.1))
    hidden1_bias = tf.get_variable("d_hidden1_bias",[HIDDEN_UNIT_SIZE], initializer = tf.constant_initializer(0.1))
    output_weight = tf.get_variable("d_output_weight",[HIDDEN_UNIT_SIZE, 1],initializer = tf.random_normal_initializer(0, 0.1))
    output_bias = tf.get_variable("d_output_bias",[1],initializer = tf.constant_initializer(0.1))
    hidden1_output = tf.nn.relu(tf.matmul(input, hidden1_weight) + hidden1_bias)
    output = tf.sigmoid(tf.matmul(hidden1_output, output_weight) + output_bias)
    return output

def get_train_params():
    hidden1_weight = tf.get_variable("get_hidden1_weight", [INPUT_SIZE, HIDDEN_UNIT_SIZE])
    hidden1_bias = tf.get_variable("get_hidden1_bias", [HIDDEN_UNIT_SIZE])
    output_weight = tf.get_variable("get_output_weight", [HIDDEN_UNIT_SIZE, 1])
    output_bias = tf.get_variable("get_output_bias", [1])
    return [hidden1_weight, hidden1_bias, output_weight, output_bias]

def loss(output_from_given_data, output_from_noise):
    loss_1 = tf.reduce_sum(tf.log(output_from_given_data))
    loss_2 = tf.reduce_sum(tf.log(1 - output_from_noise))
    return [loss_1, loss_2]

def training(loss, learning_rate):
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
    return train_step



＃ placeholders
d_given_data_placeholder = tf.placeholder("float", [None, INPUT_SIZE], name="g_given_data_placeholder")


＃ inference output

d_output_from_given_data = trainable_inference(d_given_data_placeholder)


＃エラーメッセージ

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-253495f74891> in <module>()
     13 
     14 
---> 15 d_output_from_given_data = trainable_inference(d_given_data_placeholder)
     16 

<ipython-input-10-5d4f0de9f4e8> in trainable_inference(input)
     17 
     18 def trainable_inference(input):
---> 19     hidden1_weight = tf.get_variable("d_hidden1_weight",[INPUT_SIZE, HIDDEN_UNIT_SIZE], initializer = tf.random_normal_initializer(0, 0.1))
     20     hidden1_bias = tf.get_variable("d_hidden1_bias",[HIDDEN_UNIT_SIZE], initializer = tf.constant_initializer(0.1))
     21     output_weight = tf.get_variable("d_output_weight",[HIDDEN_UNIT_SIZE, 1],initializer = tf.random_normal_initializer(0, 0.1))

/Users/hagiharatatsuya/anaconda/envs/TensorFlow/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in get_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
   1047       collections=collections, caching_device=caching_device,
   1048       partitioner=partitioner, validate_shape=validate_shape,
-> 1049       use_resource=use_resource, custom_getter=custom_getter)
   1050 get_variable_or_local_docstring = (
   1051     """%s

/Users/hagiharatatsuya/anaconda/envs/TensorFlow/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in get_variable(self, var_store, name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
    946           collections=collections, caching_device=caching_device,
    947           partitioner=partitioner, validate_shape=validate_shape,
--> 948           use_resource=use_resource, custom_getter=custom_getter)
    949 
    950   def _get_partitioned_variable(self,

/Users/hagiharatatsuya/anaconda/envs/TensorFlow/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in get_variable(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
    354           reuse=reuse, trainable=trainable, collections=collections,
    355           caching_device=caching_device, partitioner=partitioner,
--> 356           validate_shape=validate_shape, use_resource=use_resource)
    357 
    358   def _get_partitioned_variable(

/Users/hagiharatatsuya/anaconda/envs/TensorFlow/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in _true_getter(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource)
    339           trainable=trainable, collections=collections,
    340           caching_device=caching_device, validate_shape=validate_shape,
--> 341           use_resource=use_resource)
    342 
    343     if custom_getter is not None:

/Users/hagiharatatsuya/anaconda/envs/TensorFlow/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in _get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource)
    651                          " Did you mean to set reuse=True in VarScope? "
    652                          "Originally defined at:\n\n%s" % (
--> 653                              name, "".join(traceback.format_list(tb))))
    654       found_var = self._vars[name]
    655       if not shape.is_compatible_with(found_var.get_shape()):

ValueError: Variable d_hidden1_weight already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:

  File "<ipython-input-3-5d4f0de9f4e8>", line 19, in trainable_inference
    hidden1_weight = tf.get_variable("d_hidden1_weight",[INPUT_SIZE, HIDDEN_UNIT_SIZE], initializer = tf.random_normal_initializer(0, 0.1))
  File "<ipython-input-5-a8b7f043c063>", line 23, in <module>
    d_output_from_given_data = trainable_inference(d_given_data_placeholder)
  File "/Users/hagiharatatsuya/anaconda/envs/TensorFlow/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)