ValueError: Initializer for variable conv2d_2/kernel/ is from inside a control-flow construct, such as a loop or conditional. When creating a variable inside a loop or conditional, use a lambda as the initializer.

[SadAngelF]

when I run the run.ipynb in jupyter. I get the things in the following:
ValueError Traceback (most recent call last)
in
22
23 # create an untrained neural network objects from the config file
---> 24 current_NN = Residual_CNN(config.REG_CONST, config.LEARNING_RATE, (2,) + env.grid_shape, env.action_size, config.HIDDEN_CNN_LAYERS)
25 best_NN = Residual_CNN(config.REG_CONST, config.LEARNING_RATE, (2,) + env.grid_shape, env.action_size, config.HIDDEN_CNN_LAYERS)
26

~/DeepReinforcementLearning-master/model.py in init(self, reg_const, learning_rate, input_dim, output_dim, hidden_layers)
112 self.hidden_layers = hidden_layers
113 self.num_layers = len(hidden_layers)
--> 114 self.model = self._build_model()
115
116 def residual_layer(self, input_block, filters, kernel_size):

~/DeepReinforcementLearning-master/model.py in _build_model(self)
223 main_input = Input(shape = self.input_dim, name = 'main_input')
224
--> 225 x = self.conv_layer(main_input, self.hidden_layers[0]['filters'], self.hidden_layers[0]['kernel_size'])
226
227 if len(self.hidden_layers) > 1:

~/DeepReinforcementLearning-master/model.py in conv_layer(self, x, filters, kernel_size)
146 , activation='linear'
147 , kernel_regularizer = regularizers.l2(self.reg_const)
--> 148 )(x)
149
150 x = BatchNormalization(axis=1)(x)

~/miniconda3/envs/py36/lib/python3.6/site-packages/keras/engine/base_layer.py in call(self, inputs, **kwargs)
429 'You can build it manually via: '
430 'layer.build(batch_input_shape)')
--> 431 self.build(unpack_singleton(input_shapes))
432 self.built = True
433

~/miniconda3/envs/py36/lib/python3.6/site-packages/keras/layers/convolutional.py in build(self, input_shape)
139 name='kernel',
140 regularizer=self.kernel_regularizer,
--> 141 constraint=self.kernel_constraint)
142 if self.use_bias:
143 self.bias = self.add_weight(shape=(self.filters,),

~/miniconda3/envs/py36/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your ' + object_name + ' call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper

~/miniconda3/envs/py36/lib/python3.6/site-packages/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
250 dtype=dtype,
251 name=name,
--> 252 constraint=constraint)
253 if regularizer is not None:
254 with K.name_scope('weight_regularizer'):

~/miniconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in variable(value, dtype, name, constraint)
400 v._uses_learning_phase = False
401 return v
--> 402 v = tf.Variable(value, dtype=tf.as_dtype(dtype), name=name)
403 if isinstance(value, np.ndarray):
404 v._keras_shape = value.shape

~/miniconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/variables.py in init(self, initial_value, trainable, collections, validate_shape, caching_device, name, variable_def, dtype, expected_shape, import_scope, constraint)
257 dtype=dtype,
258 expected_shape=expected_shape,
--> 259 constraint=constraint)
260
261 def repr(self):

~/miniconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/variables.py in _init_from_args(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, expected_shape, constraint)
385 "construct, such as a loop or conditional. When creating a "
386 "variable inside a loop or conditional, use a lambda as the "
--> 387 "initializer." % name)
388 # pylint: enable=protected-access
389 shape = (self._initial_value.get_shape()

ValueError: Initializer for variable conv2d_2/kernel/ is from inside a control-flow construct, such as a loop or conditional. When creating a variable inside a loop or conditional, use a lambda as the initializer.

[SadAngelF]

Do it means something wrong happens in the NN? How I can solve it?

I have tried to change the code, but failed.