Variable batch_normalization_1/moving_mean/biased already exists, disallowed error

[kktam]

Hi

I am running facenet on following configuration in Anaconda when I encounter a variable disallowed error.

Anaconda: 1.6.12
numpy: 1.12.1
Tensorflow: 1.10
Keras: 2.1.6

Have you seen this issue? Which version did you run in when you last executed the test run?

The error occurred when running block 20:

im_in = Input(shape=(200,200,4))
#wrong = Input(shape=(200,200,3))

x1 = modelsqueeze(im_in)
#x = Convolution2D(64, (5, 5), padding='valid', strides =(2,2))(x)

#x1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x1)

"""
x1 = Convolution2D(256, (3,3), padding='valid', activation="relu")(x1)
x1 = Dropout(0.4)(x1)

x1 = MaxPooling2D(pool_size=(3, 3), strides=(1, 1))(x1)

x1 = Convolution2D(256, (3,3), padding='valid', activation="relu")(x1)
x1 = BatchNormalization()(x1)
x1 = Dropout(0.4)(x1)

x1 = Convolution2D(64, (1,1), padding='same', activation="relu")(x1)
x1 = BatchNormalization()(x1)
x1 = Dropout(0.4)(x1)
"""



x1 = Flatten()(x1)

x1 = Dense(512, activation="relu")(x1)
x1 = Dropout(0.2)(x1)
#x1 = BatchNormalization()(x1)
feat_x = Dense(128, activation="linear")(x1)
feat_x = Lambda(lambda  x: K.l2_normalize(x,axis=1))(feat_x)


model_top = Model(inputs = [im_in], outputs = feat_x)

model_top.summary()

im_in1 = Input(shape=(200,200,4))
im_in2 = Input(shape=(200,200,4))

feat_x1 = model_top(im_in1)
feat_x2 = model_top(im_in2)


lambda_merge = Lambda(euclidean_distance)([feat_x1, feat_x2])


model_final = Model(inputs = [im_in1, im_in2], outputs = lambda_merge)

model_final.summary()

adam = Adam(lr=0.001)

sgd = SGD(lr=0.001, momentum=0.9)

model_final.compile(optimizer=adam, loss=contrastive_loss)

The error is:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-20-d87010ad9d0a> in <module>()
      2 #wrong = Input(shape=(200,200,3))
      3 
----> 4 x1 = modelsqueeze(im_in)
      5 #x = Convolution2D(64, (5, 5), padding='valid', strides =(2,2))(x)
      6 

~/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py in __call__(self, inputs, **kwargs)
    617 
    618             # Actually call the layer, collecting output(s), mask(s), and shape(s).
--> 619             output = self.call(inputs, **kwargs)
    620             output_mask = self.compute_mask(inputs, previous_mask)
    621 

~/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py in call(self, inputs, mask)
   2083             return self._output_tensor_cache[cache_key]
   2084         else:
-> 2085             output_tensors, _, _ = self.run_internal_graph(inputs, masks)
   2086             return output_tensors
   2087 

~/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py in run_internal_graph(self, inputs, masks)
   2233                                 if 'mask' not in kwargs:
   2234                                     kwargs['mask'] = computed_mask
-> 2235                             output_tensors = _to_list(layer.call(computed_tensor, **kwargs))
   2236                             output_masks = layer.compute_mask(computed_tensor,
   2237                                                               computed_mask)

~/anaconda3/lib/python3.6/site-packages/keras/layers/normalization.py in call(self, inputs, training)
    191         self.add_update([K.moving_average_update(self.moving_mean,
    192                                                  mean,
--> 193                                                  self.momentum),
    194                          K.moving_average_update(self.moving_variance,
    195                                                  variance,

~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in moving_average_update(x, value, momentum)
   1003     """
   1004     return moving_averages.assign_moving_average(
-> 1005         x, value, momentum, zero_debias=True)
   1006 
   1007 

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/moving_averages.py in assign_moving_average(variable, value, decay, zero_debias, name)
     68         decay = math_ops.cast(decay, variable.dtype.base_dtype)
     69       if zero_debias:
---> 70         update_delta = _zero_debias(variable, value, decay)
     71       else:
     72         update_delta = (variable - value) * decay

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/moving_averages.py in _zero_debias(unbiased_var, value, decay)
    178         local_step_initializer = init_ops.zeros_initializer()
    179       biased_var = variable_scope.get_variable(
--> 180           "biased", initializer=biased_initializer, trainable=False)
    181       local_step = variable_scope.get_variable(
    182           "local_step",

~/anaconda3/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

~/anaconda3/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,

~/anaconda3/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(

~/anaconda3/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:

~/anaconda3/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 batch_normalization_1/moving_mean/biased already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:

  File "/Users/xyz/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 1005, in moving_average_update
    x, value, momentum, zero_debias=True)
  File "/Users/xyz/anaconda3/lib/python3.6/site-packages/keras/layers/normalization.py", line 193, in call
    self.momentum),
  File "/Users/xyz/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py", line 619, in __call__
    output = self.call(inputs, **kwargs)