I am running facenet on following configuration in Anaconda when I encounter a variable disallowed error.
Have you seen this issue? Which version did you run in when you last executed the test run?
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)
---------------------------------------------------------------------------
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)
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