I’ve also had the same issue. The error first pops up when loading the parameters from the VGG-19 model. It’s the second cell in the notebook and I have not changed anything in the code.
The full error I receive is as follows:
---------------------------------------------------------------------------
InternalError Traceback (most recent call last)
<ipython-input-2-c78346570a28> in <module>
4 vgg = tf.keras.applications.VGG19(include_top=False,
5 input_shape=(img_size, img_size, 3),
----> 6 weights='pretrained-model/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5')
7
8 vgg.trainable = False
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/applications/vgg19.py in VGG19(include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation)
142 x = layers.Conv2D(
143 64, (3, 3), activation='relu', padding='same', name='block1_conv1')(
--> 144 img_input)
145 x = layers.Conv2D(
146 64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
924 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
925 return self._functional_construction_call(inputs, args, kwargs,
--> 926 input_list)
927
928 # Maintains info about the `Layer.call` stack.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
1096 # Build layer if applicable (if the `build` method has been
1097 # overridden).
-> 1098 self._maybe_build(inputs)
1099 cast_inputs = self._maybe_cast_inputs(inputs, input_list)
1100
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _maybe_build(self, inputs)
2641 # operations.
2642 with tf_utils.maybe_init_scope(self):
-> 2643 self.build(input_shapes) # pylint:disable=not-callable
2644 # We must set also ensure that the layer is marked as built, and the build
2645 # shape is stored since user defined build functions may not be calling
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/convolutional.py in build(self, input_shape)
202 constraint=self.kernel_constraint,
203 trainable=True,
--> 204 dtype=self.dtype)
205 if self.use_bias:
206 self.bias = self.add_weight(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint, partitioner, use_resource, synchronization, aggregation, **kwargs)
612 synchronization=synchronization,
613 aggregation=aggregation,
--> 614 caching_device=caching_device)
615 if regularizer is not None:
616 # TODO(fchollet): in the future, this should be handled at the
/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py in _add_variable_with_custom_getter(self, name, shape, dtype, initializer, getter, overwrite, **kwargs_for_getter)
748 dtype=dtype,
749 initializer=initializer,
--> 750 **kwargs_for_getter)
751
752 # If we set an initializer and the variable processed it, tracking will not
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py in make_variable(name, shape, dtype, initializer, trainable, caching_device, validate_shape, constraint, use_resource, collections, synchronization, aggregation, partitioner)
143 synchronization=synchronization,
144 aggregation=aggregation,
--> 145 shape=variable_shape if variable_shape else None)
146
147
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py in __call__(cls, *args, **kwargs)
258 def __call__(cls, *args, **kwargs):
259 if cls is VariableV1:
--> 260 return cls._variable_v1_call(*args, **kwargs)
261 elif cls is Variable:
262 return cls._variable_v2_call(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py in _variable_v1_call(cls, initial_value, trainable, collections, validate_shape, caching_device, name, variable_def, dtype, expected_shape, import_scope, constraint, use_resource, synchronization, aggregation, shape)
219 synchronization=synchronization,
220 aggregation=aggregation,
--> 221 shape=shape)
222
223 def _variable_v2_call(cls,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py in <lambda>(**kwargs)
197 shape=None):
198 """Call on Variable class. Useful to force the signature."""
--> 199 previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
200 for _, getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access
201 previous_getter = _make_getter(getter, previous_getter)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in default_variable_creator(next_creator, **kwargs)
2595 synchronization=synchronization,
2596 aggregation=aggregation,
-> 2597 shape=shape)
2598 else:
2599 return variables.RefVariable(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py in __call__(cls, *args, **kwargs)
262 return cls._variable_v2_call(*args, **kwargs)
263 else:
--> 264 return super(VariableMetaclass, cls).__call__(*args, **kwargs)
265
266
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py in __init__(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, variable_def, import_scope, constraint, distribute_strategy, synchronization, aggregation, shape)
1516 aggregation=aggregation,
1517 shape=shape,
-> 1518 distribute_strategy=distribute_strategy)
1519
1520 def _init_from_args(self,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py in _init_from_args(self, initial_value, trainable, collections, caching_device, name, dtype, constraint, synchronization, aggregation, distribute_strategy, shape)
1649 with ops.name_scope("Initializer"), device_context_manager(None):
1650 initial_value = ops.convert_to_tensor(
-> 1651 initial_value() if init_from_fn else initial_value,
1652 name="initial_value", dtype=dtype)
1653 if shape is not None:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/initializers/initializers_v2.py in __call__(self, shape, dtype)
395 (via `tf.keras.backend.set_floatx(float_dtype)`)
396 """
--> 397 return super(VarianceScaling, self).__call__(shape, dtype=_get_dtype(dtype))
398
399
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops_v2.py in __call__(self, shape, dtype)
559 else:
560 limit = math.sqrt(3.0 * scale)
--> 561 return self._random_generator.random_uniform(shape, -limit, limit, dtype)
562
563 def get_config(self):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops_v2.py in random_uniform(self, shape, minval, maxval, dtype)
1042 op = random_ops.random_uniform
1043 return op(
-> 1044 shape=shape, minval=minval, maxval=maxval, dtype=dtype, seed=self.seed)
1045
1046 def truncated_normal(self, shape, mean, stddev, dtype):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
199 """Call target, and fall back on dispatchers if there is a TypeError."""
200 try:
--> 201 return target(*args, **kwargs)
202 except (TypeError, ValueError):
203 # Note: convert_to_eager_tensor currently raises a ValueError, not a
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/random_ops.py in random_uniform(shape, minval, maxval, dtype, seed, name)
286 maxval = 1
287 with ops.name_scope(name, "random_uniform", [shape, minval, maxval]) as name:
--> 288 shape = tensor_util.shape_tensor(shape)
289 # In case of [0,1) floating results, minval and maxval is unused. We do an
290 # `is` comparison here since this is cheaper than isinstance or __eq__.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py in shape_tensor(shape)
1027 # not convertible to Tensors because of mixed content.
1028 shape = tuple(map(tensor_shape.dimension_value, shape))
-> 1029 return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
1030
1031
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1497
1498 if ret is None:
-> 1499 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1500
1501 if ret is NotImplemented:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
336 as_ref=False):
337 _ = as_ref
--> 338 return constant(v, dtype=dtype, name=name)
339
340
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
262 """
263 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 264 allow_broadcast=True)
265
266
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
273 with trace.Trace("tf.constant"):
274 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
--> 275 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
276
277 g = ops.get_default_graph()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
298 def _constant_eager_impl(ctx, value, dtype, shape, verify_shape):
299 """Implementation of eager constant."""
--> 300 t = convert_to_eager_tensor(value, ctx, dtype)
301 if shape is None:
302 return t
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
95 except AttributeError:
96 dtype = dtypes.as_dtype(dtype).as_datatype_enum
---> 97 ctx.ensure_initialized()
98 return ops.EagerTensor(value, ctx.device_name, dtype)
99
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/context.py in ensure_initialized(self)
537 if self._use_tfrt is not None:
538 pywrap_tfe.TFE_ContextOptionsSetTfrt(opts, self._use_tfrt)
--> 539 context_handle = pywrap_tfe.TFE_NewContext(opts)
540 finally:
541 pywrap_tfe.TFE_DeleteContextOptions(opts)
InternalError: CUDA runtime implicit initialization on GPU:0 failed. Status: out of memory
