Using TensorFlow backend.
Tensorflow: 1.12.0-rc0
Traceback (most recent call last):
  File "/home/neopostmodern/.PyCharm2018.1/config/scratches/scratch_3.py", line 10, in <module>
    model.add(CuDNNLSTM(64, input_shape=(10, 39), return_sequences=True))
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/keras/engine/sequential.py", line 165, in add
    layer(x)
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/keras/layers/recurrent.py", line 532, in __call__
    return super(RNN, self).__call__(inputs, **kwargs)
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/keras/engine/base_layer.py", line 431, in __call__
    self.build(unpack_singleton(input_shapes))
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/keras/layers/cudnn_recurrent.py", line 425, in build
    from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/tensorflow/contrib/__init__.py", line 40, in <module>
    from tensorflow.contrib import distribute
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/tensorflow/contrib/distribute/__init__.py", line 34, in <module>
    from tensorflow.contrib.distribute.python.tpu_strategy import TPUStrategy
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/tensorflow/contrib/distribute/python/tpu_strategy.py", line 29, in <module>
    from tensorflow.contrib.tpu.python.ops import tpu_ops
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/tensorflow/contrib/tpu/__init__.py", line 69, in <module>
    from tensorflow.contrib.tpu.python.ops.tpu_ops import *
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/tensorflow/contrib/tpu/python/ops/tpu_ops.py", line 39, in <module>
    resource_loader.get_path_to_datafile("_tpu_ops.so"))
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/tensorflow/contrib/util/loader.py", line 56, in load_op_library
    ret = load_library.load_op_library(path)
  File "/home/neopostmodern/.local/lib/python3.6/site-packages/tensorflow/python/framework/load_library.py", line 60, in load_op_library
    lib_handle = py_tf.TF_LoadLibrary(library_filename)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Invalid name: 
An op that loads optimization parameters into HBM for embedding. Must be
preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
embedding table configuration. For example, this op is used to install
parameters that are loaded from a checkpoint before a training loop is
executed.

parameters: A tensor containing the initial embedding table parameters to use in embedding
lookups using the Adagrad optimization algorithm.
accumulators: A tensor containing the initial embedding table accumulators to use in embedding
lookups using the Adagrad optimization algorithm.
table_name: Name of this table; must match a name in the
  TPUEmbeddingConfiguration proto (overrides table_id).
num_shards: Number of shards into which the embedding tables are divided.
shard_id: Identifier of shard for this operation.
table_id: Index of this table in the EmbeddingLayerConfiguration proto
  (deprecated).
 (Did you use CamelCase?); in OpDef: name: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" input_arg { name: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" description: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" type: DT_FLOAT type_attr: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" number_attr: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" type_list_attr: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" } input_arg { name: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" description: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" type: DT_FLOAT type_attr: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" number_attr: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" type_list_attr: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" } attr { name: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" type: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" default_value { i: -1 } description: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" has_minimum: true minimum: -1 } attr { name: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" type: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" default_value { s: "" } description: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" } attr { name: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" type: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" description: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" } attr { name: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" type: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" description: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" } summary: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" description: "\nAn op that loads optimization parameters into HBM for embedding. Must be\npreceded by a ConfigureTPUEmbeddingHost op that sets up the correct\nembedding table configuration. For example, this op is used to install\nparameters that are loaded from a checkpoint before a training loop is\nexecuted.\n\nparameters: A tensor containing the initial embedding table parameters to use in embedding\nlookups using the Adagrad optimization algorithm.\naccumulators: A tensor containing the initial embedding table accumulators to use in embedding\nlookups using the Adagrad optimization algorithm.\ntable_name: Name of this table; must match a name in the\n  TPUEmbeddingConfiguration proto (overrides table_id).\nnum_shards: Number of shards into which the embedding tables are divided.\nshard_id: Identifier of shard for this operation.\ntable_id: Index of this table in the EmbeddingLayerConfiguration proto\n  (deprecated).\n" is_stateful: true

import tensorflow as tf
from keras.models import Sequential
from keras.layers import CuDNNLSTM

tf.set_random_seed(42)

print(f'Tensorflow: {tf.__version__}')

model = Sequential()
model.add(CuDNNLSTM(64, input_shape=(10, 39), return_sequences=True))