Python tensorflow.sparse_concat() Examples
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code examples of tensorflow.sparse_concat().
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Example #1
| Source File: tensorflow_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #2
| Source File: tensorflow_backend.py From keras-lambda with MIT License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #3
| Source File: tf_sequence_example_decoder.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def tensors_to_item(self, keys_to_tensors):
"""Maps the given dictionary of tensors to a concatenated list of bboxes.
Args:
keys_to_tensors: a mapping of TF-Example keys to parsed tensors.
Returns:
[time, num_boxes, 4] tensor of bounding box coordinates, in order
[y_min, x_min, y_max, x_max]. Whether the tensor is a SparseTensor
or a dense Tensor is determined by the return_dense parameter. Empty
positions in the sparse tensor are filled with -1.0 values.
"""
sides = []
for key in self._full_keys:
value = keys_to_tensors[key]
expanded_dims = tf.concat(
[tf.to_int64(tf.shape(value)),
tf.constant([1], dtype=tf.int64)], 0)
side = tf.sparse_reshape(value, expanded_dims)
sides.append(side)
bounding_boxes = tf.sparse_concat(2, sides)
if self._return_dense:
bounding_boxes = tf.sparse_tensor_to_dense(
bounding_boxes, default_value=self._default_value)
return bounding_boxes
Example #4
| Source File: tf_sequence_example_decoder.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def tensors_to_item(self, keys_to_tensors):
"""Maps the given dictionary of tensors to a concatenated list of bboxes.
Args:
keys_to_tensors: a mapping of TF-Example keys to parsed tensors.
Returns:
[time, num_boxes, 4] tensor of bounding box coordinates, in order
[y_min, x_min, y_max, x_max]. Whether the tensor is a SparseTensor
or a dense Tensor is determined by the return_dense parameter. Empty
positions in the sparse tensor are filled with -1.0 values.
"""
sides = []
for key in self._full_keys:
value = keys_to_tensors[key]
expanded_dims = tf.concat(
[tf.to_int64(tf.shape(value)),
tf.constant([1], dtype=tf.int64)], 0)
side = tf.sparse_reshape(value, expanded_dims)
sides.append(side)
bounding_boxes = tf.sparse_concat(2, sides)
if self._return_dense:
bounding_boxes = tf.sparse_tensor_to_dense(
bounding_boxes, default_value=self._default_value)
return bounding_boxes
Example #5
| Source File: tensorflow_backend.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #6
| Source File: node_edge_models.py From gcnn-survey-paper with Apache License 2.0 | 6 votes |
|
def compute_inference(self, node_features_in, sp_adj_matrix, is_training):
with tf.variable_scope('edge-model'):
z_latent = gcn_module(node_features_in, sp_adj_matrix, self.n_hidden_edge,
self.p_drop_edge, is_training, self.input_dim,
self.sparse_features)
adj_matrix_pred = compute_adj(z_latent, self.att_mechanism,
self.p_drop_edge, is_training)
self.adj_matrix_pred = adj_matrix_pred
with tf.variable_scope('node-model'):
z_latent = tf.sparse_concat(
axis=1,
sp_inputs=[
tf.contrib.layers.dense_to_sparse(z_latent), node_features_in
])
sparse_features = True
input_dim = self.n_hidden_edge[-1] + self.input_dim
logits = gcn_module(
z_latent,
sp_adj_matrix,
self.n_hidden_node,
self.p_drop_node,
is_training,
input_dim,
sparse_features=sparse_features)
return logits, adj_matrix_pred
Example #7
| Source File: model_utils.py From gcnn-survey-paper with Apache License 2.0 | 6 votes |
|
def get_sp_topk(adj_pred, sp_adj_train, nb_nodes, k):
"""Returns binary matrix with topK."""
_, indices = tf.nn.top_k(tf.reshape(adj_pred, (-1,)), k)
indices = tf.reshape(tf.cast(indices, tf.int64), (-1, 1))
sp_adj_pred = tf.SparseTensor(
indices=indices,
values=tf.ones(k),
dense_shape=(nb_nodes * nb_nodes,))
sp_adj_pred = tf.sparse_reshape(sp_adj_pred,
shape=(nb_nodes, nb_nodes, 1))
sp_adj_train = tf.SparseTensor(
indices=sp_adj_train.indices,
values=tf.ones_like(sp_adj_train.values),
dense_shape=sp_adj_train.dense_shape)
sp_adj_train = tf.sparse_reshape(sp_adj_train,
shape=(nb_nodes, nb_nodes, 1))
sp_adj_pred = tf.sparse_concat(
sp_inputs=[sp_adj_pred, sp_adj_train], axis=-1)
return tf.sparse_reduce_max(sp_adj_pred, axis=-1)
Example #8
| Source File: tensorflow_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #9
| Source File: tensorflow_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #10
| Source File: tensorflow_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #11
| Source File: tensorflow_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #12
| Source File: tensorflow_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #13
| Source File: tensorflow_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #14
| Source File: sparse_concat_op_test.py From deep_image_model with Apache License 2.0 | 6 votes |
|
def testConcat3(self):
with self.test_session(use_gpu=False) as sess:
# concat(A, B, C):
# [ 1 ]
# [2 1 1 ]
# [3 4 2 1 0 2 ]
sp_a = self._SparseTensor_3x3()
sp_b = self._SparseTensor_3x5()
sp_c = self._SparseTensor_3x2()
for concat_dim in (-1, 1):
sp_concat = tf.sparse_concat(concat_dim, [sp_a, sp_b, sp_c])
self.assertEqual(sp_concat.indices.get_shape(), [10, 2])
self.assertEqual(sp_concat.values.get_shape(), [10])
self.assertEqual(sp_concat.shape.get_shape(), [2])
concat_out = sess.run(sp_concat)
self.assertAllEqual(concat_out.indices, [[0, 2], [1, 0], [1, 4], [1, 8],
[2, 0], [2, 2], [2, 3], [2, 6],
[2, 7], [2, 8]])
self.assertAllEqual(concat_out.values, [1, 2, 1, 1, 3, 4, 2, 1, 0, 2])
self.assertAllEqual(concat_out.shape, [3, 10])
Example #15
| Source File: sparse_concat_op_test.py From deep_image_model with Apache License 2.0 | 6 votes |
|
def testConcatDim0(self):
with self.test_session(use_gpu=False) as sess:
# concat(A, D):
# [ 1]
# [2 ]
# [3 4]
# [ 1 ]
# [1 2]
sp_a = self._SparseTensor_3x3()
sp_d = self._SparseTensor_2x3()
for concat_dim in (-2, 0):
sp_concat = tf.sparse_concat(concat_dim, [sp_a, sp_d])
self.assertEqual(sp_concat.indices.get_shape(), [7, 2])
self.assertEqual(sp_concat.values.get_shape(), [7])
self.assertEqual(sp_concat.shape.get_shape(), [2])
concat_out = sess.run(sp_concat)
self.assertAllEqual(
concat_out.indices,
[[0, 2], [1, 0], [2, 0], [2, 2], [3, 1], [4, 0], [4, 2]])
self.assertAllEqual(concat_out.values, np.array([1, 2, 3, 4, 1, 1, 2]))
self.assertAllEqual(concat_out.shape, np.array([5, 3]))
Example #16
| Source File: sparse_concat_op_test.py From deep_image_model with Apache License 2.0 | 6 votes |
|
def testConcat2(self):
with self.test_session(use_gpu=False) as sess:
# concat(A, B):
# [ 1 ]
# [2 1 ]
# [3 4 2 1 0]
for sp_a in (self._SparseTensorValue_3x3(), self._SparseTensor_3x3()):
for sp_b in (self._SparseTensorValue_3x5(), self._SparseTensor_3x5()):
for concat_dim in (-1, 1):
sp_concat = tf.sparse_concat(concat_dim, [sp_a, sp_b])
self.assertEqual(sp_concat.indices.get_shape(), [8, 2])
self.assertEqual(sp_concat.values.get_shape(), [8])
self.assertEqual(sp_concat.shape.get_shape(), [2])
concat_out = sess.run(sp_concat)
self.assertAllEqual(concat_out.indices, [[0, 2], [1, 0], [1, 4],
[2, 0], [2, 2], [2, 3],
[2, 6], [2, 7]])
self.assertAllEqual(concat_out.values, [1, 2, 1, 3, 4, 2, 1, 0])
self.assertAllEqual(concat_out.shape, [3, 8])
Example #17
| Source File: sparse_concat_op_test.py From deep_image_model with Apache License 2.0 | 6 votes |
|
def testConcat1(self):
with self.test_session(use_gpu=False) as sess:
# concat(A):
# [ 1]
# [2 ]
# [3 4]
for sp_a in (self._SparseTensorValue_3x3(), self._SparseTensor_3x3()):
# Note that we ignore concat_dim in this case since we short-circuit the
# single-input case in python.
for concat_dim in (-2000, 1, 2000):
sp_concat = tf.sparse_concat(concat_dim, [sp_a])
self.assertEqual(sp_concat.indices.get_shape(), [4, 2])
self.assertEqual(sp_concat.values.get_shape(), [4])
self.assertEqual(sp_concat.shape.get_shape(), [2])
concat_out = sess.run(sp_concat)
self.assertAllEqual(concat_out.indices,
[[0, 2], [1, 0], [2, 0], [2, 2]])
self.assertAllEqual(concat_out.values, [1, 2, 3, 4])
self.assertAllEqual(concat_out.shape, [3, 3])
Example #18
| Source File: tensorflow_backend.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
# Arguments
tensors: list of tensors to concatenate.
axis: concatenation axis.
# Returns
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
return tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
Example #19
| Source File: sparse_concat_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testShapeInferenceUnknownShapes(self):
with self.test_session(use_gpu=False):
sp_inputs = [
self._SparseTensor_UnknownShape(),
self._SparseTensor_UnknownShape(val_shape=[3]),
self._SparseTensor_UnknownShape(ind_shape=[1, 3]),
self._SparseTensor_UnknownShape(shape_shape=[3])]
for concat_dim in (-2, 0):
sp_concat = tf.sparse_concat(concat_dim, sp_inputs)
self.assertEqual(sp_concat.indices.get_shape().as_list(), [None, 3])
self.assertEqual(sp_concat.values.get_shape().as_list(), [None])
self.assertEqual(sp_concat.shape.get_shape(), [3])
Example #20
| Source File: tf_ops.py From nucleus7 with Mozilla Public License 2.0 | 5 votes |
|
def concat_padded(list_of_tensors: List[tf.Tensor], axis: int = 0,
expand_nonconcat_dim: bool = True) -> tf.Tensor:
"""
Concatenate tensors and pad tensors with smaller dimension.
Uses sparse concatenation inside, so can be slow
Parameters
----------
list_of_tensors
list of tensors
axis
axis to concatenate
expand_nonconcat_dim
whether to allow the expansion in the non-concat dimensions.
Returns
-------
concatenated_tensor
concatenated tensor
"""
t_sparse = [dense_to_sparse(t, tf.shape(t, out_type=tf.int64))
for t in list_of_tensors]
t_concatenated_sparse = tf.sparse_concat(
axis, t_sparse, expand_nonconcat_dim=expand_nonconcat_dim)
return tf.sparse_tensor_to_dense(t_concatenated_sparse)
Example #21
| Source File: sparse_concat_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testMismatchedShapesExpandNonconcatDim(self):
with self.test_session(use_gpu=False) as sess:
sp_a = self._SparseTensor_3x3()
sp_b = self._SparseTensor_3x5()
sp_c = self._SparseTensor_3x2()
sp_d = self._SparseTensor_2x3()
for concat_dim0 in (-2, 0):
for concat_dim1 in (-1, 1):
sp_concat_dim0 = tf.sparse_concat(
concat_dim0, [sp_a, sp_b, sp_c, sp_d], expand_nonconcat_dim=True)
sp_concat_dim1 = tf.sparse_concat(
concat_dim1, [sp_a, sp_b, sp_c, sp_d], expand_nonconcat_dim=True)
sp_concat_dim0_out = sess.run(sp_concat_dim0)
sp_concat_dim1_out = sess.run(sp_concat_dim1)
self.assertAllEqual(sp_concat_dim0_out.indices,
[[0, 2], [1, 0], [2, 0], [2, 2], [4, 1], [5, 0],
[5, 3], [5, 4], [7, 0], [8, 0], [9, 1], [10, 0],
[10, 2]])
self.assertAllEqual(sp_concat_dim0_out.values,
[1, 2, 3, 4, 1, 2, 1, 0, 1, 2, 1, 1, 2])
self.assertAllEqual(sp_concat_dim0_out.shape, [11, 5])
self.assertAllEqual(sp_concat_dim1_out.indices,
[[0, 2], [0, 11], [1, 0], [1, 4], [1, 8], [1, 10],
[1, 12], [2, 0], [2, 2], [2, 3], [2, 6], [2, 7],
[2, 8]])
self.assertAllEqual(sp_concat_dim1_out.values,
[1, 1, 2, 1, 1, 1, 2, 3, 4, 2, 1, 0, 2])
self.assertAllEqual(sp_concat_dim1_out.shape, [3, 13])
Example #22
| Source File: sparse_concat_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testMismatchedShapes(self):
with self.test_session(use_gpu=False) as sess:
sp_a = self._SparseTensor_3x3()
sp_b = self._SparseTensor_3x5()
sp_c = self._SparseTensor_3x2()
sp_d = self._SparseTensor_2x3()
for concat_dim in (-1, 1):
sp_concat = tf.sparse_concat(concat_dim, [sp_a, sp_b, sp_c, sp_d])
# Shape mismatches can only be caught when the op is run
with self.assertRaisesOpError("Input shapes must match"):
sess.run(sp_concat)
Example #23
| Source File: sparse_concat_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testMismatchedRankExpandNonconcatDim(self):
with self.test_session(use_gpu=False):
sp_a = self._SparseTensor_3x3()
sp_e = self._SparseTensor_2x3x4()
# Rank mismatches should be caught at shape-inference time, even for
# expand_nonconcat_dim=True.
for concat_dim in (-1, 1):
with self.assertRaises(ValueError):
tf.sparse_concat(concat_dim, [sp_a, sp_e], expand_nonconcat_dim=True)
Example #24
| Source File: sparse_concat_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testMismatchedRank(self):
with self.test_session(use_gpu=False):
sp_a = self._SparseTensor_3x3()
sp_e = self._SparseTensor_2x3x4()
# Rank mismatches can be caught at shape-inference time
for concat_dim in (-1, 1):
with self.assertRaises(ValueError):
tf.sparse_concat(concat_dim, [sp_a, sp_e])
Example #25
| Source File: node_edge_models.py From gcnn-survey-paper with Apache License 2.0 | 5 votes |
|
def compute_inference(self, node_features_in, sp_adj_matrix, is_training):
with tf.variable_scope('edge-model'):
z_latent = gcn_module(node_features_in, sp_adj_matrix, self.n_hidden_edge,
self.p_drop_edge, is_training, self.input_dim,
self.sparse_features)
adj_matrix_pred = compute_adj(z_latent, self.att_mechanism,
self.p_drop_edge, is_training)
self.adj_matrix_pred = adj_matrix_pred
with tf.variable_scope('node-model'):
z_latent = tf.sparse_concat(
axis=1,
sp_inputs=[
tf.contrib.layers.dense_to_sparse(z_latent), node_features_in
])
sparse_features = True
input_dim = self.n_hidden_edge[-1] + self.input_dim
sp_adj_train = tf.SparseTensor(
indices=sp_adj_matrix.indices,
values=tf.ones_like(sp_adj_matrix.values),
dense_shape=sp_adj_matrix.dense_shape)
logits = gat_module(
z_latent,
sp_adj_train,
self.n_hidden_node,
self.n_att_node,
self.p_drop_node,
is_training,
input_dim,
sparse_features=sparse_features,
average_last=True)
return logits, adj_matrix_pred
Example #26
| Source File: sparse_split_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testSliceConcat(self):
for sp_input in (
self._SparseTensorValue_3x4x2(), self._SparseTensor_3x4x2()):
with self.test_session(use_gpu=False):
sparse_tensors = tf.sparse_split(1, 2, sp_input)
concat_tensor = tf.sparse_concat(1, sparse_tensors)
expected_output = self._SparseTensor_3x4x2()
self.assertAllEqual(concat_tensor.indices.eval(),
expected_output.indices.eval())
Example #27
| Source File: model_utils.py From nucleus7 with Mozilla Public License 2.0 | 5 votes |
|
def combine_predictions_from_devices(
predictions_devices: List[Dict[str, tf.Tensor]],
predictions_have_variable_shape: bool = False) -> Dict[str, tf.Tensor]:
"""
Combines (concatenates) the predictions from multiple devices
Parameters
----------
predictions_devices
list of dicts with same structure from multiple devices
predictions_have_variable_shape
if predictions from different devices may have different shapes; if so,
it will use sparse operations to combine them
Returns
-------
dict with same structure as first element in predictions_devices with
concatenated over first dimension (batch dimension) values. If inputs
have variable shape, then concatenation is done using
:obj:`tf.sparse_concat` instead of :obj:`tf.concat`
"""
if len(predictions_devices) == 1:
return _dict_identity(predictions_devices[0])
if predictions_have_variable_shape:
combine_fun = lambda x: tf_ops.concat_padded(x, axis=0)
else:
combine_fun = lambda x: tf_ops.concat_or_stack(x, axis=0)
with tf.variable_scope('combine_predictions'):
predictions = nest_utils.combine_nested(predictions_devices,
combine_fun=combine_fun)
return predictions
Example #28
| Source File: node_edge_models.py From gcnn-survey-paper with Apache License 2.0 | 4 votes |
|
def compute_inference(self, node_features_in, sp_adj_matrix, is_training):
with tf.variable_scope('edge-model'):
z_latent = gat_module(
node_features_in,
sp_adj_matrix,
self.n_hidden_edge,
self.n_att_edge,
self.p_drop_edge,
is_training,
self.input_dim,
self.sparse_features,
average_last=False)
adj_matrix_pred = compute_adj(z_latent, self.att_mechanism,
self.p_drop_edge, is_training)
self.adj_matrix_pred = adj_matrix_pred
with tf.variable_scope('node-model'):
concat = True
if concat:
z_latent = tf.sparse_concat(
axis=1,
sp_inputs=[
tf.contrib.layers.dense_to_sparse(z_latent), node_features_in
],
)
sparse_features = True
input_dim = self.n_hidden_edge[-1] * self.n_att_edge[
-1] + self.input_dim
else:
sparse_features = False
input_dim = self.n_hidden_edge[-1] * self.n_att_edge[-1]
logits = gat_module(
z_latent,
sp_adj_matrix,
self.n_hidden_node,
self.n_att_node,
self.p_drop_node,
is_training,
input_dim,
sparse_features=sparse_features,
average_last=False)
return logits, adj_matrix_pred
Example #29
| Source File: example.py From rgat with Apache License 2.0 | 4 votes |
|
def model_fn(features, labels, mode, params):
training = mode == ModeKeys.TRAIN
if params.model == "rgat":
model_class = RGATNModel
elif params.model == "rgc":
model_class = RGCNModel
else:
raise ValueError(
"Unknown model {}. Must be one of `'rgat'` or `'rgc'`".format(
params.model))
model = model_class(params=params, training=training)
inputs, support = features['features'], features['support']
# Combine dict of supports into a single matrix
support = tf.sparse_concat(axis=1, sp_inputs=list(support.values()),
name="combine_supports")
logits = model(inputs=inputs, support=support)
predictions = tf.argmax(logits, axis=-1, name='predictions')
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode, predictions={'logits': logits, 'predictions': predictions})
mask, labels = labels['mask'], labels['labels']
# Get only unmasked labels, logits and predictions
labels, logits = tf.gather(labels, mask), tf.gather(logits, mask)
predictions = tf.gather(predictions, mask)
loss = tf.losses.sparse_softmax_cross_entropy(
labels=labels, logits=logits)
with tf.name_scope('metrics'):
accuracy = tf.metrics.accuracy(
labels=labels, predictions=predictions)
metrics = {'accuracy': accuracy}
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=metrics)
assert mode == tf.estimator.ModeKeys.TRAIN
optimizer = tf.train.AdamOptimizer(learning_rate=params.learning_rate)
global_step = tf.train.get_global_step()
train_op = optimizer.minimize(loss, global_step=global_step)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
