def testEmptyRnnInput(self):
observation_values = tf.SparseTensor(
indices=tf.reshape(tf.constant([], dtype=tf.int64), shape=[0, 3]),
values=tf.constant([], dtype=tf.float32),
dense_shape=[2, 0, 1])
observation_code_ids = tf.SparseTensor(
indices=observation_values.indices,
values=tf.constant([], dtype=tf.string),
dense_shape=observation_values.dense_shape)
delta_time, obs_values, indicator = osm.construct_input(
{
'Observation.code':
observation_code_ids,
'Observation.valueQuantity.value':
observation_values,
'deltaTime':
tf.reshape(tf.constant([[], []], dtype=tf.int64), [2, 0, 1])
}, ['loinc:1', 'loinc:2', 'MISSING'],
'Observation.code',
'Observation.valueQuantity.value',
mode=tf.estimator.ModeKeys.TRAIN,
normalize=False,
momentum=0.9,
min_value=-10000000,
max_value=10000000,
input_keep_prob=1.0)
result = tf.concat([delta_time, indicator, obs_values], axis=2)
with self.test_session() as sess:
sess.run(tf.tables_initializer())
actual_result, = sess.run([tf.shape(result)])
self.assertAllClose([2, 0, 7], actual_result)
def test_intersect_indices_sparse(self):
obs_code = tf.SparseTensor(indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0],
[1, 1, 0], [1, 2, 0], [1, 3, 0],
[2, 0, 0], [2, 2, 0]],
values=[
'loinc:1', 'loinc:1', 'loinc:2',
'loinc:4', 'loinc:4', 'loinc:2',
'loinc:1', 'loinc:4'
],
dense_shape=[3, 4, 1])
obs_harm_code = tf.SparseTensor(indices=[[0, 0, 0], [0, 1,
0], [1, 1, 0],
[2, 1, 0], [2, 2, 0]],
values=[
'pulse', 'pulse', 'blood_pressure',
'temperature', 'temperature'
],
dense_shape=[3, 2, 3])
indices = [[0, 0, 0], [0, 1, 0], [1, 0, 0], [2, 1, 0]]
values = [b'loinc:1', b'loinc:1', b'loinc:4', b'loinc:4']
dense_shape = [3, 2, 1]
new_obs_code = input_fn._intersect_indices(obs_code, obs_harm_code)
with self.test_session() as sess:
acutal_obs_code = sess.run(new_obs_code)
self.assertAllEqual(values, acutal_obs_code.values)
self.assertAllEqual(indices, acutal_obs_code.indices)
self.assertAllEqual(dense_shape, acutal_obs_code.dense_shape)
def graph_attention_layer(self, A, M, v, layer):
with tf.variable_scope("layer_%s" % layer):
# drop
# M = tf.nn.dropout(M, 1 - self.dropout)
f1 = tf.matmul(M, v[0]) # (?,1)
f1 = A * f1 # (?,) element-wise product
f2 = tf.matmul(M, v[1]) # (?,1)
f2 = A * tf.transpose(
f2, [1, 0]) # (?,?) transpose: for the coefficient of h_j
logits = tf.sparse_add(f1, f2) # (?,)
unnormalized_attentions = tf.SparseTensor(
indices=logits.indices,
values=tf.nn.sigmoid(logits.values),
dense_shape=logits.dense_shape)
attentions = tf.sparse_softmax(unnormalized_attentions) # (?,)
attentions = tf.SparseTensor(
indices=attentions.indices,
values=attentions.values,
dense_shape=attentions.dense_shape) # (?,)
# attention dropout, each node is exposed to a stochastically sampled neighborhood
if FLAGS.gat_dropout > 0:
attentions = tf.SparseTensor(
indices=attentions.indices,
values=tf.nn.dropout(
attentions.values,
1.0 - self.placeholders['gat_dropout']),
dense_shape=attentions.dense_shape) # (?,)
return attentions
def _make_adjacency(self, sources, dests, num_nodes=None, tensor=True):
if num_nodes is None:
num_nodes = len(self.node_encode.classes_)
if tensor:
try:
adj = tf.SparseTensor(
[[sources.values[i, 0], dests.values[i, 0]]
for i in range(sources.values.shape[0])],
[1.0 for i in range(sources.values.shape[0])],
dense_shape=(num_nodes, num_nodes))
except:
adj = tf.SparseTensor([[sources[i], dests[i]]
for i in range(sources.shape[0])],
[1.0 for i in range(sources.shape[0])],
dense_shape=(num_nodes, num_nodes))
else:
try:
adj = csr_matrix(
([1.0 for i in range(sources.values.shape[0])], ([
sources.values[i, 0]
for i in range(sources.values.shape[0])
], [
dests.values[i, 0]
for i in range(sources.values.shape[0])
])),
shape=(num_nodes, num_nodes))
except:
adj = csr_matrix(
([1.0 for i in range(sources.shape[0])],
([sources[i] for i in range(sources.shape[0])
], [dests[i] for i in range(sources.shape[0])])),
shape=(num_nodes, num_nodes))
return adj
def _extend_with_dummy(extend_with, to_extend, dummy_value='n/a'):
"""Extends one SparseTensor with dummy_values at positions of other."""
dense_shape = tf.to_int64(
tf.concat([[tf.shape(extend_with)[0]],
[tf.maximum(tf.shape(extend_with)[1],
tf.shape(to_extend)[1])],
[tf.maximum(tf.shape(extend_with)[2],
tf.shape(to_extend)[2])]],
axis=0))
additional_indices = tf.sets.set_difference(
tf.SparseTensor(
indices=extend_with.indices,
values=tf.zeros_like(extend_with.values, dtype=tf.int32),
dense_shape=dense_shape),
tf.SparseTensor(
indices=to_extend.indices,
values=tf.zeros([tf.shape(to_extend.indices)[0]], dtype=tf.int32),
dense_shape=dense_shape)).indices
# Supply defaults for all other indices.
default = tf.tile(
tf.constant([dummy_value]), multiples=[tf.shape(additional_indices)[0]])
string_value = (
tf.as_string(to_extend.values)
if to_extend.values.dtype != tf.string else to_extend.values)
return tf.sparse_reorder(
tf.SparseTensor(
indices=tf.concat([to_extend.indices, additional_indices], axis=0),
values=tf.concat([string_value, default], axis=0),
dense_shape=dense_shape))
def _build_inputs(self):
self.pos_indices = tf.placeholder(tf.int64,
shape=[None, 2],
name='pos_indices')
self.pos_values = tf.placeholder(tf.float32,
shape=[None],
name='pos_values')
self.pos_shape = tf.placeholder(tf.int64, shape=[2], name='pos_shape')
self.neg_indices = tf.placeholder(tf.int64,
shape=[None, 2],
name='neg_indices')
self.neg_values = tf.placeholder(tf.float32,
shape=[None],
name='neg_values')
self.neg_shape = tf.placeholder(tf.int64, shape=[2], name='neg_shape')
self.mess_dropout = tf.placeholder(tf.float32,
shape=[None],
name='mess_dropout')
# Input positive features, shape=(batch_size * feature_dim)
self.sp_pos_feats = tf.SparseTensor(self.pos_indices, self.pos_values,
self.pos_shape)
# Input negative features, shape=(batch_size * feature_dim)
self.sp_neg_feats = tf.SparseTensor(self.neg_indices, self.neg_values,
self.neg_shape)
def _create_implicit_feedback(self, implicit_feedback, dual=False):
"""Returns the (tuple of) sparse tensor(s) of implicit feedback.
"""
with tf.variable_scope('implicit_feedback'):
if not dual:
N = tf.SparseTensor(**implicit_feedback)
return N
else:
N = tf.SparseTensor(**implicit_feedback[0])
H = tf.SparseTensor(**implicit_feedback[1])
return N, H
def testRnnInput(self):
observation_values = tf.SparseTensor(
indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [1, 0, 0], [1, 1, 0],
[1, 2, 0]],
values=[100.0, 2.3, 9999999.0, 0.5, 0.0, 4.0],
dense_shape=[2, 3, 1])
observation_code_ids = tf.SparseTensor(
indices=observation_values.indices,
values=[
'loinc:2', 'loinc:1', 'loinc:2', 'loinc:1', 'MISSING',
'loinc:1'
],
dense_shape=observation_values.dense_shape)
delta_time, obs_values, indicator = osm.construct_input(
{
'Observation.code':
observation_code_ids,
'Observation.valueQuantity.value':
observation_values,
'deltaTime':
tf.constant([[[2 * 60 * 60], [3 * 60 * 60], [0]],
[[1 * 60 * 60], [3 * 60 * 60], [6 * 60 * 60]]],
dtype=tf.int64)
}, ['loinc:1', 'loinc:2', 'MISSING'],
'Observation.code',
'Observation.valueQuantity.value',
mode=tf.estimator.ModeKeys.TRAIN,
normalize=False,
momentum=0.9,
min_value=-10000000,
max_value=10000000,
input_keep_prob=1.0)
result = tf.concat([delta_time, indicator, obs_values], axis=2)
expected_result = [
[
[0, 0, 1, 0, 0, 100, 0],
[-1, 1, 0, 0, 2.3, 0, 0],
# value 9999999.0 was filtered.
[3, 0, 0, 0, 0, 0, 0]
],
[[0, 1, 0, 0, 0.5, 0, 0], [-2, 0, 0, 1, 0, 0, 0],
[-3, 1, 0, 0, 4.0, 0, 0]]
]
with self.test_session() as sess:
sess.run(tf.tables_initializer())
actual_result = sess.run(result)
print(actual_result)
self.assertAllClose(expected_result, actual_result, atol=0.01)
def _make_sparse_tensor_dict():
rel_name1 = 'real_stuff'
# Note, these matrices are transposed.
sparse_tensor1 = tf.SparseTensor(indices=[[0, 0], [99, 1]],
values=[1., 2.],
dense_shape=[100, 2])
rel_name2 = 'other_stuff'
sparse_tensor2 = tf.SparseTensor(indices=[[100, 0]],
values=[3.],
dense_shape=[1000, 2])
return {
rel_name1: tf.Session().run(sparse_tensor1),
rel_name2: tf.Session().run(sparse_tensor2)
}
def get_tf_tensor(self, rel_name, shard_id=-1):
"""Get the Tensor that represents a relation.
Args:
rel_name: string naming a declared relation
shard_id: the i'th shard of the matrix. -1 if the matrix is not sharded.
Returns:
tf.SparseTensor
Raises:
RuntimeError: If the expression has no initial value.
"""
if shard_id < 0:
return super(DistributedNeuralQueryContext,
self).get_tf_tensor(rel_name)
sharded_rel_name = self._sharded_rel_name(rel_name, shard_id)
# construct tensor if it's not been created before, and cache it.
if sharded_rel_name not in self._cached_tensor:
if sharded_rel_name not in self._np_initval:
raise RuntimeError(
'KG relation named %r has no initial value.' %
sharded_rel_name)
if self.is_dense(rel_name):
raise TypeError(
'DistributedNeuralQueryContext does not support dense relation %d'
% rel_name)
m = self._np_initval[sharded_rel_name]
n_rows, n_cols = m.shape
data_m = np.transpose(np.vstack([m.row, m.col]))
if self.is_trainable(
rel_name): # construct tf variable if trainable
data_var_name = 'nql/%s_values' % rel_name
data_var = tf.Variable(m.data,
trainable=True,
name=data_var_name)
sparse_tensor = tf.SparseTensor(data_m, data_var,
[n_rows, n_cols])
assert self._declaration[rel_name].underlying_parameter is None
self._declaration[rel_name].underlying_parameters[
shard_id] = data_var
else: # if not trainable, construct a constant tensor
sparse_tensor = tf.SparseTensor(data_m, m.data,
[n_rows, n_cols])
self._cached_tensor[sharded_rel_name] = sparse_tensor
return self._cached_tensor[sharded_rel_name]
def compute_sparse_matrix(self, stencils, batch_size, grid_size):
with tf.device(self.device):
indexes, values_indices = self.get_indices_compute_A((batch_size, grid_size))
tau = tf.SparseTensor(indices=indexes,
values=tf.gather_nd(params=stencils, indices=values_indices),
dense_shape=(batch_size, grid_size ** 2, grid_size ** 2))
return tau
def dense_to_sparse(dense_tensor, out_type, ignore_value=-1):
indices = tf.where(
tf.not_equal(dense_tensor,
tf.constant(ignore_value, dense_tensor.dtype)))
values = tf.gather_nd(dense_tensor, indices)
shape = tf.shape(dense_tensor, out_type=out_type)
return tf.SparseTensor(indices, values, shape)
def chebyshev5(self, x, L, Fout, K):
N, M, Fin = x.get_shape()
N, M, Fin = int(N), int(M), int(Fin)
# Rescale Laplacian and store as a TF sparse tensor. Copy to not modify the shared L.
L = scipy.sparse.csr_matrix(L)
L = graph.rescale_L(L, lmax=2)
L = L.tocoo()
indices = np.column_stack((L.row, L.col))
L = tf.SparseTensor(indices, L.data, L.shape)
L = tf.sparse_reorder(L)
# Transform to Chebyshev basis
x0 = tf.transpose(x, perm=[1, 2, 0]) # M x Fin x N
x0 = tf.reshape(x0, [M, Fin*N]) # M x Fin*N
x = tf.expand_dims(x0, 0) # 1 x M x Fin*N
print("Test")
def concat(x, x_):
x_ = tf.expand_dims(x_, 0) # 1 x M x Fin*N
return tf.concat([x, x_], axis=0) # K x M x Fin*N
if K > 1:
x1 = tf.sparse_tensor_dense_matmul(L, x0)
x = concat(x, x1)
print(" K = 1")
for k in range(2, K):
x2 = 2 * tf.sparse_tensor_dense_matmul(L, x1) - x0 # M x Fin*N
x = concat(x, x2)
x0, x1 = x1, x2
x = tf.reshape(x, [K, M, Fin, N]) # K x M x Fin x N
x = tf.transpose(x, perm=[3,1,2,0]) # N x M x Fin x K
x = tf.reshape(x, [N*M, Fin*K]) # N*M x Fin*K
# Filter: Fin*Fout filters of order K, i.e. one filterbank per feature pair.
W = self._weight_variable([Fin*K, Fout], regularization=False)
x = tf.matmul(x, W) # N*M x Fout
return tf.reshape(x, [N, M, Fout]) # N x M x Fout
def rotation_matrix(self, plane):
i = plane.first_axis
j = plane.second_axis
theta = plane.theta
cos_theta = theta
sin_thesta = tf.stop_gradient(tf.sin(tf.acos(theta)))
# rotation = np.eye(self.dim).tolist()
# rotation[i][i] = cos_theta
# rotation[i][j] = -sin_thesta
# rotation[j][i] = sin_thesta
# rotation[j][j] = cos_theta
# rotation = tf.stack(rotation)
idx = []
values = []
for k in range(self.dim):
idx.append([k, k])
if k == i or k == j:
values.append(cos_theta)
else:
values.append(1)
idx.append([i, j])
values.append(-sin_thesta)
idx.append([j, i])
values.append(sin_thesta)
rotation = tf.SparseTensor(idx,
values=tf.stack(values),
dense_shape=[self.dim, self.dim])
return rotation
def chebyshev(self, x, L, Fout, K , normalized=False, algo='LB'):
'''normalized or not, algo='LB' or 'gL' (graph Laplacian)
will affact the value of "lmax" (maximum eigenvalue)'''
N, M, Fin = x.get_shape()
N, M, Fin = int(N), int(M), int(Fin)
# Rescale Laplacian and store as a TF sparse tensor. Copy to not modify the shared L.
L = scipy.sparse.csr_matrix(L)
lmax=graph.lmax(L, normalized, algo) # 202000912
# L = graph.rescale_L(L, lmax=2)
L = graph.rescale_L(L, lmax) # 202000912
L = L.tocoo()
indices = np.column_stack((L.row, L.col))
L = tf.SparseTensor(indices, L.data, L.shape)
L = tf.sparse_reorder(L)
# Transform to Chebyshev basis
x0 = tf.transpose(x, perm=[1, 2, 0]) # M x Fin x N
x0 = tf.reshape(x0, [M, Fin*N]) # M x Fin*N
x = tf.expand_dims(x0, 0) # 1 x M x Fin*N
def concat(x, x_):
x_ = tf.expand_dims(x_, 0) # 1 x M x Fin*N
return tf.concat([x, x_], axis=0) # K x M x Fin*N
if K > 1:
x1 = tf.sparse_tensor_dense_matmul(L, x0)
x = concat(x, x1)
for k in range(1, K-1):
x2 = 2 * tf.sparse_tensor_dense_matmul(L, x1) - x0 # M x Fin*N
x = concat(x, x2)
x0, x1 = x1, x2
x = tf.reshape(x, [K, M, Fin, N]) # K x M x Fin x N
x = tf.transpose(x, perm=[3,1,2,0]) # N x M x Fin x K
x = tf.reshape(x, [N*M, Fin*K]) # N*M x Fin*K
# Filter: Fin*Fout filters of order K, i.e. one filterbank per feature pair.
W = self._weight_variable([Fin*K, Fout], regularization=False)
x = tf.matmul(x, W) # N*M x Fout
return tf.reshape(x, [N, M, Fout]) # N x M x Fout
def _slice_with_actions(embeddings, actions):
"""Slice a Tensor.
Take embeddings of the form [batch_size, num_actions, embed_dim]
and actions of the form [batch_size, 1], and return the sliced embeddings
like embeddings[:, actions, :].
Args:
embeddings: Tensor of embeddings to index.
actions: int Tensor to use as index into embeddings
Returns:
Tensor of embeddings indexed by actions
"""
batch_size, num_actions = embeddings.get_shape()[:2]
# Values are the 'values' in a sparse tensor we will be setting
act_indx = tf.cast(actions, tf.int64)[:, None]
values = tf.reshape(tf.cast(tf.ones(tf.shape(actions)), tf.bool), [-1])
# Create a range for each index into the batch
act_range = tf.range(0, batch_size, dtype=tf.int64)[:, None]
# Combine this into coordinates with the action indices
indices = tf.concat([act_range, act_indx], 1)
actions_mask = tf.SparseTensor(indices, values, [batch_size, num_actions])
actions_mask = tf.stop_gradient(
tf.sparse_tensor_to_dense(actions_mask, default_value=False))
sliced_emb = tf.boolean_mask(embeddings, actions_mask)
return sliced_emb
def multihot_embedding(sess, slot_id):
slotx_emb_table = tf.get_variable(
name='multi_hot_emb_slot_%s' % str(slot_id),
shape=(g_dict_len, g_emb_size),
initializer=tf.glorot_uniform_initializer())
'''
slotx_emb_table = tf.constant([[6.4, 1.2, 0.5, 3.3],
[0.3, 0.4, 0.5, 0.8],
[1.5, 0.3, 2.2, 1.9],
[0.4, 0.9, 1.1, 4.3]])
'''
#定义稀疏矩阵, indices是位置[0,0]表示矩阵的第0行第0列，这样拼出来稀疏矩阵. values是对应emb_table中的索引。dense_shape是稀疏矩阵的长*宽
#这个稀疏矩阵就是下面这个样子，每一行是一个multihot，行数代表batch_size，列数代表multihot最多允许多少个hot。N表示稀疏矩阵这位置没有存
#[[1, 2, 3, N, N],
# [N, N, 2, N, N],
# [N, N, 3, 1, N]]
slotx_idx = tf.SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [1, 2],
[2, 2], [2, 3]],
values=[1, 2, 3, 2, 3, 1],
dense_shape=(10, 5))
print("slotx_emb_table.shape=", slotx_emb_table.shape)
slotx_emb = tf.nn.embedding_lookup_sparse(
slotx_emb_table, slotx_idx, sp_weights=None,
combiner="sum") #combiner=sum表示multihot用sum方式聚合
sess.run(tf.global_variables_initializer())
#print("emb_table(slot"+str(slot_id)+")=\n", sess.run(slotx_emb_table))
print("emb(slot" + str(slot_id) + ")=\n", sess.run(slotx_emb))
return slotx_emb
def get_bag_vectors(model):
"""
Represents snapshots as a bag of clinical observations. Specifically, returns a V-length
binary vector such that the v-th index is 1 iff the v-th observation occurs in the given snapshot
:param model: CANTRIP model
:type model: modeling.CANTRIPModel
:return: clinical snapshot encoding
"""
# 1. Evaluate which entries in model.observations are non-zero
mask = tf.not_equal(model.observations, 0)
where = tf.where(mask)
# 2. Get the vocabulary indices for non-zero observations
vocab_indices = tf.boolean_mask(model.observations, mask)
vocab_indices = tf.expand_dims(vocab_indices[:], axis=-1)
vocab_indices = tf.cast(vocab_indices, dtype=tf.int64)
# 3. Get batch and sequence indices for non-zero observations
tensor_indices = where[:, :-1]
# Concat batch, sequence, and vocabulary indices
indices = tf.concat([tensor_indices, vocab_indices], axis=-1)
# Our sparse tensor will be 1 for observed observations, 0, otherwise
ones = tf.ones_like(indices[:, 0], dtype=tf.float32)
# The dense shape will be the same as model.observations, but using the entire vocabulary as the final dimension
dense_shape = model.observations.get_shape().as_list()
dense_shape[2] = model.vocabulary_size
# Store as a sparse tensor because they're neat
st = tf.SparseTensor(indices=indices, values=ones, dense_shape=dense_shape)
return tf.sparse.reorder(st)
def build_rating_sparse_tensor(ratings_df):
indices = ratings_df[['user_id', 'movie_id']].values
values = ratings_df['rating'].values
return tf.SparseTensor(
indices=indices,
values=values,
dense_shape=[users.shape[0], movies.shape[0]])
def testCombine(self):
"""Low-level test for the results of combine_observation_code_and_values."""
observation_code_ids = tf.SparseTensor(indices=[[0, 0, 0], [0, 1, 0],
[1, 0, 0], [1, 1, 0],
[1, 2, 0]],
values=tf.constant(
[1, 1, 3, 0, 1],
dtype=tf.int64),
dense_shape=[2, 3, 1])
vocab_size = 5
expected_result = [[[0, 100.0, 0, 0, 0], [0, 2.3, 0, 0, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 0.5, 0], [0.0, 0, 0, 0, 0],
[0, 4.0, 0, 0, 0]]]
expected_indicator = [[[0, 1.0, 0, 0, 0], [0, 1, 0, 0, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 1, 0], [1, 0, 0, 0, 0],
[0, 1, 0, 0, 0]]]
acutal_result, acutal_indicator = osm.combine_observation_code_and_values(
observation_code_ids=observation_code_ids,
observation_values=self.observation_values,
vocab_size=vocab_size,
mode=tf.estimator.ModeKeys.TRAIN,
normalize=False,
momentum=0.9,
min_value=-10000000,
max_value=10000000)
with self.test_session() as sess:
a_result, a_indicator = sess.run([acutal_result, acutal_indicator])
self.assertAllClose(expected_result, a_result, atol=0.01)
self.assertAllClose(expected_indicator, a_indicator, atol=0.01)
def rfunc(triple_list, ent_num, rel_num):
head = dict()
tail = dict()
rel_count = dict()
r_mat_ind = list()
r_mat_val = list()
head_r = np.zeros((ent_num, rel_num))
tail_r = np.zeros((ent_num, rel_num))
for triple in triple_list:
head_r[triple[0]][triple[1]] = 1
tail_r[triple[2]][triple[1]] = 1
r_mat_ind.append([triple[0], triple[2]])
r_mat_val.append(triple[1])
if triple[1] not in rel_count:
rel_count[triple[1]] = 1
head[triple[1]] = set()
tail[triple[1]] = set()
head[triple[1]].add(triple[0])
tail[triple[1]].add(triple[2])
else:
rel_count[triple[1]] += 1
head[triple[1]].add(triple[0])
tail[triple[1]].add(triple[2])
r_mat = tf.SparseTensor(indices=r_mat_ind,
values=r_mat_val,
dense_shape=[ent_num, ent_num])
return head, tail, head_r, tail_r, r_mat
def CreateCombinedFeedDict(
input_feed_dict,
baseline_feed_dict=None,
baseline_transform_info=baseline_transform_info):
"""Combine baseline and input feed dicts into a common feed dict."""
combined_feed_dict = input_feed_dict.copy()
if baseline_feed_dict is None:
baseline_feed_dict = input_feed_dict
for key, feed_value in baseline_feed_dict.items():
if isinstance(key, tf.Tensor):
combined_feed_dict[baseline_transform_info.transformed(
key)] = (feed_value)
elif isinstance(key, six.text_type):
if six.PY2:
tensor = graph.get_tensor_by_name(key.decode())
else:
tensor = graph.get_tensor_by_name(key)
combined_feed_dict[baseline_transform_info.transformed(
tensor)] = (feed_value)
elif isinstance(key, tf.SparseTensor):
sparse_transformed_tensor = tf.SparseTensor(
baseline_transform_info.transformed(key.indices),
baseline_transform_info.transformed(key.values),
baseline_transform_info.transformed(key.dense_shape))
combined_feed_dict[sparse_transformed_tensor] = feed_value
else:
raise ValueError('Invalid key type %s in Feed Dict.' %
type(key))
return combined_feed_dict
def get_tensor(self, tensor_dict):
"""Construct a SparseTensor representation of the relation from tensors.
This can either be called with a decoded tf.Example, in which case this
returns a sparse tensor for an individual instance, or with the result
of `batching_loop_results`, in which case it returns a sparse tensor for
the whole batch.
Args:
tensor_dict: A dictionary mapping key names to tensors.
Returns:
A SparseTensor representation of the shepherd's data.
"""
dense_shape = tensor_dict[self.dense_shape_key()]
source_indices = tensor_dict[self.source_indices_key()]
dest_indices = tensor_dict[self.dest_indices_key()]
values = tensor_dict[self.values_key()]
# 'indices' is a [source_indices.shape[0], 2] with each row representing
# a (row, column) pair where there is a nonzero entry in the SparseTensor.
indices = tf.concat([tf.expand_dims(source_indices, 1),
tf.expand_dims(dest_indices, 1)], axis=1)
return tf.sparse_reorder(
tf.SparseTensor(indices=indices,
values=values,
dense_shape=dense_shape))
def testAddBOWIntegratedGradientsOps(self):
with tf.Graph().as_default() as graph:
# pyformat: disable
embedding_weights = tf.constant([[1., 3., 5.], [4., 6., 8.],
[4., 5., 4.]])
batch_size = tf.placeholder_with_default(tf.constant(1, tf.int64),
[])
sparse_ids = tf.SparseTensor([[0, 0], [0, 1], [0, 2]], [2, 0, 2],
[batch_size, 3])
# pyformat: enable
sparse_embedding = contrib_layers.safe_embedding_lookup_sparse(
embedding_weights,
sparse_ids,
combiner='sum',
partition_strategy='div')
vector = tf.constant([1., 2., 4.])
output_tensor = tf.reduce_sum(vector * sparse_embedding, axis=1)
embedding_lookup = tf.nn.embedding_lookup(
embedding_weights,
tf.sparse_tensor_to_dense(sparse_ids),
partition_strategy='div')
bow_attribution_hooks = integrated_gradients.AddBOWIntegratedGradientsOps(
graph, [embedding_lookup], [sparse_embedding], [],
output_tensor)
with tf.Session(graph=graph) as sess:
sess.run(tf.global_variables_initializer())
result = sess.run(bow_attribution_hooks['bow_attributions'])
self.assertTupleEqual(result[0].shape, (3, ))
# Since the output is a sum of dot products, attributions are simply dot
# products of the embedding with [1., 2., 4.].
self.assertAlmostEqual(result[0][0], 30.)
self.assertAlmostEqual(result[0][1], 27.)
self.assertAlmostEqual(result[0][2], 30.)
def setupCTC(self):
"create CTC loss and decoder and return them"
# BxTxC -> TxBxC
self.ctcIn3dTBC = tf.transpose(self.rnnOut3d, [1, 0, 2])
# ground truth text as sparse tensor
self.gtTexts = tf.SparseTensor(tf.placeholder(tf.int64, shape=[None, 2]) , tf.placeholder(tf.int32, [None]), tf.placeholder(tf.int64, [2]))
# calc loss for batch
self.seqLen = tf.placeholder(tf.int32, [None])
self.loss = tf.reduce_mean(tf.nn.ctc_loss(labels=self.gtTexts, inputs=self.ctcIn3dTBC, sequence_length=self.seqLen, ctc_merge_repeated=True))
# calc loss for each element to compute label probability
self.savedCtcInput = tf.placeholder(tf.float32, shape=[Model.maxTextLen, None, len(self.charList) + 1])
self.lossPerElement = tf.nn.ctc_loss(labels=self.gtTexts, inputs=self.savedCtcInput, sequence_length=self.seqLen, ctc_merge_repeated=True)
# decoder: either best path decoding or beam search decoding
if self.decoderType == DecoderType.BestPath:
self.decoder = tf.nn.ctc_greedy_decoder(inputs=self.ctcIn3dTBC, sequence_length=self.seqLen)
elif self.decoderType == DecoderType.BeamSearch:
self.decoder = tf.nn.ctc_beam_search_decoder(inputs=self.ctcIn3dTBC, sequence_length=self.seqLen, beam_width=50, merge_repeated=False)
elif self.decoderType == DecoderType.WordBeamSearch:
# import compiled word beam search operation (see https://github.com/githubharald/CTCWordBeamSearch)
word_beam_search_module = tf.load_op_library('TFWordBeamSearch.so')
# prepare information about language (dictionary, characters in dataset, characters forming words)
chars = str().join(self.charList)
wordChars = open('./model/model/wordCharList.txt').read().splitlines()[0]
corpus = open('./data/corpus.txt').read()
# decode using the "Words" mode of word beam search
self.decoder = word_beam_search_module.word_beam_search(tf.nn.softmax(self.ctcIn3dTBC, dim=2), 50, 'Words', 0.0, corpus.encode('utf8'), chars.encode('utf8'), wordChars.encode('utf8'))
def _sparse_intersect_indices(sp_tensor, required_sp_tensor):
"""Filters timestamps in sp_tensor to those present in required_sp_tensor."""
# We extend both sp_tensor and required_sp_tensor with each others indices
# so that they have the same indices.
# E.g. their dense representation of one batch entry could be:
# [dummy, dummy, 1 ]
dummy_value = 'n/a'
dummy_required_sp_tensor = _extend_with_dummy(
sp_tensor, required_sp_tensor, dummy_value)
dummy_sp_tensor = _extend_with_dummy(required_sp_tensor, sp_tensor,
dummy_value)
# We get rid to dummy values both for indices in the required_sp_tensor and
# the sp_tensor.
# First get rid of indices with dummy values in dummy_required_sp_tensor.
in_required = tf.sparse_retain(
dummy_sp_tensor,
tf.logical_not(tf.equal(dummy_required_sp_tensor.values, dummy_value)))
# Remove empty timesteps so that the timesteps align with the original
# required_sp_tensor.
# Then remove the indices with dummy values.
in_required = tf.sparse_retain(
_remove_empty_timesteps(in_required),
tf.logical_not(tf.equal(in_required.values, dummy_value)))
if sp_tensor.values.dtype != tf.string:
in_required = tf.SparseTensor(
indices=in_required.indices, dense_shape=in_required.dense_shape,
values=tf.strings.to_number(
in_required.values, out_type=sp_tensor.values.dtype))
return in_required
def __init__(self,
input_dim,
output_dim,
adj,
nodes_num,
dropout_rate,
is_sparse_input=False,
use_bias=True,
activation=None,
name="alinet"):
self.input_dim = input_dim
self.output_dim = output_dim
self.adjs = [
tf.SparseTensor(indices=adj[0][0],
values=adj[0][1],
dense_shape=adj[0][2])
]
self.dropout_rate = dropout_rate
self.is_sparse_input = is_sparse_input
self.nodes_num = nodes_num
self.use_bias = use_bias
self.activation = activation
self.name = name
self.data_type = tf.float32
self._get_variable()
def _from_proto_sparse_tensor(sparse_tensor_proto, process_leafs):
"""Deserializes a `tf.SparseTensor` from `sparse_tensor_proto`.
Args:
sparse_tensor_proto: A proto representing a `tf.SparseTensor`.
process_leafs: A function to be applied to the leaf valued of the nested
structure.
Returns:
An instance of `tf.SparseTensor`.
"""
if not sparse_tensor_proto.HasField("named_tuple"):
raise base_errors.ModuleInfoError(
"Error while deserializing a SparseTensor: expected proto tuple.")
if sparse_tensor_proto.named_tuple.name != _SPARSE_TENSOR_NAME:
raise base_errors.ModuleInfoError(
"Error while deserializing a SparseTensor: The name of the tuple "
"should have been {} but was {}.".format(
_SPARSE_TENSOR_NAME, sparse_tensor_proto.named_tuple.name))
named_tuple_map = sparse_tensor_proto.named_tuple.map
return tf.SparseTensor(
indices=process_leafs(named_tuple_map["indices"].value),
values=process_leafs(named_tuple_map["values"].value),
dense_shape=process_leafs(named_tuple_map["dense_shape"].value),
)
def __init__(self,
input_dim,
output_dim,
adj,
num_features_nonzero,
dropout_rate=0.0,
name='GCN',
is_sparse_inputs=False,
activation=tf.tanh,
use_bias=True):
self.activation = activation
self.input_dim = input_dim
self.output_dim = output_dim
self.adjs = [
tf.SparseTensor(indices=am[0], values=am[1], dense_shape=am[2])
for am in adj
]
self.num_features_nonzero = num_features_nonzero
self.dropout_rate = dropout_rate
self.is_sparse_inputs = is_sparse_inputs
self.use_bias = use_bias
self.kernels = list()
self.bias = list()
self.name = name
self.data_type = tf.float32
self._get_variable()
def call(self, inputs):
inputs = self.batch_normlization(inputs)
mapped_inputs = tf.matmul(inputs, self.kernel)
attention_inputs1 = tf.matmul(inputs, self.kernel1)
attention_inputs2 = tf.matmul(inputs, self.kernel2)
con_sa_1 = tf.reduce_sum(tf.multiply(attention_inputs1, inputs),
1,
keepdims=True)
con_sa_2 = tf.reduce_sum(tf.multiply(attention_inputs2, inputs),
1,
keepdims=True)
con_sa_1 = tf.keras.activations.tanh(con_sa_1)
con_sa_2 = tf.keras.activations.tanh(con_sa_2)
if self.dropout_rate > 0.0:
con_sa_1 = tf.nn.dropout(con_sa_1, self.dropout_rate)
con_sa_2 = tf.nn.dropout(con_sa_2, self.dropout_rate)
con_sa_1 = tf.cast(self.adjs[0], dtype=tf.float32) * con_sa_1
con_sa_2 = tf.cast(self.adjs[0], dtype=tf.float32) * tf.transpose(
con_sa_2, [1, 0])
weights = tf.sparse_add(con_sa_1, con_sa_2)
weights = tf.SparseTensor(indices=weights.indices,
values=tf.nn.leaky_relu(weights.values),
dense_shape=weights.dense_shape)
attention_adj = tf.sparse_softmax(weights)
attention_adj = tf.sparse_reshape(
attention_adj, shape=[self.nodes_num, self.nodes_num])
value = tf.sparse_tensor_dense_matmul(attention_adj, mapped_inputs)
return self.activation(value)