def construct_placeholders(num_classes):
placeholders = {
'labels':
tf.placeholder(DTYPE, shape=(None, num_classes), name='labels'),
'node_subgraph':
tf.placeholder(tf.int32, shape=(None), name='node_subgraph'),
'dropout':
tf.placeholder(DTYPE, shape=(None), name='dropout'),
'adj_subgraph':
tf.sparse_placeholder(DTYPE, name='adj_subgraph', shape=(None, None)),
'adj_subgraph_0':
tf.sparse_placeholder(DTYPE, name='adj_subgraph_0'),
'adj_subgraph_1':
tf.sparse_placeholder(DTYPE, name='adj_subgraph_1'),
'adj_subgraph_2':
tf.sparse_placeholder(DTYPE, name='adj_subgraph_2'),
'adj_subgraph_3':
tf.sparse_placeholder(DTYPE, name='adj_subgraph_3'),
'adj_subgraph_4':
tf.sparse_placeholder(DTYPE, name='adj_subgraph_4'),
'adj_subgraph_5':
tf.sparse_placeholder(DTYPE, name='adj_subgraph_5'),
'adj_subgraph_6':
tf.sparse_placeholder(DTYPE, name='adj_subgraph_6'),
'adj_subgraph_7':
tf.sparse_placeholder(DTYPE, name='adj_subgraph_7'),
'dim0_adj_sub':
tf.placeholder(tf.int64, shape=(None), name='dim0_adj_sub'),
'norm_loss':
tf.placeholder(DTYPE, shape=(None), name='norm_loss'),
'is_train':
tf.placeholder(tf.bool, shape=(None), name='is_train')
}
return placeholders
def init(self):
assert self.args.alignment_module == 'mapping'
assert self.args.neg_triple_num > 1
assert self.args.learning_rate >= 0.01
self.num_supports = self.args.support_number
self.utils = GCN_Utils(self.args, self.kgs)
self.attr = load_attr(self.kgs.entities_num, self.kgs)
self.adj, self.ae_input, self.train = self.utils.load_data(self.attr)
self.e = self.ae_input[2][0]
self.support = [self.utils.preprocess_adj(self.adj)]
self.ph_ae = {
"support": [
tf.sparse_placeholder(tf.float32)
for _ in range(self.args.support_number)
],
"features":
tf.sparse_placeholder(tf.float32),
"dropout":
tf.placeholder_with_default(0., shape=()),
"num_features_nonzero":
tf.placeholder_with_default(0, shape=())
}
self.ph_se = {
"support": [
tf.sparse_placeholder(tf.float32)
for _ in range(self.args.support_number)
],
"features":
tf.placeholder(tf.float32),
"dropout":
tf.placeholder_with_default(0., shape=()),
"num_features_nonzero":
tf.placeholder_with_default(0, shape=())
}
self.model_ae = GCN_Align_Unit(self.args,
self.ph_ae,
input_dim=self.ae_input[2][1],
output_dim=self.args.ae_dim,
ILL=self.train,
sparse_inputs=True,
featureless=False,
logging=False)
self.model_se = GCN_Align_Unit(self.args,
self.ph_se,
input_dim=self.e,
output_dim=self.args.se_dim,
ILL=self.train,
sparse_inputs=False,
featureless=True,
logging=False)
self.session = load_session()
tf.global_variables_initializer().run(session=self.session)
def get_placeholder(adj):
tf.compat.v1.disable_eager_execution()
placeholders = {
'features': tf.sparse_placeholder(tf.float32),
'adj': tf.sparse_placeholder(tf.float32),
'adj_orig': tf.sparse_placeholder(tf.float32),
'dropout': tf.placeholder_with_default(0., shape=()),
'real_distribution': tf.placeholder(dtype=tf.float32, shape=[adj.shape[0], FLAGS.hidden2],
name='real_distribution'),
'sample': tf.placeholder(tf.float32)
}
return placeholders
def __init__(self,
atom_types,
offset=0.0,
offset_trainable=False,
name="ANN"):
self.atom_types = atom_types
self.name = name
self.pairPot = {}
self.F = _tf.identity
self.rho = {}
self.inputs = {}
self.b_maps = {}
for t in atom_types:
self.inputs[t] = _tf.placeholder(shape=(None, 1),
dtype=precision,
name="ANN_input_%s" % t)
self.b_maps[t] = _tf.sparse_placeholder(dtype=precision,
name="b_map_{}".format(t))
self.offset = _tf.Variable(offset,
dtype=precision,
trainable=offset_trainable,
name="offset",
collections=[
_tf.GraphKeys.MODEL_VARIABLES,
_tf.GraphKeys.GLOBAL_VARIABLES
])
_tf.summary.scalar("offset", self.offset, family="modelParams")
def pre_process_sentences(messages):
input_placeholder = tf.sparse_placeholder(tf.int64, shape=[None, None])
encodings = module(
inputs=dict(
values=input_placeholder.values,
indices=input_placeholder.indices,
dense_shape=input_placeholder.dense_shape))
with tf.Session() as sess:
spm_path = sess.run(module(signature="spm_path"))
sp = spm.SentencePieceProcessor()
sp.Load(spm_path)
print("SentencePiece model loaded at {}.".format(spm_path))
def process_to_IDs_in_sparse_format(sp, sentences):
ids = [sp.EncodeAsIds(x) for x in sentences]
max_len = max(len(x) for x in ids)
dense_shape=(len(ids), max_len)
values=[item for sublist in ids for item in sublist]
indices=[[row,col] for row in range(len(ids)) for col in range(len(ids[row]))]
return (values, indices, dense_shape)
values, indices, dense_shape = process_to_IDs_in_sparse_format(sp, messages)
logging.set_verbosity(logging.ERROR)
with tf.Session() as session:
session.run([tf.global_variables_initializer(), tf.tables_initializer()])
message_embeddings = session.run(encodings,feed_dict={input_placeholder.values: values,
input_placeholder.indices: indices,
input_placeholder.dense_shape: dense_shape})
return message_embeddings
def __init__(self, lr, batch_size, dimension, util_train, util_test, campaign, reg_lambda, nn=False):
# hyperparameters
self.lr = lr
self.batch_size = batch_size
self.util_train = util_train
self.util_test = util_test
self.reg_lambda = reg_lambda
self.train_data_amt = util_train.get_data_amt()
self.test_data_amt = util_test.get_data_amt()
# output dir
model_name = "{}_{}_{}".format(self.lr, self.reg_lambda, self.batch_size)
if nn:
self.output_dir = "output/coxnn/{}/{}/".format(campaign, model_name)
else:
self.output_dir = "output/cox/{}/{}/".format(campaign, model_name)
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
# reset graph
ops.reset_default_graph()
# placeholders, sorted value
tfc.disable_eager_execution()
self.X = tfc.sparse_placeholder(tf.float64)
self.z = tfc.placeholder(tf.float64)
self.b = tfc.placeholder(tf.float64)
self.y = tfc.placeholder(tf.float64)
# computation graph, linear estimator or neural network
if nn:
hidden_size = 20
self.w1 = tf.Variable(initial_value=tfc.truncated_normal(shape=[dimension, hidden_size], dtype=tf.float64),
name='w1')
self.w2 = tf.Variable(initial_value=tfc.truncated_normal(shape=[hidden_size, 1], dtype=tf.float64),
name='w2')
self.hidden_values = tf.nn.relu(tfc.sparse_tensor_dense_matmul(self.X, self.w1))
self.index = tf.matmul(self.hidden_values, self.w2)
self.reg = tf.nn.l2_loss(self.w1[1:, ]) + tf.nn.l2_loss(self.w2[1:, ])
else:
self.w = tf.Variable(initial_value=tfc.truncated_normal(shape=[dimension, 1], dtype=tf.float64), name='w')
self.index = tfc.sparse_tensor_dense_matmul(self.X, self.w)
self.reg = tf.reduce_sum(tf.abs(self.w[1:, ]))
self.multiple_times = tf.exp(self.index)
self.loss = -tf.reduce_sum((self.index - tfc.log(tf.cumsum(self.multiple_times, reverse=True))) * self.y) + \
self.reg
self.optimizer = tfc.train.GradientDescentOptimizer(self.lr)
self.train_step = self.optimizer.minimize(self.loss)
# for test h0
self.base = self.z * self.y + self.b * (1 - self.y)
self.candidate = (1 / tf.cumsum(tf.exp(self.index), reverse=True)) * self.y
# session initialization
config = tfc.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tfc.Session(config=config)
tfc.global_variables_initializer().run(session=self.sess)
def __init__(self):
# placeholder
self.sph_user = tf.sparse_placeholder(tf.int32, name='sph_user')
self.sph_doc = tf.sparse_placeholder(tf.int32, name='sph_doc')
self.sph_con = tf.sparse_placeholder(tf.int32, name='sph_con')
self.ph_reward = tf.placeholder(tf.float32, name='ph_reward')
self.ph_nq = tf.placeholder(
tf.float32,
shape=[pd['batch_size'], pd['rnn_max_len']],
name='ph_nq')
# main networks
self.dst_embed, self.mq = self.build_net('main')
# target networks
_, self.tq = self.build_net('target')
diff = tf.reshape(self.ph_reward, [-1]) + tf.scalar_mul(
tf.constant(pd['gamma']), tf.reshape(
self.ph_nq, [-1])) - tf.reshape(self.mq, [-1])
self.loss = tf.reduce_mean(tf.square(diff))
self.a_grads = tf.clip_by_global_norm(
tf.gradients(self.mq, self.dst_embed), pd['grad_clip'])[0]
vs = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='main/value')
vs.extend(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='main/feat_embedding'))
self.grads = tf.clip_by_global_norm(tf.gradients(self.loss, vs),
pd['grad_clip'])[0]
with tf.variable_scope('train_value'):
optimizer = tf.train.AdamOptimizer(pd['lr'])
self.opt = optimizer.apply_gradients(zip(self.grads, vs))
self.m_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="main/value")
self.m_params.extend(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='main/feat_embedding'))
self.t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="target/value")
self.t_params.extend(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='target/feat_embedding'))
alpha = pd['double_networks_sync_step']
self.sync_op = [
tf.assign(t, (1.0 - alpha) * t + alpha * m)
for t, m in zip(self.t_params, self.m_params)
]
self.total_loss, self.batch_counter = 0.0, 0
def get_placeholder(adj):
placeholders = {
'features':
tf.sparse_placeholder(tf.float32),
'adj':
tf.sparse_placeholder(tf.float32),
'adj_orig':
tf.sparse_placeholder(tf.float32),
'dropout':
tf.placeholder_with_default(0., shape=()),
'real_distribution':
tf.placeholder(dtype=tf.float32,
shape=[adj.shape[0], FLAGS.hidden2],
name='real_distribution')
}
return placeholders
def __init__(self):
# placeholder
self.sph_user = tf.sparse_placeholder(tf.int32, name='sph_user')
self.sph_doc = tf.sparse_placeholder(tf.int32, name='sph_doc')
self.sph_con = tf.sparse_placeholder(tf.int32, name='sph_con')
# policy gradient
self.a_grads = tf.placeholder(tf.float32)
# policy network
self.doc_embed, self.mpa, self.mea = self.build_net('main')
# target network
_, self.tpa, self.tea = self.build_net('target')
# optional supervised signal, to avoid instability of actions in extreme cases
self.loss = tf.losses.mean_squared_error(self.doc_embed, self.mea)
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='main/policy')
params.extend(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='main/feat_embedding'))
self.grads = tf.clip_by_global_norm(tf.gradients(self.loss, params),
pd['grad_clip'])[0]
policy_grads = \
tf.clip_by_global_norm(tf.gradients(ys=self.mea, xs=params, grad_ys=self.a_grads), pd['grad_clip'])[0]
opt1 = tf.train.AdamOptimizer(-pd['lr'])
opt2 = tf.train.AdamOptimizer(pd['lr'])
with tf.variable_scope("train_policy"):
self.opt_a1 = opt1.apply_gradients(zip(policy_grads, params))
self.opt_a2 = opt2.apply_gradients(zip(self.grads, params))
self.m_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="main/policy")
self.m_params.extend(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='main/feat_embedding'))
self.t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="target/policy")
self.t_params.extend(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='target/feat_embedding'))
alpha = pd['double_networks_sync_step']
self.sync_op = [
tf.assign(t, (1.0 - alpha) * t + alpha * m)
for t, m in zip(self.t_params, self.m_params)
]
self.total_loss, self.batch_counter = 0.0, 0
def _get_weighted_json_serving_input_fn(self):
inputs = {
'age':
tf.placeholder(shape=[None], name='age', dtype=tf.float32),
'language':
tf.placeholder(shape=[None], name='language', dtype=tf.string),
'class_identity':
tf.placeholder(shape=[None], name='class_identity', dtype=tf.int32),
'language_weights': tf.sparse_placeholder(tf.float32)
}
return tf.estimator.export.ServingInputReceiver(inputs, inputs)
def __init__(self,
d,
nc,
hidden_size,
nlayers,
lr,
weight_decay,
dropout,
sparse_features=True):
self.nc = nc
self.sparse_features = sparse_features
if sparse_features:
self.batch_feats = tf.sparse_placeholder(tf.float32, None,
'features')
else:
self.batch_feats = tf.placeholder(tf.float32, [None, d],
'features')
self.batch_pprw = tf.placeholder(tf.float32, [None], 'ppr_weights')
self.batch_idx = tf.placeholder(tf.int32, [None], 'idx')
self.batch_labels = tf.placeholder(tf.int32, [None], 'labels')
Ws = [tf.get_variable('W1', [d, hidden_size])]
for i in range(nlayers - 2):
Ws.append(tf.get_variable(f'W{i + 2}', [hidden_size, hidden_size]))
Ws.append(tf.get_variable(f'W{nlayers}', [hidden_size, nc]))
feats_drop = mixed_dropout(self.batch_feats, dropout)
if sparse_features:
h = tf.sparse.sparse_dense_matmul(feats_drop, Ws[0])
else:
h = tf.matmul(feats_drop, Ws[0])
for W in Ws[1:]:
h = tf.nn.relu(h)
h_drop = tf.nn.dropout(h, rate=dropout)
h = tf.matmul(h_drop, W)
self.logits = h
weighted_logits = tf.tensor_scatter_nd_add(
tf.zeros((tf.shape(self.batch_labels)[0], nc)),
self.batch_idx[:, None], self.logits * self.batch_pprw[:, None])
loss_per_node = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.batch_labels, logits=weighted_logits)
l2_reg = tf.add_n([tf.nn.l2_loss(weight) for weight in Ws])
self.loss = tf.reduce_mean(loss_per_node) + weight_decay * l2_reg
self.preds = tf.argmax(weighted_logits, 1)
self.update_op = tf.train.AdamOptimizer(lr).minimize(self.loss)
self.cached = {}
def __init__(self, atom_types, error_scaling=1.):
self.target = _tf.placeholder(shape=(None, ),
dtype=precision,
name="target")
self.atom_types = atom_types
self.error_scaling = error_scaling
self.ANNs = {}
self.atom_maps = {}
for t in self.atom_types:
self.atom_maps[t] = _tf.sparse_placeholder(shape=(None, None),
dtype=precision,
name="%s_map" % t)
def __init__(self):
# init some parameters
self.g_type = 'powerlaw' #'erdos_renyi', 'powerlaw', 'small-world', 'barabasi_albert'
self.embedding_size = EMBEDDING_SIZE
self.learning_rate = LEARNING_RATE
self.reg_hidden = REG_HIDDEN
self.TrainSet = graph.py_GSet()
self.TestSet = graph.py_GSet()
self.utils = utils.py_Utils()
self.TrainBetwList = []
self.TestBetwList = []
self.metrics = metrics.py_Metrics()
self.inputs = dict()
self.activation = tf.nn.leaky_relu #leaky_relu relu selu elu
self.ngraph_train = 0
self.ngraph_test = 0
# [node_cnt, node_feat_dim]
self.node_feat = tf.placeholder(tf.float32, name="node_feat")
# [node_cnt, aux_feat_dim]
self.aux_feat = tf.placeholder(tf.float32, name="aux_feat")
# [node_cnt, node_cnt]
self.n2nsum_param = tf.sparse_placeholder(tf.float64,
name="n2nsum_param")
# [node_cnt,1]
self.label = tf.placeholder(tf.float32, shape=[None, 1], name="label")
# sample node pairs to compute the ranking loss
self.pair_ids_src = tf.placeholder(tf.int32,
shape=[1, None],
name='pair_ids_src')
self.pair_ids_tgt = tf.placeholder(tf.int32,
shape=[1, None],
name='pair_ids_tgt')
self.loss, self.trainStep, self.betw_pred, self.node_embedding, self.param_list = self.BuildNet(
)
self.saver = tf.train.Saver(max_to_keep=None)
config = tf.ConfigProto(
device_count={"CPU": 8}, # limit to num_cpu_core CPU usage
inter_op_parallelism_threads=100,
intra_op_parallelism_threads=100,
log_device_placement=False)
config.gpu_options.allow_growth = True
self.session = tf.Session(config=config)
self.session.run(tf.global_variables_initializer())
def load_persisting_model(self):
'''
Load the TF module and model alongwith graph to the machine's memory
to speed up future machine inference calls.
'''
with tf.Session(graph=self.graph) as session:
self.module = hub.Module("./models/use_lite")
self.input_placeholder = tf.sparse_placeholder(tf.int64,
shape=[None, None])
self.encodings = self.module(
inputs=dict(values=self.input_placeholder.values,
indices=self.input_placeholder.indices,
dense_shape=self.input_placeholder.dense_shape))
self.load_sp_model()
def init_embeddings_model():
"""
Initialize model for embedding pieces of content into a vector
:return: None
"""
g = tfv1.Graph()
with g.as_default():
model_path = "https://tfhub.dev/google/universal-sentence-encoder-lite/2"
module = hub.Module(model_path)
sts_input1_ = tfv1.sparse_placeholder(tfv1.int64, shape=[None, None])
embeddings_ = module(inputs=dict(values=sts_input1_.values,
indices=sts_input1_.indices,
dense_shape=sts_input1_.dense_shape))
return g, sts_input1_, embeddings_, module
def construct_placeholders(edge_types): #构建形成稀疏张量
placeholders = {
'batch':
tf.placeholder(tf.int32, name='batch'),
'batch_edge_type_idx':
tf.placeholder(tf.int32, shape=(), name='batch_edge_type_idx'),
'batch_row_edge_type':
tf.placeholder(tf.int32, shape=(), name='batch_row_edge_type'),
'batch_col_edge_type':
tf.placeholder(tf.int32, shape=(), name='batch_col_edge_type'),
'degrees':
tf.placeholder(tf.int32, shape=(), name='degress'),
'dropout':
tf.placeholder_with_default(0., shape=(), name='dropout'),
}
placeholders.update({
'adj_mats_%d,%d,%d' % (i, j, k): tf.sparse_placeholder(tf.float32)
for i, j in edge_types for k in range(edge_types[i, j])
})
placeholders.update({
'feat_%d' % i: tf.sparse_placeholder(tf.float32)
for i, _ in edge_types
})
return placeholders
def __init__(self):
_logger.info('Downloading the model from TensorFlow Hub...')
with tf.Session() as sess:
module = tfhub.Module("https://tfhub.dev/google/universal-sentence-encoder-lite/2")
spm_path = sess.run(module(signature="spm_path")) # spm_path now contains a path to the SentencePiece model stored inside the TF-Hub module
self.sp = spm.SentencePieceProcessor()
self.sp.Load(spm_path)
self.input_placeholder = tf.sparse_placeholder(tf.int64, shape=[None, None])
self.embeddings_model = module(
inputs=dict(
values=self.input_placeholder.values,
indices=self.input_placeholder.indices,
dense_shape=self.input_placeholder.dense_shape
)
)
def use_lite_embed():
module = hub.Module(
"https://tfhub.dev/google/universal-sentence-encoder-lite/2")
global graph
with graph.as_default():
input_placeholder = tf.sparse_placeholder(tf.int64, shape=[None, None])
encodings = module(inputs=dict(
values=input_placeholder.values,
indices=input_placeholder.indices,
dense_shape=input_placeholder.dense_shape,
))
with tf.Session() as sess:
spm_path = sess.run(module(signature="spm_path"))
sp = spm.SentencePieceProcessor()
sp.Load(spm_path)
def process_to_IDs_in_sparse_format(sp, sentences):
ids = [sp.EncodeAsIds(x) for x in sentences]
max_len = max(len(x) for x in ids)
dense_shape = (len(ids), max_len)
values = [item for sublist in ids for item in sublist]
indices = [[row, col] for row in range(len(ids))
for col in range(len(ids[row]))]
return (values, indices, dense_shape)
def embed(msgs):
values, indices, dense_shape = process_to_IDs_in_sparse_format(
sp, msgs)
global graph
with graph.as_default(), tf.Session() as session:
session.run(
[tf.global_variables_initializer(),
tf.tables_initializer()])
return session.run(
encodings,
feed_dict={
input_placeholder.values: values,
input_placeholder.indices: indices,
input_placeholder.dense_shape: dense_shape,
},
)
return embed
def __init__(
self,
model_link="https://tfhub.dev/google/universal-sentence-encoder-lite/2"
):
tf.disable_v2_behavior()
self.graph = tf.Graph()
with self.graph.as_default():
module = hub.Module(model_link)
self.input_placeholder = tf.sparse_placeholder(tf.int64,
shape=[None, None])
self.encodings = module(
inputs=dict(values=self.input_placeholder.values,
indices=self.input_placeholder.indices,
dense_shape=self.input_placeholder.dense_shape))
with tf.Session() as sess:
spm_path = sess.run(module(signature="spm_path"))
self.sp = spm.SentencePieceProcessor()
self.sp.Load(spm_path)
print("SentencePiece model loaded at {}.".format(spm_path))
def rank_semantic(self, query, hits):
values, indices, dense_shape = self.process_to_IDs_in_sparse_format([query] + hits)
with tf.Session(graph=self.graph) as sess:
input_placeholder = tf.sparse_placeholder(tf.int64, shape=[None, None])
encodings = self.embed_module(
inputs=dict(
values=input_placeholder.values,
indices=input_placeholder.indices,
dense_shape=input_placeholder.dense_shape))
sess.run([tf.global_variables_initializer(), tf.tables_initializer()])
message_embeddings = sess.run(
encodings,
feed_dict={input_placeholder.values: values,
input_placeholder.indices: indices,
input_placeholder.dense_shape: dense_shape})
message_embeddings = np.array(message_embeddings).tolist()
query_embedding, hits_embeddings = message_embeddings[0], message_embeddings[1:]
scores = np.inner(query_embedding, hits_embeddings)
return scores
def GNN(A, X):
# 使用graph_hi构造图邻接矩阵A
np.random.seed(0) # for reproducibility
ITER = 10000
# Parameters
P = OrderedDict([
('es_patience', ITER),
('dataset', ['cora'
]), # 'cora', 'citeseer', 'pubmed', 'cloud', or 'synth'
('H_', [None]),
('n_channels', [16]),
('learning_rate', [5e-4])
])
############################################################################
# LOAD DATASET
############################################################################
A = np.maximum(A, A.T)
A = sp.csr_matrix(A, dtype=np.float32)
X = X.todense()
n_feat = X.shape[-1]
############################################################################
# GNN MODEL
############################################################################
X_in = Input(
tensor=tf.placeholder(tf.float32, shape=(None, n_feat), name='X_in'))
A_in = Input(tensor=tf.sparse_placeholder(tf.float32, shape=(None, None)),
name='A_in',
sparse=True)
# S_in = Input(tensor=tf.placeholder(tf.int32, shape=(None,), name='segment_ids_in'))
A_norm = normalized_adjacency(A)
X_1 = GCSConv(P['n_channels'],
kernel_initializer='he_normal',
activation='elu')([X_in, A_in])
pool1, adj1, C = MinCutPool(k=n_classes,
h=P['H_'],
activation='elu',
return_mask=True)([X_1, A_in])
model = Model([X_in, A_in], [pool1, adj1, C])
model.compile('adam', None)
############################################################################
# TRAINING
############################################################################
# Setup
sess = K.get_session()
loss = model.total_loss
opt = tf.train.AdamOptimizer(learning_rate=P['learning_rate'])
train_step = opt.minimize(loss)
# Initialize all variables
init_op = tf.global_variables_initializer()
sess.run(init_op)
# Fit layer
tr_feed_dict = {X_in: X, A_in: sp_matrix_to_sp_tensor_value(A_norm)}
best_loss = np.inf
patience = P['es_patience']
tol = 1e-5
for _ in tqdm(range(ITER)):
outs = sess.run([train_step, model.losses[0], model.losses[1], C],
feed_dict=tr_feed_dict)
# c = np.argmax(outs[3], axis=-1)
if outs[1] + outs[2] + tol < best_loss:
best_loss = outs[1] + outs[2]
patience = P['es_patience']
else:
patience -= 1
if patience == 0:
break
############################################################################
# RESULTS
############################################################################
C_ = sess.run([C], feed_dict=tr_feed_dict)[0]
c = np.argmax(C_, axis=-1)
K.clear_session()
return c
def __init__(self, lr_1, lr_2, l2_loss_weight, batch_size, dimension,
theta0, util_train, util_test, campaign):
self.lr_1 = lr_1
self.lr_2 = lr_2
self.util_train = util_train
self.util_test = util_test
self.train_data_amt = util_train.get_data_amt()
self.test_data_amt = util_test.get_data_amt()
self.batch_size = batch_size
self.batch_num = int(self.train_data_amt / self.batch_size)
self.l2_loss_weight = l2_loss_weight
self.campaign = campaign
# output directory
model_name = "{0}_{1}_{2}_{3}".format(self.lr_1, self.lr_2,
self.l2_loss_weight,
self.batch_size)
self.output_dir = 'output/gamma/{}/{}/'.format(campaign, model_name)
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
# reset graph
tf.reset_default_graph()
# placeholders
self.X = tf.sparse_placeholder(tf.float64)
self.z = tf.placeholder(tf.float64)
self.b = tf.placeholder(tf.float64)
self.y = tf.placeholder(tf.float64)
# trainable variables
self.theta = tf.Variable([theta0], name='theta', dtype=tf.float64)
# tf.reshape(self.theta, [1, 1])
all_train_data = self.util_train.get_all_data_origin()
self.init_ks_value = all_train_data[3] * all_train_data[2] / theta0 + (
1 - all_train_data[3]) * all_train_data[1] / theta0
self.ks = tf.Variable(self.init_ks_value, name='ks', dtype=tf.float64)
self.w = tf.Variable(initial_value=tf.truncated_normal(
shape=[dimension, 1], dtype=tf.float64),
name='w')
# computation graph phase1
self.ps = tf.pow(self.z, (self.ks - 1.)) * tf.exp(-self.z / self.theta) \
/ tf.exp(tf.lgamma(self.ks)) / tf.pow(self.theta, self.ks)
self.cs = tf.igamma(self.ks, self.b / self.theta) / tf.exp(
tf.lgamma(self.ks))
self.loss_win = tf.log(tf.clip_by_value(self.ps, 1e-8, 1.0))
self.loss_lose = tf.log(tf.clip_by_value(1 - self.cs, 1e-8, 1.0))
self.loss_phase1 = -tf.reduce_mean(self.y * self.loss_win +
(1 - self.y) * self.loss_lose)
self.optimizer1 = tf.train.GradientDescentOptimizer(self.lr_1)
self.train_step1 = self.optimizer1.minimize(self.loss_phase1)
# phase 2
self.label_phase2 = tf.placeholder(tf.float64)
self.log_label_phase2 = tf.log(
tf.clip_by_value(self.label_phase2, 1e-8, 1.0))
self.loss_phase2 = tf.reduce_mean(tf.square(tf.sparse_tensor_dense_matmul(self.X, self.w) - self.log_label_phase2)) \
+ self.l2_loss_weight * tf.nn.l2_loss(self.w)
self.optimizer2 = tf.train.MomentumOptimizer(self.lr_2, 0.9)
self.train_step2 = self.optimizer2.minimize(self.loss_phase2)
# session initialization
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
DATA_FILE = "data/posts/posts.json"
ENDPOINT = "http://localhost:9200/"
BATCH_SIZE = 1000
SEARCH_SIZE = 5
GPU_LIMIT = 0.5
print("Downloading pre-trained embeddings from tensorflow hub...")
embed = hub.Module("https://tfhub.dev/google/universal-sentence-encoder-lite/2")
with tf.Session() as sess:
spm_path = sess.run(module(signature="spm_path"))
sp = spm.SentencePieceProcessor()
sp.Load(spm_path)
print("SentencePiece model loaded at {}.".format(spm_path))
input_placeholder = tf.sparse_placeholder(tf.int64, shape=[None, None])
inputs=dict(
values=input_placeholder.values,
indices=input_placeholder.indices,
dense_shape=input_placeholder.dense_shape)
embeddings = embed(inputs)
print("Done.")
print("Creating tensorflow session...")
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = GPU_LIMIT
session = tf.Session(config=config)
session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
print("Done.")
import numpy as np
import pickle
tf.disable_v2_behavior()
bert_config = bm.BertConfig.from_json_file(BERT_CONFIG_PATH) # 配置文件地址。
# Define placeholders
placeholders = {
'input_ids': tf.placeholder(tf.int64,
shape=(None, bert_config.max_length)),
'input_mask': tf.placeholder(tf.int64,
shape=(None, bert_config.max_length)),
'targets': tf.placeholder(tf.int64, shape=(None, bert_config.max_length)),
'targets_pos': tf.placeholder(tf.int64,
shape=(None, bert_config.max_length)),
'adj_graph': [tf.sparse_placeholder(tf.float32) for _ in range(1)],
}
def construct_feed_dict(input_ids, input_mask, targets, targets_pos,
placeholders):
"""Construct feed dictionary."""
feed_dict = dict()
feed_dict.update({placeholders['input_ids']: input_ids})
feed_dict.update({placeholders['input_mask']: input_mask})
feed_dict.update({placeholders['targets']: targets})
feed_dict.update({placeholders['targets_pos']: targets_pos})
# feed_dict.update({placeholders['adj_graph'][i]: support[i] for i in range(len(support))})
return feed_dict
def __init__(self, lr, batch_size, dimension, util_train, util_test, campaign, reg_lambda, sigma):
# hyperparameters
self.lr = lr
self.batch_size = batch_size
self.util_train = util_train
self.util_test = util_test
self.reg_lambda = reg_lambda
self.sigma = sigma
self.emb_size = 20
self.train_data_amt = util_train.get_data_amt()
self.test_data_amt = util_test.get_data_amt()
# output dir
model_name = "{}_{}_{}_{}".format(self.lr, self.reg_lambda, self.batch_size, self.sigma)
self.output_dir = "output/deephit/{}/{}/".format(campaign, model_name)
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
# reset graph
tf.reset_default_graph()
# field params
self.field_sizes = self.util_train.feat_sizes
self.field_num = len(self.field_sizes)
# placeholders
self.X = [tf.sparse_placeholder(tf.float64) for i in range(0, self.field_num)]
self.z = tf.placeholder(tf.float64)
self.b = tf.placeholder(tf.float64)
self.y = tf.placeholder(tf.float64)
# embedding layer
self.var_map = {}
# for truncated
self.var_map['embed_0'] = tf.Variable(
tf.truncated_normal([self.field_sizes[0], 1], dtype=tf.float64))
for i in range(1, self.field_num):
self.var_map['embed_%d' % i] = tf.Variable(
tf.truncated_normal([self.field_sizes[i], self.emb_size], dtype=tf.float64))
# after embedding
w0 = [self.var_map['embed_%d' % i] for i in range(self.field_num)]
self.dense_input = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(self.field_num)], 1)
# shared network
self.hidden1 = tf.Variable(initial_value=tf.truncated_normal(shape=[(self.field_num - 1) * self.emb_size + 1, HIDDEN_SIZE1], dtype=tf.float64), name='h1')
self.out1 = tf.Variable(initial_value=tf.truncated_normal(shape=[HIDDEN_SIZE1, OUT_SIZE1], dtype=tf.float64), name='o1')
self.hidden2 = tf.Variable(initial_value=tf.truncated_normal(shape=[OUT_SIZE1, HIDDEN_SIZE2], dtype=tf.float64), name='h2')
self.out2 = tf.Variable(initial_value=tf.truncated_normal(shape=[HIDDEN_SIZE2, OUT_SIZE2], dtype=tf.float64), name='o2')
# cause-specific network
self.hidden1_val = tf.nn.relu(tf.matmul(self.dense_input, self.hidden1))
self.out1_val = tf.sigmoid(tf.matmul(self.hidden1_val, self.out1))
self.hidden2_val = tf.nn.relu(tf.matmul(self.out1_val, self.hidden2))
self.out2_val = tf.sigmoid(tf.matmul(self.hidden2_val, self.out2))
# p_z and w_b
self.p = tf.nn.softmax(self.out2_val)
self.w = tf.cumsum(self.p, exclusive=True, axis = 1)
idx_z = tf.stack([tf.reshape(tf.range(tf.shape(self.z)[0]), (-1,1)), tf.cast(self.z - 1, tf.int32)], axis=-1)
idx_b = tf.stack([tf.reshape(tf.range(tf.shape(self.b)[0]), (-1,1)), tf.cast(self.b - 1, tf.int32)], axis=-1)
self.pz = tf.gather_nd(self.p, idx_z)
self.wb = tf.gather_nd(self.w, idx_b)
self.wz = tf.gather_nd(self.w, idx_z)
# loss and train step
self.loss1 = -tf.reduce_sum(tf.log(tf.clip_by_value(self.pz, 1e-8, 1.0)) * self.y)
self.loss2 = -tf.reduce_sum(tf.log(tf.clip_by_value(1 - self.wb, 1e-8, 1.0)) * (1 - self.y))
self.reg_loss = tf.nn.l2_loss(self.hidden1[1:,]) + tf.nn.l2_loss(self.hidden2[1:,]) + \
tf.nn.l2_loss(self.out1[1:,]) + tf.nn.l2_loss(self.out2[1:,])
# get ranking loss
self.w_of_pair = tf.transpose(tf.nn.embedding_lookup(tf.transpose(self.w), tf.cast(self.z[:,0] - 1, tf.int32)))
self.w_of_self = tf.reshape(tf.tile(tf.reshape(self.wz, (self.batch_size, )), [self.batch_size]), (self.batch_size, self.batch_size))
self.win_label = tf.reshape(tf.tile(tf.reshape(self.y, (self.batch_size, )), [self.batch_size]), (self.batch_size, self.batch_size))
self.delta = self.w_of_self - self.w_of_pair
self.candidate = tf.exp(-self.delta / self.sigma)
self.rank_loss = tf.reduce_sum(tf.matrix_band_part(self.candidate, -1, 0) * self.win_label)
self.loss = self.loss1 + self.loss2 + self.reg_lambda * self.reg_loss + self.rank_loss
self.optimizer = tf.train.GradientDescentOptimizer(self.lr)
self.train_step = self.optimizer.minimize(self.loss)
# session initialization
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def get_full_graph(sess, seed, edges, vertices, adj, labels, source, sink, reward_states, other_sources=[],other_sinks=[]):
features = np.eye(len(vertices), dtype=np.int32) # One-hot encoding for the features (i.e. feature-less)
features = sparse_to_tuple(sp.lil_matrix(features))
np.random.seed(seed)
tf.set_random_seed(seed)
y_train, y_val, train_mask, val_mask = get_splits(labels, source, sink, reward_states)
support = [preprocess_adj(adj)]
num_supports = 1
model_func = GCN
adj =adj.toarray()
deg = np.diag(np.sum(adj,axis=1))
laplacian = deg - adj
# Define placeholders
placeholders = {
'adj': tf.placeholder(tf.float32, shape=(None, None)) , #unnormalized adjancy matrix
'support': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'features': tf.sparse_placeholder(tf.float32, shape=tf.constant(features[2], dtype=tf.int64)),
'labels': tf.placeholder(tf.float32, shape=(None, y_train.shape[1])),
'labels_mask': tf.placeholder(tf.int32),
'dropout': tf.placeholder_with_default(0., shape=()),
'num_features_nonzero': tf.placeholder(tf.int32), # helper variable for sparse dropout
'learning_rate': tf.placeholder(tf.float32)
}
model = model_func(placeholders,edges,laplacian, input_dim=features[2][1], logging=True, FLAGS=FLAGS)
sess.run(tf.global_variables_initializer())
feed_dict = construct_feed_dict(adj, features, support, y_train, train_mask, placeholders)
feed_dict.update({placeholders['learning_rate']: FLAGS.learning_rate})
cost_val = []
start = time.time()
for epoch in range(FLAGS.epochs):
t = time.time()
feed_dict = construct_feed_dict(adj, features, support, y_train, train_mask, placeholders)
feed_dict.update({placeholders['learning_rate']: FLAGS.learning_rate})
outs = sess.run([model.opt_op, model.loss, model.accuracy,model.learning_rate], feed_dict=feed_dict)
print("Total time for gcn {}".format(time.time()-start))
print("Optimization Finished!")
outputs = sess.run([tf.nn.softmax(model.outputs)], feed_dict=feed_dict)[0]
return outputs[:,1]
def ctc_crnn(params):
# TODO Assert parameters
input = tf.placeholder(
shape=(None, params['img_height'], params['img_width'],
params['img_channels']), # [batch, height, width, channels]
dtype=tf.float32,
name='model_input')
input_shape = tf.shape(input)
width_reduction = 1
height_reduction = 1
# Convolutional blocks
x = input
for i in range(params['conv_blocks']):
x = tf.layers.conv2d(inputs=x,
filters=params['conv_filter_n'][i],
kernel_size=params['conv_filter_size'][i],
padding="same",
activation=None)
x = tf.layers.batch_normalization(x)
x = leaky_relu(x)
x = tf.layers.max_pooling2d(inputs=x,
pool_size=params['conv_pooling_size'][i],
strides=params['conv_pooling_size'][i])
width_reduction = width_reduction * params['conv_pooling_size'][i][1]
height_reduction = height_reduction * params['conv_pooling_size'][i][0]
# Prepare output of conv block for recurrent blocks
features = tf.transpose(
x, perm=[2, 0, 3,
1]) # -> [width, batch, height, channels] (time_major=True)
feature_dim = params['conv_filter_n'][-1] * (params['img_height'] /
height_reduction)
feature_width = input_shape[2] / width_reduction
features = tf.reshape(features,
tf.stack([
tf.cast(feature_width, 'int32'), input_shape[0],
tf.cast(feature_dim, 'int32')
])) # -> [width, batch, features]
tf.constant(params['img_height'], name='input_height')
tf.constant(width_reduction, name='width_reduction')
# Recurrent block
rnn_keep_prob = tf.placeholder(dtype=tf.float32, name="keep_prob")
rnn_hidden_units = params['rnn_units']
rnn_hidden_layers = params['rnn_layers']
rnn_outputs, _ = tf.nn.bidirectional_dynamic_rnn(
tf.contrib.rnn.MultiRNNCell([
tf.nn.rnn_cell.DropoutWrapper(
tf.contrib.rnn.BasicLSTMCell(rnn_hidden_units),
input_keep_prob=rnn_keep_prob)
for _ in range(rnn_hidden_layers)
]),
tf.contrib.rnn.MultiRNNCell([
tf.nn.rnn_cell.DropoutWrapper(
tf.contrib.rnn.BasicLSTMCell(rnn_hidden_units),
input_keep_prob=rnn_keep_prob)
for _ in range(rnn_hidden_layers)
]),
features,
dtype=tf.float32,
time_major=True,
)
rnn_outputs = tf.concat(rnn_outputs, 2)
logits = tf.contrib.layers.fully_connected(
rnn_outputs,
params['vocabulary_size'] + 1, # BLANK
activation_fn=None,
)
tf.add_to_collection("logits", logits) # for restoring purposes
# CTC Loss computation
seq_len = tf.placeholder(tf.int32, [None], name='seq_lengths')
targets = tf.sparse_placeholder(dtype=tf.int32, name='target')
ctc_loss = tf.nn.ctc_loss(labels=targets,
inputs=logits,
sequence_length=seq_len,
time_major=True)
loss = tf.reduce_mean(ctc_loss)
# CTC decoding
decoded, log_prob = tf.nn.ctc_greedy_decoder(logits, seq_len)
# decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits,seq_len,beam_width=50,top_paths=1,merge_repeated=True)
return input, seq_len, targets, decoded, loss, rnn_keep_prob
read_data = read_data_tsp
elif args.problem == 'vrp':
read_data = read_data_vrp
elif args.problem in ['mis', 'ds', 'vc', 'ca']:
read_data = read_data_general
elif args.problem == 'sc':
read_data = read_data_sc
else:
raise Exception('unknown problem!')
####### model #######
feat_dim = 57
FLAGS = set_train_params()
placeholders = {
'support':
[tf.sparse_placeholder(tf.float32)
for _ in range(FLAGS.num_supports)] if FLAGS.matrix_type == 'sparse'
else [tf.placeholder(tf.float32) for _ in range(FLAGS.num_supports)],
'features':
tf.sparse_placeholder(tf.float32, shape=(None, feat_dim))
if FLAGS.matrix_type == 'sparse' else tf.placeholder(
tf.float32, shape=(None, feat_dim)), # featureless: #points
'labels':
tf.placeholder(tf.float32, shape=(None, 2)), # 0: not linked, 1:linked
'labels_mask':
tf.placeholder(tf.int32),
'dropout':
tf.placeholder_with_default(0., shape=()),
'num_features_nonzero':
tf.placeholder(tf.int32) # helper variable for sparse dropout
}
def main(unused_argv):
"""Main function for running experiments."""
# Load data
(train_adj, full_adj, train_feats, test_feats, y_train, y_val, y_test,
train_mask, val_mask, test_mask, _, val_data, test_data, num_data,
visible_data) = load_data(FLAGS.data_prefix, FLAGS.dataset, FLAGS.precalc)
# Partition graph and do preprocessing
if FLAGS.bsize > 1:
_, parts = partition_utils.partition_graph(train_adj, visible_data,
FLAGS.num_clusters)
parts = [np.array(pt) for pt in parts]
else:
(parts, features_batches, support_batches, y_train_batches,
train_mask_batches) = utils.preprocess(train_adj, train_feats,
y_train, train_mask,
visible_data,
FLAGS.num_clusters,
FLAGS.diag_lambda)
(_, val_features_batches, val_support_batches, y_val_batches,
val_mask_batches) = utils.preprocess(full_adj, test_feats, y_val,
val_mask, np.arange(num_data),
FLAGS.num_clusters_val,
FLAGS.diag_lambda)
(_, test_features_batches, test_support_batches, y_test_batches,
test_mask_batches) = utils.preprocess(full_adj, test_feats, y_test,
test_mask, np.arange(num_data),
FLAGS.num_clusters_test,
FLAGS.diag_lambda)
idx_parts = list(range(len(parts)))
# Some preprocessing
model_func = models.GCN
# Define placeholders
placeholders = {
'support': tf.sparse_placeholder(tf.float32),
'features': tf.placeholder(tf.float32),
'labels': tf.placeholder(tf.float32, shape=(None, y_train.shape[1])),
'labels_mask': tf.placeholder(tf.int32),
'dropout': tf.placeholder_with_default(0., shape=()),
'num_features_nonzero':
tf.placeholder(tf.int32) # helper variable for sparse dropout
}
# Create model
model = model_func(placeholders,
input_dim=test_feats.shape[1],
logging=True,
multilabel=FLAGS.multilabel,
norm=FLAGS.layernorm,
precalc=FLAGS.precalc,
num_layers=FLAGS.num_layers)
# Initialize session
sess = tf.Session()
tf.set_random_seed(seed)
# Init variables
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
cost_val = []
total_training_time = 0.0
# Train model
for epoch in range(FLAGS.epochs):
t = time.time()
np.random.shuffle(idx_parts)
if FLAGS.bsize > 1:
(features_batches, support_batches, y_train_batches,
train_mask_batches) = utils.preprocess_multicluster(
train_adj, parts, train_feats, y_train, train_mask,
FLAGS.num_clusters, FLAGS.bsize, FLAGS.diag_lambda)
for pid in range(len(features_batches)):
# Use preprocessed batch data
features_b = features_batches[pid]
support_b = support_batches[pid]
y_train_b = y_train_batches[pid]
train_mask_b = train_mask_batches[pid]
# Construct feed dictionary
feed_dict = utils.construct_feed_dict(features_b, support_b,
y_train_b, train_mask_b,
placeholders)
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
# Training step
outs = sess.run([model.opt_op, model.loss, model.accuracy],
feed_dict=feed_dict)
else:
np.random.shuffle(idx_parts)
for pid in idx_parts:
# Use preprocessed batch data
features_b = features_batches[pid]
support_b = support_batches[pid]
y_train_b = y_train_batches[pid]
train_mask_b = train_mask_batches[pid]
# Construct feed dictionary
feed_dict = utils.construct_feed_dict(features_b, support_b,
y_train_b, train_mask_b,
placeholders)
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
# Training step
outs = sess.run([model.opt_op, model.loss, model.accuracy],
feed_dict=feed_dict)
total_training_time += time.time() - t
print_str = 'Epoch: %04d ' % (
epoch + 1) + 'training time: {:.5f} '.format(
total_training_time) + 'train_acc= {:.5f} '.format(outs[2])
# Validation
if FLAGS.validation:
cost, acc, micro, macro = evaluate(sess, model,
val_features_batches,
val_support_batches,
y_val_batches, val_mask_batches,
val_data, placeholders)
cost_val.append(cost)
print_str += 'val_acc= {:.5f} '.format(
acc) + 'mi F1= {:.5f} ma F1= {:.5f} '.format(micro, macro)
tf.logging.info(print_str)
if epoch > FLAGS.early_stopping and cost_val[-1] > np.mean(
cost_val[-(FLAGS.early_stopping + 1):-1]):
tf.logging.info('Early stopping...')
break
tf.logging.info('Optimization Finished!')
# Save model
saver.save(sess, FLAGS.save_name)
# Load model (using CPU for inference)
with tf.device('/cpu:0'):
sess_cpu = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
sess_cpu.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess_cpu, FLAGS.save_name)
# Testing
test_cost, test_acc, micro, macro = evaluate(
sess_cpu, model, test_features_batches, test_support_batches,
y_test_batches, test_mask_batches, test_data, placeholders)
print_str = 'Test set results: ' + 'cost= {:.5f} '.format(
test_cost) + 'accuracy= {:.5f} '.format(
test_acc) + 'mi F1= {:.5f} ma F1= {:.5f}'.format(micro, macro)
tf.logging.info(print_str)
model_func = GCN
elif FLAGS.model == 'gcn_cheby':
support = chebyshev_polynomials(adj, FLAGS.max_degree)
num_supports = 1 + FLAGS.max_degree
model_func = GCN
elif FLAGS.model == 'dense':
support = [preprocess_adj(adj)] # Not used
num_supports = 1
model_func = MLP
else:
raise ValueError('Invalid argument for model: ' + str(FLAGS.model))
# Define placeholders
placeholders = {
'support':
[tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'features':
tf.sparse_placeholder(tf.float32,
shape=tf.constant(features[2], dtype=tf.int64)),
'labels':
tf.placeholder(tf.float32, shape=(None, y_train.shape[1])),
'labels_mask':
tf.placeholder(tf.int32),
'dropout':
tf.placeholder_with_default(0., shape=()),
'num_features_nonzero':
tf.placeholder(tf.int32) # helper variable for sparse dropout
}
# Create model
model = model_func(placeholders, input_dim=features[2][1], logging=True)
