seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
result_file = f"results/{args.problem}_test_{time.strftime('%Y%m%d-%H%M%S')}"
result_file = result_file + '.csv'
os.makedirs('results', exist_ok=True)
### TENSORFLOW SETUP ###
if args.gpu == -1:
os.environ['CUDA_VISIBLE_DEVICES'] = ''
else:
os.environ['CUDA_VISIBLE_DEVICES'] = f'{args.gpu}'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tf.enable_eager_execution(config)
tf.executing_eagerly()
test_files = list(pathlib.Path(f"data/samples/{problem_folder}/test").glob('sample_*.pkl'))
test_files = [str(x) for x in test_files]
print(f"{len(test_files)} test samples")
evaluated_policies = [['gcnn', model] for model in gcnn_models] + \
[['ml-competitor', model] for model in other_models]
fieldnames = [
'policy',
'seed',
] + [
| tensorflow.executing_eagerly | 1,700 |
import tensorflow as tf
This lower bound on the number of true positives given `logits` and `labels`
is the same one used in the global objectives loss functions.
Args:
labels: A `Tensor` of shape [batch_size] or [batch_size, num_labels].
logits: A `Tensor` of shape [batch_size, num_labels] or
[batch_size, num_labels, num_anchors]. If the third dimension is present,
the lower bound is computed on each slice [:, :, k] independently.
weights: Per-example loss coefficients, with shape broadcast-compatible with
that of `labels`.
surrogate_type: Either 'xent' or 'hinge', specifying which upper bound
should be used for indicator functions.
Returns:
A `Tensor` of shape [num_labels] or [num_labels, num_anchors].
"""
maybe_log2 = tf.log(2.0) if surrogate_type == 'xent' else 1.0
maybe_log2 = tf.cast(maybe_log2, logits.dtype.base_dtype)
if logits.get_shape().ndims == 3 and labels.get_shape().ndims < 3:
labels = tf.expand_dims(labels, 2)
loss_on_positives = losses_utils.weighted_surrogate_loss(
labels, logits, surrogate_type, negative_weights=0.0) / maybe_log2
return tf.reduce_sum(weights * (labels - loss_on_positives), 0)
def false_positives_upper_bound(labels, logits, weights, surrogate_type):
"""Calculate an upper bound on the number of false positives.
This upper bound on the number of false positives given `logits` and `labels`
is the same one used in the global objectives loss functions.
Args:
labels: A `Tensor` of shape [batch_size, num_labels]
logits: A `Tensor` of shape [batch_size, num_labels] or
| tensorflow.cast | 1,701 |
import tensorflow as tf
return output_spec
return model_fn
def get_masked_lm_output(
bert_config, input_tensor, output_weights, positions, label_ids, label_weights
):
"""Get loss and log probs for the masked LM."""
input_tensor = gather_indexes(input_tensor, positions)
with tf.variable_scope("cls/predictions"):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.variable_scope("transform"):
input_tensor = tf.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range
),
)
| tensorflow.variable_scope | 1,702 |
import tensorflow as tf
output_z_ = lrelu(linear(trans_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(tf.concat([output_h0, tgtctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(tf.concat([output_h1, tgtctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(tf.concat([output_h2, tgtctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(tf.concat([output_h3, tgtctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
| tensorflow.concat | 1,703 |
from tensorflow.python.training import moving_averages
Returns
-------
The same Numpy array, cast to its new type.
"""
return np.asarray(x, dtype=tf.float32)
def moving_average_update(variable, value, momentum):
try:
return moving_averages.assign_moving_average(
variable, value, momentum, zero_debias=False)
except TypeError:
return moving_averages.assign_moving_average(variable, value, momentum)
def int_shape(x):
"""Returns the shape of a Keras tensor or a Keras variable as a tuple of
integers or None entries.
Arguments
---------
x: Tensor or variable.
Returns
-------
| tensorflow.python.training.moving_averages.assign_moving_average | 1,704 |
import tensorflow as tf
N, PL, QL, CL, d, dc, nh = self._params()
if self.config.fix_pretrained_vector:
dc = self.char_mat.get_shape()[-1]
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(tf.nn.embedding_lookup(
self.char_mat, self.ch), [N * PL * self.max_p_num, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(
self.char_mat, self.qh), [N * QL * self.max_p_num, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
ch_emb = conv(ch_emb, d,
bias=True, activation=tf.nn.relu, kernel_size=5, name="char_conv", reuse=None)
| tensorflow.nn.embedding_lookup | 1,705 |
import tensorflow as tf
bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
num_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
| tensorflow.trainable_variables | 1,706 |
from tensorflow.python.framework import tensor_util
normal = dists.Normal(mu, sigma,
validate_args=True)
self.assertTrue(tensor_util.constant_value(normal.is_scalar_event))
self.assertTrue(tensor_util.constant_value(normal.is_scalar_batch))
normal = dists.Normal([mu], [sigma],
| tensorflow.python.framework.tensor_util.constant_value | 1,707 |
import tensorflow as tf
name: A string used as the name for this variable scope.
Returns:
(tf.Tensor) A single value tensor containing the loss.
"""
loss = None
with tf.name_scope(name, "click_weighted_log_loss",[output]):
click_prob = tf.sigmoid(output)
loss = tf.losses.log_loss(labels, click_prob, propensity_weights)
return loss
| tensorflow.sigmoid | 1,708 |
import tensorflow as tf
raise ValueError('anchors must be an BoxList')
if not isinstance(groundtruth_boxes, box_list.BoxList):
raise ValueError('groundtruth_boxes must be an BoxList')
if groundtruth_labels is None:
groundtruth_labels = tf.ones(tf.expand_dims(groundtruth_boxes.num_boxes(),
0))
groundtruth_labels = tf.expand_dims(groundtruth_labels, -1)
shape_assert = tf.assert_equal(tf.shape(groundtruth_labels)[1:],
tf.shape(self._unmatched_cls_target))
with tf.control_dependencies([shape_assert]):
match_quality_matrix = self._similarity_calc.compare(groundtruth_boxes,
anchors)
match = self._matcher.match(match_quality_matrix, **params)
reg_targets = self._create_regression_targets(anchors,
groundtruth_boxes,
match)
| tensorflow.shape | 1,709 |
import tensorflow as tf
epsilon = 1e-5
mean, var = tf.nn.moments(x, [1, 2], keep_dims=True)
scale = tf.get_variable('scale',[x.get_shape()[-1]],
initializer=tf.truncated_normal_initializer(mean=1.0, stddev=0.02))
offset = tf.get_variable('offset',[x.get_shape()[-1]],initializer=tf.constant_initializer(0.0))
out = scale*tf.div(x-mean, tf.sqrt(var+epsilon)) + offset
return out
| tensorflow.constant_initializer | 1,710 |
from tensorflow.contrib.boosted_trees.proto import learner_pb2
classifier.evaluate(input_fn=_eval_input_fn, steps=1)
classifier.export(self._export_dir_base)
def testThatLeafIndexIsInPredictions(self):
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
learner_config.constraints.max_tree_depth = 1
model_dir = tempfile.mkdtemp()
| tensorflow.contrib.boosted_trees.proto.learner_pb2.LearnerConfig | 1,711 |
import tensorflow as tf
def viz3(name, a, b, c):
with tf.name_scope(name):
im = tf.concat([a, b, c], axis=3)
im = tf.transpose(im, [0, 2, 3, 1])
im = (im + 1.0) * 128
im = tf.clip_by_value(im, 0, 255)
im = tf.cast(im, tf.uint8, name='viz')
| tensorflow.transpose | 1,712 |
import tensorflow as tf
rank_assertions = []
for i in range(len(image_list)):
image_rank = tf.rank(image_list[i])
rank_assert = tf.Assert(
| tensorflow.rank | 1,713 |
import tensorflow as tf
#cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=glabels, logits=cls_pred)
# Create a tensor named cross_entropy for logging purposes.
tf.identity(cross_entropy, name='cross_entropy_loss')
tf.summary.scalar('cross_entropy_loss', cross_entropy)
loc_loss = tf.cond(n_positives > 0., lambda: modified_smooth_l1(location_pred, tf.stop_gradient(gtargets), sigma=1.), lambda: tf.zeros_like(location_pred))
#loc_loss = modified_smooth_l1(location_pred, tf.stop_gradient(gtargets))
loc_loss = tf.reduce_mean(tf.reduce_sum(loc_loss, axis=-1))
loc_loss = tf.identity(loc_loss, name='location_loss')
tf.summary.scalar('location_loss', loc_loss)
tf.losses.add_loss(loc_loss)
# Add weight decay to the loss. We exclude the batch norm variables because
# doing so leads to a small improvement in accuracy.
loss = cross_entropy + loc_loss + params['weight_decay'] * tf.add_n(
[tf.nn.l2_loss(v) for v in tf.trainable_variables()
if 'batch_normalization' not in v.name])
total_loss = tf.identity(loss, name='total_loss')
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
| tensorflow.losses.add_loss | 1,714 |
from tensorflow.python.framework import ops
# Now, we have the implicit threshold, so compute the sensitivity:
return math_ops.div(tp[tf_index],
tp[tf_index] + fn[tf_index] + kepsilon,
name)
sensitivity = compute_sensitivity_at_specificity('value')
with ops.control_dependencies(
[tp_update_op, fn_update_op, tn_update_op, fp_update_op]):
update_op = compute_sensitivity_at_specificity('update_op')
if metrics_collections:
ops.add_to_collections(metrics_collections, sensitivity)
| tensorflow.python.framework.ops.control_dependencies | 1,715 |
import tensorflow as tf
# for both the shortcut and non-shortcut paths as part of the first
# block's projection. Cf. Appendix of [2].
if self.resnet_version == 1:
inputs = batch_norm(inputs, training, self.data_format)
inputs = tf.nn.relu(inputs)
if self.first_pool_size:
inputs = tf.layers.max_pooling2d(
inputs=inputs, pool_size=self.first_pool_size,
strides=self.first_pool_stride, padding='SAME',
data_format=self.data_format)
inputs = tf.identity(inputs, 'initial_max_pool')
for i, num_blocks in enumerate(self.block_sizes):
num_filters = self.num_filters * (2**i)
inputs = block_layer(
inputs=inputs, filters=num_filters, bottleneck=self.bottleneck,
block_fn=self.block_fn, blocks=num_blocks,
strides=self.block_strides[i], training=training,
name='block_layer{}'.format(i + 1), data_format=self.data_format)
# Only apply the BN and ReLU for model that does pre_activation in each
| tensorflow.identity | 1,716 |
import tensorflow as tf
)
# Before and after flux.
before_flux = batch_win_shaped(band_features["before_flux"])
after_flux = batch_win_shaped(band_features["after_flux"])
before_time = batch_win_shaped(band_features["before_time"])
after_time = batch_win_shaped(band_features["after_time"])
self.dtime = batch_2win_shaped(
tf.concat([before_time, after_time], axis=1) - tile_to_2win(closest_time),
)
self.dflux = batch_2win_shaped(
tf.concat([before_flux, after_flux], axis=1) - tile_to_2win(closest_flux),
)
# Masking tensor.
left_mask = _left_mask(
batch_shaped(
band_features["before_padding"]),
window_size)
right_mask = _right_mask(
batch_shaped(band_features["after_padding"]), window_size
)
| tensorflow.concat | 1,717 |
import tensorflow as tf
[size_assertion],
tf.slice(image, offsets, cropped_shape))
return tf.reshape(image, cropped_shape)
| tensorflow.reshape | 1,718 |
import tensorflow as tf
_,h,w,c = input_var.shape.as_list()
_t = tf.image.resize_nearest_neighbor(input_var, [h*2, w*2])
_t = tf.pad(_t,self.padding, mode='SYMMETRIC')
return tf.nn.bias_add(
| tensorflow.pad | 1,719 |
import tensorflow as tf
data_format_ = 'NHWC' if data_format=='channels_last' else 'NCHW'
if data_format_ == 'NHWC':
inputs = tf.transpose(inputs, [0, 2, 3, 1])
ksize = int(6 * sigma + 1.)
x = tf.expand_dims(tf.range(ksize, delta=1, dtype=tf.float32), axis=1)
y = tf.transpose(x, [1, 0])
kernel_matrix = tf.exp(- ((x - ksize/2.) ** 2 + (y - ksize/2.) ** 2) / (2 * sigma ** 2))
#print(kernel_matrix)
| tensorflow.range | 1,720 |
import tensorflow as tf
anchors_w_1,
arc_seq,
tf.constant([0.0], dtype=tf.float32, name="entropy"),
tf.constant([0.0], dtype=tf.float32, name="log_prob"),
]
loop_outputs = tf.while_loop(_condition, _body, loop_vars,
parallel_iterations=1)
arc_seq = loop_outputs[-3].stack()
arc_seq = tf.reshape(arc_seq, [-1])
entropy = tf.reduce_sum(loop_outputs[-2])
log_prob = tf.reduce_sum(loop_outputs[-1])
last_c = loop_outputs[-7]
last_h = loop_outputs[-6]
return arc_seq, entropy, log_prob, last_c, last_h
def build_trainer(self, child_model):
| tensorflow.reshape | 1,721 |
import tensorflow as tf
truthoutput_h1 = lrelu(deconv2d(tf.concat([truthoutput_h0, tgtctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(tf.concat([truthoutput_h1, tgtctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
truthoutput_h3 = lrelu(deconv2d(tf.concat([truthoutput_h2, tgtctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
truthoutput_h4 = deconv2d(tf.concat([truthoutput_h3, tgtctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
self.simloss = tf.reduce_mean((trans_z - tgtimg_z) ** 2) * 1e3
mean, var = tf.nn.moments(tgtimg_z, axes=[0])
print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
print(tgtimg_z.get_shape())
self.out = output_h4# + contextimg#tf.nn.tanh(h4)
self.out2 = truthoutput_h4
self.recon1 = tf.nn.l2_loss(tgtimg - self.out)
self.recon2 = tf.nn.l2_loss(tgtimg - self.out2)
| tensorflow.reduce_mean | 1,722 |
import tensorflow as tf
logits = tf.reduce_mean(logits, axis=1)
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
| tensorflow.squeeze | 1,723 |
import tensorflow as tf
values = []
bleu = 100 * bleu_hook.bleu_wrapper(
decode_hparams.decode_reference, decode_hparams.decode_to_file)
values.append(tf.Summary.Value(tag="BLEU", simple_value=bleu))
tf.logging.info("%s: BLEU = %6.2f" % (decode_hparams.decode_to_file, bleu))
if hook_args.hparams.mlperf_mode:
current_step = decode_hparams.mlperf_decode_step
mlperf_log.transformer_print(
| tensorflow.logging.info | 1,724 |
import tensorflow as tf
loss = loss_pg + loss_vf + loss_entropy
opt = tf.train.AdamOptimizer(self.LR)
self.train_op = opt.minimize(loss, global_step=self.global_step, var_list=pi_params + vf_params)
self.pi_new_params = [oldp.assign(p) for p, oldp in zip(pi_params, pi_old_params)]
self.vf_new_params = [oldp.assign(p) for p, oldp in zip(vf_params, vf_old_params)]
self.sess.run(tf.global_variables_initializer())
# Tensorboard
if summary_dir is not None:
self.writer = tf.summary.FileWriter(summary_dir)
tf.summary.scalar('Loss/Policy', loss_pg)
tf.summary.scalar('Loss/Value', loss_vf)
tf.summary.scalar('Loss/Entropy', loss_entropy)
tf.summary.scalar('Loss/Total', loss)
tf.summary.scalar('Var/Epsilon', epsilon_decay)
tf.summary.scalar('Var/Policy Mode', tf.reduce_mean(pi.mode()))
tf.summary.scalar('Var/Policy Sigma', tf.reduce_mean(pi.stddev()))
tf.summary.scalar('Var/Value', tf.reduce_mean(self.vf))
self.summarise = tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES))
# AC net
def build_anet(self, state_in, name, reuse=False, batch_size=64):
reg = None
with tf.variable_scope(name, reuse=reuse):
layer_a1 = tf.layers.dense(state_in, 512, tf.nn.relu, kernel_regularizer=reg)
| tensorflow.summary.scalar | 1,725 |
import tensorflow as tf
"char_mat", initializer=tf.constant(char_mat, dtype=tf.float32))
self.c_mask = tf.cast(self.c, tf.bool)
self.q_mask = tf.cast(self.q, tf.bool)
self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1)
self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1)
if opt:
# we have to hardcode the max batch size here! use the batch size from the generator as this will be used for PG
N, CL = config.batch_size if not self.demo else config.batch_size, config.char_limit
self.c_maxlen = tf.reduce_max(self.c_len)
self.q_maxlen = tf.reduce_max(self.q_len)
self.c = tf.slice(self.c, [0, 0], [N, self.c_maxlen])
self.q = tf.slice(self.q, [0, 0], [N, self.q_maxlen])
self.c_mask = tf.slice(self.c_mask, [0, 0], [N, self.c_maxlen])
self.q_mask = tf.slice(self.q_mask, [0, 0], [N, self.q_maxlen])
self.ch = tf.slice(self.ch, [0, 0, 0], [N, self.c_maxlen, CL])
self.qh = tf.slice(self.qh, [0, 0, 0], [N, self.q_maxlen, CL])
self.y1 = tf.argmax(tf.slice(self.y1, [0, 0], [N, self.c_maxlen]),axis=-1)
self.y2 = tf.argmax(tf.slice(self.y2, [0, 0], [N, self.c_maxlen]),axis=-1)
else:
self.c_maxlen, self.q_maxlen = config.para_limit, config.ques_limit
self.ch_len = tf.reshape(tf.reduce_sum(
tf.cast(tf.cast(self.ch, tf.bool), tf.int32), axis=2), [-1])
self.qh_len = tf.reshape(tf.reduce_sum(
tf.cast(tf.cast(self.qh, tf.bool), tf.int32), axis=2), [-1])
| tensorflow.slice | 1,726 |
import tensorflow as tf
if isinstance(facts, tuple):
# In case of Bi-RNN, concatenate the forward and the backward RNN outputs.
facts = tf.concat(facts, 2)
print ("querry_size mismatch")
query = tf.concat(values = [
query,
query,
], axis=1)
| tensorflow.concat | 1,727 |
import tensorflow as tf
with tf.Session() as sess:
sess.run(local_init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(a.workers):
t = threading.Thread(target=worker, args=(coord,))
t.start()
| tensorflow.train.start_queue_runners | 1,728 |
import tensorflow as tf
pi_eval, _ = self.build_anet(self.state, 'pi', reuse=True)
vf_old, vf_old_params = self.build_cnet(batch['state'], 'oldvf')
self.vf, vf_params = self.build_cnet(batch['state'], 'vf')
self.vf_eval, _ = self.build_cnet(self.state, 'vf', reuse=True)
self.sample_action = tf.squeeze(pi_eval.sample(1), axis=0)
self.eval_action = pi_eval.mode()
self.global_step = tf.train.get_or_create_global_step()
self.saver = tf.train.Saver()
# Loss functions and training
epsilon_decay = tf.train.polynomial_decay(self.EPSILON, self.global_step, self.EPS_LEN, 0.1, power=0)
ratio = tf.maximum(pi.prob(batch['actions']), 1e-6) / tf.maximum(pi_old.prob(batch['actions']), 1e-6)
ratio = tf.clip_by_value(ratio, 0, 10)
surr1 = batch['advantage'] * ratio
surr2 = batch['advantage'] * tf.clip_by_value(ratio, 1 - epsilon_decay, 1 + epsilon_decay)
loss_pg = - 2.0 * tf.reduce_mean(tf.minimum(surr1, surr2))
loss_vf = 0.5 * tf.reduce_mean(tf.square(batch['rewards'] - self.vf))
| tensorflow.train.Saver | 1,729 |
import tensorflow as tf
else:
self.embedding_W = tf.Variable(tf.random_uniform([num_quantized_chars, embedding_size], -1.0, 1.0),name="embedding_W")
self.embedded_characters = tf.nn.embedding_lookup(self.embedding_W, self.input_x)
embedded_text_expand = tf.expand_dims(self.embedded_characters, -1)
with tf.device('/cpu:0'), tf.name_scope("embedding_tags"):
W_tags = tf.get_variable("embed_W_tags", [tags_vocab_size, embedding_size], initializer=initializer)
embedded_tags = tf.nn.embedding_lookup(W_tags, self.input_tags)
embedded_tags_expanded = tf.expand_dims(embedded_tags, -1)
with tf.device('/cpu:0'), tf.name_scope("embedding_deps"):
W_deps = tf.get_variable("embed_W_deps", [deps_vocab_size, embedding_size], initializer=initializer)
embedded_deps = tf.nn.embedding_lookup(W_deps, self.input_deps)
embedded_deps_expanded = tf.expand_dims(embedded_deps, -1)
with tf.device('/cpu:0'), tf.name_scope("embedding_head"):
| tensorflow.expand_dims | 1,730 |
from tensorflow.python.platform import tf_logging as logging
self._save(step, session)
if self._save_secs is not None:
if time.time() >= self._last_saved_time + self._save_secs:
self._save(step, session)
def end(self, session=None):
super(CheckpointSaver, self).end(session)
self._save(self._last_begin_step, session)
def _save(self, step, session):
"""Saves the latest checkpoint."""
if step == self._last_saved_step:
return
logging.info("Saving checkpoints for %d into %s.", step, self._save_path)
self._last_saved_time = time.time()
self._last_saved_step = step
if self._saver is None:
self._scaffold.saver.save(session, self._save_path, global_step=step)
else:
self._saver.save(session, self._save_path, global_step=step)
self._summary_writer.add_session_log(
SessionLog(
status=SessionLog.CHECKPOINT, checkpoint_path=self._save_path),
step)
class StepCounter(EveryN):
| tensorflow.python.platform.tf_logging.info | 1,731 |
import tensorflow as tf
# The two terms 'term1' and 'term2' which come from normalizers of the
# 1. Original policy distribution
# 2. The distribution after completing the square
sigma = tf.matrix_inverse(prec)
term1 = -0.5 * param_eta * tf.log(tf.matrix_determinant(2 * np.pi * sigma))
if self.beta == 0:
term2 = 0.5 * param_eta * tf.log(tf.matrix_determinant(2 * np.pi * param_eta * HaaInv))
| tensorflow.matrix_inverse | 1,732 |
from tensorflow.python.framework import ops
ops.RegisterShape("Neg")(common_shapes.unchanged_shape)
ops.RegisterShape("Real")(common_shapes.unchanged_shape)
ops.RegisterShape("Rsqrt")(common_shapes.unchanged_shape)
ops.RegisterShape("Sign")(common_shapes.unchanged_shape)
| tensorflow.python.framework.ops.RegisterShape | 1,733 |
import tensorflow as tf
Returns:
a `float` decov loss
"""
with tf.name_scope(name):
x = tf.reshape(xs, [int(xs.get_shape()[0]), -1])
m = tf.reduce_mean(x, 0, True)
z = tf.expand_dims(x - m, 2)
corr = tf.reduce_mean(tf.matmul(z, tf.transpose(z, perm=[0, 2, 1])), 0)
corr_frob_sqr = tf.reduce_sum(tf.square(corr))
corr_diag_sqr = tf.reduce_sum(tf.square(tf.diag_part(corr)))
loss = 0.5 * (corr_frob_sqr - corr_diag_sqr)
return loss
def center_loss(features, label, alpha, num_classes, name='center_loss'):
"""Center loss based on the paper "A Discriminative Feature Learning Approach
for Deep Face Recognition" (http://ydwen.github.io/papers/WenECCV16.pdf)
| tensorflow.diag_part | 1,734 |
import tensorflow as tf
candidate_mention_scores = tf.squeeze(candidate_mention_scores, 1) # [k]
k = tf.to_int32(tf.floor(tf.to_float(tf.shape(context_outputs)[0]) * self.config["top_span_ratio"]))
top_span_indices = coref_ops.extract_spans(tf.expand_dims(candidate_mention_scores, 0),
tf.expand_dims(candidate_starts, 0),
tf.expand_dims(candidate_ends, 0),
tf.expand_dims(k, 0),
util.shape(context_outputs, 0),
True) # [1, k]
top_span_indices.set_shape([1, None])
top_span_indices = tf.squeeze(top_span_indices, 0) # [k]
top_span_starts = tf.gather(candidate_starts, top_span_indices) # [k]
top_span_ends = tf.gather(candidate_ends, top_span_indices) # [k]
top_span_emb = tf.gather(candidate_span_emb, top_span_indices) # [k, emb]
top_span_cluster_ids = tf.gather(candidate_cluster_ids, top_span_indices) # [k]
top_span_mention_scores = tf.gather(candidate_mention_scores, top_span_indices) # [k]
top_span_sentence_indices = tf.gather(candidate_sentence_indices, top_span_indices) # [k]
top_span_speaker_ids = tf.gather(speaker_ids, top_span_starts) # [k]
| tensorflow.squeeze | 1,735 |
import tensorflow as tf
self._label_file = label_file
self._num_classes = num_classes
self._score_threshold = score_threshold
self._image_sz = image_sz[0:2]
self._config = ConfigProto()
self._config.gpu_options.allow_growth = True
self._graph = tf.Graph()
with self._graph.as_default():
self._sess = tf.Session(config=self._config)
tf.saved_model.load(
self._sess, [tag_constants.SERVING], self._model_path)
self._image_tensor = self._sess.graph.get_tensor_by_name(
'serving_default_input_1:0')
self._output_tensor = self._sess.graph.get_tensor_by_name(
'StatefulPartitionedCall:0')
self._boxes = tf.placeholder(
| tensorflow.Session | 1,736 |
import tensorflow as tf
'foo': tf.convert_to_tensor([0, 1, 2, 3], dtype=tf.int64),
'bar': tf.convert_to_tensor([0, 2, 0, 2], dtype=tf.int64),
}
# Annotate an arbitrary proto at the schema level (not sure what global
# schema boundaries would mean, but hey I'm just a test).
boundaries = tf.constant([[1.0]])
message_type = annotations_pb2.BucketBoundaries.DESCRIPTOR.full_name
sizes = tf.expand_dims([tf.size(boundaries)], axis=0)
message_proto = tf.raw_ops.EncodeProto(
sizes=sizes, values=[tf.cast(boundaries, tf.float32)],
field_names=['boundaries'], message_type=message_type)[0]
type_url = os.path.join('type.googleapis.com', message_type)
schema_inference.annotate(type_url, message_proto)
with tf.compat.v1.Session(graph=graph) as session:
schema = schema_inference.infer_feature_schema(outputs, graph, session)
self.assertLen(schema.annotation.extra_metadata, 1)
for annotation in schema.annotation.extra_metadata:
# Extract the annotated message and validate its contents
message = annotations_pb2.BucketBoundaries()
annotation.Unpack(message)
self.assertAllClose(list(message.boundaries), [1])
def test_infer_feature_schema_with_ragged_tensor(self):
with tf.compat.v1.Graph().as_default() as graph:
outputs = {
'foo': tf.RaggedTensor.from_row_splits(
values=tf.constant([3, 1, 4, 1, 5, 9, 2, 6], tf.int64),
row_splits=[0, 4, 4, 7, 8, 8]),
| tensorflow.compat.v1.Session | 1,737 |
import tensorflow as tf
def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
with tf.variable_scope(name):
| tensorflow.variable_scope | 1,738 |
import tensorflow as tf
moving_averages.assign_moving_average(
self.ema_means, dw, self.hparams.decay,
zero_debias=False)
n = tf.reduce_sum(updated_ema_count, axis=-1, keep_dims=True)
updated_ema_count = ((updated_ema_count + self.hparams.epsilon) / (
n + 2**self.hparams.z_size * self.hparams.epsilon) * n)
updated_ema_means = updated_ema_means / tf.expand_dims(
updated_ema_count, axis=-1)
with tf.control_dependencies([e_loss]):
update_means = tf.assign(self.means, updated_ema_means)
with tf.control_dependencies([update_means]):
loss += self.hparams.beta * e_loss
else:
# Use a gradient based loss for learning the cluster centers
loss += q_loss + self.hparams.beta * e_loss
# Get the discrete latent representation
| tensorflow.control_dependencies | 1,739 |
import tensorflow as tf
## End new version
if self._normalize_cols:
logits_vec = logits_vec - tf.math.reduce_logsumexp(
logits_vec, axis=0)[None]
relabel_indices = tf.random.categorical(logits=logits_vec, num_samples=1)
### Metrics
global_step = tf.compat.v1.train.get_or_create_global_step()
| tensorflow.random.categorical | 1,740 |
import tensorflow as tf
class CommonImageAttentionTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(common_image_attention.DistributionType.DMOL, 5, 50),
(common_image_attention.DistributionType.CAT, None, 256),
)
def testPostProcessImageTrainMode(self, likelihood, num_mixtures, depth):
batch = 1
rows = 8
cols = 24
hparams = tf.contrib.training.HParams(
hidden_size=2,
likelihood=likelihood,
mode=tf.estimator.ModeKeys.TRAIN,
num_mixtures=num_mixtures,
)
inputs = tf.random_uniform([batch, rows, cols, hparams.hidden_size],
minval=-1., maxval=1.)
outputs = common_image_attention.postprocess_image(
inputs, rows, cols, hparams)
| tensorflow.contrib.training.HParams | 1,741 |
import tensorflow as tf
(total_loss, per_example_loss, logits, probabilities) = create_model(
bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
num_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
| tensorflow.train.init_from_checkpoint | 1,742 |
from tensorflow.python.framework import random_seed
self._config.training_worker_session_startup_stagger_secs)
if sleep_secs:
logging.info('Waiting %d secs before starting task %d.', sleep_secs,
self._config.task)
time.sleep(sleep_secs)
# Device allocation
device_fn = device_fn or self._device_fn
with ops.Graph().as_default() as g, g.device(device_fn):
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, targets = input_fn()
self._check_inputs(features, targets)
train_op, loss_op = self._get_train_ops(features, targets)
return train(
graph=g,
output_dir=self._model_dir,
train_op=train_op,
loss_op=loss_op,
| tensorflow.python.framework.random_seed.set_random_seed | 1,743 |
import tensorflow as tf
stddev=0.02, data_format='NCHW',padding='SAME') :
with tf.variable_scope(name) :
assert(data_format == 'NCHW' or data_format == 'NHWC')
self.w = tf.get_variable('w', [k_h, k_w, input_dim, output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
self.b = tf.get_variable('b',[output_dim], initializer=tf.constant_initializer(0.0))
if( data_format == 'NCHW' ) :
self.strides = [1, 1, d_h, d_w]
| tensorflow.truncated_normal_initializer | 1,744 |
import tensorflow as tf
save.restore(sess, save_path)
# Check that the parameter nodes have been restored.
self.assertEqual(10.0, v0.eval())
self.assertEqual(20.0, v1.eval())
# Build another graph with 2 nodes, initialized
# differently, and a Restore node for them.
with self.test_session(graph=tf.Graph()) as sess:
v0_2 = tf.Variable(1000.0, name="v0")
v1_2 = tf.Variable(2000.0, name="v1")
save2 = tf.train.Saver([v0_2, v1_2])
tf.initialize_all_variables().run()
# Check that the parameter nodes have been initialized.
| tensorflow.Graph | 1,745 |
from tensorflow.python.framework import ops
pass
def inference_graph(self, data):
with ops.device(self.device_assigner):
# Compute activations for the neural network.
nn_activations = layers.fully_connected(data, 1)
| tensorflow.python.framework.ops.device | 1,746 |
import tensorflow as tf
normalizer_fn=None,
data_format='NDHWC',
scope='Conv2d_0c_1x1')
# Temporal average pooling.
logits = tf.reduce_mean(logits, axis=1)
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
| tensorflow.reduce_mean | 1,747 |
import tensorflow as tf
sdf = sdf * -1.0 # inside positive, outside zero
samples_object = centernet_utils.transform_pointcloud(
tf.reshape(samples_world, [1, 1, -1, 3]),
tf.reshape(poses[2][i], [1, 1, 3]),
tf.reshape(poses[0][i], [1, 1, 3, 3]),
tf.reshape(poses[1][i], [1, 1, 3]), inverse=True) * 2.0
samples_object = (samples_object * (29.0/32.0) / 2.0 + 0.5) * 32.0 - 0.5
samples = tf.squeeze(samples_object)
interpolated = trilinear.interpolate(sdf, samples)
occupancy_value = tf.math.sign(tf.nn.relu(interpolated + self.tol))
| tensorflow.reshape | 1,748 |
import tensorflow as tf
def dense_maxnorm(var_matrix, maxnorm=1.0):
'''Similar to dense_maxnorm_update(), except this returns a new Tensor
instead of an operation that modifies var_matrix.
Args:
var_matrix: 2D tensor (Variable)
maxnorm: the maximum Euclidean norm
Returns:
A new tensor where all rows have been scaled as necessary
'''
axis_norms = tf.sqrt(tf.reduce_sum(tf.square(var_matrix), 1))
scaling = maxnorm / tf.maximum(axis_norms, maxnorm)
return var_matrix * tf.expand_dims(scaling, 1)
class BaseModel(object):
''' Base class for embedding-based relational learning models that use
maxnorm regularization. Subclasses must implement _create_model() and
populate self.train_step, and can optionally populate self.post_step for
post-processing.
Note: When model_type is 'ranking_margin', the mini-batch provider returned
by _create_batch_provider() must provide instances in alternating
pos/neg pairs: [pos, neg, pos, neg, ...]. This is satisfied when using
ContrastiveTrainingProvider; be careful if you use a different one.
Args:
| tensorflow.expand_dims | 1,749 |
import tensorflow as tf
return output
else:
activation = non_linear_fn(output)
return activation
def batch_norm(x, b_train, scope, reuse=False):
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
n_out = x.get_shape().as_list()[-1]
beta = tf.get_variable('beta', initializer=tf.constant(0.0, shape=[n_out]))
gamma = tf.get_variable('gamma', initializer=tf.constant(1.0, shape=[n_out]))
batch_mean, batch_var = tf.nn.moments(x, [0], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.9)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(b_train,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
return normed
| tensorflow.nn.moments | 1,750 |
import tensorflow as tf
# hardware related configuration
tf.app.flags.DEFINE_integer(
'num_readers', 16,#16
'The number of parallel readers that read data from the dataset.')
tf.app.flags.DEFINE_integer(
'num_preprocessing_threads', 48,#48
'The number of threads used to create the batches.')
tf.app.flags.DEFINE_integer(
'num_cpu_threads', 0,
'The number of cpu cores used to train.')
tf.app.flags.DEFINE_float(
'gpu_memory_fraction', 1., 'GPU memory fraction to use.')
# scaffold related configuration
tf.app.flags.DEFINE_string(
'data_dir', '../Datasets/tfrecords',#'/media/rs/0E06CD1706CD0127/Kapok/Chi/Datasets/tfrecords',
'The directory where the dataset input data is stored.')
tf.app.flags.DEFINE_string(
'dataset_name', '{}_????', 'The pattern of the dataset name to load.')
tf.app.flags.DEFINE_string(
'model_dir', './logs_sext_cpn/',
'The parent directory where the model will be stored.')
tf.app.flags.DEFINE_integer(
'log_every_n_steps', 10,
'The frequency with which logs are print.')
tf.app.flags.DEFINE_integer(
'save_summary_steps', 100,
| tensorflow.app.flags.DEFINE_string | 1,751 |
import tensorflow as tf
#Construct graph
images, labels = input_name.build_input(
FLAGS.dataset, FLAGS.eval_data_path, hps.batch_size, FLAGS.mode)#FLAGS.mode='attack', batch_size=200
Res = model_name.ResNet(hps, images, FLAGS.mode, Reuse=False)
Res.build_graph()
saver = tf.train.Saver()
#Open session and restore checkpoint
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
tf.train.start_queue_runners(sess)
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root) # Choose dir according to rt
tf.logging.info('Loading checkpoint %s', ckpt_state.model_checkpoint_path)
num_sample = hps.batch_size*FLAGS.eval_batch_count
# Initialize results to save
entropy_test_adv_all = np.array([])
confidence_test_adv_all = np.array([])
entropy_test_nor_all = np.array([])
confidence_test_nor_all = np.array([])
logits_adv_all = np.reshape(np.array([]), (0, 64))
| tensorflow.train.get_checkpoint_state | 1,752 |
import tensorflow as tf
target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_q_func')
# construct optimization op (with gradient clipping)
self.learning_rate = tf.placeholder(tf.float32, (), name="learning_rate")
optimizer = self.optimizer_spec.constructor(learning_rate=self.learning_rate, **self.optimizer_spec.kwargs)
self.train_fn = minimize_and_clip(optimizer, self.total_error,
var_list=q_func_vars, clip_val=grad_norm_clipping)
# update_target_fn will be called periodically to copy Q network to target Q network
update_target_fn = []
for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name),
sorted(target_q_func_vars, key=lambda v: v.name)):
update_target_fn.append(var_target.assign(var))
self.update_target_fn = tf.group(*update_target_fn)
# construct the replay buffer
self.replay_buffer = ReplayBuffer(replay_buffer_size, frame_history_len, lander=lander)
self.replay_buffer_idx = None
###############
# RUN ENV #
###############
self.model_initialized = False
self.num_param_updates = 0
self.mean_episode_reward = -float('nan')
self.best_mean_episode_reward = -float('inf')
self.last_obs = self.env.reset()
| tensorflow.group | 1,753 |
import tensorflow as tf
self.ch = tf.placeholder(tf.int32, [None, config.test_para_limit, config.char_limit],"context_char")
self.qh = tf.placeholder(tf.int32, [None, config.test_ques_limit, config.char_limit],"question_char")
self.y1 = tf.placeholder(tf.int32, [None, config.test_para_limit],"answer_index1")
self.y2 = tf.placeholder(tf.int32, [None, config.test_para_limit],"answer_index2")
| tensorflow.placeholder | 1,754 |
import tensorflow as tf
def inference(self, forward_only=None):
embed_inputs = tf.nn.embedding_lookup(self.embedding_init, self.x) ## (batch_size, seq_len, 100)
with tf.variable_scope('hidden', reuse=forward_only):
with tf.variable_scope('lstm_cell'):
lstm_cell = tf.nn.rnn_cell.LSTMCell(num_units=self.num_hidden, use_peepholes=False,
# forget_bias=0.0,
activation=tf.nn.relu,
# initializer=tf.truncated_normal_initializer(stddev=0.1),
# initializer=tf.random_uniform_initializer(-0.003, 0.003),
initializer=tf.contrib.layers.xavier_initializer(),
state_is_tuple=True)
if not forward_only:
lstm_cell = tf.nn.rnn_cell.DropoutWrapper(cell=lstm_cell, output_keep_prob=self.dropout_output)
# lstm_cell = tf.nn.rnn_cell.MultiRNNCell(cells=[lstm_cell] * 4, state_is_tuple=True)
if not forward_only:
embed_inputs = tf.nn.dropout(embed_inputs, keep_prob=self.dropout_input)
rnn_outputs, output_states = tf.nn.dynamic_rnn(
cell=lstm_cell,
| tensorflow.contrib.layers.xavier_initializer | 1,755 |
import tensorflow as tf
annotation = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="annotation")
z = tf.placeholder(tf.float32, shape=[None, 4, 4, 128], name="z")
# pred_annotation, logits = inference(image, keep_probability,z)
# tf.summary.image("input_image", image, max_outputs=2)
# tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
# tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
# loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
# labels=tf.squeeze(annotation, squeeze_dims=[3]),
# name="entropy")))
mask_ = tf.ones([FLAGS.batch_size,64,64,3])
mask = tf.pad(mask_, [[0,0],[32,32],[32,32],[0,0]])
mask2__ = tf.ones([FLAGS.batch_size,78,78,3])
mask2_ = tf.pad(mask2__, [[0,0],[25,25],[25,25],[0,0]])
mask2 = mask2_ - mask
pred_annotation, logits = inference((1-mask)*image + mask*255, keep_probability,z)
tf.summary.image("input_image", image, max_outputs=2)
tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
# loss0 = tf.reduce_mean(tf.abs(z))
loss = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square((image - logits)),[1,2,3])))
# loss2 = tf.reduce_mean(tf.square((image - logits)*mask2))
# loss = loss1 + loss2 + loss0
# loss = tf.reduce_mean(tf.squared_difference(logits ,annotation ))
| tensorflow.ones | 1,756 |
from tensorflow.python.framework import ops
# models/layers made in tf.compat.v1.Graph.as_default() with models/layers
# created outside of it. Converting a model to an estimator (via
# model_to_estimator) invalidates all models/layers made before the
# conversion (even if they were not the model converted to an estimator).
# Similarly, making a layer or a model inside a a tf.compat.v1.Graph
# invalidates all layers/models you previously made outside of the graph.
self._originally_built_as_v1 = True
@property
def _saved_model_loader(self) -> saved_transform_io_v2.SavedModelLoader:
"""A `saved_transform_io_v2.SavedModelLoader`."""
if self._saved_model_loader_value is None:
self._saved_model_loader_value = saved_transform_io_v2.SavedModelLoader(
self._tft_output.transform_savedmodel_dir)
self._loaded_saved_model_graph = ops.get_default_graph()
# TODO(b/160294509): Use tf.compat.v1 when we stop supporting TF 1.15.
if ops.executing_eagerly_outside_functions():
return self._saved_model_loader_value
else:
assert not self._exported_as_v1
# TODO(b/149997088): Raise an exception once we no longer support using
# the Keras layer with estimator based Trainer.
tf.compat.v1.logging.warning('Loading a TF2 SavedModel but eager mode '
'seems disabled.')
# If exported as TF2 SavedModel but not invoked in eager mode,
# re-initialize the saved_model_loader_value as __init__ could have been
# called in a different graph context.
| tensorflow.python.framework.ops.get_default_graph | 1,757 |
import tensorflow as tf
if res_increase == 1:
# already in the target shape
return input_tensor
# resize y-z
squeeze_b_x = tf.reshape(input_tensor, [-1, y_size, z_size, c_size], name='reshape_bx')
resize_b_x = tf.compat.v1.image.resize_bilinear(squeeze_b_x, [y_size_new, z_size_new], align_corners=align)
resume_b_x = tf.reshape(resize_b_x, [-1, x_size, y_size_new, z_size_new, c_size], name='resume_bx')
# Reorient
| tensorflow.reshape | 1,758 |
import tensorflow as tf
else:
ones_like_x = tf.ones_like(x, dtype=tf.int64)
| tensorflow.ones_like | 1,759 |
import tensorflow as tf
"""
box1 = box1.numpy() if isinstance(box1, tf.Tensor) else box1
box2 = box2.numpy() if isinstance(box2, tf.Tensor) else box2
box1 = box1.astype(np.float32)
box2 = box2.astype(np.float32)
# rotates around z, while we rotate around y so need to swap
center_1 = tf.reshape(box1[0:3][[0, 2, 1]], [1, 3])
center_2 = tf.reshape(box2[0:3][[0, 2, 1]], [1, 3])
rotation_z_1 = tf.reshape(box1[-1], [1])
rotation_z_2 = tf.reshape(box2[-1], [1])
length_1 = tf.reshape(box1[3 + 0], [1])
height_1 = tf.reshape(box1[3 + 2], [1])
width_1 = tf.reshape(box1[3 + 1], [1])
length_2 = tf.reshape(box2[3 + 0], [1])
height_2 = tf.reshape(box2[3 + 2], [1])
width_2 = tf.reshape(box2[3 + 1], [1])
iou = np.squeeze(np_box_ops.iou3d_7dof_box(
length_1, height_1, width_1, center_1, rotation_z_1,
| tensorflow.reshape | 1,760 |
import tensorflow as tf
log_cdf_plus = plus_in - tf.nn.softplus(plus_in)
log_one_minus_cdf_min = -tf.nn.softplus(min_in)
cdf_delta = cdf_plus - cdf_min
mid_in = inv_stdv * centered_inputs
log_pdf_mid = mid_in - log_scales - 2. * tf.nn.softplus(mid_in)
log_probs = tf.select(
inputs < -0.999, log_cdf_plus,
tf.select(
| tensorflow.nn.softplus | 1,761 |
import tensorflow as tf
rnorm_var = tf.random_normal([row_dim, col_dim], mean=0.0, stddev=1.0)
runif_var = tf.random_uniform([row_dim, col_dim], minval=0, maxval=4)
print(sess.run(rnorm_var))
| tensorflow.random_uniform | 1,762 |
import tensorflow as tf
return tf.squeeze(z,axis=0)
zs = tf.map_fn(loop_hyper_encoder, ys, dtype=tf.float32, parallel_iterations=1, back_prop=False)
print("Hyper Encoder")
z_hats, _ = entropy_bottleneck(zs, False)
print("Quantize hyperprior")
def loop_hyper_deocder(z):
z = tf.expand_dims(z, 0)
loc, scale = hyper_decoder(z)
return tf.squeeze(loc, [0]), tf.squeeze(scale, [0])
locs, scales = tf.map_fn(loop_hyper_deocder, z_hats, dtype=(tf.float32, tf.float32),
parallel_iterations=1, back_prop=False)
lower_bound = 1e-9# TODO
scales = tf.maximum(scales, lower_bound)
print("Hyper Decoder")
z_strings, z_min_v, z_max_v = entropy_bottleneck.compress(zs)
z_shape = tf.shape(zs)[:]
print("Entropy Encode (Hyper)")
| tensorflow.squeeze | 1,763 |
import tensorflow as tf
self.assertAllClose(
self.evaluate(log_prob),
self._scipy_pareto(concentration_v, scale_v).logpdf(x))
pdf = pareto.prob(x)
self.assertEqual(pdf.shape, (6,))
self.assertAllClose(
self.evaluate(pdf),
self._scipy_pareto(concentration_v, scale_v).pdf(x))
def testParetoLogPdfValidateArgs(self):
batch_size = 3
scale = tf.constant([2., 3., 4.])
concentration = tf.constant([2.] * batch_size)
pareto = tfd.Pareto(concentration, scale, validate_args=True)
with self.assertRaisesOpError("not in the support"):
x = tf.placeholder_with_default(input=[2., 3., 3.], shape=[3])
log_prob = pareto.log_prob(x)
self.evaluate(log_prob)
with self.assertRaisesOpError("not in the support"):
x = tf.placeholder_with_default(input=[2., 2., 5.], shape=[3])
log_prob = pareto.log_prob(x)
self.evaluate(log_prob)
| tensorflow.constant | 1,764 |
import tensorflow as tf
layer_last = feats_audio
nfilt_last = audio_nchannels
for i, ((ntime, nband, nfilt), (ptime, pband)) in enumerate(zip(cnn_filter_shapes, cnn_pool)):
layer_name = 'cnn_{}'.format(i)
with tf.variable_scope(layer_name):
filters = tf.get_variable('filters', [ntime, nband, nfilt_last, nfilt], initializer=cnn_init, dtype=dtype)
biases = tf.get_variable('biases', [nfilt], initializer=tf.constant_initializer(0.1), dtype=dtype)
if cnn_rnn_zack:
padding = 'SAME'
else:
padding = 'VALID'
conv = tf.nn.conv2d(layer_last, filters, [1, 1, 1, 1], padding=padding)
biased = tf.nn.bias_add(conv, biases)
convolved = tf.nn.relu(biased)
pool_shape = [1, ptime, pband, 1]
pooled = tf.nn.max_pool(convolved, ksize=pool_shape, strides=pool_shape, padding='SAME')
print('{}: {}'.format(layer_name, pooled.get_shape()))
export_feat_tensors[layer_name] = pooled
# TODO: CNN dropout?
| tensorflow.nn.conv2d | 1,765 |
import tensorflow as tf
def do_cls(avg_pool, num_classes, name='dense'):
"""Applies classification."""
with tf.variable_scope('target_CLS', reuse=tf.AUTO_REUSE):
logits = tf.layers.dense(
inputs=avg_pool,
| tensorflow.variable_scope | 1,766 |
from tensorflow.python.ops import state_ops
train_ops = self._get_linear_training_ops(
linear_grads, linear_vars) + self._get_dnn_training_ops(dnn_grads,
dnn_vars)
train_step = control_flow_ops.group(*train_ops, name="combined_training_op")
with ops.control_dependencies([train_step]):
with ops.get_default_graph().colocate_with(global_step):
return state_ops.assign_add(global_step, 1).op, loss
def _run_metrics(self, predictions, targets, metrics, weights):
result = {}
targets = math_ops.cast(targets, predictions.dtype)
for name, metric in six.iteritems(metrics or {}):
if "weights" in inspect.getargspec(metric)[0]:
| tensorflow.python.ops.state_ops.assign_add | 1,767 |
import tensorflow as tf
sess.run(tf.global_variables_initializer())
print(f'\nl1={sess.run(l1)} l2={sess.run(l2)}')
a = np.array([1, 2, 3], dtype=np.float32)
tf_v = tf.Variable(5, dtype=tf.float32)
sess.run(tf.global_variables_initializer())
print(f'a * tf_v = {sess.run(a * tf_v)}')
weights = tf.constant([[1.0, -2], [-3, 4]]);
regular_l1 = tf.contrib.layers.l1_regularizer(0.5)(weights)
regular_l2 = tf.contrib.layers.l2_regularizer(0.5)(weights)
print(f'\nregular_l1={sess.run(regular_l1)} regular_l2={sess.run(regular_l2)}')
val_val = sess.run(val)
print('\nval=' + str(val_val))
print(f'\nargmax_0={val_val.argmax(0)} argmax_1={val_val.argmax(1)}')
print('\ntf.argmax(val, 0)=' + str(sess.run(tf.argmax(val, 0))))
print('tf.argmax(val, 1)=' + str(sess.run(tf.argmax(val, 1))))
| tensorflow.constant | 1,768 |
import tensorflow as tf
# input shape, so we confuse it about the input shape.
initial_output = tf.slice(initial_output, [0, 0, 0, 0],
common_layers.shape_list(initial_output))
target_modality = self._problem_hparams.target_modality
if target_modality.is_class_modality:
decode_length = 1
else:
decode_length = common_layers.shape_list(
features["inputs"])[1] + decode_length
# Initial values of result, logits and loss.
result = initial_output
# tensor of shape [batch_size, time, 1, 1, vocab_size]
logits = tf.zeros((batch_size, 0, 1, 1, target_modality.top_dimensionality))
if not context.in_eager_mode():
logits.set_shape([None, None, None, None, None])
loss = 0.0
def while_exit_cond(result, logits, loss): # pylint: disable=unused-argument
"""Exit the loop either if reach decode_length or EOS."""
length = common_layers.shape_list(result)[1]
not_overflow = length < decode_length
if self._problem_hparams.stop_at_eos:
| tensorflow.zeros | 1,769 |
from tensorflow.python.platform import tf_logging as logging
try:
self._last_export_dir = self._estimator.export(
self.export_dir,
exports_to_keep=self.exports_to_keep,
signature_fn=self.signature_fn,
input_fn=self._input_fn,
default_batch_size=self._default_batch_size,
input_feature_key=self._input_feature_key,
use_deprecated_input_fn=self._use_deprecated_input_fn)
except RuntimeError:
logging.info("Skipping exporting for the same step.")
class CheckpointSaver(BaseMonitor):
"""Saves checkpoints every N steps."""
def __init__(self,
checkpoint_dir,
save_secs=None,
save_steps=None,
| tensorflow.python.platform.tf_logging.info | 1,770 |
import tensorflow as tf
key_vocabulary_filename=key_vocabulary_filename)
if key_vocabulary_filename is not None:
return numeric_combine_result
keys, counts = numeric_combine_result
if key_dtype is not tf.string:
keys = tf.strings.to_number(keys, key_dtype)
return keys, counts
@common.log_api_use(common.ANALYZER_COLLECTION)
def mean(x: common_types.TensorType,
| tensorflow.strings.to_number | 1,771 |
import tensorflow as tf
tf.split(1, max_sequence_len, embeddings)]
# Need to prepare a mask to zero out the padding symbols.
# Make a batch_size x max_sequence_len matrix where each
# row contains the length repeated max_sequence_len times.
lengths_transposed = tf.expand_dims(tf.to_int32(self.seq_lens), 1)
lengths_tiled = tf.tile(lengths_transposed, [1, max_sequence_len])
# Make a matrix where each row contains [0, 1, ..., max_sequence_len]
r = tf.range(0, max_sequence_len, 1)
range_row = tf.expand_dims(r, 0)
range_tiled = tf.tile(range_row, [batch_size, 1])
self.lengths_transposed = lengths_transposed
self.lengths_tiled = lengths_tiled
self.range_row = range_row
self.range_tiled = range_tiled
# Use the logical operations to create a mask
indicator = tf.less(range_tiled, lengths_tiled+1) #i.e. where seq len is less than index
trim = np.ones(indicator.get_shape())
| tensorflow.expand_dims | 1,772 |
import tensorflow as tf
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testAttentionDecoder2(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.GRUCell(2)
inp = [tf.constant(0.5, shape=[2, 2])] * 2
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4,
num_heads=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
| tensorflow.nn.rnn_cell.GRUCell | 1,773 |
import tensorflow as tf
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
| tensorflow.control_dependencies | 1,774 |
import tensorflow.contrib.rnn as rnn
# 0. Reformat input shape to become a sequence
x = tf.split(features[TIMESERIES_COL], N_INPUTS, 1)
# 1. Configure the RNN
lstm_cell = rnn.BasicLSTMCell(LSTM_SIZE, forget_bias = 1.0)
outputs, _ = rnn.static_rnn(lstm_cell, x, dtype = tf.float32)
# Slice to keep only the last cell of the RNN
| tensorflow.contrib.rnn.BasicLSTMCell | 1,775 |
import tensorflow as tf
return out_img
def build_discriminator(self,image,reuse=False,name='discriminator'):
with tf.variable_scope(name):
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
def lrelu(x, alpha,name='lrelu'):
with tf.variable_scope(name):
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
| tensorflow.get_variable_scope | 1,776 |
import tensorflow as tf
# check that we have some nodes to checkpoint
if not checkpoints:
raise Exception('no checkpoints nodes found or given as input! ')
# disconnect dependencies between checkpointed tensors
checkpoints_disconnected = {}
for x in checkpoints:
if x.op and x.op.name is not None:
grad_node = tf.stop_gradient(x, name=x.op.name+"_sg")
else:
grad_node = tf.stop_gradient(x)
grad_node.op._set_device(x.op.node_def.device)
checkpoints_disconnected[x] = grad_node
# partial derivatives to the checkpointed tensors and xs
ops_to_copy = fast_backward_ops(seed_ops=[y.op for y in ys],
stop_at_ts=checkpoints, within_ops=fwd_ops)
debug_print("Found %s ops to copy within fwd_ops %s, seed %s, stop_at %s",
len(ops_to_copy), fwd_ops, [r.op for r in ys], checkpoints)
debug_print("ops_to_copy = %s", ops_to_copy)
| tensorflow.stop_gradient | 1,777 |
import tensorflow as tf
mask2 = mask2_ - mask
pred_annotation, logits = inference((1-mask)*image + mask*255, keep_probability,z)
tf.summary.image("input_image", image, max_outputs=2)
tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
# loss0 = tf.reduce_mean(tf.abs(z))
loss = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square((image - logits)),[1,2,3])))
# loss2 = tf.reduce_mean(tf.square((image - logits)*mask2))
# loss = loss1 + loss2 + loss0
# loss = tf.reduce_mean(tf.squared_difference(logits ,annotation ))
loss_summary = tf.summary.scalar("entropy", loss)
grads = train_z(loss,z)
trainable_var = tf.trainable_variables()
if FLAGS.debug:
for var in trainable_var:
utils.add_to_regularization_and_summary(var)
train_op = train(loss, trainable_var)
print("Setting up summary op...")
summary_op = tf.summary.merge_all()
| tensorflow.summary.scalar | 1,778 |
import tensorflow as tf
tf.float32)
h = tf.zeros([config.num_layers, self.batch_size, config.hidden_size],
tf.float32)
self._initial_state = (tf.contrib.rnn.LSTMStateTuple(h=h, c=c),)
outputs, h, c = self._cell(inputs, h, c, self._rnn_params, is_training)
outputs = tf.transpose(outputs, [1, 0, 2])
outputs = tf.reshape(outputs, [-1, config.hidden_size])
return outputs, (tf.contrib.rnn.LSTMStateTuple(h=h, c=c),)
def _get_lstm_cell(self, config, is_training):
| tensorflow.transpose | 1,779 |
import tensorflow as tf
squeeze_b_z = tf.reshape(reoriented, [-1, y_size_new, x_size, c_size], name='reshape_bz')
resize_b_z = tf.compat.v1.image.resize_bilinear(squeeze_b_z, [y_size_new, x_size_new], align_corners=align)
resume_b_z = tf.reshape(resize_b_z, [-1, z_size_new, y_size_new, x_size_new, c_size], name='resume_bz')
| tensorflow.reshape | 1,780 |
import tensorflow as tf
self.weight_initializer = tf.contrib.layers.xavier_initializer()
| tensorflow.contrib.layers.xavier_initializer | 1,781 |
import tensorflow as tf
)(self.rgb_images_placeholder, is_training)
init = tf.global_variables_initializer()
config = tf.ConfigProto(log_device_placement=False)
if self.on_gpu:
config.gpu_options.allow_growth = True
| tensorflow.ConfigProto | 1,782 |
import tensorflow as tf
Wsa = tf.placeholder(dtype=tf.float32, shape=[None, None], name="Wsa")
wa = tf.placeholder(dtype=tf.float32, shape=[None, None], name="wa")
| tensorflow.placeholder | 1,783 |
import tensorflow as tf
logits = tf.reduce_sum(tf.multiply(output_layer,output_weights),-1)
| tensorflow.multiply | 1,784 |
import tensorflow as tf
x_discrete = self.bit_to_int(
tf.to_int32(x_means_bits), num_bits=self.hparams.z_size, base=2)
# Reshape x_discrete
shape_x = common_layers.shape_list(x)
shape_discrete = shape_x[:-1]
x_discrete = tf.reshape(x_discrete, shape_discrete)
x_means = tf.reshape(x_means, shape=shape_x)
h1 = x + tf.stop_gradient(x_means - x)
h2 = tf.layers.dense(tf.nn.relu(h1), self.hparams.filter_size, name="vch2")
res = tf.layers.dense(
tf.nn.relu(h2), self.hparams.hidden_size, name="vcfin")
embed_fn = partial(self.embed)
return {
"dense": res,
"discrete": x_discrete,
"loss": loss,
"embed": embed_fn
}
| tensorflow.nn.relu | 1,785 |
import tensorflow as tf
logits = tf.layers.dense(
x, self.hparams.problem.num_actions, name="dense2"
)
logits = clip_logits(logits, self.hparams)
logits = tf.expand_dims(logits, axis=1)
value = tf.layers.dense(x, self.distributional_value_size)
return {"target_policy": logits, "target_value": value}
| tensorflow.expand_dims | 1,786 |
import tensorflow as tf
dims_in = [dim_in] + dim_hid[:-1]
dims_out = dim_hid
res = input_
bias = (not use_batch_norm)
with tf.variable_scope(name):
for layer_idx in xrange(len(dim_hid)):
res = conv_layer(
input_=res,
filter_size=filter_sizes[layer_idx],
| tensorflow.variable_scope | 1,787 |
import tensorflow as tf
# Input weight matrix:
# (uniform initialization as in pycog)
self.W_in = \
tf.get_variable('W_in', [N_rec, N_in],
initializer=W_in_initializer,
trainable=self.W_in_train)
| tensorflow.get_variable | 1,788 |
import tensorflow as tf
def test_minimum_batch_size(self):
with self.test_session() as session:
@dynamic_batching.batch_fn_with_options(
minimum_batch_size=2, timeout_ms=1000)
def f(a, b):
batch_size = tf.shape(a)[0]
return a + b, tf.tile([batch_size], [batch_size])
output = f(tf.constant([[1, 3]]), tf.constant([2]))
tf.train.start_queue_runners()
start = datetime.datetime.now()
session.run(output)
duration = datetime.datetime.now() - start
# There should have been a timeout here because only one sample was added
# and the minimum batch size is 2.
self.assertLessEqual(.9, duration.total_seconds())
self.assertGreaterEqual(1.5, duration.total_seconds())
| tensorflow.train.start_queue_runners | 1,789 |
import tensorflow as tf
ops.reset_default_graph()
sess = tf.Session()
my_var = tf.Variable(tf.zeros([1,20]))
merged = tf.summary.merge_all()
| tensorflow.zeros | 1,790 |
import tensorflow as tf
import anchors
import learning_rates
import losses
import mask_rcnn_architecture
_WEIGHT_DECAY = 1e-4
def create_optimizer(learning_rate, params):
"""Creates optimized based on the specified flags."""
if params['optimizer'] == 'momentum':
optimizer = tf.train.MomentumOptimizer(
learning_rate, momentum=params['momentum'])
elif params['optimizer'] == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
elif params['optimizer'] == 'adadelta':
optimizer = tf.train.AdadeltaOptimizer(learning_rate)
elif params['optimizer'] == 'adagrad':
optimizer = tf.train.AdagradOptimizer(learning_rate)
elif params['optimizer'] == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
learning_rate, momentum=params['momentum'])
elif params['optimizer'] == 'lars':
optimizer = tf.contrib.opt.LARSOptimizer(
learning_rate,
momentum=params['momentum'],
weight_decay=params['lars_weight_decay'],
skip_list=['batch_normalization', 'bias'])
else:
| tensorflow.train.AdamOptimizer | 1,791 |
import tensorflow as tf
fake_loss = tf.reduce_sum(tf.square(logits))
grad_norms = [
_get_grad_norm(
fake_loss, tf.trainable_variables('.*/progressive_gan_block_1/.*')),
_get_grad_norm(
fake_loss, tf.trainable_variables('.*/progressive_gan_block_2/.*')),
_get_grad_norm(
fake_loss, tf.trainable_variables('.*/progressive_gan_block_3/.*'))
]
grad_norms_output = None
with self.test_session(use_gpu=True) as sess:
sess.run(tf.global_variables_initializer())
grad_norms_output = np.array([
| tensorflow.trainable_variables | 1,792 |
import tensorflow as tf
)
)
'''
with tf.train.MonitoredTrainingSession(
checkpoint_dir=params.output, hooks=train_hooks,
save_checkpoint_secs=None, config=config) as sess:
while not sess.should_stop():
| tensorflow.train.MonitoredTrainingSession | 1,793 |
import tensorflow as tf
vsize = input_shape[1]
passage_length = tf.shape(passage_word_idx)[1]
with tf.variable_scope('final_distribution'):
vocab_dist = p_gen * vocab_dist
attn_dist = (1.0-p_gen) * attn_dist
# Concatenate some zeros to each vocabulary dist, to hold the probabilities for phrases
extended_vsize = vsize
if self.max_phrase_size is not None:
extended_vsize += self.max_phrase_size
extra_zeros = tf.zeros((batch_size, self.max_phrase_size))
vocab_dist = tf.concat(values=[vocab_dist, extra_zeros], axis=1) # [batch_size, extended_vsize]
if self.options.add_first_word_prob_for_phrase: # add prob of the first word to each phrase
attn_dist = add_first_word_prob_to_atten_dists(self.in_passage_words, self.phrase_starts,
vocab_dist, attn_dist)
# match attn_dist[batch_size, passage_length] to sparse one-hot representation [batch_size, passage_length, extended_vsize]
batch_nums = tf.range(0, limit=batch_size) # shape (batch_size)
batch_nums = tf.expand_dims(batch_nums, axis=1) # shape (batch_size, 1)
batch_nums = tf.tile(batch_nums, [1, passage_length]) # shape (batch_size, passage_length)
step_nums = tf.range(0, limit=passage_length) # [passage_length]
step_nums = tf.expand_dims(step_nums, axis=0) # shape (1, passage_length)
step_nums = tf.tile(step_nums, [batch_size, 1]) # shape (batch_size, passage_length)
| tensorflow.concat | 1,794 |
import tensorflow as tf
.AvgPooling('downsample', 2)
.Conv2D('conv0', 20, 5, padding='VALID')
.MaxPooling('pool0', 2)
.Conv2D('conv1', 20, 5, padding='VALID')
.FullyConnected('fc1', out_dim=32)
.FullyConnected('fct', out_dim=6, nl=tf.identity,
W_init=tf.constant_initializer(),
b_init=tf.constant_initializer([1, 0, HALF_DIFF, 0, 1, HALF_DIFF]))())
# output 6 parameters for affine transformation
stn = tf.reshape(stn, [-1, 2, 3], name='affine') # bx2x3
stn = tf.reshape(tf.transpose(stn, [2, 0, 1]), [3, -1]) # 3 x (bx2)
coor = tf.reshape(tf.matmul(xys, stn),
[WARP_TARGET_SIZE, WARP_TARGET_SIZE, -1, 2])
coor = tf.transpose(coor, [2, 0, 1, 3], 'sampled_coords') # b h w 2
sampled = ImageSample('warp', [image, coor], borderMode='constant')
return sampled
with argscope([Conv2D, FullyConnected], nl=tf.nn.relu):
with tf.variable_scope('STN1'):
sampled1 = get_stn(image)
| tensorflow.transpose | 1,795 |
import tensorflow as tf
def get_params(self):
"""See base class."""
return {}, {}
def encode(self, x, encode_params):
"""See base class."""
del encode_params # Unused.
signs = tf.sign(x)
abs_vals = tf.abs(x)
ints = tf.floor(abs_vals)
floats = abs_vals - ints
return {
self.ENCODED_SIGNS_KEY: signs,
self.ENCODED_INTS_KEY: ints,
self.ENCODED_FLOATS_KEY: floats
}
| tensorflow.abs | 1,796 |
import tensorflow as tf
with tf.variable_scope(name) as scope:
| tensorflow.variable_scope | 1,797 |
import tensorflow as tf
def contra_step_lossV2(pred, tgt):
# Step-wise contrastive loss
pred1, pred2 = tf.split(pred, 2, axis=0)
tgt1, tgt2 = tf.split(tgt, 2, axis=0)
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
tgt_small = tf.where(geq, tgt2, tgt1)
pred_larg = tf.where(geq, pred1, pred2)
pred_small = tf.where(geq, pred2, pred1)
loss = tf.maximum(0.0, (tgt_larg - tgt_small) - (pred_larg - pred_small))
loss = tf.reduce_mean(loss)
return loss
def contra_step_lossV3(pred, tgt, margin=1.0):
# Step-wise contrastive loss
pred1, pred2 = tf.split(pred, 2, axis=0)
tgt1, tgt2 = tf.split(tgt, 2, axis=0)
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
tgt_small = tf.where(geq, tgt2, tgt1)
| tensorflow.reduce_mean | 1,798 |
import tensorflow as tf
if self.dale_ratio:
reg += self.L1_out * tf.reduce_mean(tf.matmul(tf.abs(self.W_out) * self.output_Connectivity, self.Dale_out))
else:
reg += self.L1_out * tf.reduce_mean(tf.abs(self.W_out) * self.output_Connectivity)
# L2 weight regularization
reg += self.L2_in * tf.reduce_mean(tf.square(tf.abs(self.W_in) * self.input_Connectivity))
reg += self.L2_rec * tf.reduce_mean(tf.square(tf.abs(self.W_rec) * self.rec_Connectivity))
if self.dale_ratio:
reg += self.L2_out * tf.reduce_mean(tf.square(
tf.matmul(tf.abs(self.W_out) * self.output_Connectivity, self.Dale_out)))
else:
reg += self.L2_out * tf.reduce_mean(tf.square(tf.abs(self.W_out) * self.output_Connectivity))
# L2 firing rate regularization
reg += self.L2_firing_rate * tf.reduce_mean(tf.square(tf.nn.relu(self.states)))
# susillo regularization
reg += self.sussillo_constant * self.sussillo_reg()
return reg
# implement one step of the RNN
def rnn_step(self, rnn_in, state):
| tensorflow.abs | 1,799 |
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