seed stringlengths 25 2.89k | seed_api stringlengths 14 102 | index int64 0 14.8k |
|---|---|---|
import tensorflow as tf
from src.nn_utils.general import exp_mask_for_high_rank, mask_for_high_rank
from src.nn_utils.integration_func import directional_attention_with_dense
from src.nn_utils.nn import bn_dense_layer, linear
def bi_directional_simple_block_attention(
rep_tensor, rep_mask, block_len=5, scope... | tensorflow.variable_scope | 14,700 |
import tensorflow as tf
num_or_size_splits=num_of_joints,
axis=-1)
losses = [] # 计算每一个关键点的损失值，并累加求平均
for i in range(num_of_joints):
heatmap_pred = tf.squeeze(heatmap_pred_list[i])
heatmap_true = tf.squeeze(heatmap_true_list... | tensorflow.squeeze | 14,701 |
import tensorflow as tf
[self.vocab.word_size() - 2, self.vocab.word_embed_dim],
dtype=tf.float32,
initializer=tf.constant_initializer(
... | tensorflow.concat | 14,702 |
import tensorflow as tf
log_probs = tf.nn.log_softmax(logits, axis=-1)
labels = tf.reshape(labels, [-1])
| tensorflow.reshape | 14,703 |
import tensorflow as tf
step_callback: (optional) A function that will be called before each
optimization step, step_callback(iteration, feed_dict)
'''
if self.sess is not None:
self.sess.close()
self.sess = tf.Session(graph=self.graph)
with self... | tensorflow.Session | 14,704 |
import tensorflow as tf
w1_a = tf.get_variable('w1_a', [self.a_dim, n_l1], initializer=init_w, trainable=trainable)
b1 = tf.get_variable('b1', [1, n_l1], initializer=init_b, trainable=trainable)
| tensorflow.get_variable | 14,705 |
import tensorflow as tf
else:
return tf.reshape(tf.stack(values=h, axis=1), [-1])
def lstm(xs, ms, s, scope, nh, init_scale=1.0):
nbatch, nin = [v.value for v in xs[0].get_shape()]
with tf.variable_scope(scope):
wx = tf.get_variable("wx", [nin, nh*4], initializer=ortho_init(init_scale))
... | tensorflow.matmul | 14,706 |
import tensorflow as tf
left_in.append(tf.random_normal((1, size * 2)))
right_in.append(tf.random_normal((1, size * 2)))
tracking.append(tf.random_normal((1, tracker_size * 2)))
out = reducer(left_in, right_in, tracking=tracking)
self.assertEqual(batch_size, len(out))
self.as... | tensorflow.device | 14,707 |
import tensorflow as tf
gan_train_ops = tf.contrib.gan.gan_train_ops(gan_model, gan_loss, gen_optimizer, dis_optimizer)
while_loop = tf.contrib.tpu.while_loop if params['use_tpu'] else tf.while_loop
# train the discriminator 100 steps
inputs = [tf.constant(0), tf.constant(0.0)]
... | tensorflow.constant | 14,708 |
import tensorflow as tf
# Create connected layers: fc1, fc2
with tf.contrib.framework.arg_scope([tf.contrib.layers.fully_connected],
normalizer_fn=tf.contrib.layers.batch_norm,
normalizer_params={... | tensorflow.name_scope | 14,709 |
import tensorflow as tf
kernel = _variable_with_weight_decay('weights',
shape=kernel_shape,
use_xavier=use_xavier,
stddev=stddev,
w... | tensorflow.constant_initializer | 14,710 |
import tensorflow as tf
if alpha > 0:
return tf.maximum(alpha * x, x, name=name)
else:
return tf.nn.relu(x, name=name)
| tensorflow.nn.relu | 14,711 |
import tensorflow as tf
val_losses = np.array(val_losses)
return (training_losses,val_losses, int(parameter_num))
"""
Test RNN graph 0 step
"""
def test_rnn(test_data_x,test_data_y, g, checkpoint, input_prob, output_prob, state_prob, num_test):
with tf.Session() as sess:
... | tensorflow.Session | 14,712 |
import tensorflow as tf
self.is_training = tf.placeholder(tf.bool)
initializer = tf.contrib.layers.variance_scaling_initializer()
# Embedding Lookup 16
with tf.device('/cpu:0'), tf.name_scope("embedding"):
if use_he_uniform:
self.embedding_W = tf.ge... | tensorflow.get_variable | 14,713 |
import tensorflow as tf
numpy.random.seed(42)
tf.set_random_seed(1234)
sess = tf.Session(graph=tf.get_default_graph())
| tensorflow.set_random_seed | 14,714 |
import tensorflow as tf
pred, K, reprojected, crit_fake = model(x2d)
crit_real = model.crit(x3d)
crit_dis = tf.reduce_mean(tf.square(crit_real - tf.ones_like(crit_real))) + tf.reduce_mean(tf.square(crit_fake - tf.zeros_like(crit_fake)))
crit_gen = tf.reduce_mean(tf.square(crit_fake - tf.ones_like(crit_fake)))... | tensorflow.zeros_like | 14,715 |
import tensorflow as tf
f2 = tf.reduce_sum(half(masked, 1), 2) / tf.reduce_sum(half(mask, 1))
return tf.concat([x, f1, f2], 1)
def batch_norm(x, train, name, decay=0.99, epsilon=1e-5):
shape = x.get_shape().as_list()
with tf.variable_scope(name):
beta = tf.get_variable('beta', [shape[-... | tensorflow.constant_initializer | 14,716 |
import tensorflow as tf
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with tf.gfile... | tensorflow.gfile.Open | 14,717 |
import tensorflow as tf
log_r = tf.cond(
tf.less(t + 1, self.max_seq_len),
lambda: self.tilt(rnn_out, latent_encoded, self.targets_ta.read(t+1)),
lambda: 0.)
# On the last step, log_r = 0.
log_r *= tf.to_float(t < self.seq_lengths - 1)
weights += log_r - prev... | tensorflow.to_float | 14,718 |
import tensorflow as tf
Returns:
A boolean tensor of shape [M, N], True for entries which are sampled.
"""
def _minibatch_subsample_fn(inputs):
indicators, targets = inputs
return sample_balanced_positive_negative(tf.cast(indicators, tf.bool),
... | tensorflow.cast | 14,719 |
import tensorflow as tf
mask = tf.equal(mask, tf.ones_like(mask))
hidden_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
input_size = query.get_shape().as_list()[-1]
# Trainable parameters
w1 = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1)... | tensorflow.tensordot | 14,720 |
import tensorflow as tf
# model related configuration
tf.app.flags.DEFINE_integer(
'train_image_size', 352,
'The size of the input image for the model to use.')
tf.app.flags.DEFINE_integer(
'resnet_size', 50,
'The size of the ResNet model to use.')
tf.app.flags.DEFINE_integer(
| tensorflow.app.flags.DEFINE_integer | 14,721 |
import tensorflow as tf
"""Define a single cell with variational dropout"""
def get_a_cell(state_size,input_prob,state_prob,num_input):
if cell_type == 'LSTM':
if activation == 'linear':
lstm=tf.nn.rnn_cell.LSTMCell(num_units=state_size, activation = tf.identity, state_i... | tensorflow.nn.rnn_cell.LSTMCell | 14,722 |
import tensorflow as tf
g = tf.gradients(U, x, grad_ys=self.dummy_x1_tf)[0]
return tf.gradients(g, self.dummy_x1_tf)[0]
| tensorflow.gradients | 14,723 |
import tensorflow as tf
* Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR '18
"""
def __init__(self, data_set, exp_settings, forward_only=False):
"""Create the model.
Args:... | tensorflow.contrib.training.HParams | 14,724 |
import tensorflow as tf
callback.on_rollout_start()
if step % self.update_buffer_interval ==0 and step>self.learning_starts:
mean_agent = sum(all_r)/sum(all_r_step)
mean_exp = sum(all_exp_r)/sum(all_exp_r_step)
add_r = me... | tensorflow.Summary.Value | 14,725 |
import tensorflow as tf
rnn_params,
base_variable_scope="Model/RNN")
tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, params_saveable)
self._cost = tf.get_collection_ref(util.with_prefix(self._name, "cost"))[0]
| tensorflow.add_to_collection | 14,726 |
import tensorflow as tf
return tf.reduce_mean(loss)
loss = tf.map_fn(fn=lambda inp: sample_compute(inp), elems=tf.range(resample), dtype=tf.float32,
parallel_iterations=32)
final_loss = tf.reduce_mean(loss)
return final_loss
def contra_traj_lossV1(pred, tgt, temp=10.0):... | tensorflow.reduce_mean | 14,727 |
import tensorflow as tf
pred_mat = tf.get_variable('pred_mat', [in_size, self._out_vocab_size])
pred_bias = tf.get_variable('pred_bias', [self._out_vocab_size])
# Make a prediction for each tweet.
def GetWordPred(o_):
logits = tf.nn.xw_plus_b(o_, pred_mat, pred_bias)
return tf.nn.softmax(l... | tensorflow.nn.softmax | 14,728 |
import tensorflow as tf
def train_rnn_multi(raw_data_x, raw_data_y, val_data_x, val_data_y, timeindex_train, timeindex_val, g, num_epochs, num_steps, batch_size, input_prob, output_prob, state_prob, epoch_before_val = 50, max_checks_without_progress=50,epoch_overlap=None, verbose=True, save=False):
with tf.Sess... | tensorflow.Session | 14,729 |
import tensorflow as tf
examples_per_sec = num_epochs * num_batches * batch_size / wall_time
self.report_benchmark(
name="eager_train_%s" %
("gpu" if tfe.num_gpus() > 0 else "cpu"),
iters=num_epochs * num_batches,
extras={"examples_per_sec": examples_per_sec},
... | tensorflow.test.main | 14,730 |
import tensorflow as tf
Args:
var_name: name of variable as a string.
"""
if var_name not in self._initializers:
if var_name == self.GAMMA:
self._initializers[self.GAMMA] = tf.ones_initializer()
elif var_name == self.BETA:
self._initializers[self.BETA] = tf.zeros_initiali... | tensorflow.zeros_initializer | 14,731 |
import tensorflow as tf
n_neg_to_select = tf.cast(params['negative_ratio'] * n_positives, tf.int32)
n_neg_to_select = tf.minimum(n_neg_to_select, tf.cast(n_negtives, tf.int32))
| tensorflow.cast | 14,732 |
import tensorflow as tf
"""
max_time = 8
batch_size = 16
inputs = tf.random_uniform([batch_size, max_time],
maxval=30521, dtype=tf.int32)
| tensorflow.random_uniform | 14,733 |
from tensorflow.python.ops import random_ops
else:
gradient_shape = gradient.get_shape()
noise = random_ops.truncated_normal(gradient_shape) * gradient_noise_scale
noisy_gradients.append(gradient + noise)
| tensorflow.python.ops.random_ops.truncated_normal | 14,734 |
from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
path_values: A `Tensor` of type `float32`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
"""
result = _op_def_lib.apply_op("UnpackPath", path=path,
path_values=path_val... | tensorflow.core.framework.op_def_pb2.OpList | 14,735 |
import tensorflow as tf
by mistake.
"""
def fun_(*args, **kwargs):
try:
return fun(*args, **kwargs)
except ValueError as e:
if 'reuse' in str(e):
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
return fun(*args, **kwar... | tensorflow.get_variable_scope | 14,736 |
import tensorflow as tf
min_score_thresh=0.65,
min_iou_thresh=0.5,
is_class_agnostic=False)
nms_masks_expected2 = tf.stack([mask2, mask0, mask5, mask4])
nms_scores_expected2 = tf.constant([0.95, 1.0, 0.8, 0.7], dtype=tf.float32)
nms_classes_expected2 = tf.constant([0, 1, 2, 2], d... | tensorflow.constant | 14,737 |
import tensorflow as tf
else:
return -tf.reduce_sum(log_sum_exp(log_probs), [1, 2])
def mse_loss(pred, labels):
try:
batch_size = tf.cast(pred.shape[0], tf.float32)
except Exception as e:
print('Pred is a tf tensor %s' % str(e.message))
batch_size = tf.cast(tf.shape(pred)[0], tf.float32)
... | tensorflow.nn.l2_loss | 14,738 |
import tensorflow as tf
x,
y_size[:-1],
kernel,
align_corners=False)
resized = tf.nn.conv3d_transpose(
value=resized,
filter=kernel,
output_shape=y_size,
strides=[1, 1, 1, 1, 1],
... | tensorflow.nn.bias_add | 14,739 |
import tensorflow as tf
td_map[self.train_model.states_ph] = states
td_map[self.train_model.dones_ph] = masks
td_map[self.polyak_model.states_ph] = states
td_map[self.polyak_model.dones_ph] = masks
if writer is not None:
# run loss backprop with summ... | tensorflow.RunOptions | 14,740 |
import tensorflow as tf
"target_action": tf.zeros(
obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32),
"target_reward": tf.zeros(
obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32),
"target_policy": tf.zeros(
obs_shape[:1] + [num_target_frames] + [action_space.... | tensorflow.zeros | 14,741 |
import tensorflow as tf
arc_seq = arc_seq.write(start_id + 2 * i, index)
curr_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(
| tensorflow.nn.sparse_softmax_cross_entropy_with_logits | 14,742 |
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten
# Block 1
conv1a = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[8, 8], strides=4, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(self.inputs)
conv1b = Conv2D(padding="same", filters=RNN_SIZE//... | tensorflow.keras.layers.Conv2D | 14,743 |
import tensorflow as tf
def dense(x, num_units, scope="dense", training=True, ema=None, init=False, bias_initializer=tf.constant_initializer(0.)):
with tf.variable_scope(scope):
| tensorflow.constant_initializer | 14,744 |
import tensorflow as tf
if isinstance(metric, ShapeAccuracyMetric):
labels = sample['shapes']
weights = tf.math.sign(labels + 1) # -1 is mapped to zero, else 1
metric.update(labels, detections['shapes_logits'], weights)
elif isinstance(metric, BoxIoUMetric):
scene_id = str(... | tensorflow.reshape | 14,745 |
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
from Bunch import Bunch
tf.app.flags.DEFINE_string('input_path', '../data/tmp/grid03.14.c.tar.gz', 'input folder')
tf.app.flags.DEFINE_string('input_name', '', 'input folder')
tf.app.flags.DEFINE_string('test_path', '', 'test set fo... | tensorflow.app.flags.DEFINE_string | 14,746 |
import tensorflow as tf
# Gain bias
bias_shape = [1, 1, 1, 1, self.hgru_k[idx]]
if self.gate_bias_init == 'chronos':
bias_init = -tf.log(
tf.random_uniform(
bias_shape,
mi... | tensorflow.get_variable | 14,747 |
import tensorflow as tf
print('\rTrained in %.3fs. Global step %i' % (time() - start, step+1))
return summary
class PPO_HC(PPO):
def build_anet(self, state_in, name, reuse=False):
reg = tf.contrib.layers.l2_regularizer(1e-3)
with tf.variable_scope(name, reuse=reuse):
l... | tensorflow.layers.dense | 14,748 |
import tensorflow as tf
REPLACE_ITER_C = 1500
MEMORY_CAPACITY = 200000
BATCH_SIZE = 32
DISPLAY_THRESHOLD = 100 # display until the running reward > 100
DATA_PATH = './data'
LOAD_MODEL = False
SAVE_MODEL_ITER = 100000
RENDER = False
OUTPUT_GRAPH = False
ENV_NAME = 'BipedalWalker-v2'
GLOBAL_STEP = tf.Variable(0, train... | tensorflow.train.exponential_decay | 14,749 |
import tensorflow as tf
def build_value(self, _input):
with tf.variable_scope('VF'):
hidden = tf.layers.dense(inputs=_input,
units=self.vf_hidden_size,
activation=tf.nn.elu)
w = tf.get_variable("weights", (se... | tensorflow.stop_gradient | 14,750 |
import tensorflow as tf
def avg_norm(t):
return tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(t), axis=-1)))
def gradient_add(g1, g2, param):
print([g1, g2, param.name])
assert (not (g1 is None and g2 is None)), param.name
if g1 is None:
return g2
elif g2 is None:
return g1
e... | tensorflow.nn.moments | 14,751 |
import tensorflow as tf
def assign_lr(self, session, lr_value):
session.run(self._lr_update, feed_dict={self._new_lr: lr_value})
def export_ops(self, name):
"""Exports ops to collections."""
self._name = name
ops = {util.with_prefix(self._name, "cost"): self._cost}
if self._is_training:
... | tensorflow.add_to_collection | 14,752 |
import tensorflow as tf
@registry.register_model
class FeedForwardCategoricalPolicy(PolicyBase):
"""Feed-forward categorical."""
def body(self, features):
observations = features["inputs_raw"]
observations = tf.cast(observations, tf.float32)
flat_observations = tf.layers.flatten(observations)
wit... | tensorflow.layers.flatten | 14,753 |
import tensorflow as tf
layer_c2 = tf.layers.dense(layer_c1, 256, tf.nn.relu, kernel_regularizer=reg)
vf = tf.layers.dense(layer_c2, 1, kernel_regularizer=reg)
| tensorflow.layers.dense | 14,754 |
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_sparse_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import sparse_ops
class BatchedS... | tensorflow.load_op_library | 14,755 |
import tensorflow as tf
# 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_normalizat... | tensorflow.constant | 14,756 |
import tensorflow as tf
elif decoder.update_first:
output, state = update(state, input_, None, input_symbol)
context, new_weights = look(time, output, input_, pos=pos, prev_weights=prev_weights, context=context)
if decoder.conditional_rnn:
with tf.variable_scope('condi... | tensorflow.argmax | 14,757 |
import tensorflow as tf
def evaluate_legendre_polynomial(degree_l: TensorLike,
order_m: TensorLike,
x: TensorLike) -> TensorLike:
degree_l = tf.convert_to_tensor(value=degree_l)
order_m = tf.convert_to_tensor(value=order_m)
x = tf.convert_to_ten... | tensorflow.equal | 14,758 |
import tensorflow as tf
Arguments:
Y_labels -- ground truth vector
N_classes -- the number of classes in the ground truth vector
N_ch -- number of channels, if any (for the feature vector only)
Returns:
one_hot -- one hot matrix encoding
"""
# Create a tensot flow constant equal to t... | tensorflow.expand_dims | 14,759 |
import tensorflow as tf
with tf.variable_scope('target_q'):
self.target_q = R + self.gamma * self.q_
with tf.variable_scope('abs_TD'):
self.abs_td = tf.abs(self.target_q - self.q)
self.ISWeights = tf.placeholder(tf.float32, [None, 1], name='IS_weights')
with tf... | tensorflow.placeholder | 14,760 |
import tensorflow as tf
def correlation_loss(source_samples, target_samples, weight, name='corr_loss'):
"""Adds a similarity loss term, the correlation between two representations.
Args:
source_samples: a tensor of shape [num_samples, num_features]
target_samples: a tensor of shape [num_samples, num_f... | tensorflow.reduce_mean | 14,761 |
import tensorflow as tf
q_values_adaptive = q_func(observations_ph.get(), num_actions, scope="adaptive_q_func")
perturb_for_adaption = perturb_vars(original_scope="q_func", perturbed_scope="adaptive_q_func")
kl = tf.reduce_sum(tf.nn.softmax(q_values) * (tf.log(tf.nn.softmax(q_values)) - tf.log(... | tensorflow.nn.softmax | 14,762 |
from tensorflow.python.platform import gfile
self.assertEqual([], save.last_checkpoints)
s1 = save.save(sess, os.path.join(save_dir, "s1"))
self.assertEqual([s1], save.last_checkpoints)
self.assertTrue(gfile.Exists(s1))
s2 = save.save(sess, os.path.join(save_dir, "s2"))
self.asser... | tensorflow.python.platform.gfile.Exists | 14,763 |
import tensorflow as tf
trainable=True
)
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)... | tensorflow.cast | 14,764 |
import tensorflow as tf
W_p = self._make_var('W_p', (1, 1, ch_mul * ch, ch))
X = tf.nn.relu(X)
| tensorflow.nn.relu | 14,765 |
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