seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
hard_negative_loss, hard_negative_active_loss, hard_negative_summaries = (
compute_loss_and_create_summaries(use_semi_hard=False))
(semi_hard_negative_loss, semi_hard_negative_active_loss,
semi_hard_negative_summaries) = (
compute_loss_and_create_summaries(use_semi_hard=True))
summaries = {
'triplet_loss/Margin':
tf.constant(margin),
'triplet_loss/Anchor/Positive/Distance/Mean':
tf.math.reduce_mean(anchor_positive_distances),
'triplet_mining/Anchor/Positive/Distance/Mean':
tf.math.reduce_mean(anchor_positive_mining_distances),
}
if summarize_percentiles:
summaries.update({
'triplet_loss/Anchor/Positive/Distance/Median':
tfp.stats.percentile(anchor_positive_distances, q=50),
'triplet_mining/Anchor/Positive/Distance/Median':
tfp.stats.percentile(anchor_positive_mining_distances, q=50),
})
summaries.update(hard_negative_summaries)
summaries.update(semi_hard_negative_summaries)
if use_semi_hard:
| tensorflow.math.reduce_mean | 400 |
import tensorflow as tf
sync_warmup_vfn = tf.group(*[tf.assign(w_v, p_v) for w_v, p_v in zip(warmup_vfn.parameters(), vfn.parameters())])
model = DynamicsModel(dim_state, dim_action, normalizers, FLAGS.model.hidden_sizes)
lazy_model = DynamicsModel(dim_state, dim_action, normalizers, FLAGS.model.hidden_sizes)
warmup_model = DynamicsModel(dim_state, dim_action, normalizers, FLAGS.model.hidden_sizes)
sync_warmup_model = tf.group(*[tf.assign(w_v, p_v) for w_v, p_v in zip(warmup_model.parameters(), model.parameters())])
shadow_models = [DynamicsModel(dim_state, dim_action, normalizers, FLAGS.model.hidden_sizes) for n in range(FLAGS.warmup.n_shadow_models)]
sync_model_from_lazymodel = tf.group(*[tf.assign(w_v, p_v) for w_v, p_v in zip(model.parameters(), lazy_model.parameters())])
sync_model_to_lazymodel = tf.group(*[tf.assign(w_v, p_v) for w_v, p_v in zip(lazy_model.parameters(), model.parameters())])
| tensorflow.assign | 401 |
from tensorflow.python.framework import ops
return math_ops.select(
math_ops.greater(true_positives + false_negatives, 0),
math_ops.div(true_positives, true_positives + false_negatives),
0,
name)
recall = compute_recall(true_positives, false_negatives, 'value')
with ops.control_dependencies([true_positives_update_op,
false_negatives_update_op]):
update_op = compute_recall(true_positives, false_negatives, 'update_op')
if metrics_collections:
ops.add_to_collections(metrics_collections, recall)
| tensorflow.python.framework.ops.control_dependencies | 402 |
import tensorflow as tf
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state('./model_pretrain')
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("loading checkpoint...")
| tensorflow.train.checkpoint_exists | 403 |
import tensorflow as tf
cast0 = tf.dtypes.as_string(add if not swap else sub, name="TOSTR0")
else:
cast0 = tf.cast(add if not swap else sub, tf_output0_dtype, "CAST0")
if tf_output1_dtype == tf.string:
cast1 = tf.dtypes.as_string(sub if not swap else add, name="TOSTR1")
else:
cast1 = tf.cast(sub if not swap else add, tf_output1_dtype, "CAST1")
out0 = tf.identity(cast0, "OUTPUT0")
| tensorflow.dtypes.as_string | 404 |
import tensorflow as tf
def __init__(self, include_mask=False):
self._include_mask = include_mask
self._keys_to_features = {
'image/encoded':
tf.io.FixedLenFeature((), tf.string),
'image/source_id':
tf.io.FixedLenFeature((), tf.string),
'image/height':
tf.io.FixedLenFeature((), tf.int64),
'image/width':
tf.io.FixedLenFeature((), tf.int64),
'image/object/bbox/xmin':
tf.io.VarLenFeature(tf.float32),
'image/object/bbox/xmax':
tf.io.VarLenFeature(tf.float32),
'image/object/bbox/ymin':
tf.io.VarLenFeature(tf.float32),
'image/object/bbox/ymax':
tf.io.VarLenFeature(tf.float32),
'image/object/class/label':
| tensorflow.io.FixedLenFeature | 405 |
import tensorflow as tf
correct = tf.cast(correct, tf.float32)
accuracy = tf.reduce_mean(correct)*100.0
tf.summary.scalar(scope+'accuracy',accuracy)
return accuracy
def num_correct_prediction(logits, labels):
'''
Evaluate the quality of the logits at predicting the label
'''
correct = tf.equal(tf.arg_max(logits,1), tf.arg_max(labels,1))
correct = tf.cast(correct, tf.int32)
n_correct = tf.reduce_sum(correct)
return n_correct
def optimize(loss, learning_rate, global_step):
'''
Optimization, use Gradient Descent as default
'''
| tensorflow.arg_max | 406 |
import tensorflow as tf
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
query = tf.layers.dense(query, facts_size, activation=None, name='f1_trans_shine' + stag)
query = prelu(query)
queries = tf.tile(query, [1, tf.shape(facts)[1]])
queries = tf.reshape(queries, tf.shape(facts))
din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1)
d_layer_1_all = tf.layers.dense(din_all, facts_size, activation=tf.nn.sigmoid, name='f1_shine_att' + stag)
d_layer_2_all = tf.layers.dense(d_layer_1_all, facts_size, activation=tf.nn.sigmoid, name='f2_shine_att' + stag)
d_layer_2_all = tf.reshape(d_layer_2_all, tf.shape(facts))
output = d_layer_2_all
return output
| tensorflow.layers.dense | 407 |
import tensorflow as tf
:param padding: [string] Convolution padding method, `VALID` or `SAME`.
"""
assert len(mask.get_shape()) in [3, 4], 'Mask shape must be 3D or 4D.'
if len(mask.get_shape()) == 3:
mask_ = tf.expand_dims(mask, 3)
elif len(mask.get_shape()) == 4:
mask_ = mask
assert mask.get_shape()[-1] == 1, '4D mask last dimension must be 1.'
ksize = [int(ss) for ss in w.get_shape()]
psize = [1, ksize[0], ksize[1], 1]
mask_ = tf.nn.max_pool(mask_, psize, strides, padding)
return tf.nn.conv2d(x, w, strides, padding) * mask_
| tensorflow.nn.max_pool | 408 |
import tensorflow as tf
N, PL, QL, CL, d, dc, nh = config.batch_size if not self.demo else config.batch_size, self.c_maxlen, self.q_maxlen, config.char_limit, config.hidden, config.char_dim, config.num_heads
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(tf.nn.embedding_lookup(
self.char_mat, self.ch), [N * PL, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(
self.char_mat, self.qh), [N * QL, 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)
| tensorflow.nn.embedding_lookup | 409 |
import tensorflow as tf
dec_inp_dict2 = {}
dec_inp_dict2["0"] = [
tf.constant(0, tf.int32, shape=[2]) for _ in range(3)]
dec_inp_dict2["1"] = [
tf.constant(0, tf.int32, shape=[2]) for _ in range(4)]
with tf.variable_scope("other"):
outputs_dict3, _ = tf.nn.seq2seq.one2many_rnn_seq2seq(
enc_inp, dec_inp_dict2, cell, 2, dec_symbols_dict,
embedding_size=2, feed_previous=tf.constant(True))
sess.run([tf.global_variables_initializer()])
| tensorflow.variable_scope | 410 |
import tensorflow as tf
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)
sdf_values += tf.math.sign(tf.nn.relu(interpolated + self.tol))
status2 = False
if status2:
a = 2
| tensorflow.squeeze | 411 |
import tensorflow as tf
params = [self.w0, self.b0, self.w1, self.b1]
grads = [tf.cast(grad, tf.float32) for grad in grads]
with tf.name_scope('update'):
update_op = tf.group(*[
param.assign(param - grad * self.LEARNING_RATE)
for param, grad in zip(params, grads)
])
# return update_op
with tf.name_scope('validate'):
x, y = self._build_data_pipeline()
y_hat, loss = self._build_validation_model(x, y)
with tf.control_dependencies([update_op]):
return tf.print('expect', loss, y, y_hat, summarize=50)
class DataOwner:
| tensorflow.name_scope | 412 |
import tensorflow as tf
output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as writer:
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, prediction) in enumerate(result):
probabilities = prediction["probabilities"]
| tensorflow.logging.info | 413 |
import tensorflow as tf
self._dim = None
self._p = p
def _compute(self, x, y):
self._dim = x._rank()
kernel = np.zeros((tf.size(x), tf.size(y)))
for l in tf.range(start=0, limit=tf.size(x), delta=1, dtype=None, name='l_range'):
for m in tf.range(start=0, limit=tf.size(y), delta=1, dtype=None, name='m_range'):
vx = tf.contrib.lookup.MutableHashTable(key_dtype=tf.string,
value_dtype=tf.int64,
default_value=-1)
vz = tf.contrib.lookup.MutableHashTable(key_dtype=tf.string,
value_dtype=tf.int64,
default_value=-1)
vx_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1))
vz_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1))
| tensorflow.contrib.lookup.MutableHashTable | 414 |
import tensorflow as tf
# session info
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = False
self.sess = tf.Session(config=sess_config)
self._build_graph()
# save info
self.saver = tf.train.Saver()
# initialize the model
self.sess.run(tf.global_variables_initializer())
def _build_graph(self):
"""
Builds the computation graph with Tensorflow
| tensorflow.train.Saver | 415 |
import tensorflow as tf
time_steps = tf.shape(hidden)[1]
if encoder.attn_keep_prob is not None:
state_noise_shape = [1, tf.shape(state)[1]] if encoder.pervasive_dropout else None
state = tf.nn.dropout(state, keep_prob=encoder.attn_keep_prob, noise_shape=state_noise_shape)
hidden_noise_shape = [1, 1, tf.shape(hidden)[2]] if encoder.pervasive_dropout else None
| tensorflow.shape | 416 |
import tensorflow as tf
z1 = z0 + 1
x0_clip = tf.clip_by_value(x0, zero, max_x)
x1_clip = tf.clip_by_value(x1, zero, max_x)
y0_clip = tf.clip_by_value(y0, zero, max_y)
y1_clip = tf.clip_by_value(y1, zero, max_y)
z0_clip = tf.clip_by_value(z0, zero, max_z)
z1_clip = tf.clip_by_value(z1, zero, max_z)
dim3 = width
dim2 = width * height
dim1 = width * height * depth
base = _repeat(
tf.range(num_batch) * dim1, out_depth * out_height * out_width)
base_z0_y0 = base + z0_clip * dim2 + y0_clip * dim3
| tensorflow.clip_by_value | 417 |
import tensorflow as tf
context_size = initial_context.shape[1].value
def get_logits(state, ids, time): # for beam-search decoding
with tf.variable_scope('decoder_{}'.format(decoder.name)):
state, context, pos, prev_weights = tf.split(state, [cell_state_size, context_size, 1, -1], axis=1)
input_ = embed(ids)
pos = tf.squeeze(pos, axis=1)
pos = tf.cond(tf.equal(time, 0),
lambda: pos,
lambda: update_pos(pos, ids, encoder_input_length[align_encoder_id]))
if decoder.cell_type.lower() == 'lstm' and decoder.use_lstm_full_state:
output = state
else:
# Output is always the right-most part of the state (even with multi-layer RNNs)
| tensorflow.equal | 418 |
import tensorflow as tf
c = c
h = h
z = tf.matmul(x, wx) + tf.matmul(h, wh) + b
i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z)
| tensorflow.matmul | 419 |
import tensorflow as tf
.inference_with_all_cores)
else:
save_checkpoints_steps = (FLAGS.save_checkpoints_steps or
FLAGS.iterations_per_loop)
run_config = tf.estimator.RunConfig(
model_dir=FLAGS.model_dir,
save_checkpoints_steps=save_checkpoints_steps)
image_classifier = tf.estimator.Estimator(
model_fn=model.model_fn,
config=run_config,
params=estimator_parmas)
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
| tensorflow.estimator.Estimator | 420 |
import tensorflow as tf
self.q = max(q,1)
# Initialize NN
self.weights, self.biases = self.initialize_NN(layers)
# Initialize parameters
self.lambda_1 = tf.Variable([0.0], dtype=tf.float32)
self.lambda_2 = tf.Variable([-6.0], dtype=tf.float32)
# Load IRK weights
tmp = np.float32(np.loadtxt('../../Utilities/IRK_weights/Butcher_IRK%d.txt' % (q), ndmin = 2))
weights = np.reshape(tmp[0:q**2+q], (q+1,q))
| tensorflow.Variable | 421 |
from tensorflow.python.framework import ops
@ops.RegisterShape("Any")
@ops.RegisterShape("Max")
@ops.RegisterShape("Mean")
@ops.RegisterShape("Min")
@ops.RegisterShape("Prod")
@ops.RegisterShape("Sum")
def _ReductionShape(op):
"""Common shape function for reduction ops."""
| tensorflow.python.framework.ops.RegisterShape | 422 |
import tensorflow.contrib as contrib
"scale": True,
"updates_collections": None
}
):
fc1_1 = contrib.layers.fully_connected(X, 32, scope="fc1_1")
fc1_2 = contrib.layers.fully_connected(X, 32, scope="fc1_2")
if cross_stitch_enabled:
with tf.variable_scope("cross_stitch_1"):
stitch1_1, stitch1_2 = apply_cross_stitch(fc1_1, fc1_2)
else:
stitch1_1, stitch1_2 = fc1_1, fc1_2
fc2_1 = contrib.layers.fully_connected(stitch1_1, 32, scope="fc2_1")
fc2_2 = contrib.layers.fully_connected(stitch1_2, 32, scope="fc2_2")
if cross_stitch_enabled:
with tf.variable_scope("cross_stitch_2"):
stitch2_1, stitch2_2 = apply_cross_stitch(fc2_1, fc2_2)
else:
stitch2_1, stitch2_2 = fc2_1, fc2_2
dropout2_1 = contrib.layers.dropout(stitch2_1, keep_prob=keep_prob, is_training=is_training,
scope="dropout2_1")
dropout2_2 = contrib.layers.dropout(stitch2_2, keep_prob=keep_prob, is_training=is_training,
scope="dropout2_2")
| tensorflow.contrib.layers.fully_connected | 423 |
import tensorflow as tf
b_init_args = {}
self.inputs = prev_layer.outputs
if self.inputs.get_shape().ndims != 2:
raise Exception("The input dimension must be rank 2")
n_in = int(self.inputs.get_shape()[-1])
self.n_units = n_units
with tf.variable_scope(name):
W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=LayersConfig.tf_dtype, **W_init_args)
b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, dtype=LayersConfig.tf_dtype, **b_init_args)
# self.outputs = act(tf.matmul(self.inputs, W) + b)
LayersConfig.set_keep[name] = tf.placeholder(tf.float32)
W_dropcon = tf.nn.dropout(W, LayersConfig.set_keep[name])
self.outputs = act(tf.matmul(self.inputs, W_dropcon) + b)
# self.all_layers = list(layer.all_layers)
# self.all_params = list(layer.all_params)
# self.all_drop = dict(layer.all_drop)
self.all_drop.update({LayersConfig.set_keep[name]: keep})
self.all_layers.append(self.outputs)
self.all_params.extend([W, b])
| tensorflow.placeholder | 424 |
import tensorflow as tf
# tf.divide(tf.cast(n_neg_to_select, tf.float32), n_negtives),
# tf.zeros_like(tf.cast(n_neg_to_select, tf.float32)),
# name='rand_select_negtive')
# include both selected negtive and all positive examples
final_mask = tf.stop_gradient(tf.logical_or(tf.logical_and(negtive_mask, selected_neg_mask), positive_mask))
total_examples = tf.reduce_sum(tf.cast(final_mask, tf.float32))
# add mask for glabels and cls_pred here
glabels = tf.boolean_mask(tf.clip_by_value(glabels, 0, FLAGS.num_classes), tf.stop_gradient(final_mask))
cls_pred = tf.boolean_mask(cls_pred, tf.stop_gradient(final_mask))
location_pred = tf.boolean_mask(location_pred, tf.stop_gradient(positive_mask))
gtargets = tf.boolean_mask(gtargets, tf.stop_gradient(positive_mask))
predictions = {
'classes': tf.argmax(cls_pred, axis=-1),
'probabilities': tf.reduce_max(tf.nn.softmax(cls_pred, name='softmax_tensor'), axis=-1),
'bboxes_predict': tf.reshape(bboxes_pred, [-1, 4]) }
| tensorflow.stop_gradient | 425 |
import tensorflow as tf
l1, l2 = tf.nn.sigmoid(hyp_params[0]), tf.exp(hyp_params[1])
epsilon = tf.sinh(epsilon*l2)/tf.cosh(epsilon*l2)**l1/l2
# Compute A_{h+1}
A = tf.tile(alpha_mean+epsilon*alpha_std, [1, tf.shape(X)[0], 1, 1])
# Compute z_{h}A_{h+1}
Z1 = tf.matmul(Z, A[:,:,:n_basis//2,:])/tf.sqrt(n_basis*.5)
Z2 = tf.matmul(Z, A[:,:,n_basis//2:,:])/tf.sqrt(n_basis*.5)
# Compute u_{h+1} and v_{h+1}
U, V = tf.cos(Z1)+tf.cos(Z2), tf.sin(Z1)+tf.sin(Z2)
Z = tf.concat([U, V], 3)/tf.sqrt(n_out*1.)
KL += tf.reduce_mean(alpha_std**2+alpha_mean**2-2*alpha_logstd-1)/2.
# Output layer
else:
F = tf.squeeze(tf.layers.dense(Z, n_out), [2])
return F, KL | tensorflow.reduce_mean | 426 |
import tensorflow as tf
norm_grads_q, norm_grads_policy, avg_norm_grads_f = None, None, None
avg_norm_k, avg_norm_g, avg_norm_k_dot_g, avg_norm_adj = None, None, None, None
if self.trust_region:
# [n_envs * n_steps, n_act]
grad = tf.gradients(- (loss_policy - self.ent_coef * entropy) * self.n_steps * self.n_envs,
phi_i)
# [n_envs * n_steps, n_act] # Directly computed gradient of KL divergence wrt f
kl_grad = - f_polyak_i / (f_i_ + eps)
k_dot_g = tf.reduce_sum(kl_grad * grad, axis=-1)
adj = tf.maximum(0.0, (tf.reduce_sum(kl_grad * grad, axis=-1) - self.delta) / (
tf.reduce_sum(tf.square(kl_grad), axis=-1) + eps)) # [n_envs * n_steps]
# Calculate stats (before doing adjustment) for logging.
avg_norm_k = avg_norm(kl_grad)
avg_norm_g = avg_norm(grad)
avg_norm_k_dot_g = tf.reduce_mean(tf.abs(k_dot_g))
avg_norm_adj = tf.reduce_mean(tf.abs(adj))
| tensorflow.reduce_sum | 427 |
import tensorflow as tf
self.assertEqual((2, 2), res[0].h.shape)
def testEmbeddingRNNSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in range(2)]
dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
dec, mem = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
| tensorflow.nn.rnn_cell.BasicLSTMCell | 428 |
import tensorflow as tf
trans_z = linear(tf.nn.dropout(trans_h0, keep_prob), featsize, 'trans_z')
self.translated_z = trans_z
s_h, s_w = self.output_height, self.output_width
s_h0, s_h1, s_h2, s_h3 = \
int(s_h/ns0), int(s_h/ns0/ns1), int(s_h/ns0/ns1/ns2), int(s_h/ns0/ns1/ns2/ns3)
s_w0, s_w1, s_w2, s_w3 = \
int(s_w/ns0), int(s_w/ns0/ns1), int(s_w/ns0/ns1/ns2), int(s_w/ns0/ns1/ns2/ns3)
def decode(z, skip_h3, skip_h2, skip_h1, skip_h0):
z_ = lrelu(linear(tf.nn.dropout(z, keep_prob), nf3*s_h3*s_w3, 'd_h0_lin'))
h0 = tf.nn.dropout(tf.reshape(z_, [-1, s_h3, s_w3, nf3]), keep_prob)
import IPython
IPython.embed()
h1 = lrelu(deconv2d(tf.concat([h0, skip_h3], 3),
[self.batch_size, s_h2, s_w2, nf2], name='d_h1', d_h=ns3, d_w=ns3))
h2 = lrelu(deconv2d(tf.concat([h1, skip_h2], 3),
[self.batch_size, s_h1, s_w1, nf1], name='d_h2', d_h=ns2, d_w=ns2))
h3 = lrelu(deconv2d(tf.concat([h2, skip_h1], 3),
[self.batch_size, s_h0, s_w0, nf0], name='d_h3', d_h=ns1, d_w=ns1))
print(h3.get_shape())
h4 = deconv2d(tf.concat([h3, skip_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4', d_h=ns0, d_w=ns0)
return h4
with tf.variable_scope("deconv") as scope:
output_h4 = decode(trans_z, tgtctx_h3, tgtctx_h2, tgtctx_h1, tgtctx_h0)
scope.reuse_variables()
truthoutput_h4 = decode(tgtimg_z, tgtctx_h3, tgtctx_h2, tgtctx_h1, tgtctx_h0)
| tensorflow.concat | 429 |
from tensorflow.python.framework import ops
hessians[a][b][c], where [a] runs over the epoch. For fixed [a], hessians stores the value of the hessian matrix
evaluated at the critical points; this is a nxn matrix indexed by [b][c]. The size of the matrix is predetermined
by the number of parameters in the network.
residuals -- list containing the value of the residuals at predefinite training intervals. As we are only interested in the
sign of the residuals, we define it as the difference between the predicted output \hat{y} (an in the code)
and the training labels y (Y in the code).
"""
ops.reset_default_graph() # reset the computational graph
tf.set_random_seed(1) # to keep consistent results
#----------training/test set features-------------------------
X_tr = np.transpose(X_train) # the transpose is taken to adapt to TF convenntion. This is also
f , m = X_tr.shape # f: number of features, m: number of training examples
X_tst = np.transpose(X_test) # the transpose is taken to adapt to TF convenntion. This is also
| tensorflow.python.framework.ops.reset_default_graph | 430 |
import tensorflow as tf
obs_shape = common_layers.shape_list(observations)
(frame_height, frame_width) = obs_shape[2:4]
# TODO(afrozm): We have these dummy problems mainly for hparams, so cleanup
# when possible and do this properly.
if hparams.policy_problem_name == "dummy_policy_problem_ttt":
tf.logging.info("Using DummyPolicyProblemTTT for the policy.")
policy_problem = tic_tac_toe_env.DummyPolicyProblemTTT()
else:
tf.logging.info("Using DummyPolicyProblem for the policy.")
policy_problem = DummyPolicyProblem(action_space, frame_height, frame_width)
trainer_lib.add_problem_hparams(hparams, policy_problem)
hparams.force_full_predict = True
model = registry.model(hparams.policy_network)(
hparams, tf.estimator.ModeKeys.TRAIN
)
try:
num_target_frames = hparams.video_num_target_frames
| tensorflow.logging.info | 431 |
import tensorflow as tf
net = tf.layers.dense(net, 20, activation=tf.nn.relu, kernel_initializer=init_w,
bias_initializer=init_b, name='l2', trainable=trainable)
with tf.variable_scope('q'):
q = tf.layers.dense(net, 1, kernel_initializer=init_w, bias_initializer=init_b, trainable=trainable) # Q(s,a)
return q
def learn(self, s, a, r, s_, ISW):
_, abs_td = self.sess.run([self.train_op, self.abs_td], feed_dict={S: s, self.a: a, R: r, S_: s_, self.ISWeights: ISW})
if self.t_replace_counter % self.t_replace_iter == 0:
self.sess.run([tf.assign(t, e) for t, e in zip(self.t_params, self.e_params)])
self.t_replace_counter += 1
return abs_td
class SumTree(object):
"""
This SumTree code is modified version and the original code is from:
https://github.com/jaara/AI-blog/blob/master/SumTree.py
| tensorflow.assign | 432 |
import tensorflow as tf
weights = np.random.normal(scale=0.01, size=shape).astype('f')
return safe_get(name, list(shape), initializer=tf.constant_initializer(weights), dtype=tf.float32)
def init_bias(shape, name=None):
return safe_get(name, initializer=tf.zeros(shape, dtype=tf.float32))
def init_fc_weights_xavier(shape, name=None):
fc_initializer = tf.contrib.layers.xavier_initializer(dtype=tf.float32)
return safe_get(name, list(shape), initializer=fc_initializer, dtype=tf.float32)
def init_conv_weights_xavier(shape, name=None):
conv_initializer = tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32)
return safe_get(name, list(shape), initializer=conv_initializer, dtype=tf.float32)
| tensorflow.contrib.layers.xavier_initializer | 433 |
import tensorflow as tf
- Output class tensor of the model.
"""
# Weights and biases for output softmax layer.
out_weights = tf.Variable(
tf.random_normal([self.num_units, self.n_classes]))
out_bias = tf.Variable(tf.random_normal([self.n_classes]))
# input image placeholder
| tensorflow.random_normal | 434 |
import tensorflow as tf
query = tf.matmul(query, self.v_attn)
logits = tf.reshape(query, [1, layer_id])
if self.temperature is not None:
logits /= self.temperature
if self.tanh_constant is not None:
logits = self.tanh_constant * tf.tanh(logits)
index = tf.multinomial(logits, 1)
index = tf.to_int32(index)
index = tf.reshape(index, [1])
arc_seq = arc_seq.write(start_id + 2 * i, index)
curr_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=index)
log_prob += curr_log_prob
curr_ent = tf.stop_gradient(tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=tf.nn.softmax(logits)))
entropy += curr_ent
| tensorflow.reshape | 435 |
import tensorflow as tf
cast0 = tf.dtypes.as_string(add if not swap else sub, name="TOSTR0")
else:
cast0 = tf.cast(add if not swap else sub, tf_output0_dtype, "CAST0")
if tf_output1_dtype == tf.string:
cast1 = tf.dtypes.as_string(sub if not swap else add, name="TOSTR1")
else:
cast1 = tf.cast(sub if not swap else add, tf_output1_dtype, "CAST1")
out0 = tf.identity(cast0, "TENSOR_OUTPUT0")
out1 = tf.identity(cast1, "TENSOR_OUTPUT1")
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"savedmodel_nobatch" if max_batch == 0 else "savedmodel", input_dtype,
output0_dtype, output1_dtype)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
| tensorflow.identity | 436 |
import tensorflow as tf
log_lambdas = self.parameterizer(x1)
x2, ildj = exponentiate(z2, log_lambdas, inverse=tf.constant(True))
| tensorflow.constant | 437 |
import tensorflow as tf
epoch = features["epoch"] + tf.zeros([x_shape[0]], dtype=tf.int32)
# Randomly set epoch to 0 in some cases as that's the inference value.
rand = tf.random.uniform([x_shape[0]])
epoch = tf.where(rand < 0.1, tf.zeros_like(epoch), epoch)
# Embed the epoch number.
emb_epoch = common_layers.embedding(epoch, 32, 32) # [batch, 32]
flat_x = tf.concat([flat_x, emb_epoch], axis=1)
flat_x = tf.layers.dropout(flat_x, rate=dropout)
x = tf.layers.dense(flat_x, 128, activation=tf.nn.relu)
logits = tf.layers.dense(
x, self.hparams.problem.num_actions, name="dense2"
| tensorflow.concat | 438 |
import tensorflow as tf
pi_dropout_mask_phs = pi_dropout_mask_generator.generate_dropout_mask_placeholders()
pi, pi_reg = mlp_variational(x, pi_dropout_mask_phs, list(hidden_sizes) + [act_dim], activation, output_activation, dropout_rate)
pi = act_limit * pi
with tf.variable_scope('q1'):
q1_in_ph = tf.concat([x, a], axis=-1)
q1_in_dim = q1_in_ph.shape.as_list()[1]
q1_dropout_mask_generator = DropoutMaskGenerator(q1_in_dim, hidden_sizes, model_prob=1.0 - dropout_rate)
q1_dropout_mask_phs = q1_dropout_mask_generator.generate_dropout_mask_placeholders()
q1, q1_reg = mlp_variational(q1_in_ph, q1_dropout_mask_phs, list(hidden_sizes) + [1],
activation, None, dropout_rate)
q1 = tf.squeeze(q1, axis=2)
with tf.variable_scope('q1', reuse=True):
q1_pi, q1_pi_reg = mlp_variational(tf.concat([x, pi[0]], axis=-1), q1_dropout_mask_phs, list(hidden_sizes) + [1],
activation, None, dropout_rate)
q1_pi = tf.squeeze(q1_pi, axis=2)
with tf.variable_scope('q2'):
q2_in_ph = tf.concat([x, a], axis=-1)
q2_in_dim = q2_in_ph.shape.as_list()[1]
q2_dropout_mask_generator = DropoutMaskGenerator(q2_in_dim, hidden_sizes, model_prob=1.0 - dropout_rate)
q2_dropout_mask_phs = q2_dropout_mask_generator.generate_dropout_mask_placeholders()
q2, q2_reg = mlp_variational(q2_in_ph, q2_dropout_mask_phs, list(hidden_sizes) + [1],
activation, None, dropout_rate)
q2 = tf.squeeze(q2, axis=2)
| tensorflow.variable_scope | 439 |
import tensorflow as tf
n_l1 = 1000
n_l2 = 1000
z_dim = 10
batch_size = 100
n_epochs = 1000
learning_rate = 0.001
beta1 = 0.9
results_path = './Results/Semi_Supervised'
n_labels = 10
n_labeled = 1000
# Placeholders for input data and the targets
x_input = tf.placeholder(dtype=tf.float32, shape=[batch_size, input_dim], name='Input')
x_input_l = tf.placeholder(dtype=tf.float32, shape=[batch_size, input_dim], name='Labeled_Input')
y_input = tf.placeholder(dtype=tf.float32, shape=[batch_size, n_labels], name='Labels')
x_target = tf.placeholder(dtype=tf.float32, shape=[batch_size, input_dim], name='Target')
real_distribution = tf.placeholder(dtype=tf.float32, shape=[batch_size, z_dim], name='Real_distribution')
categorial_distribution = tf.placeholder(dtype=tf.float32, shape=[batch_size, n_labels],
name='Categorical_distribution')
manual_decoder_input = tf.placeholder(dtype=tf.float32, shape=[1, z_dim + n_labels], name='Decoder_input')
def form_results():
"""
Forms folders for each run to store the tensorboard files, saved models and the log files.
:return: three string pointing to tensorboard, saved models and log paths respectively.
"""
folder_name = "/{0}_{1}_{2}_{3}_{4}_{5}_Semi_Supervised". \
format(datetime.datetime.now(), z_dim, learning_rate, batch_size, n_epochs, beta1)
tensorboard_path = results_path + folder_name + '/Tensorboard'
| tensorflow.placeholder | 440 |
import tensorflow as tf
clipped_grads_and_vars = self._clip_gradients(self.grads_and_vars, self._grad_clipping_tuple)
# compute norms in case they need to be logged
self.gradient_norms = [tf.norm(g) + NUMTOL for (g, v) in clipped_grads_and_vars]
self.weight_norms = [tf.norm(v) + NUMTOL for (g, v) in clipped_grads_and_vars]
# check that gradients are finite
grads = [tf.check_numerics(g, "grads is not finite") for (g, v) in clipped_grads_and_vars]
variables = [tf.check_numerics(v, "grads is not finite") for (g, v) in clipped_grads_and_vars]
| tensorflow.norm | 441 |
import tensorflow as tf
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)
| tensorflow.constant | 442 |
from tensorflow.python.framework import ops
# avoid division by zero
epsilon = 1e-7
def compute_recall(name):
recall = math_ops.div(true_positives,
epsilon + true_positives + false_negatives,
name='recall_' + name)
return recall
recall = compute_recall('value')
with ops.control_dependencies([true_positives_compute_op,
false_negatives_compute_op]):
update_op = compute_recall('update_op')
if metrics_collections:
ops.add_to_collections(metrics_collections, recall)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
| tensorflow.python.framework.ops.control_dependencies | 443 |
import tensorflow as tf
input_dtype,
output0_dtype,
output1_dtype,
swap=False):
if not tu.validate_for_tf_model(input_dtype, output0_dtype, output1_dtype,
input_shape, output0_shape, output1_shape):
return
tf_input_dtype = np_to_tf_dtype(input_dtype)
tf_output0_dtype = np_to_tf_dtype(output0_dtype)
tf_output1_dtype = np_to_tf_dtype(output1_dtype)
# Create the model. If non-batching then don't include the batch
# dimension.
tf.reset_default_graph()
if max_batch == 0:
in0 = tf.placeholder(tf_input_dtype, tu.shape_to_tf_shape(input_shape),
"INPUT0")
in1 = tf.placeholder(tf_input_dtype, tu.shape_to_tf_shape(input_shape),
"INPUT1")
else:
in0 = tf.placeholder(tf_input_dtype, [
None,
] + tu.shape_to_tf_shape(input_shape), "INPUT0")
in1 = tf.placeholder(tf_input_dtype, [
None,
] + tu.shape_to_tf_shape(input_shape), "INPUT1")
# If the input is a string, then convert each string to the
| tensorflow.reset_default_graph | 444 |
import tensorflow as tf
average_grads.append(grad_and_var)
return average_grads
def train():
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0), trainable=False)
| tensorflow.Graph | 445 |
import tensorflow as tf
name_to_features = {
"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([seq_length], tf.int64),
"segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
"label_ids": tf.FixedLenFeature([], tf.int64),
"is_real_example": tf.FixedLenFeature([], tf.int64),
}
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
| tensorflow.FixedLenFeature | 446 |
import tensorflow as tf
w = tf.get_variable("w", wshape, initializer=ortho_init(init_scale))
b = tf.get_variable("b", bias_var_shape, initializer=tf.constant_initializer(0.0))
| tensorflow.constant_initializer | 447 |
from tensorflow.python.framework import ops
next_size = _next_array_size(new_size)
next_shape = array_ops.pack([next_size] + fixed_shape)
new_value = array_ops.zeros(next_shape, dtype=values.dtype)
old_value = array.value()
assign_op = state_ops.assign(array, new_value, validate_shape=False)
with ops.control_dependencies([assign_op]):
copy_op = array[:size].assign(old_value[:size])
# return value needs to be the same dtype as no_op() for cond
with ops.control_dependencies([copy_op]):
return control_flow_ops.no_op()
| tensorflow.python.framework.ops.control_dependencies | 448 |
import tensorflow as tf
Returns
-------
A tf.Variable, filled with drawn samples.
"""
shape = tuple(map(int, shape))
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e8)
value = tf.random_normal_initializer(
mean, scale, dtype=dtype, seed=seed)(shape)
return tf.Variable(value, dtype=dtype, name=name)
def max(x, axis=None, keepdims=False):
"""Maximum value in a tensor.
| tensorflow.random_normal_initializer | 449 |
import tensorflow as tf
updates_collections=None,
scope=scope,
reuse=self.reuse,
is_training=self.train)
_, l3_h2 = self.hgru_ops(
i0=i0,
x=processed_l2_h2,
h2=l3_h2,
layer='h3',
layer_idx=1)
if self.batch_norm:
with tf.variable_scope(
'l3_h2_bn',
reuse=self.scope_reuse) as scope:
l3_h2 = tf.contrib.layers.batch_norm(
inputs=l3_h2,
scale=True,
center=True,
fused=True,
renorm=False,
param_initializers=self.param_initializer,
updates_collections=None,
scope=scope,
reuse=self.reuse,
is_training=self.train)
# l3-l2 feedback (FEEDBACK KERNEL is 2x channels)
_, temp_l2_h2 = self.hgru_ops(
| tensorflow.contrib.layers.batch_norm | 450 |
import tensorflow as tf
wall_time = (time.time() - start_time) / hparams.n_iters
examples_per_sec = hparams.n_samples / wall_time
self.report_benchmark(
name="eager_train_%s%s_%d" %
("gpu" if tf.test.is_gpu_available() else "cpu",
"_defun" if defun else "", sample_size),
iters=hparams.n_iters,
extras={"examples_per_sec": examples_per_sec},
wall_time=wall_time)
| tensorflow.test.is_gpu_available | 451 |
from tensorflow.python.framework import ops
self._lr = learning_rate
self._beta1 = beta1
self._beta2 = beta2
# Tensor versions of the constructor arguments, created in _prepare().
self._lr_t = None
self._beta1_t = None
self._beta2_t = None
def _prepare(self):
self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate")
self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1")
self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2")
def _create_slots(self, var_list):
# Create slots for the first and second moments.
for v in var_list:
self._zeros_slot(v, "m", self._name)
self._zeros_slot(v, "v", self._name)
self._zeros_slot(v, "g", self._name)
def _apply_dense(self, grad, var):
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
| tensorflow.python.framework.ops.convert_to_tensor | 452 |
import tensorflow as tf
# Loss op and other outputs. The log(2.0) term corrects for
# logloss not being an upper bound on the indicator function.
weighted_loss = weights * losses_utils.weighted_surrogate_loss(
labels,
logits,
surrogate_type=surrogate_type,
positive_weights=1.0,
negative_weights=lambdas)
maybe_log2 = tf.log(2.0) if surrogate_type == 'xent' else 1.0
maybe_log2 = tf.cast(maybe_log2, logits.dtype.base_dtype)
lambda_term = lambdas * target_rate * (1.0 - label_priors) * maybe_log2
loss = tf.reshape(weighted_loss - lambda_term, original_shape)
other_outputs = {
'lambdas':
lambdas_variable,
'label_priors':
label_priors,
'true_positives_lower_bound':
true_positives_lower_bound(labels, logits, weights, surrogate_type),
'false_positives_upper_bound':
false_positives_upper_bound(labels, logits, weights, surrogate_type)
}
| tensorflow.reshape | 453 |
import tensorflow as tf
# pad_input_data_to_static_shape assumes that image is already concatenated
# with additional channels.
self.assertAllEqual(
padded_tensor_dict[fields.InputDataFields.image].shape.as_list(),
[5, 6, 5])
self.assertAllEqual(
padded_tensor_dict[fields.InputDataFields.image_additional_channels]
.shape.as_list(), [5, 6, 2])
def test_images_and_additional_channels_errors(self):
input_tensor_dict = {
fields.InputDataFields.image:
tf.placeholder(tf.float32, [None, None, 3]),
fields.InputDataFields.image_additional_channels:
tf.placeholder(tf.float32, [None, None, 2]),
fields.InputDataFields.original_image:
tf.placeholder(tf.float32, [None, None, 3]),
}
with self.assertRaises(ValueError):
_ = inputs.pad_input_data_to_static_shapes(
tensor_dict=input_tensor_dict,
max_num_boxes=3,
num_classes=3,
spatial_image_shape=[5, 6])
| tensorflow.placeholder | 454 |
from tensorflow.contrib.summary import summary_test_util
dev_data = data.SnliData(fake_train_file, word2index)
test_data = data.SnliData(fake_train_file, word2index)
# 2. Create a fake config.
config = _test_spinn_config(
data.WORD_VECTOR_LEN, 4,
logdir=os.path.join(self._temp_data_dir, "logdir"))
# 3. Test training of a SPINN model.
trainer = spinn.train_or_infer_spinn(
embed, word2index, train_data, dev_data, test_data, config)
# 4. Load train loss values from the summary files and verify that they
# decrease with training.
summary_file = glob.glob(os.path.join(config.logdir, "events.out.*"))[0]
events = summary_test_util.events_from_file(summary_file)
train_losses = [event.summary.value[0].simple_value for event in events
if event.summary.value
and event.summary.value[0].tag == "train/loss"]
self.assertEqual(config.epochs, len(train_losses))
self.assertLess(train_losses[-1], train_losses[0])
# 5. Verify that checkpoints exist and contains all the expected variables.
self.assertTrue(glob.glob(os.path.join(config.logdir, "ckpt*")))
ckpt_variable_names = [
item[0] for item in checkpoint_utils.list_variables(config.logdir)]
self.assertIn("global_step", ckpt_variable_names)
for v in trainer.variables:
variable_name = v.name[:v.name.index(":")] if ":" in v.name else v.name
self.assertIn(variable_name, ckpt_variable_names)
| tensorflow.contrib.summary.summary_test_util.events_from_file | 455 |
import tensorflow as tf
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(per_example_loss, label_ids, logits, is_real_example):
# predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
# ones=tf.get_variable('ones',shape=logits.shape,initializer=tf.ones_initializer)
# zeros=tf.get_variable('zeros',shape=logits.shape,initializer=tf.zeros_initializer)
predictions=tf.where(logits>=0,tf.ones(tf.shape(logits)),tf.zeros(tf.shape(logits)))
accuracy = tf.metrics.accuracy(
labels=label_ids, predictions=predictions, weights=is_real_example)
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
}
| tensorflow.metrics.accuracy | 456 |
import tensorflow as tf
with tf.Session(config=config) as sess:
t6 = time.time()
sv3 = sess.run(approx_scskconv)
t5 = time.time()
for i in range(0, num_trials):
sess.run(approx_scskconv)
t6 = time.time()
ts = abs(t6 - t5) / max(num_trials,1)
print("time approx sparse: ", ts)
tf.reset_default_graph()
time.sleep(1)
if dense:
td = 0
with tf.device("/gpu:0"):
conv = nn_ops.conv3d(d1, d2, strides, padding)
with tf.Session(config=config) as sess:
t22 = time.time()
expected = sess.run(conv)
t11 = time.time()
for i in range(0, num_trials):
sess.run(conv)
t22 = time.time()
td = abs(t22 - t11) / max(num_trials,1)
print("time dense gpu: ", td)
tf.reset_default_graph()
print("time ratio: ", ts / td)
return [expected, sv3, ts, td]
| tensorflow.device | 457 |
import tensorflow as tf
with tf.control_dependencies([train_op]), tf.name_scope('ema'):
ema = tf.train.ExponentialMovingAverage(decay=MOVING_AVERAGE_DECAY, num_updates=global_step)
train_op = ema.apply(tf.trainable_variables())
return tf.estimator.EstimatorSpec(mode, loss=total_loss, train_op=train_op)
def add_weight_decay(weight_decay):
"""Add L2 regularization to all (or some) trainable kernel weights."""
| tensorflow.estimator.EstimatorSpec | 458 |
import tensorflow as tf
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
else:
probabilities = logits
logits = tf.squeeze(logits, [-1])
predictions = logits
per_example_loss = tf.square(logits - labels)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, probabilities, logits, predictions)
| tensorflow.squeeze | 459 |
import tensorflow as tf
y = tf.py_func(lambda x: x + 1, [x], [tf.float32])
z = tf.py_func(lambda x: x * 2, [x], [tf.float32])
| tensorflow.py_func | 460 |
import tensorflow as tf
h_conv1 = self.conv2d('h_conv1', s, W_conv1, 4, b_conv1) # 构造第一个卷积层输出为conv1
h_pool1 = self.max_pool_2x2('h_pool1', h_conv1)
h_conv2 = self.conv2d('h_conv2', h_pool1, W_conv2, 2, b_conv2)
h_conv3 = self.conv2d('h_conv3', h_conv2, W_conv3, 1, b_conv3)
h_conv3_flat = tf.reshape(h_conv3, [-1, 1600], 'h_conv3_flat')
h_fc1 = tf.nn.relu(tf.add(tf.matmul(h_conv3_flat, W_fc1), b_fc1, 'h_fc1'))
readout = tf.add(tf.matmul(h_fc1, W_fc2), b_fc2, 'h_fc2')
return s, readout, h_fc1
def creat_optimizer(self,readout):
| tensorflow.reshape | 461 |
import tensorflow as tf
if FLAGS.do_serve:
def serving_input_fn():
with tf.variable_scope("foo"):
feature_spec = {
"input_ids": tf.FixedLenFeature([FLAGS.max_seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([FLAGS.max_seq_length], tf.int64),
| tensorflow.variable_scope | 462 |
import tensorflow as tf
Returns:
X -- placeholder for the data input, of shape [n_x, None] and dtype "float"
Y -- placeholder for the input labels, of shape [n_y, None] and dtype "float"
"""
X = tf.placeholder(shape= [Nfeat, None], dtype= "float64" )
Y = tf.placeholder(shape= [Nlab, None], dtype= "float64" )
return X, Y
| tensorflow.placeholder | 463 |
import tensorflow as tf
'''
def set_optimizer(self):
with tf.variable_scope('optimizer'):
self._optimizer, self._learning_rate = TFOptimizers.instantiate_optimizer(self, self.optimizer_tuple)
def set_training_op(self):
| tensorflow.variable_scope | 464 |
import tensorflow as tf
if __name__ == "__main__":
tf.app.run()
| tensorflow.app.run | 465 |
import tensorflow as tf
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64),
})
if FLAGS.contrast_norm == 'areafactor':
image = tf.decode_raw(features['image_raw'], tf.float32)
| tensorflow.FixedLenFeature | 466 |
import tensorflow as tf
if params['data_format'] == 'channels_first':
cls_pred = tf.transpose(cls_pred, [0, 2, 3, 1])
| tensorflow.transpose | 467 |
import tensorflow as tf
with tf.variable_scope(name):
# Encoder definition
o_c1 = self.general_conv2d(input_data, self.base_number_of_features, 3, stride = 1, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_conv2d_1')
o_mp1 = tf.layers.max_pooling2d(o_c1, 2, 2, name = name + '_maxpooling_1')
o_c2 = self.general_conv2d(o_mp1, self.base_number_of_features * 2, 3, stride = 1, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_conv2d_2')
o_mp2 = tf.layers.max_pooling2d(o_c2, 2, 2, name = name + '_maxpooling_2')
| tensorflow.layers.max_pooling2d | 468 |
from tensorflow.python.ops import math_ops
total = _create_local('total', shape=[])
count = _create_local('count', shape=[])
if weights is not None:
weights = math_ops.to_float(weights)
values = math_ops.mul(values, weights)
num_values = math_ops.reduce_sum(_broadcast_weights(weights, values))
else:
num_values = math_ops.to_float(array_ops.size(values))
total_compute_op = state_ops.assign_add(total, math_ops.reduce_sum(values))
| tensorflow.python.ops.math_ops.mul | 469 |
import tensorflow as tf
def get_batch(image,label,batch_size,crop_size):
#print(image.shape)
#print(label.shape)
images,labels=tf.train.shuffle_batch([image,label],
batch_size=batch_size,num_threads=10,capacity=10000,min_after_dequeue=200)
return tf.reshape(images,[batch_size,4096]),tf.reshape(labels,[batch_size])
def get_test_batch(image,label,batch_size):
images,labels=tf.train.batch([image,label],batch_size=batch_size)
return tf.reshape(images,[batch_size,4096]),tf.reshape(labels,[batch_size])
def get_valid_batch(image,label,batch_size):
images,labels=tf.train.batch([image,label],batch_size=batch_size)
return tf.reshape(images,[batch_size,4096]),tf.reshape(labels,[batch_size])
class trainwork(object):
def __init__(self):
with tf.variable_scope('scop'):
| tensorflow.reshape | 470 |
import tensorflow as tf
sliced_output = tf.unstack(output, axis=1)
sliced_label = tf.unstack(labels, axis=1)
sliced_propensity = tf.unstack(propensity_weights, axis=1)
for i in range(len(sliced_output)):
for j in range(i+1, len(sliced_output)):
cur_label_weight = tf.math.sign(sliced_label[i] - sliced_label[j])
cur_propensity = sliced_propensity[i] * sliced_label[i] + sliced_propensity[j] * sliced_label[j]
cur_pair_loss = -tf.exp(sliced_output[i]) / (tf.exp(sliced_output[i]) + tf.exp(sliced_output[j]))
if loss == None:
loss = cur_label_weight * cur_pair_loss * cur_propensity
| tensorflow.math.sign | 471 |
from tensorflow.python.framework import ops
ops.RegisterShape("Imag")(common_shapes.unchanged_shape)
ops.RegisterShape("Inv")(common_shapes.unchanged_shape)
ops.RegisterShape("IsFinite")(common_shapes.unchanged_shape)
ops.RegisterShape("IsInf")(common_shapes.unchanged_shape)
ops.RegisterShape("IsNan")(common_shapes.unchanged_shape)
ops.RegisterShape("Log")(common_shapes.unchanged_shape)
ops.RegisterShape("LogicalNot")(common_shapes.unchanged_shape)
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)
ops.RegisterShape("Sin")(common_shapes.unchanged_shape)
ops.RegisterShape("Sqrt")(common_shapes.unchanged_shape)
ops.RegisterShape("Square")(common_shapes.unchanged_shape)
ops.RegisterShape("Sigmoid")(common_shapes.unchanged_shape)
ops.RegisterShape("Tanh")(common_shapes.unchanged_shape)
ops.RegisterShape("Cast")(common_shapes.unchanged_shape)
ops.RegisterShape("ComplexAbs")(common_shapes.unchanged_shape)
@ops.RegisterShape("Add")
@ops.RegisterShape("Complex")
@ops.RegisterShape("Div")
@ops.RegisterShape("Equal")
@ops.RegisterShape("Greater")
@ops.RegisterShape("GreaterEqual")
@ops.RegisterShape("Less")
@ops.RegisterShape("LessEqual")
@ops.RegisterShape("LogicalAnd")
@ops.RegisterShape("LogicalOr")
| tensorflow.python.framework.ops.RegisterShape | 472 |
import tensorflow as tf
"""Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
var: tensor to regularize.
weight_l1: an optional weight to modulate the l1 loss.
weight_l2: an optional weight to modulate the l2 loss.
name: Optional scope/name for op_scope.
Returns:
the l1+L2 loss op.
"""
with tf.name_scope(name):
weight_l1_t = tf.convert_to_tensor(weight_l1, dtype=var.dtype.base_dtype, name='weight_l1')
weight_l2_t = tf.convert_to_tensor(weight_l2, dtype=var.dtype.base_dtype, name='weight_l2')
reg_l1 = tf.multiply(weight_l1_t, tf.reduce_sum(tf.abs(var)), name='value_l1')
reg_l2 = tf.multiply(weight_l2_t, tf.nn.l2_loss(var), name='value_l2')
return tf.add(reg_l1, reg_l2, name='value')
def l1_regularizer(scale, name='l1_regularizer'):
"""Returns a function that can be used to apply L1 regularization to weights.
L1 regularization encourages sparsity.
| tensorflow.name_scope | 473 |
import tensorflow as tf
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord)
# Sleep to make sure the queue runner has started the first run call.
time.sleep(_SLEEP_TIME)
# Session closed.
coord.request_stop()
coord.join()
def test_batcher_closed(self):
with tf.Graph().as_default():
@dynamic_batching.batch_fn
def f(a):
return a
f(tf.constant([1]))
# Intentionally using tf.Session() instead of self.test_session() to have
# control over closing the session. test_session() is a cached session.
with tf.Session():
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord)
time.sleep(_SLEEP_TIME)
coord.request_stop() # Calls close operation.
coord.join()
# Session closed.
def test_minimum_batch_size(self):
with self.test_session() as session:
@dynamic_batching.batch_fn_with_options(
| tensorflow.constant | 474 |
import tensorflow as tf
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
return tf.nn.relu(x) - alpha * tf.nn.relu(-x)
def instance_norm(x,name='instance_norm'):
with tf.variable_scope(name):
| tensorflow.nn.relu | 475 |
import tensorflow as tf
new_value = assign.eval()
return p.eval(), new_value
def _initAssignAddFetch(self, x, y, use_gpu=False):
"""Initialize a param to init, and compute param += y."""
with self.test_session(use_gpu=use_gpu):
p = tf.Variable(x)
add = tf.assign_add(p, y)
p.initializer.run()
new_value = add.eval()
return p.eval(), new_value
def _initAssignSubFetch(self, x, y, use_gpu=False):
"""Initialize a param to init, and compute param -= y."""
| tensorflow.assign_add | 476 |
import tensorflow as tf
# 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())
outputs = [
f(tf.constant([[1, 3]]), tf.constant([2])),
f(tf.constant([[1, 3]]), tf.constant([2]))
]
start = datetime.datetime.now()
(_, batch_size), _ = session.run(outputs)
duration = datetime.datetime.now() - start
| tensorflow.constant | 477 |
import tensorflow as tf
raw_data = reader.ptb_raw_data(FLAGS.data_path)
train_data, valid_data, test_data, _ = raw_data
config = get_config()
eval_config = get_config()
eval_config.batch_size = 1
eval_config.num_steps = 1
train_graph = tf.Graph()
eval_graph = tf.Graph()
infer_graph = tf.Graph()
with train_graph.as_default():
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
with tf.name_scope('Train'):
train_input = DataInput(config=config, data=train_data, name='TrainInput')
with tf.variable_scope('Model', reuse=None, initializer=initializer):
m = Model(is_training=True, config=config, input_=train_input, graph=train_graph)
tf.summary.scalar('Training Loss', m.cost)
tf.summary.scalar('Learning rate', m.lr)
latest_ckpt = tf.train.latest_checkpoint(FLAGS.save_path)
| tensorflow.random_uniform_initializer | 478 |
import tensorflow as tf
phi: TensorLike,
name: str = "spherical_harmonics_evaluate_spherical_harmonics") -> TensorLike: # pylint: disable=line-too-long
with tf.name_scope(name):
degree_l = tf.convert_to_tensor(value=degree_l)
order_m = tf.convert_to_tensor(value=order_m)
theta = tf.convert_to_tensor(value=theta)
phi = tf.convert_to_tensor(value=phi)
var_type = theta.dtype
sign_m = tf.math.sign(order_m)
order_m = tf.abs(order_m)
zeros = tf.zeros_like(order_m)
result_m_zero = _spherical_harmonics_normalization(
degree_l, zeros, var_type) * evaluate_legendre_polynomial(
degree_l, zeros, tf.cos(theta))
result_branch = _evaluate_spherical_harmonics_branch(
degree_l, order_m, theta, phi, sign_m, var_type)
return tf.where(tf.equal(order_m, zeros), result_m_zero, result_branch)
| tensorflow.abs | 479 |
import tensorflow as tf
# (T,B,D) => (B,T,D)
facts = tf.array_ops.transpose(facts, [1, 0, 2])
| tensorflow.array_ops.transpose | 480 |
from tensorflow.python.ops import array_ops
# total average precision.
with ops.name_scope(None, 'max', (average_precision,)) as max_scope:
# `max` is the max possible precision. Since max for any row is 1.0:
# - For the unweighted case, this is just the number of rows.
# - For the weighted case, it's the sum of the weights broadcast across
# `average_precision` rows.
max_var = contrib_variables.local_variable(
array_ops.zeros([], dtype=dtypes.float64), name=max_scope)
if weights is None:
batch_max = math_ops.to_double(
array_ops.size(average_precision, name='batch_max'))
else:
# TODO(ptucker): More efficient way to broadcast?
broadcast_weights = math_ops.mul(
weights, array_ops.ones_like(average_precision),
name='broadcast_weights')
batch_max = math_ops.reduce_sum(broadcast_weights, name='batch_max')
max_update = state_ops.assign_add(max_var, batch_max, name='update')
with ops.name_scope(None, 'total', (average_precision,)) as total_scope:
total_var = contrib_variables.local_variable(
array_ops.zeros([], dtype=dtypes.float64), name=total_scope)
batch_total = math_ops.reduce_sum(average_precision, name='batch_total')
total_update = state_ops.assign_add(total_var, batch_total, name='update')
# Divide total by max to get mean, for both vars and the update ops.
mean_average_precision = _safe_scalar_div(total_var, max_var, name='mean')
update = _safe_scalar_div(total_update, max_update, name=scope)
| tensorflow.python.ops.array_ops.ones_like | 481 |
import tensorflow as tf
in0 = tf.strings.to_number(in0, tf.int32)
in1 = tf.strings.to_number(in1, tf.int32)
add = tf.add(in0, in1, "ADD")
sub = tf.subtract(in0, in1, "SUB")
# Cast or convert result to the output dtype.
if tf_output0_dtype == tf.string:
cast0 = tf.dtypes.as_string(add if not swap else sub, name="TOSTR0")
else:
cast0 = tf.cast(add if not swap else sub, tf_output0_dtype, "CAST0")
if tf_output1_dtype == tf.string:
cast1 = tf.dtypes.as_string(sub if not swap else add, name="TOSTR1")
else:
cast1 = tf.cast(sub if not swap else add, tf_output1_dtype, "CAST1")
| tensorflow.dtypes.as_string | 482 |
import tensorflow as tf
"question")
self.ch = tf.placeholder(tf.int32, [self.config.batch_size * self.max_p_num, self.config.max_p_len,
self.config.max_ch_len], "context_char")
self.qh = tf.placeholder(tf.int32, [self.config.batch_size * self.max_p_num, self.config.max_q_len,
self.config.max_ch_len], "question_char")
self.start_label = tf.placeholder(tf.int32, [self.config.batch_size], "answer_label1")
self.end_label = tf.placeholder(tf.int32, [self.config.batch_size], "answer_label2")
self.position_emb = position_embedding(self.c, 2 * self.config.hidden_size)
self.c_mask = tf.cast(self.c, tf.bool) # index 0 is padding symbol N x self.max_p_num, max_p_len
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)
self.dropout = tf.placeholder(tf.float32, name="dropout")
self.global_step = tf.Variable(0, name="global_step", trainable=False)
"""
:descrition: The embedding layer, question and passage share embeddings
"""
| tensorflow.cast | 483 |
import tensorflow as tf
# metrics_correct_rate, golden, predict = correct_rate(tags, pred_ids)
# metrics_correct_rate = correct_rate(tags, pred_ids, weights)
metrics = {
"acc": tf.metrics.accuracy(tags, pred_ids, weights),
"precision": precision(tags, pred_ids, num_tags, indices, weights),
"recall": recall(tags, pred_ids, num_tags, indices, weights),
"f1": f1(tags, pred_ids, num_tags, indices, weights),
| tensorflow.metrics.accuracy | 484 |
import tensorflow as tf
self.U1_pred = self.net_U1(self.x1_tf) # N1 x q
self.loss = tf.reduce_sum(tf.square(self.u0_tf - self.U0_pred)) + \
tf.reduce_sum(tf.square(self.u1_tf - self.U1_pred))
| tensorflow.square | 485 |
import tensorflow as tf
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, init_string
)
| tensorflow.train.init_from_checkpoint | 486 |
import tensorflow as tf
return merge_states(tf.transpose(x, [0, 2, 1, 3]))
def conv1d(x, scope, nf, rf, w_init=tf.random_normal_initializer(stddev=0.02), b_init=tf.constant_initializer(0), pad='VALID', train=False):
with tf.variable_scope(scope):
#x = [-1,n_ctx,512]
nx = shape_list(x)[-1]
#rf = 1,nx=emb,nf=3*emb
w = tf.get_variable("w", [rf, nx, nf], initializer=w_init)
b = tf.get_variable("b", [nf], initializer=b_init)
if rf == 1: #faster 1x1 conv
c = tf.reshape(tf.matmul(tf.reshape(x, [-1, nx]), tf.reshape(w, [-1, nf]))+b, shape_list(x)[:-1]+[nf])
else: #was used to train LM
c = tf.nn.conv1d(x, w, stride=1, padding=pad)+b
return c
def attn(x, scope, n_state, n_head, train=False, scale=False):
assert n_state%n_head==0
with tf.variable_scope(scope):
#c [-1,n_ctx,3*emb]
c = conv1d(x, 'c_attn', n_state*3, 1, train=train)
| tensorflow.reshape | 487 |
import tensorflow as tf
V = tf.random_normal(V_shape, dtype=L.dtype)
samples = tf.expand_dims(tf.transpose(mu), -1) + tf.batch_matmul(L, V)
return tf.transpose(samples)
#samples = []
| tensorflow.transpose | 488 |
import tensorflow as tf
tf.float32, shape=(None, None, None, 4))
self._scores = tf.placeholder(
tf.float32, shape=(None, None, self._num_classes))
self._boxes_predi, self._scores_predi, self._classes_predi,\
self._valid_detections_predi = \
tf.image.combined_non_max_suppression(
boxes=self._boxes, scores=self._scores,
max_output_size_per_class=50, max_total_size=50,
iou_threshold=0.45, score_threshold=self._score_threshold)
self._label_map = self._load_labelmap(self._label_file)
| tensorflow.image.combined_non_max_suppression | 489 |
from tensorflow.python.ops import check_ops
return (self.alpha * math_ops.log(self.beta) -
math_ops.lgamma(self.alpha) -
(self.alpha + 1.) * math_ops.log(x) - self.beta / x)
def _prob(self, x):
return math_ops.exp(self._log_prob(x))
def _log_cdf(self, x):
return math_ops.log(self._cdf(x))
def _cdf(self, x):
x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
self.validate_args else [], x)
# Note that igammac returns the upper regularized incomplete gamma
# function Q(a, x), which is what we want for the CDF.
return math_ops.igammac(self.alpha, self.beta / x)
@distribution_util.AppendDocstring(
"""This is defined to be
```
entropy = alpha - log(beta) + log(Gamma(alpha))
| tensorflow.python.ops.check_ops.assert_positive | 490 |
import tensorflow as tf
def get_config(self):
return {
"vocab_path": self._args["vocab_path"],
"spm_model": self._args["spm_model"],
"languages": self._args["languages"],
"with_src_lang_tag": self._with_src_lang_tag,
"trg_lang_tag_position": self._trg_lang_tag_position,
}
def inputs_signature(self, mode):
""" Returns the input dtypes and signatures. """
dtypes = {"feature": tf.int64, "src_lang": tf.int64, "trg_lang": tf.int64}
signatures = {"feature": tf.TensorShape([None, None]),
"src_lang": tf.TensorShape([None, ]),
"trg_lang": tf.TensorShape([None, ])}
if mode == compat.ModeKeys.INFER:
return dtypes, signatures
dtypes["label"] = tf.int64
signatures["label"] = tf.TensorShape([None, None])
return dtypes, signatures
def build_model(self, args, name=None):
""" Builds and return a keras model. """
model = build_model(args, self._multilingual_dp.meta,
| tensorflow.TensorShape | 491 |
import tensorflow as tf
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return (loss, per_example_loss, log_probs)
def get_next_sentence_output(bert_config, input_tensor, labels):
"""Get loss and log probs for the next sentence prediction."""
# Simple binary classification. Note that 0 is "next sentence" and 1 is
# "random sentence". This weight matrix is not used after pre-training.
| tensorflow.reduce_sum | 492 |
import tensorflow as tf
name="start_pointer"), -1)
end_logits = tf.squeeze(
conv(self._attention(tf.concat([self.enc[1], self.enc[3]], axis=-1), name="attn2"), 1, bias=False,
name="end_pointer"), -1)
else:
start_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[2]], axis=-1), 1, bias=False, name="start_pointer"), -1)
end_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[3]], axis=-1), 1, bias=False, name="end_pointer"), -1)
self.logits = [mask_logits(start_logits, mask=tf.reshape(self.c_mask, [N, -1])),
mask_logits(end_logits, mask=tf.reshape(self.c_mask, [N, -1]))]
self.logits1, self.logits2 = [l for l in self.logits]
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(self.logits1), axis=2),
tf.expand_dims(tf.nn.softmax(self.logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, self.max_a_len)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
| tensorflow.reshape | 493 |
import tensorflow as tf
config=bert_config,
is_training=False,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
masked_lm_example_loss = get_masked_lm_output(
bert_config, model.get_sequence_output(), model.get_embedding_table(),
masked_lm_positions, masked_lm_ids)
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()
| tensorflow.trainable_variables | 494 |
import tensorflow as tf
from sklearn.metrics import classification_report
slim = tf.contrib.slim
global first
first = True
classnum=12
testnum = tf.placeholder(tf.int32)
trainnum = tf.placeholder(tf.int32)
validnum = tf.placeholder(tf.int32)
learnrate = tf.placeholder(tf.float32)
def getinputs(path):
filename_queue=tf.train.string_input_producer([path])
reader=tf.TFRecordReader()
_,serialized_example=reader.read(filename_queue)
features=tf.parse_single_example(serialized_example,
features={
'label':tf.FixedLenFeature([], tf.int64),
'img_raw' : tf.FixedLenFeature([], tf.string),
})
image=tf.decode_raw(features['img_raw'],tf.uint8)
label=tf.cast(features['label'],tf.int32)
image=tf.reshape(image,[4096,1])
return image,label
def get_batch(image,label,batch_size,crop_size):
#print(image.shape)
#print(label.shape)
| tensorflow.TFRecordReader | 495 |
import tensorflow as tf
din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1)
d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att' + stag)
d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att' + stag)
d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag)
d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(facts)[1]])
scores = d_layer_3_all
# Mask
# key_masks = tf.sequence_mask(facts_length, tf.shape(facts)[1]) # [B, T]
key_masks = tf.expand_dims(mask, 1) # [B, 1, T]
paddings = tf.ones_like(scores) * (-2 ** 32 + 1)
if not forCnn:
scores = tf.where(key_masks, scores, paddings) # [B, 1, T]
# Scale
# scores = scores / (facts.get_shape().as_list()[-1] ** 0.5)
| tensorflow.expand_dims | 496 |
from tensorflow.python.client import graph_util
@ops.RegisterStatistics("Conv2D", "weight_parameters")
def _calc_conv_weight_params(graph, node):
"""Calculates the on-disk size of the weights for Conv2D."""
input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
input_shape.assert_is_fully_defined()
filter_shape = graph_util.tensor_shape_from_node_def_name(graph,
node.input[1])
filter_shape.assert_is_fully_defined()
output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
output_shape.assert_is_fully_defined()
filter_height = int(filter_shape[0])
filter_width = int(filter_shape[1])
filter_in_depth = int(filter_shape[2])
filter_out_depth = int(filter_shape[3])
return ops.OpStats("weight_parameters", (filter_height * filter_width *
filter_in_depth * filter_out_depth))
| tensorflow.python.client.graph_util.tensor_shape_from_node_def_name | 497 |
import tensorflow as tf
tf.app.flags.DEFINE_integer('save_summary_steps', 100, '')
tf.app.flags.DEFINE_string('pretrained_model_path', None, '')
tf.app.flags.DEFINE_boolean('allow_mix_precision', False, 'whether to allow mix precision')
tf.app.flags.DEFINE_boolean('auto_tune', False, 'whether to autotune')
tf.app.flags.DEFINE_boolean('use_processed_data', False, 'whether to use processed data')
tf.app.flags.DEFINE_string('processed_data', './processed_dataset/', 'where to save preprocessed datasets')
import model
| tensorflow.app.flags.DEFINE_boolean | 498 |
import tensorflow as tf
for i in range(classes.shape[0]):
sdf = tf.expand_dims(sdfs[i], -1)
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)
| tensorflow.reshape | 499 |
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