seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
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)
| tensorflow.slice | 1,500 |
import tensorflow as tf
)
self.loss = loss_action + loss_arguments
tf.scalar_summary('loss', self.loss)
with tf.name_scope('accuracy'):
correct_prediction_action = tf.equal(
tf.argmax(one_hot_labels_action, 1),
tf.argmax(self.predictions_action, 1)
)
self.accuracy_action = tf.reduce_mean(tf.cast(correct_prediction_action, 'float'))
| tensorflow.name_scope | 1,501 |
import tensorflow as tf
ns3 = 2
# with tf.variable_scope("conv_context") as scope:
def encode(img):
img_h0 = lrelu(conv2d(img, nf0, d_h=ns0, d_w=ns0, name='h0_conv'))
img_h1 = lrelu(conv2d(img_h0, nf1, d_h=ns1, d_w=ns1, name='h1_conv'))
img_h2 = lrelu(conv2d(img_h1, nf2, d_h=ns2, d_w=ns2, name='h2_conv'))
img_h3 = lrelu(conv2d(img_h2, nf3, d_h=ns3, d_w=ns3, name='h3_conv'))
print(img_h3.get_shape())
img_h4 = lrelu(linear(tf.nn.dropout(tf.reshape(img_h3, [self.batch_size, -1]), keep_prob), featsize, 'h4_lin'))
img_z = lrelu(linear(tf.nn.dropout(img_h4, keep_prob), featsize, 'hz_lin'))
return img_h0, img_h1, img_h2, img_h3, img_h4, img_z
with tf.variable_scope("conv") as scope:
srcimg_h0, srcimg_h1, srcimg_h2, srcimg_h3, srcimg_h4, srcimg_z = encode(srcimg)
scope.reuse_variables()
tgtimg_h0, tgtimg_h1, tgtimg_h2, tgtimg_h3, tgtimg_h4, tgtimg_z = encode(tgtimg)
tgtctx_h0, tgtctx_h1, tgtctx_h2, tgtctx_h3, tgtctx_h4, tgtctx_z = encode(tgtctx)
| tensorflow.reshape | 1,502 |
import tensorflow as tf
else:
train= False
init = tf.ones_initializer()
| tensorflow.ones_initializer | 1,503 |
from tensorflow.python.platform import gfile
self.assertFalse(gfile.Exists(s1))
self.assertFalse(gfile.Exists(save._MetaGraphFilename(s1)))
self.assertTrue(gfile.Exists(s3))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s3)))
self.assertTrue(gfile.Exists(s2))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s2)))
# Adding s1 (s3 should now be deleted as oldest in list)
s1 = save.save(sess, os.path.join(save_dir, "s1"))
self.assertEqual([s2, s1], save.last_checkpoints)
self.assertFalse(gfile.Exists(s3))
self.assertFalse(gfile.Exists(save._MetaGraphFilename(s3)))
self.assertTrue(gfile.Exists(s2))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s2)))
self.assertTrue(gfile.Exists(s1))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s1)))
# Exercise the second helper.
# Adding s2 again (old s2 is removed first, then new s2 appended)
s2 = save2.save(sess, os.path.join(save_dir, "s2"))
self.assertEqual([s3, s2], save2.last_checkpoints)
# Created by the first helper.
self.assertTrue(gfile.Exists(s1))
| tensorflow.python.platform.gfile.Exists | 1,504 |
import tensorflow as tf
vector_batch_as_matricies = tf.expand_dims(vector, [1])
mult_result = tf.matmul(vector_batch_as_matricies, matrix)
squeezed_result = tf.squeeze(mult_result, [1])
return squeezed_result
def euclidean_loss_layer(a, b, multiplier=100.0, use_l1=False, eps=0.01):
""" Math: out = (action - mlp_out)'*precision*(action-mlp_out)
= (u-uhat)'*A*(u-uhat)"""
multiplier = tf.constant(multiplier, dtype='float') #for bc #10000
uP =a*multiplier-b*multiplier
if use_l1:
return tf.reduce_mean(eps*tf.square(uP) + tf.abs(uP))
return tf.reduce_mean(tf.square(uP))
def conv2d(img, w, b, strides=[1, 1, 1, 1], is_dilated=False):
if is_dilated:
| tensorflow.constant | 1,505 |
import tensorflow as tf
>>> y = tf.nn.relu(tf.matmul(inputs, W) + b)
Notes
-----
If the layer input has more than two axes, it needs to be flatten by using :class:`FlattenLayer`.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
n_units=100,
act=tf.identity,
W_init=tf.truncated_normal_initializer(stddev=0.1),
b_init=tf.constant_initializer(value=0.0),
W_init_args=None,
b_init_args=None,
name='dense',
):
super(DenseLayer, self).__init__(prev_layer=prev_layer, name=name)
logging.info("DenseLayer %s: %d %s" % (name, n_units, act.__name__))
self.inputs = prev_layer.outputs
self.n_units = n_units
if W_init_args is None:
W_init_args = {}
if b_init_args is None:
| tensorflow.constant_initializer | 1,506 |
from tensorflow.python.framework import ops
name: `String`. The name prepended to Ops created by this class.
Raises:
ValueError: if either `batch_ndims` or `event_ndims` are: `None`,
negative, not `int32`.
"""
if batch_ndims is None: raise ValueError("batch_ndims cannot be None")
if event_ndims is None: raise ValueError("event_ndims cannot be None")
self._batch_ndims = batch_ndims
self._event_ndims = event_ndims
self._validate_args = validate_args
with ops.name_scope(name) as ns:
self._name = ns
with ops.name_scope("init"):
self._batch_ndims = self._assert_non_negative_int32_scalar(
ops.convert_to_tensor(
batch_ndims, name="batch_ndims"))
self._batch_ndims_static, self._batch_ndims_is_0 = (
self._introspect_ndims(self._batch_ndims))
self._event_ndims = self._assert_non_negative_int32_scalar(
ops.convert_to_tensor(
event_ndims, name="event_ndims"))
self._event_ndims_static, self._event_ndims_is_0 = (
self._introspect_ndims(self._event_ndims))
@property
def name(self):
"""Name given to ops created by this class."""
return self._name
| tensorflow.python.framework.ops.convert_to_tensor | 1,507 |
import tensorflow as tf
# We use as_list since TensorShape comparison does not work correctly for
# unknown values, ie, Dimension(None).
self.assertAllEqual([6, 7, 2, 3, 5], y.shape.as_list())
x = tf.placeholder_with_default(
input=np.ones((6, 7, 2, 3, 5), dtype=np.float32), shape=None)
dist = fake_distribution(batch_shape=[None, 3], event_shape=[5])
sample_shape = tf.convert_to_tensor([6, 7], dtype=tf.int32)
y = dist._set_sample_static_shape(x, sample_shape)
self.assertAllEqual([6, 7, None, 3, 5], y.shape.as_list())
x = tf.placeholder_with_default(
input=np.ones((6, 7, 2, 3, 5), dtype=np.float32), shape=None)
dist = fake_distribution(batch_shape=[None, 3], event_shape=[None])
| tensorflow.convert_to_tensor | 1,508 |
import tensorflow as tf
with tf.device('/cpu:0'), tf.name_scope("embedding"):
if use_he_uniform:
self.embedding_W = tf.get_variable(name='lookup_W', shape=[num_quantized_chars, embedding_size],
initializer=tf.contrib.layers.variance_scaling_initializer())
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)
| tensorflow.nn.embedding_lookup | 1,509 |
from tensorflow.contrib.eager.python.examples.linear_regression import linear_regression
batch_size=batch_size,
num_batches=num_batches)
burn_in_dataset = dataset.take(10)
model = linear_regression.LinearModel()
with tf.device(device()):
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
| tensorflow.contrib.eager.python.examples.linear_regression.linear_regression.LinearModel | 1,510 |
import tensorflow as tf
self.a_grads = tf.gradients(self.q, a)[0] # tensor of gradients of each sample (None, a_dim)
def _build_net(self, s, a, scope, trainable):
with tf.variable_scope(scope):
init_w = tf.random_normal_initializer(0., 0.01)
init_b = tf.constant_initializer(0.01)
with tf.variable_scope('l1'):
n_l1 = 700
# combine the action and states together in this way
w1_s = tf.get_variable('w1_s', [self.s_dim, n_l1], initializer=init_w, trainable=trainable)
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)
net = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a) + b1)
with tf.variable_scope('l2'):
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
| tensorflow.matmul | 1,511 |
import tensorflow as tf
# print("----y_conv-----")
# print(y_conv)
# exit()
# Exchange dim 1 and dim 0
# Start at: [0,1,2] = [batch_size, 128, 9] => [batch_size, 32, 36]
feature_mat = tf.transpose(feature_mat, [1, 0, 2])
# New feature_mat's shape: [time_steps, batch_size, n_inputs] [128, batch_size, 9]
print("----feature_mat-----")
print(feature_mat)
# exit()
# Temporarily crush the feature_mat's dimensions
feature_mat = tf.reshape(feature_mat, [-1, config.n_inputs]) # 9
# New feature_mat's shape: [time_steps*batch_size, n_inputs] # 128 * batch_size, 9
# Linear activation, reshaping inputs to the LSTM's number of hidden:
hidden = tf.nn.relu(tf.matmul(
feature_mat, config.W['hidden']
) + config.biases['hidden'])
# New feature_mat (hidden) shape: [time_steps*batch_size, n_hidden] [128*batch_size, 32]
print("--n_steps--")
print(config.n_steps)
print("--hidden--")
print(hidden)
| tensorflow.reshape | 1,512 |
from tensorflow.contrib.eager.python.examples.l2hmc import l2hmc
tf.reset_default_graph()
with tf.Graph().as_default():
energy_fn, _, _ = l2hmc.get_scg_energy_fn()
x = tf.random_normal([hparams.n_samples, hparams.x_dim],
| tensorflow.contrib.eager.python.examples.l2hmc.l2hmc.get_scg_energy_fn | 1,513 |
import tensorflow as tf
# Deletes the file
gfile.Remove(filename)
with self.assertRaisesWithPredicateMatch(
IOError, lambda e: "does not exist"):
tf.train.import_meta_graph(filename)
def testSliceVariable(self):
test_dir = self._TestDir("slice_saver")
filename = os.path.join(test_dir, "metafile")
with self.test_session():
v1 = tf.Variable([20.0], name="v1")
v2 = tf.Variable([20.0], name="v2")
v2._set_save_slice_info(tf.Variable.SaveSliceInfo("v1", [1], [0], [1]))
# The names are different and will work.
slice_saver = tf.train.Saver({"first": v1, "second": v2})
tf.initialize_all_variables().run()
# Exports to meta_graph
meta_graph_def = slice_saver.export_meta_graph(filename)
with tf.Graph().as_default():
# Restores from MetaGraphDef.
new_saver = tf.train.import_meta_graph(filename)
# Generates a new MetaGraphDef.
new_meta_graph_def = new_saver.export_meta_graph()
# It should be the same as the original.
self.assertProtoEquals(meta_graph_def, new_meta_graph_def)
def _testGraphExtensionSave(self):
test_dir = self._TestDir("graph_extension")
| tensorflow.train.Saver | 1,514 |
from tensorflow.python.ops import state_ops
weighted_predictions = predictions * weights
weighted_labels = labels * weights
update_count = state_ops.assign_add(count, batch_count) # n_AB in eqn
prev_count = update_count - batch_count # n_A in update equation
# We update the means by Delta=Error*BatchCount/(BatchCount+PrevCount)
# batch_mean_prediction is E[x_B] in the update equation
batch_mean_prediction = _safe_div(
math_ops.reduce_sum(weighted_predictions), batch_count,
'batch_mean_prediction')
delta_mean_prediction = _safe_div(
(batch_mean_prediction - mean_prediction) * batch_count, update_count,
'delta_mean_prediction')
update_mean_prediction = state_ops.assign_add(mean_prediction,
delta_mean_prediction)
# prev_mean_prediction is E[x_A] in the update equation
prev_mean_prediction = update_mean_prediction - delta_mean_prediction
# batch_mean_label is E[y_B] in the update equation
batch_mean_label = _safe_div(
math_ops.reduce_sum(weighted_labels), batch_count, 'batch_mean_label')
delta_mean_label = _safe_div((batch_mean_label - mean_label) * batch_count,
update_count, 'delta_mean_label')
update_mean_label = state_ops.assign_add(mean_label, delta_mean_label)
# prev_mean_label is E[y_A] in the update equation
prev_mean_label = update_mean_label - delta_mean_label
| tensorflow.python.ops.state_ops.assign_add | 1,515 |
import tensorflow as tf
initializer=tf.truncated_normal_initializer(stddev=stddev))
self.b = tf.get_variable('b',[output_dim], initializer=tf.constant_initializer(0.0))
self.strides = [1,1,1]
self.dilates = [d_t, d_h, d_w]
def __call__(self,input_var,name=None) :
k_t,k_h,k_w,_,_ = self.w.get_shape().as_list()
_t = tf.pad(input_var, [[0,0],[0,0],[k_h//2,k_h//2],[k_w//2,k_w//2],[0,0]], "SYMMETRIC")
return tf.nn.bias_add(
tf.nn.convolution(_t, self.w,
strides=self.strides, dilation_rate=self.dilates,
padding='VALID'),
self.b,name=name)
| tensorflow.pad | 1,516 |
import tensorflow as tf
with tf.variable_scope(scope):
V = tf.get_variable('V', shape=list(filter_size) + [int(x.get_shape()[-1]), num_filters], dtype=tf.float32,
initializer=tf.random_normal_initializer(0, 0.05), trainable=True)
g = tf.get_variable('g', shape=[num_filters], dtype=tf.float32,
initializer=tf.constant_initializer(1.), trainable=True)
b = tf.get_variable('b', shape=[num_filters], dtype=tf.float32,
initializer=bias_initializer, trainable=True)
def maybe_avg(v):
if ema is not None and not init:
v = tf.cond(training, lambda: v, lambda: ema.average(v))
| tensorflow.get_variable | 1,517 |
import tensorflow as tf
d['surface_point_samples'])
def parse_example(filename):
"""A tensorflow function to return a dataset element when mapped"""
return tf.py_func(_example_dict_tf_func_wrapper, [filename], [
tf.float32, tf.float32, tf.string, tf.float32, tf.float32, tf.float32,
tf.float32])
| tensorflow.py_func | 1,518 |
import tensorflow as tf
# 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 ))
loss_summary = tf.summary.scalar("entropy", loss)
grads = train_z(loss,z)
trainable_var = tf.trainable_variables()
if FLAGS.debug:
| tensorflow.cast | 1,519 |
from tensorflow.python.framework import ops
@ops.RegisterShape("SoftsignGrad")
def _BinaryElementwiseShape(op):
"""Returns same shape as both inputs to op.
Args:
op: Input operation.
Returns:
Shape of both inputs to `op`.
"""
return [op.inputs[0].get_shape().merge_with(op.inputs[1].get_shape())]
ops.RegisterShape("L2Loss")(common_shapes.scalar_shape)
ops.RegisterShape("LRN")(common_shapes.unchanged_shape_with_rank(4))
@ops.RegisterShape("LRNGrad")
def _LRNGradShape(op):
"""Shape function for LRNGrad op."""
in_grads_shape = op.inputs[0].get_shape().with_rank(4)
in_image_shape = op.inputs[1].get_shape().with_rank(4)
out_image_shape = op.inputs[2].get_shape().with_rank(4)
return [in_grads_shape.merge_with(in_image_shape).merge_with(out_image_shape)]
| tensorflow.python.framework.ops.RegisterShape | 1,520 |
import tensorflow as tf
print('Num of sample per eps is %d' % (num_sample))
# Construct predictions
image = tf.placeholder(tf.float32, shape=[hps.batch_size, image_size, image_size,
num_channel]) ############MNIST and CIFAR10 are different ar here
adv_image = tf.placeholder(tf.float32, shape=[hps.batch_size, image_size, image_size,
num_channel]) ############MNIST and CIFAR10 are different ar here
predict = tf.placeholder(tf.float32, shape=[hps.batch_size, 10])
predict_nor, tsne_logit_nor = models(hps, image, FLAGS.RCE_train, logits=False, tsne_logits=True)
predict_adv, tsne_logit_adv = models(hps, adv_image, FLAGS.RCE_train, logits=False, tsne_logits=True)
# Calculate entropy
argmax_y_onehot = tf.one_hot(tf.argmax(predict, 1), 10, on_value=0.0, off_value=1.0, axis=-1)
normalized_y_nonmaximal = tf.reduce_sum(predict * argmax_y_onehot, 1)
entropy = tf.reduce_sum(-tf.log(predict) * predict * argmax_y_onehot, 1) / normalized_y_nonmaximal + tf.log(
normalized_y_nonmaximal)
for k in range(10):
adv_image_craft = adv_craft_func(hps, image, FLAGS.attack_method, eps=0.02 * k + 0.02, RCE_train=FLAGS.RCE_train)
#adv_image_craft = adv_craft_func(hps, image, FLAGS.attack_method, eps=0.04,RCE_train=FLAGS.RCE_train)
sess.run(tf.global_variables_initializer())
saver.restore(sess, ckpt_state.model_checkpoint_path)
for i in six.moves.range(FLAGS.eval_batch_count):
time_start = time.time()
(nor_img,true_label) = sess.run([images,labels])
adv_img = sess.run(adv_image_craft,feed_dict={image:nor_img})
| tensorflow.log | 1,521 |
import tensorflow as tf
initializer=tf.constant_initializer(1.), trainable=True)
b = tf.get_variable('b', shape=[num_filters], dtype=tf.float32,
initializer=bias_initializer, trainable=True)
def maybe_avg(v):
if ema is not None and not init:
v = tf.cond(training, lambda: v, lambda: ema.average(v))
return v
if init:
x = tf.nn.conv2d(x, tf.nn.l2_normalize(V.initialized_value(), [0, 1, 2]), [1] + list(stride) + [1], pad)
init_scale=.01
m_init, v_init = tf.nn.moments(x, [0,1,2])
scale_init = init_scale / tf.sqrt(v_init + 1e-10)
with tf.control_dependencies([g.assign(g * scale_init), b.assign_add(-m_init * scale_init)]):
x = tf.reshape(scale_init, [1, 1, 1, num_filters]) * (x - tf.reshape(m_init, [1, 1, 1, num_filters]))
else:
V = maybe_avg(V)
g = maybe_avg(g)
b = maybe_avg(b)
# use weight normalization (Salimans & Kingma, 2016)
W = tf.reshape(g, [1, 1, 1, num_filters]) * tf.nn.l2_normalize(V, [0, 1, 2])
# calculate convolutional layer output
| tensorflow.nn.moments | 1,522 |
import tensorflow as tf
a = 2
values = interpolated
inter = tf.reshape(values, [self.resolution,
self.resolution,
| tensorflow.reshape | 1,523 |
import tensorflow as tf
frame_history_len: int
How many past frames to include as input to the model.
target_update_freq: int
How many experience replay rounds (not steps!) to perform between
each update to the target Q network
grad_norm_clipping: float or None
If not None gradients' norms are clipped to this value.
"""
assert type(env.observation_space) == gym.spaces.Box
assert type(env.action_space) == gym.spaces.Discrete
###############
# BUILD MODEL #
###############
if not out_dir: out_dir = os.path.join(os.getcwd(),'results',env.unwrapped.spec.id +'_' + time.strftime("%d-%m-%Y_%H-%M-%S"))
writer = tf.summary.FileWriter(out_dir)
if len(env.observation_space.shape) == 1:
# This means we are running on low-dimensional observations (e.g. RAM)
input_shape = env.observation_space.shape
else:
img_h, img_w, img_c = env.observation_space.shape
input_shape = (img_h, img_w, frame_history_len * img_c)
num_actions = env.action_space.n
# set up placeholders
# placeholder for current observation (or state)
obs_t_ph = tf.placeholder(tf.uint8, [None] + list(input_shape))
# placeholder for current action
act_t_ph = tf.placeholder(tf.int32, [None])
| tensorflow.summary.FileWriter | 1,524 |
import tensorflow as tf
return new_dropout_masks
def placeholder(dim=None):
return tf.placeholder(dtype=tf.float32, shape=(None,dim) if dim else (None,))
def placeholders(*args):
| tensorflow.placeholder | 1,525 |
import tensorflow as tf
def cross_entropy_layer(tensor, target, **opts):
if _rank(tensor) > 1:
target = tf.reshape(target, shape=(-1, ))
| tensorflow.reshape | 1,526 |
import tensorflow as tf
this file.""")
tf.flags.DEFINE_string('graph_file', None,
"""Write the model's graph definition to this
file. Defaults to binary format unless filename ends
in 'txt'.""")
tf.flags.DEFINE_string('optimizer', 'sgd',
'Optimizer to use: momentum or sgd or rmsprop')
tf.flags.DEFINE_float('learning_rate', None,
"""Initial learning rate for training.""")
tf.flags.DEFINE_float('num_epochs_per_decay', 0,
| tensorflow.flags.DEFINE_string | 1,527 |
import tensorflow as tf
p.transformer_tpl.tr_atten_tpl.num_attention_heads = 8
p.transformer_tpl.tr_fflayer_tpl.hidden_dim = 8192
return p
@base_layer.initializer
def __init__(self, params):
super(TransformerStack, self).__init__(params)
p = self.params
with tf.variable_scope(p.name):
# Add transformer layers.
transformer_layer_params = []
for i in range(p.num_transformer_layers):
params = p.transformer_tpl.Copy()
params.name = 'trans_%d' % (i)
params.source_dim = p.model_dim
params.packed_input = p.packed_input
params.has_aux_atten = p.has_aux_attention
| tensorflow.variable_scope | 1,528 |
import tensorflow as tf
return_dict = {}
# invalid position mask such as query and special symbols (PAD, SEP, CLS)
p_mask = features["p_mask"]
# logit of the start position
with tf.variable_scope("start_logits"):
start_logits = tf.layers.dense(
output,
1,
kernel_initializer=initializer)
start_logits = tf.transpose(tf.squeeze(start_logits, -1), [1, 0])
start_logits_masked = start_logits * (1 - p_mask) - 1e30 * p_mask
| tensorflow.variable_scope | 1,529 |
import tensorflow as tf
assert tag != None
# handle most of the regularizer here
weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.FLAGS.weight_decay)
if cfg.FLAGS.bias_decay:
biases_regularizer = weights_regularizer
| tensorflow.contrib.layers.l2_regularizer | 1,530 |
import tensorflow as tf
comb_ch = len(out_blocks) * block_ch
X = tf.concat(out_blocks, axis=3)
return (X, comb_ch)
def _combine_cell_blocks_dynamic(self, cell_inputs, blocks, cell_arch, w, h, block_ch, is_train=False):
ni = len(cell_inputs + blocks)
b = len(blocks)
# Count usage of inputs
block_uses = []
for bi in range(b):
idx1 = cell_arch[bi][0]
idx2 = cell_arch[bi][2]
block_use = tf.one_hot(idx1, ni, dtype=tf.int32) + tf.one_hot(idx2, ni, dtype=tf.int32)
block_uses.append(block_use)
block_uses = tf.add_n(block_uses)
unused_indices = tf.reshape(tf.cast(tf.where(tf.equal(block_uses, 0)), tf.int32), [-1])
num_out_blocks = tf.size(unused_indices)
# Select only unused blocks
with tf.variable_scope('select'):
stacked_blocks = tf.stack(cell_inputs + blocks)
out_blocks = tf.gather(stacked_blocks, unused_indices, axis=0)
out_blocks = tf.transpose(out_blocks, (1, 2, 3, 0, 4))
# Combine to constant channels
with tf.variable_scope('combine'):
| tensorflow.one_hot | 1,531 |
import tensorflow as tf
norm_grads = None
if self.max_grad_norm is not None:
grads, norm_grads = tf.clip_by_global_norm(grads, self.max_grad_norm)
grads = list(zip(grads, self.params))
with tf.variable_scope("input_info", reuse=False):
tf.summary.scalar('rewards', tf.reduce_mean(self.reward_ph))
tf.summary.scalar('learning_rate', tf.reduce_mean(self.learning_rate))
tf.summary.scalar('advantage', tf.reduce_mean(adv))
tf.summary.scalar('action_probability', tf.reduce_mean(self.mu_ph))
| tensorflow.variable_scope | 1,532 |
import tensorflow as tf
with tf.Session(
target="",
config=tf.ConfigProto(device_count={"CPU": 2})) as sess:
with sess.graph.device("/cpu:0"):
v0 = tf.Variable(10, name="v0")
with sess.graph.device("/cpu:1"):
v1 = tf.Variable(20, name="v1")
save = tf.train.Saver({"v0": v0, "v1": v1}, sharded=True)
tf.initialize_all_variables().run()
val = save.save(sess, save_path)
self.assertEqual(save_path + "-?????-of-00002", val)
meta_graph_filename = save._MetaGraphFilename(val)
self.assertEqual(save_path + ".meta", meta_graph_filename)
| tensorflow.train.Saver | 1,533 |
import tensorflow as tf
return [L_, tf.transpose(L_)]
tmp = tf.scan(fn, L_flat, initializer=init)
if isinstance(tmp, (list, tuple)):
| tensorflow.scan | 1,534 |
import tensorflow as tf
if do_dnn:
last_layer = rnn_output
last_layer_size = rnn_output_size
for i, layer_size in enumerate(dnn_sizes):
layer_name = 'dnn_{}'.format(i)
with tf.variable_scope(layer_name):
dnn_w = tf.get_variable('W', shape=[last_layer_size, layer_size], initializer=dnn_init, dtype=dtype)
dnn_b = tf.get_variable('b', shape=[layer_size], initializer=tf.constant_initializer(0.0), dtype=dtype)
projected = tf.nn.bias_add(tf.matmul(last_layer, dnn_w), dnn_b)
# TODO: argument nonlinearity, change bias to 0.1 if relu
| tensorflow.variable_scope | 1,535 |
import tensorflow as tf
session_config=config)
if FLAGS.use_hvd and hvd.rank() != 0 and not FLAGS.auto_recover:
run_config = tf.estimator.RunConfig(
model_dir=FLAGS.output_dir,
keep_checkpoint_max=FLAGS.keep_checkpoint_max,
| tensorflow.estimator.RunConfig | 1,536 |
import tensorflow as tf
def dice_loss(predictions, targets, weights=1., name='dice_loss'):
with tf.name_scope(name):
# predictions = tf.to_float(predictions)
targets = tf.to_float(targets)
intersection = 2 * tf.reduce_sum(predictions * targets) + weights
union = weights + tf.reduce_sum(predictions) + tf.reduce_sum(targets)
loss = -(intersection / (union))
return loss
| tensorflow.reduce_sum | 1,537 |
from tensorflow.contrib.distributions.python.ops import distribution_util
@distribution_util.AppendDocstring(_poisson_sample_note)
def _log_prob(self, x):
return self._log_unnormalized_prob(x) - self._log_normalization()
@distribution_util.AppendDocstring(_poisson_sample_note)
def _prob(self, x):
return math_ops.exp(self._log_prob(x))
| tensorflow.contrib.distributions.python.ops.distribution_util.AppendDocstring | 1,538 |
import tensorflow as tf
k = dense_layer_3d(to_tensor, num_attention_heads, size_per_head,
create_initializer(initializer_range), key_act, "key")
# `value_layer` = [B, T, N, H]
v = dense_layer_3d(to_tensor, num_attention_heads, size_per_head,
create_initializer(initializer_range), value_act, "value")
q = tf.transpose(q, [0, 2, 1, 3])
k = tf.transpose(k, [0, 2, 1, 3])
v = tf.transpose(v, [0, 2, 1, 3])
if attention_mask is not None:
attention_mask = tf.reshape(
| tensorflow.transpose | 1,539 |
import tensorflow as tf
batch_size_val = self.cnf['batch_size_test']
self.end_points_G = self.model.generator([batch_size_train, 100], True, None, batch_size_val)
if gpu_idx == 0:
G_means = tf.reduce_mean(self.end_points_G['softmax'], 0, keep_dims=True)
G_vars = tf.reduce_mean(tf.square(self.end_points_G['softmax'] - G_means), 0, keep_dims=True)
G = tf.Print(
self.end_points_G['softmax'],
[tf.reduce_mean(G_means), tf.reduce_mean(G_vars)],
"generator mean and average var",
first_n=1)
inputs_means = tf.reduce_mean(inputs, 0, keep_dims=True)
inputs_vars = tf.reduce_mean(tf.square(inputs - inputs_means), 0, keep_dims=True)
inputs = tf.Print(
inputs,
[tf.reduce_mean(inputs_means), tf.reduce_mean(inputs_vars)],
"image mean and average var",
first_n=1)
joint = tf.concat([inputs, G], 0)
log.info('Input size of unlabelled and generated %s' % (joint.get_shape()))
self.end_points_D = self.model.discriminator(
joint, True, None, num_classes=num_classes, batch_size=batch_size_train)
self.end_points_D_val = self.model.discriminator(
| tensorflow.square | 1,540 |
import tensorflow as tf
"""
# size: num_priors x num_targets
ious = iou_of(tf.expand_dims(gt_boxes, axis=0), tf.expand_dims(corner_form_priors, axis=1))
# size: num_priors
best_target_per_prior = tf.math.reduce_max(ious, axis=1)
best_target_per_prior_index = tf.math.argmax(ious, axis=1)
# size: num_targets
best_prior_per_target = tf.math.reduce_max(ious, axis=0)
best_prior_per_target_index = tf.math.argmax(ious, axis=0)
| tensorflow.math.reduce_max | 1,541 |
import tensorflow as tf
name='loss_logits_rl_w',
initializer=tf.initializers.zeros(),
shape=[
FLAGS.num_choices,
],
dtype=tf.float32)
dist = tfp.distributions.Categorical(logits=logits)
dist_entropy = tf.reduce_sum(dist.entropy())
sample = dist.sample()
sample_masks = 1. * tf.cast(sample, tf.float32) / FLAGS.loss_weight_scale
sample_log_prob = tf.reduce_mean(dist.log_prob(sample))
return (dist_entropy, sample_masks, sample_log_prob)
def main(unused_argv):
tf.set_random_seed(FLAGS.random_seed)
run_config_args = {
| tensorflow.cast | 1,542 |
import tensorflow as tf
cnn_output = tf.slice(cnn_output, [0, zack_hack_div_2, 0, 0], [-1, rnn_nunroll, -1, -1])
nfeats_conv = reduce(lambda x, y: x * y, [int(x) for x in cnn_output.get_shape()[-2:]])
else:
nfeats_conv = reduce(lambda x, y: x * y, [int(x) for x in cnn_output.get_shape()[-3:]])
feats_conv = tf.reshape(cnn_output, [batch_size * rnn_nunroll, nfeats_conv])
nfeats_tot = nfeats_conv + nfeats
feats_all = tf.concat(1, [feats_conv, feats_other])
print('feats_cnn: {}'.format(feats_conv.get_shape()))
print('feats_all: {}'.format(feats_all.get_shape()))
# Project to RNN size
rnn_output = feats_all
rnn_output_size = nfeats_tot
if do_rnn:
with tf.variable_scope('rnn_proj'):
rnn_proj_w = tf.get_variable('W', [nfeats_tot, rnn_size], initializer=tf.uniform_unit_scaling_initializer(factor=1.0, dtype=dtype), dtype=dtype)
rnn_proj_b = tf.get_variable('b', [rnn_size], initializer=tf.constant_initializer(0.0), dtype=dtype)
rnn_inputs = tf.nn.bias_add(tf.matmul(feats_all, rnn_proj_w), rnn_proj_b)
rnn_inputs = tf.reshape(rnn_inputs, [batch_size, rnn_nunroll, rnn_size])
rnn_inputs = tf.split(rnn_inputs, rnn_nunroll, axis=1)
rnn_inputs = [tf.squeeze(input_, [1]) for input_ in rnn_inputs]
if rnn_cell_type == 'rnn':
cell_fn = tf.nn.rnn_cell.BasicRNNCell
elif rnn_cell_type == 'gru':
cell_fn = tf.nn.rnn_cell.GRUCell
elif rnn_cell_type == 'lstm':
| tensorflow.variable_scope | 1,543 |
from tensorflow.python.ops import array_ops
ValueError: If `weights` is not `None` and has an incomptable shape.
"""
default_name = _at_k_name('true_positive', k, class_id=class_id)
with ops.name_scope(name, default_name, (predictions_idx, labels)) as scope:
tp = _sparse_true_positive_at_k(
predictions_idx=predictions_idx, labels=labels, class_id=class_id,
weights=weights)
batch_total_tp = math_ops.to_double(math_ops.reduce_sum(tp))
var = contrib_variables.local_variable(
array_ops.zeros([], dtype=dtypes.float64), name=scope)
return var, state_ops.assign_add(var, batch_total_tp, name='update')
def _sparse_false_positive_at_k(predictions_idx,
labels,
class_id=None,
weights=None):
"""Calculates false positives for precision@k.
| tensorflow.python.ops.array_ops.zeros | 1,544 |
import tensorflow as tf
})
if FLAGS.contrast_norm == 'areafactor':
image = tf.decode_raw(features['image_raw'], tf.float32)
else:
image = tf.decode_raw(features['image_raw'], tf.uint8)
image = tf.cast(image, tf.float32) * (1. / 255)
image.set_shape(np.prod([FLAGS.num_scales, FLAGS.crop_size, FLAGS.crop_size]))
image = tf.reshape(image, [FLAGS.num_scales, FLAGS.crop_size, FLAGS.crop_size, 1])
image = image - 0.5
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['label'], tf.int32)
return image, label
def inputs(name, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and test (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
| tensorflow.cast | 1,545 |
import tensorflow as tf
FLAGS.save_summaries_steps > 0):
summary_writer = tf.summary.FileWriter(FLAGS.train_dir,
tf.get_default_graph())
| tensorflow.get_default_graph | 1,546 |
import tensorflow as tf
return out
def deconv2d(x, dim=(32, [3, 3], [1, 1]), pad='SAME', scope="deconv2d", training=True, ema=None, init=False, bias_initializer=tf.constant_initializer(0.)):
num_filters, filter_size, stride = dim
xs = x.get_shape().as_list()
if pad=='SAME':
target_shape = [tf.shape(x)[0], xs[1]*stride[0], xs[2]*stride[1], num_filters]
else:
target_shape = [tf.shape(x)[0], xs[1]*stride[0] + filter_size[0]-1, xs[2]*stride[1] + filter_size[1]-1, num_filters]
with tf.variable_scope(scope):
V = tf.get_variable("V", shape=list(filter_size) + [num_filters, int(x.get_shape()[-1])], dtype=tf.float32, initializer=tf.random_normal_initializer(0, 0.05), trainable=True)
g = tf.get_variable("g", shape=[num_filters], dtype=tf.float32, initializer=tf.constant_initializer(1.), trainable=True)
b = tf.get_variable("b", shape=[num_filters], dtype=tf.float32, initializer=bias_initializer, trainable=True)
def maybe_avg(v):
if ema is not None and not init:
v = tf.cond(training, lambda: v, lambda: ema.average(v))
return v
if init:
x = tf.nn.conv2d_transpose(x, tf.nn.l2_normalize(V.initialized_value(), [0, 1, 3]), target_shape, [1] + list(stride) + [1], padding=pad)
init_scale = .01
m_init, v_init = tf.nn.moments(x, [0, 1, 2])
scale_init = init_scale / tf.sqrt(v_init + 1e-10)
with tf.control_dependencies([g.assign(g * scale_init), b.assign_add(-m_init * scale_init)]):
| tensorflow.constant_initializer | 1,547 |
import tensorflow as tf
optimiser = tf.train.AdamOptimizer(decayed_learning_rate, name="Adam").minimize(cross_entropy)
# calculate the prediction and the accuracy
accuracy, acc_op = tf.metrics.accuracy(labels=tf.argmax(y_, axis=1), predictions=tf.argmax(y_conv, axis=1))
loss_summary = tf.summary.scalar('Loss', cross_entropy)
acc_summary = tf.summary.scalar('Accuracy', accuracy)
# summaries for TensorBoard visualisation
validation_summary = tf.summary.merge([img_summary, acc_summary])
| tensorflow.summary.scalar | 1,548 |
import tensorflow as tf
if distributional_size > 1:
target_value_shape_suffix = [num_target_frames, distributional_size]
features = {
"inputs": observations,
"epoch": tf.constant(epoch + 1),
"input_action": tf.zeros(obs_shape[:2] + [1], dtype=tf.int32),
"input_reward": tf.zeros(obs_shape[:2] + [1], dtype=tf.int32),
"targets": tf.zeros(obs_shape[:1] + [num_target_frames] + obs_shape[2:]),
"target_action": tf.zeros(
obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32),
| tensorflow.zeros | 1,549 |
import tensorflow as tf
latent_variable = dense(e_dense_2, n_l2, z_dim, 'e_latent_variable')
cat_op = dense(e_dense_2, n_l2, n_labels, 'e_label')
if not supervised:
softmax_label = tf.nn.softmax(logits=cat_op, name='e_softmax_label')
else:
softmax_label = cat_op
| tensorflow.nn.softmax | 1,550 |
import tensorflow as tf
layers_ratio = (l + 1) / (L + 2)
drop_path_keep_prob = self._get_drop_path_keep_prob(layers_ratio, step, is_train, **knobs)
# Either add a reduction cell or normal cell
if l in reduction_layers:
block_ch *= 2
w >>= 1
h >>= 1
with tf.variable_scope('reduction_cell'):
if use_dynamic_arch:
self._add_dynamic_cell(reduction_arch, layers, w, h, block_ch, drop_path_keep_prob, is_train)
else:
self._add_static_cell(reduction_arch, layers, w, h, block_ch, drop_path_keep_prob, is_train,
is_reduction=True)
else:
with tf.variable_scope('normal_cell'):
if use_dynamic_arch:
| tensorflow.variable_scope | 1,551 |
from tensorflow.python.platform import gfile
# Check that s1 is still here, but s2 is gone.
self.assertTrue(gfile.Exists(s1))
self.assertFalse(gfile.Exists(s2))
self.assertTrue(gfile.Exists(s3))
| tensorflow.python.platform.gfile.Exists | 1,552 |
import tensorflow as tf
avg_norm_k_dot_g = tf.reduce_mean(tf.abs(k_dot_g))
avg_norm_adj = tf.reduce_mean(tf.abs(adj))
| tensorflow.abs | 1,553 |
import tensorflow as tf
# inputs=embedding_output,
# sequence_lengths=_true_length,
# rnn_size=config.lstm_size,
# dropout=config.bilstm_dropout_rate,
# is_training=is_training,
# num_layers=config.num_bilstm,
# direction='bidirectional')
# first_token_tensor = tf.squeeze(sequence_output[:, -1:, :], axis=1)
last_token_tensor = tf.squeeze(sequence_output[:, -1:, :], axis=1)
output_layer = tf.layers.dense(
last_token_tensor,
config.hidden_size,
activation=tf.tanh,
kernel_initializer=modeling.create_initializer(config.initializer_range))
hidden_size = output_layer.shape[-1].value
| tensorflow.squeeze | 1,554 |
import tensorflow as tf
intermediate_size,
create_initializer(initializer_range),
intermediate_act_fn,
num_attention_heads=num_attention_heads,
name="dense")
with tf.variable_scope("output"):
ffn_output = dense_layer_2d(
intermediate_output,
hidden_size,
create_initializer(initializer_range),
| tensorflow.variable_scope | 1,555 |
import tensorflow as tf
eval_examples.append(classifier_utils.PaddingInputExample())
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
eval_file = os.path.join(cached_dir, task_name + "_eval.tf_record")
if not tf.gfile.Exists(eval_file):
classifier_utils.file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer,
eval_file, task_name)
tf.logging.info("***** Running evaluation *****")
| tensorflow.gfile.Exists | 1,556 |
from tensorflow.python.framework import ops
predictions_idx=top_k_idx, labels=labels, k=k, class_id=class_id,
weights=weights)
fp, fp_update = _streaming_sparse_false_positive_at_k(
predictions_idx=top_k_idx, labels=labels, k=k, class_id=class_id,
weights=weights)
metric = math_ops.div(tp, math_ops.add(tp, fp), name=name)
update = math_ops.div(
tp_update, math_ops.add(tp_update, fp_update), name='update')
if metrics_collections:
ops.add_to_collections(metrics_collections, metric)
if updates_collections:
ops.add_to_collections(updates_collections, update)
return metric, update
# TODO(ptucker): Validate range of values in labels?
@deprecated_args(IGNORE_MASK_DATE, IGNORE_MASK_INSTRUCTIONS, 'ignore_mask')
def streaming_sparse_precision_at_k(predictions,
labels,
| tensorflow.python.framework.ops.add_to_collections | 1,557 |
import tensorflow as tf
i = tf.nn.sigmoid(i)
f = tf.nn.sigmoid(f)
o = tf.nn.sigmoid(o)
u = tf.tanh(u)
c = f*c + i*u
h = o*tf.tanh(c)
xs[idx] = h
s = tf.concat(axis=1, values=[c, h])
return xs, s
def _ln(x, g, b, e=1e-5, axes=[1]):
u, s = tf.nn.moments(x, axes=axes, keep_dims=True)
x = (x-u)/tf.sqrt(s+e)
x = x*g+b
| tensorflow.concat | 1,558 |
import tensorflow as tf
self.image_real_A = self.images_real[:,:,:,:self.input_dim]
self.image_real_B = self.images_real[:,:,:,self.input_dim:self.input_dim + self.output_dim]
self.images_fake_B = self.build_generator(self.image_real_A,reuse=False,name='generator_AB')
self.images_fake_A = self.build_generator(self.images_fake_B,reuse=False,name='generator_BA')
self.images_fake_A_ = self.build_generator(self.image_real_B,reuse=True,name='generator_BA')
self.images_fake_B_ = self.build_generator(self.images_fake_A_,reuse=True,name='generator_AB')
self.D_B_fake = self.build_discriminator(self.images_fake_B ,reuse=False, name="discriminatorB")
self.D_A_fake = self.build_discriminator(self.images_fake_A_,reuse=False, name="discriminatorA")
self.D_B_real = self.build_discriminator(self.image_real_B,reuse=True, name="discriminatorB")
self.D_A_real = self.build_discriminator(self.image_real_A,reuse=True, name="discriminatorA")
self.loss_GABA = self.lambda_l2*squared_loss(self.images_fake_A,self.image_real_A) + binary_cross_entropy_loss(labels=tf.ones_like(self.D_B_fake),logits=self.D_B_fake)
self.loss_GBAB = self.lambda_l2*squared_loss(self.images_fake_B_,self.image_real_B) + binary_cross_entropy_loss(labels=tf.ones_like(self.D_A_fake),logits=self.D_A_fake)
self.generator_loss = self.loss_GABA + self.loss_GBAB
self.D_B_loss_real = binary_cross_entropy_loss(tf.ones_like(self.D_B_real),self.D_B_real)
self.D_B_loss_fake = binary_cross_entropy_loss(tf.zeros_like(self.D_B_fake),self.D_B_fake)
self.D_B_loss = (self.D_B_loss_real + self.D_B_loss_fake) / 2.0
self.D_A_loss_real = binary_cross_entropy_loss(tf.ones_like(self.D_A_real),self.D_A_real)
self.D_A_loss_fake = binary_cross_entropy_loss(tf.zeros_like(self.D_A_fake),self.D_A_fake)
self.D_A_loss = (self.D_A_loss_real + self.D_A_loss_fake) / 2.0
self.discriminator_loss = self.D_B_loss + self.D_A_loss
| tensorflow.ones_like | 1,559 |
import tensorflow as tf
d_fake_logits=d_fake_logits)
penalty_loss = penalty_lib.get_penalty_loss(
x=images, x_fake=generated, y=y, is_training=is_training,
discriminator=self.discriminator, architecture=self._architecture)
self.d_loss += self._lambda * penalty_loss
z_projs = tf.concat([z_projs_real, z_projs_fake], 0)
z_aug_projs = tf.concat([z_aug_projs_real, z_aug_projs_fake], 0)
sims_logits = tf.matmul(z_projs, z_aug_projs, transpose_b=True)
logits_max = tf.reduce_max(sims_logits,1)
sims_logits = sims_logits - tf.reshape(logits_max, [-1, 1])
sims_probs = tf.nn.softmax(sims_logits)
sim_labels = tf.constant(np.arange(bs * 2, dtype=np.int32))
sims_onehot = tf.one_hot(sim_labels, bs * 2)
c_real_loss = - tf.reduce_mean(
tf.reduce_sum(sims_onehot * tf.log(sims_probs + 1e-10), 1))
self.d_loss += c_real_loss * self._weight_contrastive_loss_d
self._tpu_summary.scalar("loss/c_real_loss", c_real_loss)
self._tpu_summary.scalar("loss/penalty", penalty_loss)
| tensorflow.nn.softmax | 1,560 |
import tensorflow as tf
def deconv_bn_relu(self, bottom, name, kernel_size, output_channels, initializer, stride = 1, bn=False, training=False, relu=True):
input_shape = bottom.get_shape().as_list()
input_channels = input_shape[-1]
output_shape = [input_shape[0], input_shape[1]*stride, input_shape[2]*stride, output_channels]
with tf.variable_scope(name) as scope:
kernel = self.variable('weights', [kernel_size, kernel_size, output_channels, input_channels], initializer, regularizer=tf.contrib.layers.l2_regularizer(0.0005))
deconv = tf.nn.conv2d_transpose(bottom, kernel, output_shape, [1, stride, stride, 1], padding='SAME')
biases = self.variable('biases', [output_channels], tf.constant_initializer(0.0))
deconv_layer = tf.nn.bias_add(deconv, biases)
if bn:
deconv_layer = self.batch_norm_layer('batch_norm_layer',deconv_layer,training)
if relu:
deconv_layer = tf.nn.relu(deconv_layer, name=scope.name)
print('Deconv layer {0} -> {1}'.format(bottom.get_shape().as_list(),deconv_layer.get_shape().as_list()))
return deconv_layer
| tensorflow.nn.bias_add | 1,561 |
import tensorflow as tf
activation=common_layers.belu, padding="SAME")
x = tf.nn.dropout(x, rate=dropout)
x = tf.layers.conv2d(
x, 64, (4, 4), strides=(2, 2), name="conv2",
activation=common_layers.belu, padding="SAME")
x = tf.nn.dropout(x, rate=dropout)
x = tf.layers.conv2d(
x, 128, (4, 4), strides=(2, 2), name="conv3",
activation=common_layers.belu, padding="SAME")
flat_x = tf.layers.flatten(x)
flat_x = tf.nn.dropout(flat_x, rate=dropout)
| tensorflow.layers.conv2d | 1,562 |
import tensorflow as tf
"""
with tf.name_scope('Encode'):
return self._encode(boxes, anchors)
def decode(self, rel_codes, anchors):
"""Decode boxes that are encoded relative to an anchor collection.
Args:
rel_codes: a tensor representing N relative-encoded boxes
anchors: BoxList of anchors
Returns:
boxlist: BoxList holding N boxes encoded in the ordinary way (i.e.,
with corners y_min, x_min, y_max, x_max)
"""
with tf.name_scope('Decode'):
return self._decode(rel_codes, anchors)
@abstractmethod
def _encode(self, boxes, anchors):
"""Method to be overriden by implementations.
Args:
boxes: BoxList holding N boxes to be encoded
anchors: BoxList of N anchors
Returns:
a tensor representing N relative-encoded boxes
"""
pass
| tensorflow.name_scope | 1,563 |
import tensorflow as tf
:, :-1],
labels=gtboxes_and_label_r[
:, -1],
method=1)
tf.summary.image('Compare/gtboxes_h_gpu:%d' % i, gtboxes_in_img_h)
tf.summary.image('Compare/gtboxes_r_gpu:%d' % i, gtboxes_in_img_r)
if cfgs.ADD_BOX_IN_TENSORBOARD:
| tensorflow.summary.image | 1,564 |
import tensorflow as tf
tf.summary.scalar('loss', loss)
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) / (
| tensorflow.gradients | 1,565 |
import tensorflow as tf
with tf.control_dependencies([layer_output]):
# update the initial states
for i in range(2):
new_state = tf.concat(
[final_state[i][:batch_size, :],
init_states[i][batch_size:, :]], axis=0)
state_update_op = tf.assign(init_states[i], new_state)
update_ops.append(state_update_op)
layer_input = layer_output
self.mask = mask
self.sequence_lengths = sequence_lengths
self.update_state_op = tf.group(*update_ops)
def dump_token_embeddings(vocab_file, options_file, weight_file, outfile):
'''
Given an input vocabulary file, dump all the token embeddings to the
outfile. The result can be used as the embedding_weight_file when
constructing a BidirectionalLanguageModel.
'''
with open(options_file, 'r') as fin:
options = json.load(fin)
max_word_length = options['char_cnn']['max_characters_per_token']
vocab = UnicodeCharsVocabulary(vocab_file, max_word_length)
| tensorflow.group | 1,566 |
import tensorflow as tf
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
| tensorflow.reshape | 1,567 |
from tensorflow.python.summary import summary
"""
def gradient_clipping(grads_and_vars):
"""Internal function for adaptive clipping."""
grads, variables = zip(*grads_and_vars)
norm = clip_ops.global_norm(grads)
max_norm, log_mean = _adaptive_max_norm(norm, std_factor, decay,
global_step, epsilon, name)
# reports the max gradient norm for debugging
if report_summary:
summary.scalar("global_norm/adaptive_max_gradient_norm", max_norm)
# factor will be 1. if norm is smaller than max_norm
factor = array_ops.where(norm < max_norm, array_ops.ones_like(norm),
math_ops.exp(log_mean) / norm)
if static_max_norm is not None:
factor = math_ops.minimum(static_max_norm / norm, factor)
# apply factor
clipped_grads = []
for grad in grads:
if grad is None:
| tensorflow.python.summary.summary.scalar | 1,568 |
import tensorflow as tf
if not FLAGS.restore:
tf.gfile.DeleteRecursively(FLAGS.checkpoint_path)
tf.gfile.MkDir(FLAGS.checkpoint_path)
input_images = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_images')
input_score_maps = tf.placeholder(tf.float32, shape=[None, None, None, 1], name='input_score_maps')
if FLAGS.geometry == 'RBOX':
input_geo_maps = tf.placeholder(tf.float32, shape=[None, None, None, 5], name='input_geo_maps')
else:
input_geo_maps = tf.placeholder(tf.float32, shape=[None, None, None, 8], name='input_geo_maps')
input_training_masks = tf.placeholder(tf.float32, shape=[None, None, None, 1], name='input_training_masks')
global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
learning_rate = tf.train.exponential_decay(FLAGS.learning_rate, global_step, decay_steps=10000, decay_rate=0.94, staircase=True)
# add summary
tf.summary.scalar('learning_rate', learning_rate)
opt = tf.train.AdamOptimizer(learning_rate)
opt = MixedPrecisionOptimizer(opt, scale=FLAGS.loss_scale)
from npu_bridge.estimator.npu.npu_optimizer import NPUDistributedOptimizer
opt = NPUDistributedOptimizer(opt)
# split
input_images_split = tf.split(input_images, len(gpus))
input_score_maps_split = tf.split(input_score_maps, len(gpus))
input_geo_maps_split = tf.split(input_geo_maps, len(gpus))
input_training_masks_split = tf.split(input_training_masks, len(gpus))
| tensorflow.train.exponential_decay | 1,569 |
import tensorflow as tf
theta: A tensor of size [num_batch, 16].
It is the inverse camera transformation matrix (tf.float32).
out_size: A tuple representing the size of output of
transformer layer (float).
z_near: A number representing the near clipping plane (float).
z_far: A number representing the far clipping plane (float).
Returns:
A transformed tensor (tf.float32).
"""
def _repeat(x, n_repeats):
with tf.variable_scope('_repeat'):
rep = tf.transpose(
tf.expand_dims(tf.ones(shape=tf.stack([
n_repeats,
])), 1), [1, 0])
rep = tf.to_int32(rep)
x = tf.matmul(tf.reshape(x, (-1, 1)), rep)
return tf.reshape(x, [-1])
def _interpolate(im, x, y, z, out_size):
"""Bilinear interploation layer.
| tensorflow.variable_scope | 1,570 |
import tensorflow as tf
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
# Define your AdamOptimiser, using FLAGS.learning_rate to minimixe the loss function
decayed_learning_rate = tf.train.exponential_decay(FLAGS.learning_rate, tf.Variable(0, trainable=False), 1000, 0.8)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
| tensorflow.Variable | 1,571 |
import tensorflow as tf
attn_dists.append(attn_dist_t)
p_gens.append(p_gen_t)
vocab_scores.append(output_t) # The vocabulary distributions.
state_t_1 = state_t
context_t_1 = context_t
coverage_t_1 = coverage_t
if mode_gen == 'greedy':
wordidx_t = tf.argmax(output_t, 1) # [batch_size]
wordidx_t = tf.reshape(wordidx_t, [-1]) # [batch_size]
elif mode_gen == 'sample':
log_score_t = tf.log(output_t) # [batch_size, vsize]
wordidx_t = tf.multinomial(log_score_t, 1) # [batch_size, 1]
wordidx_t = tf.reshape(wordidx_t, [-1]) # [batch_size]
elif mode_gen in ('ce_train', 'loss',):
wordidx_t = answer_batch_unstack[i]
else:
assert False, 'unknown generating mode %s' % mode_gen
sampled_words.append(wordidx_t)
if len(sampled_words)!=0:
sampled_words = tf.stack(sampled_words, axis=1) # [batch_size, max_dec_steps]
| tensorflow.log | 1,572 |
import tensorflow as tf
# Transform the input features
for key, input_modality in six.iteritems(
self._problem_hparams.input_modality):
if key not in features:
tf.logging.warning("Missing feature %s - ignoring." % key)
continue
do_reuse = input_modality.name in all_previous_modalities
with tf.variable_scope(input_modality.name, reuse=do_reuse):
log_info("Transforming feature '%s' with %s.bottom", key,
input_modality.name)
transformed_features[key] = input_modality.bottom(features[key])
all_previous_modalities.append(input_modality.name)
# Transform the targets (for autoregressive models)
| tensorflow.variable_scope | 1,573 |
import tensorflow as tf
x = tf.logical_not(tf.nn.in_top_k(logits, label, topk))
return tf.cast(x, tf.float32, name=name)
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
| tensorflow.reduce_mean | 1,574 |
import tensorflow as tf
if ac_regularizers:
ac_regularizers_names = [r.name for r in ac_regularizers]
else:
ac_regularizers = [tf.zeros([1])]
ac_regularizers_names = ["none"]
ac_regularizers_fileNames = {"fileName" : "ac_regularizers"}
if custom_regularizers:
custom_regularizers_names = [r.name for r in custom_regularizers]
else:
custom_regularizers = [tf.zeros([1])]
custom_regularizers_names = ["none"]
custom_regularizers_fileNames = {"fileName": "custom_regularizers"}
# logging hooks
tensors_to_average = [[wb_regularizers], [ac_regularizers, custom_regularizers]]
tensors_to_average_names = [[wb_regularizers_names], [ac_regularizers_names, custom_regularizers_names]]
tensors_to_average_plots = [[wb_regularizers_fileNames],
[ac_regularizers_fileNames, custom_regularizers_fileNames]]
| tensorflow.zeros | 1,575 |
import tensorflow as tf
dataset.training_X, dataset.training_y, dataset.validation_X, dataset.validation_y
if not os.path.exists(weights_dir):
os.mkdir(weights_dir)
if not os.path.exists(weights_dir + '/best_models'):
os.mkdir(weights_dir + '/best_models')
# Create a saver.
saver = tf.train.Saver(max_to_keep=None)
if self.is_summary:
training_batch_summary_op = tf.merge_all_summaries(key=TRAINING_BATCH_SUMMARIES)
training_epoch_summary_op = tf.merge_all_summaries(key=TRAINING_EPOCH_SUMMARIES)
validation_batch_summary_op = tf.merge_all_summaries(key=VALIDATION_BATCH_SUMMARIES)
validation_epoch_summary_op = tf.merge_all_summaries(key=VALIDATION_EPOCH_SUMMARIES)
# Build an initialization operation to run below.
init = tf.global_variables_initializer()
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self.cnf.get('gpu_memory_fraction', 0.9))
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options))
sess.run(init)
if start_epoch > 1:
weights_from = "weights/model-epoch-%d.ckpt" % (start_epoch - 1)
if weights_from:
self._load_weights(sess, saver, weights_from)
learning_rate_value = self.lr_policy.initial_lr
log.info("Initial learning rate: %f " % learning_rate_value)
if self.is_summary:
| tensorflow.global_variables_initializer | 1,576 |
import tensorflow as tf
'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
for index, endpoint in enumerate(endpoints):
with tf.Graph().as_default():
inputs = tf.random_uniform((batch_size, height, width, 3))
out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint=endpoint)
self.assertTrue(out_tensor.op.name.startswith(
'MobilenetV1/' + endpoint))
self.assertItemsEqual(endpoints[:index + 1], end_points.keys())
| tensorflow.random_uniform | 1,577 |
import tensorflow as tf
# initial_state=self._initial_state)
outputs = []
with tf.variable_scope("RNN"):
for time_step in range(self.num_steps):
| tensorflow.variable_scope | 1,578 |
import tensorflow as tf
orig_is_opt = opt_indices == orig_indices
orig_opt_frac = tf.reduce_mean(tf.cast(orig_is_opt, tf.float32))
tf.compat.v2.summary.scalar(
name="orig_task_optimal", data=orig_opt_frac, step=global_step)
# How often is the relabelled goal optimal?
# The relabel_indices are [B, 1], so we need to remove the extra dim.
relabel_is_opt = tf.squeeze(relabel_indices) == orig_indices
relabel_opt_frac = tf.reduce_mean(tf.cast(relabel_is_opt, tf.float32))
tf.compat.v2.summary.scalar(
name="relabel_task_optimal", data=relabel_opt_frac, step=global_step)
# What are the average Q values of the original tasks?
if batch_size == num_tasks:
indices = tf.transpose(tf.stack([orig_indices, orig_indices], axis=0))
orig_q_vals = tf.gather_nd(logits_vec, indices)
| tensorflow.cast | 1,579 |
import tensorflow as tf
attention_head_size: int. Size of attention head.
attention_probs_dropout_prob: float. dropout probability for attention_layer
intermediate_size: int. Size of intermediate hidden layer.
intermediate_act_fn: (optional) Activation function for the intermediate
layer.
initializer_range: float. Range of the weight initializer.
hidden_dropout_prob: (optional) float. Dropout probability of the hidden
layer.
Returns:
layer output
"""
with tf.variable_scope("attention_1"):
with tf.variable_scope("self"):
attention_output = attention_layer(
from_tensor=layer_input,
to_tensor=layer_input,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
attention_probs_dropout_prob=attention_probs_dropout_prob,
initializer_range=initializer_range)
# Run a linear projection of `hidden_size` then add a residual
# with `layer_input`.
with tf.variable_scope("output"):
attention_output = dense_layer_3d_proj(
attention_output,
| tensorflow.variable_scope | 1,580 |
import tensorflow as tf
u = tf.contrib.layers.batch_norm(u)
u = tf.nn.relu(u)
| tensorflow.nn.relu | 1,581 |
import tensorflow as tf
# Decoder definition
o_d1 = self.general_deconv2d(o_c5, self.base_number_of_features * 8, 3, stride = 2, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_deconv2d_1')
o_me1 = tf.concat([o_d1, o_c4], 3) # Skip connection
o_d2 = self.general_deconv2d(o_me1, self.base_number_of_features * 4, 3, stride = 2, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_deconv2d_2')
o_me2 = tf.concat([o_d2, o_c3], 3) # Skip connection
o_d3 = self.general_deconv2d(o_me2, self.base_number_of_features * 2, 3, stride = 2, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_deconv2d_3')
o_me3 = tf.concat([o_d3, o_c2], 3) # Skip connection
o_d4 = self.general_deconv2d(o_me3, self.base_number_of_features, 3, stride = 2, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_deconv2d_4')
o_me4 = tf.concat([o_d4, o_c1], 3) # Skip connection
logits = tf.layers.conv2d(o_me4, self.args.num_classes, 1, 1, 'SAME', activation = None)
prediction = tf.nn.softmax(logits, name = name + '_softmax')
return logits, prediction
def general_conv2d(self, input_data, filters = 64, kernel_size = 7, stride = 1, stddev = 0.02, activation_function = "relu", padding = "VALID", do_norm=True, relu_factor = 0, name="conv2d"):
with tf.variable_scope(name):
conv = tf.layers.conv2d(input_data, filters, kernel_size, stride, padding, activation=None)
| tensorflow.concat | 1,582 |
import tensorflow as tf
marker='.', c=sdf_values.numpy()[:, 0])
plt.colorbar()
if not tf.math.is_nan(iou):
self.iou_per_class[class_id].append(iou)
| tensorflow.math.is_nan | 1,583 |
import tensorflow as tf
dataset = tf.data.TFRecordDataset(["./data/train.tfrecord"])
dataset = dataset.map(decode)
dataset = dataset.map(normalize)
dataset = dataset.batch(50)
dataset = dataset.take(1) # keep validating on the same items
dataset = dataset.repeat()
iterator = dataset.make_one_shot_iterator()
return iterator.get_next()
def update_model(self, *grads):
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)
| tensorflow.cast | 1,584 |
from tensorflow.python.platform import gfile
# Created by the first helper.
self.assertTrue(gfile.Exists(s1))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s1)))
# Deleted by the first helper.
self.assertFalse(gfile.Exists(s3))
self.assertFalse(gfile.Exists(save._MetaGraphFilename(s3)))
self.assertTrue(gfile.Exists(s2))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s2)))
# Adding s1 (s3 should not be deleted because helper is unaware of it)
s1 = save3.save(sess, os.path.join(save_dir, "s1"))
self.assertEqual([s2, s1], save3.last_checkpoints)
self.assertFalse(gfile.Exists(s3))
self.assertFalse(gfile.Exists(save._MetaGraphFilename(s3)))
self.assertTrue(gfile.Exists(s2))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s2)))
self.assertTrue(gfile.Exists(s1))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s1)))
def testSharded(self):
save_dir = os.path.join(self.get_temp_dir(), "max_to_keep_sharded")
try:
gfile.DeleteRecursively(save_dir)
except OSError:
pass # Ignore
gfile.MakeDirs(save_dir)
with tf.Session(
| tensorflow.python.platform.gfile.Exists | 1,585 |
from tensorflow.python.framework import ops
@ops.RegisterShape("Div")
@ops.RegisterShape("Equal")
@ops.RegisterShape("Greater")
@ops.RegisterShape("GreaterEqual")
@ops.RegisterShape("Less")
@ops.RegisterShape("LessEqual")
@ops.RegisterShape("LogicalAnd")
@ops.RegisterShape("LogicalOr")
@ops.RegisterShape("Maximum")
@ops.RegisterShape("Minimum")
@ops.RegisterShape("Mod")
@ops.RegisterShape("Mul")
@ops.RegisterShape("NotEqual")
@ops.RegisterShape("Pow")
@ops.RegisterShape("Sub")
def _BroadcastShape(op):
"""Common shape function for binary operators that broadcast their inputs."""
shape_x = op.inputs[0].get_shape()
shape_y = op.inputs[1].get_shape()
if shape_x.ndims is None or shape_y.ndims is None:
| tensorflow.python.framework.ops.RegisterShape | 1,586 |
import tensorflow as tf
h = x_shape[1]
w = x_shape[2]
pad_h1 = tf.mod(-h + bsize[1], bstrides[1])
pad_w1 = tf.mod(-w + bsize[2], bstrides[2])
| tensorflow.mod | 1,587 |
import tensorflow as tf
"""
with tf.variable_scope(name):
inputdata = tf.transpose(inputdata, [0, 3, 1, 2])
n, c, h, w = inputdata.get_shape().as_list()
| tensorflow.transpose | 1,588 |
import tensorflow as tf
init_bw = tf.tile(tf.Variable(
tf.zeros([1, num_units])), [batch_size, 1])
mask_fw = dropout(tf.ones([batch_size, 1, input_size_], dtype=tf.float32),
keep_prob=keep_prob, is_train=is_train, mode=None)
mask_bw = dropout(tf.ones([batch_size, 1, input_size_], dtype=tf.float32),
keep_prob=keep_prob, is_train=is_train, mode=None)
self.grus.append((gru_fw, gru_bw, ))
self.inits.append((init_fw, init_bw, ))
| tensorflow.ones | 1,589 |
import tensorflow as tf
def net_U1(self, x):
lambda_1 = self.lambda_1
lambda_2 = tf.exp(self.lambda_2)
U = self.neural_net(x, self.weights, self.biases)
U_x = self.fwd_gradients_1(U, x)
U_xx = self.fwd_gradients_1(U_x, x)
F = -lambda_1*U*U_x + lambda_2*U_xx
U1 = U + self.dt*tf.matmul(F, (self.IRK_beta - self.IRK_alpha).T)
return U1
def callback(self, loss):
print('Loss:', loss)
def train(self, nIter):
| tensorflow.matmul | 1,590 |
import tensorflow as tf
images = tf.image.random_contrast(images, 0.6, 1.5)
images = tf.image.random_flip_left_right(images)
| tensorflow.image.random_flip_left_right | 1,591 |
import tensorflow as tf
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.5
# Placeholders
self.sess = tf.Session(config=config)
self.s_dim, self.a_dim = env.observation_space.shape, env.action_space.shape[0]
self.a_bound = (env.action_space.high - env.action_space.low) / 2
self.actions = tf.placeholder(tf.float32, [None, self.a_dim], 'action')
self.state = tf.placeholder(tf.float32, [None, self.s_dim[0]], 'state')
self.advantage = tf.placeholder(tf.float32, [None, 1], 'advantage')
self.rewards = tf.placeholder(tf.float32, [None, 1], 'rewards')
self.keep_prob = tf.placeholder(tf.float32, name='dropout_keep_prob')
# Dateset with experiennce replay
self.dataset = tf.data.Dataset.from_tensor_slices({'state': self.state, 'actions': self.actions,
'rewards': self.rewards, 'advantage': self.advantage})
self.dataset = self.dataset.batch(self.MINIBATCH, drop_remainder=True)
self.data_iter = self.dataset.make_initializable_iterator()
batch = self.data_iter.get_next()
# Call ppo net
pi_old, pi_old_params, _, _ = self.build_anet(batch['state'], 'oldpi')
pi, pi_params, self.pi_state_init, self.pi_state_final = self.build_anet(batch['state'], 'pi')
| tensorflow.placeholder | 1,592 |
import tensorflow as tf
# KL Divergence
beta = 4
rho = 0.15
p_hat = tf.reduce_mean(activation_out, 0) # theano: p_hat = T.mean( self.a[i], axis=0 )
try: # TF1.0
KLD = beta * tf.reduce_sum(rho * tf.log(tf.divide(rho, p_hat)) + (1 - rho) * tf.log((1 - rho) / (tf.subtract(float(1), p_hat))))
except Exception: # TF0.12
KLD = beta * tf.reduce_sum(rho * tf.log(tf.div(rho, p_hat)) + (1 - rho) * tf.log((1 - rho) / (tf.sub(float(1), p_hat))))
# KLD = beta * tf.reduce_sum( rho * tf.log(rho/ p_hat) + (1- rho) * tf.log((1- rho)/(1- p_hat)) )
# theano: L1_a = l1_a[i] * T.sum( rho[i] * T.log(rho[i]/ p_hat) + (1- rho[i]) * T.log((1- rho[i])/(1- p_hat)) )
# Total cost
if act == tf.nn.softplus:
logging.info(' use: mse, L2_w, L1_a')
self.cost = mse + L1_a + L2_w
| tensorflow.div | 1,593 |
import tensorflow as tf
major, minor, _ = tf.version.VERSION.split('.')
if not (int(major) >= 2 and tf2.enabled()):
tf.compat.v1.logging.warning(
'Tensorflow version (%s) found. TransformFeaturesLayer is supported '
'only for TF 2.x with TF 2.x behaviors enabled and may not work as '
'intended.', tf.version.VERSION)
elif int(major) == 2 and int(minor) < 3:
# TODO(varshaan): Log a more specific warning.
tf.compat.v1.logging.warning(
'Tensorflow version (%s) found. TransformFeaturesLayer may not work '
'as intended if the SavedModel contains an initialization op.',
tf.version.VERSION)
# TODO(b/162055065): Possibly switch back to inherit from Layer when possible.
@_maybe_register_keras_serializable(package='TensorFlowTransform')
| tensorflow.compat.v1.logging.warning | 1,594 |
import tensorflow as tf
A dictionary of the initial values of the
cluster centroids, cluster indices, original weights,
the pretrained flag for marking the first training
epoch, and weight name.
"""
result = {}
weights = getattr(layer.layer, name)
if self.preserve_sparsity and not tf.reduce_any(weights == 0):
self.preserve_sparsity = False
logging.warning(
'Input layer does not contain zero weights, so apply CQAT instead.')
centroids_mask = None
centroids, lookup = get_unique(weights)
num_centroids = tf.size(centroids)
| tensorflow.reduce_any | 1,595 |
from tensorflow.contrib.layers.python.layers import feature_column
self._assertSingleClassMetrics(metrics)
def benchmarkCustomOptimizer(self):
iris = test_data.prepare_iris_data_for_logistic_regression()
cont_feature = feature_column.real_valued_column('feature', dimension=4)
bucketized_feature = feature_column.bucketized_column(
cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))
| tensorflow.contrib.layers.python.layers.feature_column.real_valued_column | 1,596 |
from tensorflow.python.ops import math_ops
A [D1, ... DN] `Tensor` of false positive counts.
"""
with ops.name_scope(None, 'false_positives', (predictions_idx, labels)):
labels, predictions_idx = _maybe_select_class_id(labels,
predictions_idx,
class_id)
fp = set_ops.set_size(set_ops.set_difference(
predictions_idx, labels, aminusb=True))
fp = math_ops.to_double(fp)
if weights is not None:
weights = math_ops.to_double(weights)
fp = math_ops.mul(fp, weights)
return fp
def _streaming_sparse_false_positive_at_k(predictions_idx,
labels,
k=None,
class_id=None,
weights=None,
name=None):
"""Calculates weighted per step false positives for precision@k.
| tensorflow.python.ops.math_ops.mul | 1,597 |
import tensorflow as tf
return tf.contrib.summary.all_summary_ops()
# To log the loss, current learning rate, and epoch for Tensorboard, the
# summary op needs to be run on the host CPU via host_call. host_call
# expects [batch_size, ...] Tensors, thus reshape to introduce a batch
# dimension. These Tensors are implicitly concatenated to
# [params['batch_size']].
global_step_t = tf.reshape(global_step, [1])
total_loss_t = tf.reshape(total_loss, [1])
total_rpn_loss_t = tf.reshape(total_rpn_loss, [1])
rpn_score_loss_t = tf.reshape(rpn_score_loss, [1])
rpn_box_loss_t = tf.reshape(rpn_box_loss, [1])
total_fast_rcnn_loss_t = tf.reshape(total_fast_rcnn_loss, [1])
fast_rcnn_class_loss_t = tf.reshape(fast_rcnn_class_loss, [1])
fast_rcnn_box_loss_t = tf.reshape(fast_rcnn_box_loss, [1])
mask_loss_t = tf.reshape(mask_loss, [1])
learning_rate_t = tf.reshape(learning_rate, [1])
| tensorflow.reshape | 1,598 |
import tensorflow as tf
wordidx_t = tf.multinomial(log_score_t, 1) # [batch_size, 1]
wordidx_t = tf.reshape(wordidx_t, [-1]) # [batch_size]
elif mode_gen in ('ce_train', 'loss',):
wordidx_t = answer_batch_unstack[i]
else:
assert False, 'unknown generating mode %s' % mode_gen
sampled_words.append(wordidx_t)
if len(sampled_words)!=0:
sampled_words = tf.stack(sampled_words, axis=1) # [batch_size, max_dec_steps]
vocab_scores = tf.stack(vocab_scores, axis=1) # [batch_size, max_dec_steps, vocab]
# calculating loss
self._loss = None
if mode_gen in ('ce_train', 'loss', ):
xent = CE_loss(vocab_scores, answer_batch, loss_weights) # [batch_size]
if mode_gen == 'loss': xent *= self.placeholders.reward # multiply with rewards
self._loss = tf.reduce_mean(xent)
| tensorflow.stack | 1,599 |
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