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import tensorflow as tf def resnet_block(x, block_name='ResBlock', channel_nr=64, scale = 1, pad='SAME'): tmp = conv3d(x, kernel_size=3, filters=channel_nr, padding=pad, activation=None, use_bias=False, initialization=None) tmp = tf.keras.layers.LeakyReLU(alpha=0.2)(tmp) tmp = conv3d(tmp, kernel_size=3, filters=channel_nr, padding=pad, activation=None, use_bias=False, initialization=None)

tensorflow.keras.layers.LeakyReLU

import tensorflow as tf image = self._do_cutout(image, w, h, cutout_size) return (image, clazz) (images, classes) = _prepare(images, classes) dataset = tf.data.Dataset.from_tensor_slices((images, classes)).repeat() if is_train: dataset = dataset.apply(tf.data.experimental.map_and_batch(map_func=_preprocess_train, batch_size=batch_size)) else: dataset = dataset.batch(batch_size) dataset_itr = dataset.make_initializable_iterator() (images_batch, classes_batch) = dataset_itr.get_next() dataset_init_op = dataset_itr.initializer

tensorflow.data.experimental.map_and_batch

import tensorflow as tf eval_spec = tf.estimator.EvalSpec(read_dataset('valid.csv', tf.estimator.ModeKeys.EVAL, 512), steps = None, exporters = exporter) tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)

tensorflow.estimator.train_and_evaluate

from tensorflow.python import debug as tf_debug hooks = [] if FLAGS.use_hvd: hooks.append(hvd.BroadcastGlobalVariablesHook(0)) if hvd.rank() == -1: #if debug, set 0 CLIDebugHook = tf_debug.LocalCLIDebugHook(ui_type='readline') CLIDebugHook.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan) hooks.append(CLIDebugHook) if FLAGS.profile and hvd.rank() == 0: ProfilerHook = tf.train.ProfilerHook(save_steps=FLAGS.hooking_frequence, output_dir=FLAGS.output_dir, show_dataflow=True, show_memory=True)

tensorflow.python.debug.LocalCLIDebugHook

from tensorflow.python.ops import image_ops from tensorflow.python.client import session from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import image_ops from tensorflow.python.ops import io_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import test def _resize_image(image, height, width): image = array_ops.expand_dims(image, 0) image = image_ops.resize_bilinear(image, [height, width]) return array_ops.squeeze(image, [0]) def _create_tfrecord_dataset(tmpdir): if not gfile.Exists(tmpdir): gfile.MakeDirs(tmpdir) data_sources = test_utils.create_tfrecord_files(tmpdir, num_files=1) keys_to_features = { 'image/encoded':

tensorflow.python.ops.image_ops.resize_bilinear

from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten # Block 1 conv1a = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[8, 8], strides=4, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(self.inputs) conv1b = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1a) conv1c = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1b) pool1 = MaxPool2D(pool_size=[2,2])(conv1c) # Block 2 conv2a = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(pool1) conv2b = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv2a) conv2c = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv2b) pool2 = MaxPool2D(pool_size=[2,2])(conv2c) # Block 3 conv3a = Conv2D(padding="same", filters=RNN_SIZE//2, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(pool2) conv3b = Conv2D(padding="same", filters=RNN_SIZE//2, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv3a) conv3c = Conv2D(padding="same", filters=RNN_SIZE//2, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv3b) pool3 = MaxPool2D(pool_size=[2,2])(conv3c)

tensorflow.keras.layers.Conv2D

import tensorflow as tf if reduce_fn is None: scalar = args[i][0] else: scalar = reduce_fn(args[i]) with tf.contrib.summary.record_summaries_every_n_global_steps( 100, global_step=step): tf.contrib.summary.scalar(prefix + name, scalar, step=step)

tensorflow.contrib.summary.record_summaries_every_n_global_steps

import tensorflow as tf indices = tf.stack((batch_nums, step_nums, passage_word_idx), axis=2) # shape (batch_size, passage_length, 3) indices = tf.reshape(indices, [-1, 3]) #[batch_size * passage_length, 3] indices = tf.cast(indices, tf.int64) shape = [batch_size, passage_length, extended_vsize] shape = tf.cast(shape, tf.int64) attn_dist = tf.reshape(attn_dist, shape=[-1]) # [batch_size*passage_length] one_hot_spare_rep = tf.SparseTensor(indices=indices, values=attn_dist, dense_shape=shape) # [batch_size, passage_length, extended_vsize] if passage_mask is not None: passage_mask = tf.expand_dims(passage_mask, axis=-1) one_hot_spare_rep = one_hot_spare_rep * passage_mask one_hot_spare_rep = tf.sparse_reduce_sum(one_hot_spare_rep, axis=1) # [batch_size, extended_vsize] vocab_dist = tf.add(vocab_dist, one_hot_spare_rep) if self.options.add_first_word_prob_for_phrase: vocab_dist = tf.nn.softmax(vocab_dist) # normalize return vocab_dist # [batch_size, extended_vsize] def linear(args, output_size, bias=True, bias_start=0.0, scope=None): if args is None or (isinstance(args, (list, tuple)) and not args): raise ValueError("`args` must be specified") if not isinstance(args, (list, tuple)): args = [args]

tensorflow.sparse_reduce_sum

import tensorflow as tf self.qnode = qnode dtype = tf.float32 if tf.keras.backend.floatx() == tf.float32 else tf.float64

tensorflow.keras.backend.floatx

from tensorflow.contrib.learn.python.learn.graph_actions import train self._check_inputs(features, targets) train_op, loss_op = self._get_train_ops(features, targets) return train( graph=g,

tensorflow.contrib.learn.python.learn.graph_actions.train

import tensorflow as tf encoder_state = tuple(encoder_state[-1] for _ in range(num_layers)) decoder_cell = attention(encoder_out, seq_lens) dense_layer = tf.layers.Dense(n_mels * resampled)

tensorflow.layers.Dense

from tensorflow.python.ops import check_ops raise ValueError("%s.ndims=%d is not 0 (scalar)" % (x.name, x.get_shape().ndims)) if x_value_static < 0: raise ValueError("%s.value=%d cannot be negative" % (x.name, x_value_static)) return x if self.validate_args: x = control_flow_ops.with_dependencies([ check_ops.assert_rank(x, 0), check_ops.assert_non_negative(x)], x) return x def _introspect_ndims(self, ndims): """Helper to establish some properties of input ndims args.""" if self._is_all_constant_helper(ndims): return (tensor_util.constant_value(ndims),

tensorflow.python.ops.check_ops.assert_rank

import tensorflow as tf [layer_input[0]['observation'], layer_input[1]], axis=1) return tf.keras.layers.Lambda(f)

tensorflow.keras.layers.Lambda

from tensorflow.contrib import losses with ops.control_dependencies([check_shape_op]): target = array_ops.reshape( target, shape=[array_ops.shape(target)[0], 1]) return losses.hinge_loss(logits, target) super(_BinarySvmTargetColumn, self).__init__(

tensorflow.contrib.losses.hinge_loss

import tensorflow as tf A tuple of possible batch sizes """ for device in device_lib.list_local_devices(): if tf.DeviceSpec.from_string(device.name).device_type == "GPU": if "K20" in device.physical_device_desc: return (16,) if "P100" in device.physical_device_desc: return (16, 32, 64) if tf.DeviceSpec.from_string(device.name).device_type == "TPU": return (32,) return (16, 32) def _force_device_sync(self): """Shamelessly copied from `resnet50_test.py`.""" tf.constant(1.).cpu()

tensorflow.DeviceSpec.from_string

import tensorflow as tf # In this example, we limit mnist data Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates) Xte, Yte = mnist.test.next_batch(200) #200 for testing # tf Graph Input xtr = tf.placeholder("float", [None, 784]) xte = tf.placeholder("float", [784]) # Nearest Neighbor calculation using L1 Distance # Calculate L1 Distance distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1) # Prediction: Get min distance index (Nearest neighbor) pred = tf.arg_min(distance, 0) accuracy = 0. # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Start training

tensorflow.negative

import tensorflow as tf if extra_inputs is None: extra_inputs = tuple() last_loss = f_loss(*(tuple(inputs) + extra_inputs)) start_time = time.time() dataset = BatchDataset(inputs, self._batch_size, extra_inputs=extra_inputs) sess = tf.compat.v1.get_default_session() for epoch in range(self._max_epochs): if self._verbose: logger.log('Epoch {}'.format(epoch)) progbar = pyprind.ProgBar(len(inputs[0])) for batch in dataset.iterate(update=True): sess.run(self._train_op, dict(list(zip(self._input_vars, batch)))) if self._verbose:

tensorflow.compat.v1.get_default_session

from tensorflow.python.ops import variables yield def _setupDense(self, is_distributed, dtype): with self._maybeWithDevice("/job:ps" if is_distributed else None): var0 = variables.Variable([[0.0, 1.0], [2.0, 3.0]], dtype=dtype) var1 = variables.Variable([4.0, 5.0], dtype=dtype) with self._maybeWithDevice("/job:worker" if is_distributed else None): grads0 = constant_op.constant([[0.1, 0.1], [0.1, 0.1]], dtype=dtype)

tensorflow.python.ops.variables.Variable

import tensorflow as tf """Checks that `perm` is valid.""" with tf.name_scope(name, 'maybe_validate_perm', [perm]): assertions = [] if not perm.dtype.is_integer: raise TypeError('`perm` must be integer type') msg = '`perm` must be a vector.' if perm.shape.ndims is not None: if perm.shape.ndims != 1: raise ValueError( msg[:-1] + ', saw rank: {}.'.format(perm.shape.ndims)) elif validate_args: assertions += [tf.compat.v1.assert_rank(perm, 1, message=msg)] perm_ = tf.get_static_value(perm) msg = '`perm` must be a valid permutation vector.' if perm_ is not None: if not np.all(np.arange(np.size(perm_)) == np.sort(perm_)): raise ValueError(msg[:-1] + ', saw: {}.'.format(perm_)) elif validate_args: assertions += [ tf.compat.v1.assert_equal( tf.sort(perm), tf.range(tf.size(input=perm)), message=msg) ] return assertions

tensorflow.get_static_value

import tensorflow as tf assert len(all_vars) == len(all_perturbed_vars) perturb_ops = [] for var, perturbed_var in zip(all_vars, all_perturbed_vars): if param_noise_filter_func(perturbed_var): # Perturb this variable. operation = tf.assign(perturbed_var, var + tf.random_normal(shape=tf.shape(var), mean=0., stddev=param_noise_scale)) else: # Do not perturb, just assign. operation = tf.assign(perturbed_var, var) perturb_ops.append(operation) assert len(perturb_ops) == len(all_vars) return tf.group(*perturb_ops) # Set up functionality to re-compute `param_noise_scale`. This perturbs yet another copy # of the network and measures the effect of that perturbation in action space. If the perturbation # is too big, reduce scale of perturbation, otherwise increase. with tf.variable_scope("adaptive_model", reuse=False): adaptive_policy = q_func(sess, ob_space, ac_space, 1, 1, None, obs_phs=obs_phs) perturb_for_adaption = perturb_vars(original_scope="model", perturbed_scope="adaptive_model/model") kl_loss = tf.reduce_sum( tf.nn.softmax(policy.q_values) * (tf.log(tf.nn.softmax(policy.q_values)) - tf.log(tf.nn.softmax(adaptive_policy.q_values))), axis=-1) mean_kl = tf.reduce_mean(kl_loss)

tensorflow.group

from tensorflow.python.ops import array_ops array_ops.size(tensor.shape) + dim, [1]) else: expand_dims = [dim] expanded_shape = array_ops.concat( 0, (array_ops.slice(tensor.shape, [0], expand_dims), [1], array_ops.slice(tensor.shape, expand_dims, [-1])), name='expanded_shape') expanded = sparse_ops.sparse_reshape(

tensorflow.python.ops.array_ops.slice

import tensorflow as tf def load_agent_ckpt(ckpt_dir, tf_agent, global_step=None): if global_step is None: global_step = tf.compat.v1.train.get_or_create_global_step() train_checkpointer = common.Checkpointer( ckpt_dir=ckpt_dir, agent=tf_agent, global_step=global_step) train_checkpointer.initialize_or_restore().assert_existing_objects_matched()

tensorflow.compat.v1.train.get_or_create_global_step

from tensorflow.contrib.layers.python.layers.layers import _build_variable_getter, _add_variable_to_collections conv_dims: Optional convolution dimensionality, when set it would use the corresponding convolution (e.g. 2 for Conv 2D, 3 for Conv 3D, ..). When leaved to None it would select the convolution dimensionality based on the input rank (i.e. Conv ND, with N = input_rank - 2). Returns: A tensor representing the output of the operation. Raises: ValueError: If `data_format` is invalid. ValueError: Both 'rate' and `stride` are not uniformly 1. """ if data_format not in [None, 'NWC', 'NCW', 'NHWC', 'NCHW', 'NDHWC', 'NCDHW']: raise ValueError('Invalid data_format: %r' % (data_format,)) layer_variable_getter = _build_variable_getter({'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope(scope, 'Conv', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) input_rank = inputs.get_shape().ndims if conv_dims is not None and conv_dims + 2 != input_rank: raise ValueError('Convolution expects input with rank %d, got %d' % (conv_dims + 2, input_rank)) if input_rank == 3: layer_class = convolutional_layers.Convolution1D elif input_rank == 4: layer_class = MyConv2D elif input_rank == 5:

tensorflow.contrib.layers.python.layers.layers._build_variable_getter

import tensorflow as tf with tf.device("/device:CPU:0"): ds = tf.data.Dataset.from_tensors(tensors).repeat() return tfe.Iterator(ds) self._benchmark_eager_train( "eager_train_dataset_with_defun", make_iterator, device_and_data_format(), defun=True) if __name__ == "__main__": tf.enable_eager_execution() tf.test.main()

tensorflow.enable_eager_execution

import tensorflow as tf "MSE": mse, "eval_loss": loss,} elif task_name == "cola": def metric_fn(per_example_loss, label_ids, logits, is_real_example): """Compute Matthew's correlations for STS-B.""" predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) # https://en.wikipedia.org/wiki/Matthews_correlation_coefficient tp, tp_op = tf.metrics.true_positives( predictions, label_ids, weights=is_real_example) tn, tn_op = tf.metrics.true_negatives( predictions, label_ids, weights=is_real_example) fp, fp_op = tf.metrics.false_positives( predictions, label_ids, weights=is_real_example) fn, fn_op = tf.metrics.false_negatives( predictions, label_ids, weights=is_real_example) # Compute Matthew's correlation

tensorflow.metrics.true_negatives

import tensorflow as tf except AttributeError: deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,

tensorflow.nn.deconv2d

from tensorflow.contrib.eager.python.examples.revnet import blocks_test self.assertEqual(len(g2_all.shape), 1) degree = blocks_test.compute_degree(g1_all, g2_all) self.assertLessEqual(degree, 1e0)

tensorflow.contrib.eager.python.examples.revnet.blocks_test.compute_degree

import tensorflow as tf # loss and optimizer self.loss = tf.reduce_mean(tf.square(tf.subtract(self.value_estimate, self.target))) self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)

tensorflow.subtract

import tensorflow as tf feed_previous=tf.constant(True)) sess.run([tf.global_variables_initializer()]) tf.get_variable_scope().reuse_variables() d1, _ = tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=True) d2, _ = tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp2, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=True) res1 = sess.run(d1) res2 = sess.run(d2) res3 = sess.run(d3)

tensorflow.nn.seq2seq.embedding_attention_seq2seq

import tensorflow as tf dep_idxs = tf.tile(tf.expand_dims(dep_org_idx, 1), [1, sl_head, 1]) head_idxs = tf.tile(tf.expand_dims(head_org_idx, 2), [1, 1, sl_dep]) if direction is None: direct_mask = tf.not_equal(head_idxs, dep_idxs) # [bs, slh, sld] else: if direction == 'forward': direct_mask = tf.greater(head_idxs, dep_idxs) # [bs, slh, sld] else: direct_mask = tf.less(head_idxs, dep_idxs) # [bs, slh, sld] # [bs, slh, slh] rep_mask_tile = tf.logical_and(tf.expand_dims(rep_dep_mask, 1), tf.expand_dims(rep_head_mask, 2)) attn_mask = tf.logical_and(direct_mask, rep_mask_tile) # [bs, slh, sld] # tensor tile rep_map_tile = tf.tile(tf.expand_dims(rep_dep_tensor, 1), [1, sl_head, 1, 1]) # bs,slh,sld,vec with tf.variable_scope('attention'): # bs,sl,sl,vec f_bias = tf.get_variable('f_bias', [ivec], tf.float32, tf.constant_initializer(0.))

tensorflow.less

from tensorflow.python.ops import math_ops tf_index = math_ops.argmin(math_ops.abs(specificities - specificity), 0) tf_index = math_ops.cast(tf_index, dtypes.int32) # Now, we have the implicit threshold, so compute the sensitivity: return math_ops.div(tp[tf_index], tp[tf_index] + fn[tf_index] + kepsilon, name)

tensorflow.python.ops.math_ops.div

import tensorflow as tf self.validateMoments([10**5], -5.0, 1.0, 2.0, np.infty) def testSmallStddev(self): self.validateKolmogorovSmirnov([10**5], 0.0, 0.1, 0.05, 0.10) class ParameterizedTruncatedNormalGpuTest(ParameterizedTruncatedNormalTest): _use_gpu = True # Benchmarking code def parameterized_vs_naive(shape, num_iters, use_gpu=False): np.random.seed(1618) # Make it reproducible. # No CSE/CF. optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0) config = tf.ConfigProto( graph_options=tf.GraphOptions(optimizer_options=optimizer_options)) with tf.Session(config=config) as sess: with tf.device("/cpu:0" if not use_gpu else None): param_op = tf.group(random_ops.parameterized_truncated_normal(shape)) naive_op = tf.group(random_ops.truncated_normal(shape)) # Burn-in to avoid session setup costs in the timing. sess.run(param_op) sess.run(param_op) param_dt = timeit.timeit(lambda: sess.run(param_op), number=num_iters) sess.run(naive_op) sess.run(naive_op)

tensorflow.OptimizerOptions

from tensorflow.contrib.learn.python.learn.graph_actions import evaluate global_step = contrib_framework.create_global_step(g) features, targets = input_fn() self._check_inputs(features, targets) eval_dict = self._get_eval_ops(features, targets, metrics or self._get_default_metric_functions()) eval_results, _ = evaluate( graph=g, output_dir=eval_dir, checkpoint_path=checkpoint_path, eval_dict=eval_dict,

tensorflow.contrib.learn.python.learn.graph_actions.evaluate

from tensorflow.contrib.learn.python.learn import ops from tensorflow.python.platform import test class OpsTest(test.TestCase): """Ops tests.""" def test_softmax_classifier(self): with self.cached_session() as session: features = array_ops.placeholder(dtypes.float32, [None, 3]) labels = array_ops.placeholder(dtypes.float32, [None, 2]) weights = constant_op.constant([[0.1, 0.1], [0.1, 0.1], [0.1, 0.1]]) biases = constant_op.constant([0.2, 0.3]) class_weight = constant_op.constant([0.1, 0.9]) prediction, loss = ops.softmax_classifier(features, labels, weights, biases, class_weight) self.assertEqual(prediction.get_shape()[1], 2) self.assertEqual(loss.get_shape(), []) value = session.run(loss, {features: [[0.2, 0.3, 0.2]], labels: [[0, 1]]}) self.assertAllClose(value, 0.55180627) def test_embedding_lookup(self): d_embed = 5 n_embed = 10 ids_shape = (2, 3, 4) embeds = np.random.randn(n_embed, d_embed) ids = np.random.randint(0, n_embed, ids_shape)

tensorflow.contrib.learn.python.learn.ops.softmax_classifier

import tensorflow as tf hparams, tf.estimator.ModeKeys.TRAIN ) try: num_target_frames = hparams.video_num_target_frames except AttributeError: num_target_frames = 1 target_value_shape_suffix = [num_target_frames] 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), "target_reward": tf.zeros( obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32), "target_policy": tf.zeros( obs_shape[:1] + [num_target_frames] + [action_space.n]), "target_value": tf.zeros( obs_shape[:1] + target_value_shape_suffix) } model.distributional_value_size = max(distributional_size, 1) model.use_epochs = hparams.use_epochs with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE): t2t_model.create_dummy_vars() (targets, _) = model(features) target_values = targets["target_value"][:, 0]

tensorflow.zeros

from tensorflow.python.framework import ops auc = compute_auc(tp, fn, tn, fp, 'value') update_op = compute_auc( tp_update_op, fn_update_op, tn_update_op, fp_update_op, 'update_op') if metrics_collections: ops.add_to_collections(metrics_collections, auc) if updates_collections: ops.add_to_collections(updates_collections, update_op)

tensorflow.python.framework.ops.add_to_collections

from tensorflow.python.ops import logging_ops as logging def after_create_session(self, session, _): assert self._init_op.graph == ops.get_default_graph() assert self._is_initialized_op.graph == self._init_op.graph while True: try: if session.run(self._is_initialized_op): break elif self._is_chief: session.run(self._init_op) else: time.sleep(1) except RuntimeError as e: logging.info(e) class GMM(estimator.Estimator): """An estimator for GMM clustering.""" SCORES = 'scores' ASSIGNMENTS = 'assignments' ALL_SCORES = 'all_scores' def __init__(self, num_clusters, model_dir=None,

tensorflow.python.ops.logging_ops.info

import tensorflow as tf for i in range(0, config.num_clones): with tf.name_scope(config.clone_scope(i)) as clone_scope: clone_device = config.clone_device(i) with tf.device(clone_device): with tf.variable_scope(tf.get_variable_scope(), reuse=True if i > 0 else None): outputs = model_fn(*args, **kwargs) clones.append(Clone(outputs, clone_scope, clone_device))

tensorflow.get_variable_scope

from tensorflow.python.ops import array_ops # Use static shape if known. num_predictions = predictions_2d.get_shape().as_list()[0] # Otherwise use dynamic shape. if num_predictions is None: num_predictions = array_ops.shape(predictions_2d)[0] thresh_tiled = array_ops.tile( array_ops.expand_dims(array_ops.constant(thresholds), [1]), array_ops.pack([1, num_predictions])) # Tile the predictions after thresholding them across different thresholds. pred_is_pos = math_ops.greater( array_ops.tile(array_ops.transpose(predictions_2d), [num_thresholds, 1]), thresh_tiled) pred_is_neg = math_ops.logical_not(pred_is_pos)

tensorflow.python.ops.array_ops.constant

import tensorflow as tf def validation_mapper(byte): image = tf.image.decode_jpeg( tf.reshape(byte, shape=[]), 3, **JPEG_OPT) image = resize_shortest_edge(image, tf.shape(image), 256) image = center_crop(image, 224) image = tf.reverse(image, axis=[2]) # to BGR return image def training_mapper(byte): jpeg_shape = tf.image.extract_jpeg_shape(byte) # hwc bbox_begin, bbox_size, distort_bbox = tf.image.sample_distorted_bounding_box(

tensorflow.reverse

from tensorflow.contrib.eager.python import tfe """Trains model on train_data using optimizer.""" tf.train.get_or_create_global_step() def model_loss(labels, chars, sequence_length): predictions = model((chars, sequence_length), training=True) loss_value = loss(labels, predictions) tf.contrib.summary.scalar("loss", loss_value) return loss_value for (batch, (labels, chars, sequence_length)) in enumerate( tfe.Iterator(train_data)): with tf.contrib.summary.record_summaries_every_n_global_steps(log_interval): batch_model_loss = functools.partial(model_loss, labels, chars, sequence_length) optimizer.minimize( batch_model_loss, global_step=tf.train.get_global_step()) if log_interval and batch % log_interval == 0: print("train/batch #%d\tloss: %.6f" % (batch, batch_model_loss())) SOURCE_TRAIN_URL = "https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/archive/extras/colorbot/data/train.csv"

tensorflow.contrib.eager.python.tfe.Iterator

from tensorflow.core.util.event_pb2 import SessionLog logging.info("Saving checkpoints for %d into %s.", step, self._save_path) self._last_saved_time = time.time() self._last_saved_step = step if self._saver is None: self._scaffold.saver.save(session, self._save_path, global_step=step) else: self._saver.save(session, self._save_path, global_step=step) self._summary_writer.add_session_log( SessionLog( status=SessionLog.CHECKPOINT, checkpoint_path=self._save_path), step) class StepCounter(EveryN): """Steps per second monitor."""

tensorflow.core.util.event_pb2.SessionLog

from tensorflow.contrib.slim.python.slim.data import tfexample_decoder shape=[1], dtype=dtypes.int64, default_value=array_ops.zeros( [1], dtype=dtypes.int64)) } items_to_handlers = { 'image': tfexample_decoder.Image(), 'label': tfexample_decoder.Tensor('image/class/label'), } decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)

tensorflow.contrib.slim.python.slim.data.tfexample_decoder.Image

import tensorflow as tf Returns: a tensor with shape [N, M] representing pairwise iou scores. """ intersections = pairwise_intersection(boxlist1, boxlist2) areas1 = area(boxlist1) areas2 = area(boxlist2) unions = ( tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections) return tf.where( tf.equal(intersections, 0.0), tf.zeros_like(intersections), tf.truediv(intersections, unions))

tensorflow.expand_dims

from tensorflow.python.ops import math_ops def compute_recall(true_positives, false_negatives, name): return math_ops.select( math_ops.greater(true_positives + false_negatives, 0), math_ops.div(true_positives, true_positives + false_negatives),

tensorflow.python.ops.math_ops.greater

import tensorflow as tf counts.update(_split_string(line)) alphabet = [k for (k, _) in counts.most_common(max_size)] alphabet.sort() return np.asarray(alphabet, dtype=np.object) chars, = tf.py_func(_unique_chars, [filename], [tf.string]) char_to_id = tf.contrib.lookup.index_table_from_tensor( chars, num_oov_buckets=num_oov_buckets) id_to_char = tf.contrib.lookup.index_to_string_table_from_tensor(chars, " ") return char_to_id, id_to_char

tensorflow.contrib.lookup.index_table_from_tensor

import tensorflow as tf soft_placement = True util.auto_parallel(metagraph, m) with tf.Graph().as_default(): tf.train.import_meta_graph(metagraph) for model in models.values(): model.import_ops() sv = tf.train.Supervisor(logdir=FLAGS.save_path) config_proto = tf.ConfigProto(allow_soft_placement=soft_placement) with sv.managed_session(config=config_proto) as session: for i in range(config.max_max_epoch): lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.0) m.assign_lr(session, config.learning_rate * lr_decay)

tensorflow.train.Supervisor

import tensorflow as tf sync_lp_time = stop - start print(f"Got {len(sync_lp_observations)} observations in {sync_lp_time:.2f}s") # %% [markdown] # ## Comparison # To compare outcomes of sync and async runs, let's plot their respective regrets side by side, and print out the running time. For this toy problem we expect async scenario to run a little bit faster on machines with multiple CPU. # %% from util.plotting import plot_regret import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 2) sync_lp_min_idx = tf.squeeze(tf.argmin(sync_lp_observations, axis=0)) async_lp_min_idx = tf.squeeze(tf.argmin(async_lp_observations, axis=0)) plot_regret( sync_lp_observations.numpy(), ax[0], num_init=len(initial_data), idx_best=sync_lp_min_idx ) ax[0].set_yscale("log") ax[0].set_ylabel("Regret") ax[0].set_ylim(0.0000001, 100) ax[0].set_xlabel("# evaluations") ax[0].set_title(f"Sync LP, {len(sync_lp_observations)} points, time {sync_lp_time:.2f}") plot_regret( async_lp_observations.numpy(), ax[1], num_init=len(initial_data), idx_best=async_lp_min_idx ) ax[1].set_yscale("log")

tensorflow.argmin

import tensorflow as tf hparams = imagetransformer_latent_tiny() hparams.mode = tf.estimator.ModeKeys.TRAIN block_dim = int(hparams.hidden_size // hparams.num_blocks) block_v_size = 2**(hparams.bottleneck_bits / (hparams.num_residuals * hparams.num_blocks)) block_v_size = int(block_v_size) means = tf.get_variable( name="means", shape=[hparams.num_residuals, hparams.num_blocks, block_v_size, block_dim], initializer=tf.uniform_unit_scaling_initializer()) hparams.bottleneck = functools.partial( discretization.discrete_bottleneck, hidden_size=hparams.hidden_size, z_size=hparams.bottleneck_bits, filter_size=hparams.filter_size, startup_steps=hparams.startup_steps, bottleneck_kind=hparams.bottleneck_kind, num_blocks=hparams.num_blocks, num_residuals=hparams.num_residuals, reshape_method=hparams.reshape_method, beta=hparams.vq_beta,

tensorflow.uniform_unit_scaling_initializer

from tensorflow.python.ops import math_ops tuple. """ predictions, labels = tensor_util.remove_squeezable_dimensions( predictions, labels) predictions.get_shape().assert_is_compatible_with(labels.get_shape()) radial_diffs = math_ops.mul(predictions, labels) radial_diffs = math_ops.reduce_sum(radial_diffs, reduction_indices=[dim,], keep_dims=True) mean_distance, update_op = streaming_mean(radial_diffs, weights, None, None, name or 'mean_cosine_distance') mean_distance = math_ops.sub(1.0, mean_distance) update_op = math_ops.sub(1.0, update_op) if metrics_collections: ops.add_to_collections(metrics_collections, mean_distance) if updates_collections: ops.add_to_collections(updates_collections, update_op) return mean_distance, update_op @deprecated_args(IGNORE_MASK_DATE, IGNORE_MASK_INSTRUCTIONS, 'ignore_mask') def streaming_percentage_less(values, threshold, ignore_mask=None, weights=None, metrics_collections=None,

tensorflow.python.ops.math_ops.sub

from tensorflow.contrib.rnn.python.ops import lstm_ops (config_name, self._GetConfigDesc(config))) def benchmarkTfRNNLSTMBlockCellTraining(self): test_configs = self._GetTestConfig() for config_name, config in test_configs.items(): num_layers = config["num_layers"] num_units = config["num_units"] batch_size = config["batch_size"] seq_length = config["seq_length"] with ops.Graph().as_default(), ops.device("/device:GPU:0"): inputs = seq_length * [ array_ops.zeros([batch_size, num_units], dtypes.float32) ] cell = lambda: lstm_ops.LSTMBlockCell(num_units=num_units) # pylint: disable=cell-var-from-loop multi_cell = rnn_cell.MultiRNNCell( [cell() for _ in range(num_layers)]) outputs, final_state = core_rnn.static_rnn( multi_cell, inputs, dtype=dtypes.float32) trainable_variables = ops.get_collection( ops.GraphKeys.TRAINABLE_VARIABLES) gradients = gradients_impl.gradients([outputs, final_state], trainable_variables) training_op = control_flow_ops.group(*gradients) self._BenchmarkOp(training_op, "tf_rnn_lstm_block_cell %s %s" % (config_name, self._GetConfigDesc(config)))

tensorflow.contrib.rnn.python.ops.lstm_ops.LSTMBlockCell

from tensorflow.python.training import checkpoint_utils # 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) class EagerSpinnSNLIClassifierBenchmark(test.Benchmark): def benchmarkEagerSpinnSNLIClassifier(self): test_device = "gpu:0" if tfe.num_gpus() else "cpu:0" with tf.device(test_device):

tensorflow.python.training.checkpoint_utils.list_variables

import tensorflow as tf logits = tf.log([1.0 - self._relabel_prob, self._relabel_prob]) mask = tf.squeeze( tf.random.categorical( logits[None], num_samples=self._sample_batch_size))

tensorflow.random.categorical

import tensorflow.contrib.eager as tfe dataset = random_dataset() if defun: model.call = tfe.defun(model.call) with tf.device(device()):

tensorflow.contrib.eager.defun

from tensorflow.python.ops import gen_nn_ops name: A name for the operation (optional). Returns: A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the softmax cross entropy loss. """ # The second output tensor contains the gradients. We use it in # _CrossEntropyGrad() in nn_grad but not here. cost, unused_backprop = gen_nn_ops._softmax_cross_entropy_with_logits( logits, labels, name=name) return cost def sparse_softmax_cross_entropy_with_logits(logits, labels, name=None): """Computes sparse softmax cross entropy between `logits` and `labels`.

tensorflow.python.ops.gen_nn_ops._softmax_cross_entropy_with_logits

from tensorflow.contrib.learn.python.learn.estimators import head as head_lib config=config, params={ "head": head_lib._regression_head( # pylint: disable=protected-access label_dimension=label_dimension, weight_column_name=weight_column_name,

tensorflow.contrib.learn.python.learn.estimators.head._regression_head

import tensorflow as tf input_shape = [batch_size, image_size, image_size, input_nchan] images = tf.truncated_normal( input_shape, dtype=input_data_type, stddev=1e-1, name='synthetic_images') labels = tf.random_uniform( [batch_size], minval=1, maxval=nclass, dtype=tf.int32, name='synthetic_labels') # Note: This results in a H2D copy, but no computation # Note: This avoids recomputation of the random values, but still # results in a H2D copy. images = tf.contrib.framework.local_variable(images, name='images') labels = tf.contrib.framework.local_variable(labels, name='labels') # Change to 0-based (don't use background class like Inception does) labels -= 1 if num_compute_devices == 1: images_splits = [images] labels_splits = [labels] else: images_splits = tf.split(images, num_compute_devices, 0) labels_splits = tf.split(labels, num_compute_devices, 0) return nclass, images_splits, labels_splits def create_config_proto(): config = tf.ConfigProto()

tensorflow.contrib.framework.local_variable

import tensorflow as tf else: graph_def.ParseFromString(f.read()) with graph.as_default(): tf.import_graph_def(graph_def, name='') tf.io.write_graph(graph_def, '/tmp/', 'optimized_graph.pb',as_text=False) return graph

tensorflow.io.write_graph

from tensorflow.python.ops import gen_math_ops \\\\(y = |x|\\\\). See [`tf.complex_abs()`](#tf_complex_abs) to compute the absolute value of a complex number. Args: x: A `Tensor` of type `float`, `double`, `int32`, or `int64`. name: A name for the operation (optional). Returns: A `Tensor` the same size and type as `x` with absolute values. """ with ops.op_scope([x], name, "Abs") as name: x = ops.convert_to_tensor(x, name="x") if x.dtype == types.complex64: return gen_math_ops.complex_abs(x, name=name) return gen_math_ops._abs(x, name=name) def pow(x, y, name=None): """Computes the power of one value to another. Given a tensor `x` and a tensor `y`, this operation computes \\\\(x^y\\\\) for corresponding elements in `x` and `y`. For example: ``` # tensor 'x' is [[2, 2]], [3, 3]] # tensor 'y' is [[8, 16], [2, 3]] tf.pow(x, y) ==> [[256, 65536], [9, 27]]

tensorflow.python.ops.gen_math_ops.complex_abs

from tensorflow.python.framework import ops if not inputs or not isinstance(inputs, (list, tuple)): raise ValueError("inputs must be a list of at least one Tensor with the " "same dtype and shape") inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs) if not all(isinstance(x, ops.Tensor) for x in inputs): raise ValueError("inputs must be a list of at least one Tensor with the "

tensorflow.python.framework.ops.convert_n_to_tensor_or_indexed_slices

import tensorflow as tf import numpy as np import tvm from tvm import relay from tvm.contrib import graph_runtime from tvm.relay.testing.config import ctx_list import keras import tensorflow as tf from tensorflow import keras as tf_keras # prevent Keras from using up all gpu memory if tf.executing_eagerly(): gpus = tf.config.list_physical_devices('GPU') for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) else: from keras.backend.tensorflow_backend import set_session config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.5 set_session(tf.Session(config=config)) def pytest_generate_tests(metafunc):

tensorflow.config.list_physical_devices

import tensorflow as tf rl_advantage = rl_reward - rl_baseline rl_empirical_loss = -tf.stop_gradient(rl_advantage) * log_prob rl_entropy_loss = -rl_entropy_regularization * rl_entropy enable_rl_optimizer = tf.cast( tf.greater_equal(target_global_step, FLAGS.first_pretrain_steps), tf.float32) rl_learning_rate = FLAGS.rl_learning_rate * enable_rl_optimizer rl_learning_rate = tf.train.piecewise_constant( target_global_step, [800,], [rl_learning_rate, rl_learning_rate * 0.1]) optimizer = tf.train.AdamOptimizer(rl_learning_rate) target_train_op = optimizer.minimize( rl_empirical_loss, target_global_step, var_list=tf.trainable_variables(rl_scope.name))

tensorflow.train.piecewise_constant

import tensorflow as tf tf.add_to_collection(self._initial_state_name, state_tuple.c) tf.add_to_collection(self._initial_state_name, state_tuple.h) for state_tuple in self._final_state: tf.add_to_collection(self._final_state_name, state_tuple.c) tf.add_to_collection(self._final_state_name, state_tuple.h) def import_state_tuples(self, state_tuples, name, num_replicas): restored = [] for i in range(len(state_tuples) * num_replicas): c = tf.get_collection_ref(name)[2 * i + 0] h = tf.get_collection_ref(name)[2 * i + 1] restored.append(tf.contrib.rnn.LSTMStateTuple(c, h)) return tuple(restored) def import_ops(self): if self._is_training: self._train_op = tf.get_collection_ref('train_op')[0] self._lr = tf.get_collection_ref('lr')[0] self._new_lr = tf.get_collection_ref('new_lr')[0] self._lr_update = tf.get_collection_ref('lr_update')[0] rnn_params = tf.get_collection_ref('rnn_params') if self._cell and rnn_params:

tensorflow.contrib.rnn.LSTMStateTuple

import tensorflow as tf # optimizer & gradients optimizer_base = tf.train.MomentumOptimizer(lrn_rate, FLAGS.momentum) if not FLAGS.enbl_multi_gpu: optimizer = optimizer_base else: optimizer = mgw.DistributedOptimizer(optimizer_base) grads_origin = optimizer.compute_gradients(loss, self.trainable_vars) grads_pruned = self.__calc_grads_pruned(grads_origin) # TF operations & model saver self.sess_train = sess with tf.control_dependencies(self.update_ops): self.train_op = optimizer.apply_gradients(grads_pruned, global_step=self.global_step) self.summary_op = tf.summary.merge_all() self.log_op = [lrn_rate, loss, pr_trainable, pr_maskable] + list(metrics.values()) self.log_op_names = ['lr', 'loss', 'pr_trn', 'pr_msk'] + list(metrics.keys()) self.init_op = tf.variables_initializer(self.vars) self.init_opt_op = tf.variables_initializer(optimizer_base.variables()) if FLAGS.enbl_multi_gpu: self.bcast_op = mgw.broadcast_global_variables(0) self.saver_train = tf.train.Saver(self.vars) def __build_eval(self): """Build the evaluation graph.""" with tf.Graph().as_default(): # create a TF session for the current graph

tensorflow.summary.merge_all

import tensorflow as tf self.Z = tf.placeholder(tf.float32, (None, None, fourier_window_size // 2 + 1)) batch_size = tf.shape(self.X)[0] seq_lens = tf.count_nonzero(tf.reduce_sum(self.decoder_inputs, -1), 1, dtype=tf.int32) + 1 def cells(reuse=False): return tf.contrib.rnn.DropoutWrapper( tf.nn.rnn_cell.LSTMCell( size_layers, initializer=tf.orthogonal_initializer(), reuse=reuse ), state_keep_prob=dropout, output_keep_prob=dropout, ) def attention(encoder_out, seq_len, reuse=False): attention_mechanism = tf.contrib.seq2seq.LuongAttention( num_units=size_layers, memory=encoder_out, memory_sequence_length=seq_len ) return tf.contrib.seq2seq.AttentionWrapper( cell=tf.nn.rnn_cell.MultiRNNCell([cells(reuse) for _ in range(num_layers)]), attention_mechanism=attention_mechanism, attention_layer_size=size_layers, alignment_history=True, ) encoder_cells = tf.nn.rnn_cell.MultiRNNCell([cells() for _ in range(num_layers)]) encoder_out, encoder_state = tf.nn.dynamic_rnn( cell=encoder_cells, inputs=forward, sequence_length=seq_lens, dtype=tf.float32 )

tensorflow.contrib.seq2seq.LuongAttention

import tensorflow as tf # train the model using Adam def train(self, sess, generator, learning_rate=.001, training_iters=50000, batch_size=64, display_step=10,weight_save_step=100, save_weights_path= None, generator_function= None, training_weights_path = None): # train with gradient clipping optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) grads = optimizer.compute_gradients(self.loss) clipped_grads = [(tf.clip_by_norm(grad, 1.0), var) if grad is not None else (grad, var) for grad, var in grads] # add vanishing gradient regularizer #out, test = self.dOmega_dWrec() #clipped_grads[0] = (tf.add(out[0], clipped_grads[0][0]), clipped_grads[0][1]) #clipped_grads[0] = (tf.Print(clipped_grads[0][0], [clipped_grads[0][0]], "gw_rec"), clipped_grads[0][1]) optimize = optimizer.apply_gradients(clipped_grads)

tensorflow.clip_by_norm

from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined gradient_multipliers=( dnn_linear_combined._extract_embedding_lr_multipliers( # pylint: disable=protected-access

tensorflow.contrib.learn.python.learn.estimators.dnn_linear_combined._extract_embedding_lr_multipliers

import tensorflow as tf # strides = np.asarray(self.pool_strides) # strides[1:] *= len(self.ff_conv_k) # kernels = np.asarray(self.pooling_kernel) # kernels[1:] *= len(self.ff_conv_k) # return tf.layers.conv3d_transpose( # inputs=x, # strides=strides, # padding=self.padding, # filters=y_size[-1], # kernel_size=kernels, # trainable=self.train, # use_bias=use_bias, # activation=self.ff_nl) resized = tf.nn.conv3d_transpose( value=x, filter=kernel, output_shape=y_size, strides=[1] + strides + [1], padding=self.padding, name='resize_x_to_y') resized = tf.nn.bias_add( resized, bias) resized = self.ff_nl(resized) return resized elif mode == 'replicate_n_transpose':

tensorflow.nn.conv3d_transpose

import tensorflow as tf env_floor=0) for i in range(N_WORKER)] # 觀察者 # workers.append(Worker(envpath='./ObstacleTower/obstacletower.exe', # wid=N_WORKER + 1, # retro=False, # realtime_mode=True, # env_seed=0, # env_floor=0)) GLOBAL_UPDATE_COUNTER, GLOBAL_EP = 0, 0 GLOBAL_RUNNING_R = [] COORD = tf.train.Coordinator() # 宣告共用記憶體 QUEUE = queue.Queue() threads = [] for worker in workers: # worker threads t = threading.Thread(target=worker.work, args=()) t.start() # training threads.append(t) # 建立模型更新的執行緒 threads.append(threading.Thread(target=GLOBAL_KPRUN.update, )) threads[-1].start() COORD.join(threads)

tensorflow.train.Coordinator

import tensorflow as tf total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu, optimizer) output_spec = contrib_tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, train_op=train_op, scaffold_fn=scaffold_fn) elif mode == tf.estimator.ModeKeys.EVAL: if task_name not in ["sts-b", "cola"]: def metric_fn(per_example_loss, label_ids, logits, is_real_example): predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) 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, } elif task_name == "sts-b": def metric_fn(per_example_loss, label_ids, logits, is_real_example): """Compute Pearson correlations for STS-B.""" # Display labels and predictions concat1 = contrib_metrics.streaming_concat(logits) concat2 = contrib_metrics.streaming_concat(label_ids) # Compute Pearson correlation pearson = contrib_metrics.streaming_pearson_correlation(

tensorflow.metrics.mean

import tensorflow as tf shape = control_flow_ops.with_dependencies([rank_assertions[i]], tf.shape(image))

tensorflow.shape

import tensorflow as tf def clip_logits(logits, config): logits_clip = getattr(config, "logits_clip", 0.) if logits_clip > 0: min_logit = tf.reduce_min(logits) return tf.minimum(logits - min_logit, logits_clip) else:

tensorflow.reduce_min

import tensorflow as tf loss = tf.maximum(0.0, (tgt_larg - tgt_small) - (pred_larg - pred_small)) loss = tf.reduce_mean(loss) return loss def contra_step_lossV3(pred, tgt, margin=1.0): # Step-wise contrastive loss pred1, pred2 = tf.split(pred, 2, axis=0) tgt1, tgt2 = tf.split(tgt, 2, axis=0) geq = tf.cast((tgt1 - tgt2) > 0, tf.bool) tgt_larg = tf.where(geq, tgt1, tgt2) tgt_small = tf.where(geq, tgt2, tgt1) pred_larg = tf.where(geq, pred1, pred2) pred_small = tf.where(geq, pred2, pred1) loss = tf.maximum(0.0, (tgt_larg - tgt_small) - (pred_larg - pred_small) + margin) loss = tf.reduce_mean(loss) return loss def contra_step_lossV4(pred, tgt): # 50*50 # Step-wise contrastive loss even = [2 * i for i in range(25)] odd = [2 * i + 1 for i in range(25)] pred1 = tf.gather(pred, even) pred2 = tf.gather(pred, odd) tgt1 = tf.gather(tgt, even)

tensorflow.where

from tensorflow.python.training import training_ops def __init__(self, learning_rate, use_locking=False, name="GradientDescent"): """Construct a new gradient descent optimizer. Args: learning_rate: A Tensor or a floating point value. The learning rate to use. use_locking: If True use locks for update operations. name: Optional name prefix for the operations created when applying gradients. Defaults to "GradientDescent". """ super(GradientDescentOptimizer, self).__init__(use_locking, name) self._learning_rate = learning_rate def _apply_dense(self, grad, var): return training_ops.apply_gradient_descent( var, self._learning_rate_tensor, grad, use_locking=self._use_locking).op def _apply_sparse(self, grad, var): delta = ops.IndexedSlices(grad.values * self._learning_rate_tensor, grad.indices, grad.dense_shape) return var.scatter_sub(delta, use_locking=self._use_locking) def _prepare(self): self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate, name="learning_rate")

tensorflow.python.training.training_ops.apply_gradient_descent

from tensorflow.python.client import device_lib def main(_): if not FLAGS.data_path: raise ValueError("Must set --data_path to PTB data directory") gpus = [ x.name for x in device_lib.list_local_devices() if x.device_type == "GPU" ] if FLAGS.num_gpus > len(gpus): raise ValueError( "Your machine has only %d gpus " "which is less than the requested --num_gpus=%d."

tensorflow.python.client.device_lib.list_local_devices

import tensorflow as tf try: if not tf.io.gfile.exists(a.crop_dir): tf.io.gfile.makedirs(a.crop_dir) except Exception as e:

tensorflow.io.gfile.makedirs

import tensorflow as tf # execute at test time return tf.nn.batch_normalization(x, pop_mean, pop_var, beta, gamma, epsilon) return tf.cond(train, func1, func2) def average_gradients(tower_grads):

tensorflow.cond

from tensorflow.python.ops import nn_ops w_c: [1,1, attention_vec_size] coverage: [batch_size, passage_len] ''' with variable_scope.variable_scope("Attention"): # Equation (11) in the paper state_features = linear(decoder_state, attention_vec_size, True) # [batch_size, attention_vec_size] state_features = tf.expand_dims(state_features, 1) # [batch_size, 1, attention_vec_size] all_features = encoder_features + state_features # [batch_size,passage_len,attention_vec_size] if use_coverage and coverage is not None: coverage_features = tf.expand_dims(coverage, axis=-1) * w_c # [batch_size, passage_len, attention_vec_size] all_features += coverage_features e = tf.reduce_sum(v * tf.tanh(all_features), axis=-1) # [batch_size, passage_len] attn_dist = nn_ops.softmax(e) # [batch_size, passage_len] attn_dist *= passage_mask if coverage is not None: # Update coverage vector coverage += attn_dist else: # first step of training coverage = attn_dist # Calculate the context vector from attn_dist and encoder_states # shape (batch_size, attn_size). context_vector = tf.reduce_sum(tf.expand_dims(attn_dist, axis=-1) * encoder_states, axis=1) # [batch_size, encoder_dim] return context_vector, attn_dist, coverage

tensorflow.python.ops.nn_ops.softmax

from tensorflow.python.ops import math_ops def _log_prob(self, x): x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if self.validate_args else [], x) 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))

tensorflow.python.ops.math_ops.log

import tensorflow as tf 'fast_rcnn_box_loss', tf.reduce_mean(fast_rcnn_box_loss), step=global_step) if params['include_mask']: tf.contrib.summary.scalar( 'mask_loss', tf.reduce_mean(mask_loss), step=global_step) tf.contrib.summary.scalar( 'learning_rate', tf.reduce_mean(learning_rate), step=global_step) 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])

tensorflow.contrib.summary.all_summary_ops

from tensorflow.python.framework import tensor_util if input_shape.ndims is None: return [tensor_shape.unknown_shape()] elif input_shape.ndims <= 1: return [tensor_shape.scalar()] dimension = tensor_util.ConstantValue(op.inputs[1]) if dimension is None: return [tensor_shape.unknown_shape(ndims=input_shape.ndims - 1)] elif 0 <= dimension and dimension < input_shape.ndims: returned_shape = []

tensorflow.python.framework.tensor_util.ConstantValue

import tensorflow as tf out = tf.matmul(l1, self.w2)+self.b2 return out def test_inference(self,images): images=tf.cast(images,tf.float32)/255.0 l1 = tf.matmul(images, self.w1)+self.b1 l1=tf.nn.relu(l1) out = tf.matmul(l1, self.w2)+self.b2

tensorflow.cast

import tensorflow as tf cols[3] / height, cols[2] / width], axis=1) # add batch dimension (assume batch_size==1) #assert image.get_shape()[0] == 1 boxes = tf.expand_dims(boxes, dim=0) image = tf.image.draw_bounding_boxes(image, boxes) # 在image上画gt_truth return tf.summary.image('ground_truth', image) def _add_act_summary(self, tensor): tf.summary.histogram('ACT/' + tensor.op.name + '/activations', tensor) tf.summary.scalar('ACT/' + tensor.op.name + '/zero_fraction', tf.nn.zero_fraction(tensor)) def _add_score_summary(self, key, tensor): tf.summary.histogram('SCORE/' + tensor.op.name + '/' + key + '/scores', tensor) def _add_train_summary(self, var): tf.summary.histogram('TRAIN/' + var.op.name, var) # Custom Layers # def _reshape_layer(self, bottom, num_dim, name): input_shape = tf.shape(bottom) with tf.variable_scope(name):

tensorflow.nn.zero_fraction

import tensorflow as tf hparams["type"] = "natural_exp_decay" hparams["kwargs"] = { "decay_steps": 1, "decay_rate": 0.5 } ned_lr_decay_fn = opt.get_learning_rate_decay_fn(hparams) ned_lr = ned_lr_decay_fn(learning_rate=1., global_step=global_step) ned_lr_true = tf.train.natural_exp_decay( 1., global_step-hparams["start_decay_step"], hparams["kwargs"]["decay_steps"], hparams["kwargs"]["decay_rate"]) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) pc_lr_, pc_lr_true_, ned_lr_, ned_lr_true_ = sess.run(

tensorflow.train.natural_exp_decay

from tensorflow.python.training import summary_io # TODO(mdan): This line looks redundant. if self._summary_writer is None: self._summary_writer = summary_io.SummaryWriter(estimator.model_dir)

tensorflow.python.training.summary_io.SummaryWriter

import tensorflow as tf centroids_mask = None centroids, lookup = get_unique(weights) num_centroids = tf.size(centroids) if self.preserve_sparsity: sparsity_mask = tf.math.divide_no_nan(weights, weights) zero_idx = tf.argmin(tf.abs(centroids), axis=-1) centroids_mask = 1.0 - tf.one_hot(zero_idx, num_centroids) result = {SPARSITY_MASK: sparsity_mask}

tensorflow.math.divide_no_nan

from tensorflow.python.ops import array_ops array_ops.expand_dims(array_ops.constant(thresholds), [1]), array_ops.pack([1, num_predictions])) # Tile the predictions after thresholding them across different thresholds. pred_is_pos = math_ops.greater( array_ops.tile(array_ops.transpose(predictions_2d), [num_thresholds, 1]), thresh_tiled) pred_is_neg = math_ops.logical_not(pred_is_pos) # Tile labels by number of thresholds label_is_pos = array_ops.tile(labels_2d, [num_thresholds, 1]) label_is_neg = math_ops.logical_not(label_is_pos) true_positives = _create_local('true_positives', shape=[num_thresholds]) false_negatives = _create_local('false_negatives', shape=[num_thresholds]) true_negatives = _create_local('true_negatives', shape=[num_thresholds]) false_positives = _create_local('false_positives', shape=[num_thresholds]) is_true_positive = math_ops.to_float( math_ops.logical_and(label_is_pos, pred_is_pos))

tensorflow.python.ops.array_ops.tile

import tensorflow as tf def _create_model(self, train_triples): # Count unique items to determine embedding matrix sizes entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2])) rel_cnt = len(set(train_triples[:,1])) init_sd = 1.0 / np.sqrt(self.embedding_size) # Embedding variables entity_var_shape = [entity_cnt, self.embedding_size] rel_var_shape = [rel_cnt, self.embedding_size] entity_init = tf.truncated_normal(entity_var_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_var_shape, stddev=init_sd) # Ensure maxnorm constraints are initially satisfied entity_init = dense_maxnorm(entity_init, self.maxnorm) self.entity_embedding_vars = tf.Variable(entity_init) self.rel_embedding_vars = tf.Variable(rel_init) # Embedding layer for each (head, rel, tail) triple being fed in as input head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input) tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) # Relationship vector acts as a translation in entity embedding space

tensorflow.truncated_normal

from tensorflow.contrib.eager.python.examples.spinn import data logdir=os.path.join(self._temp_data_dir, "logdir"), inference_sentences=("( foo ( bar . ) )", None)) with self.assertRaises(ValueError): spinn.train_or_infer_spinn(embed, word2index, None, None, None, config) def testTrainSpinn(self): """Test with fake toy SNLI data and GloVe vectors.""" # 1. Create and load a fake SNLI data file and a fake GloVe embedding file. snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0") fake_train_file = self._create_test_data(snli_1_0_dir) vocab = data.load_vocabulary(self._temp_data_dir) word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab) train_data = data.SnliData(fake_train_file, word2index) 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(

tensorflow.contrib.eager.python.examples.spinn.data.load_word_vectors

import tensorflow as tf name='logits_rl_w', initializer=tf.initializers.zeros(),

tensorflow.initializers.zeros

from tensorflow.contrib.framework import deprecated def _at_k_name(name, k=None, class_id=None): if k is not None: name = '%s_at_%d' % (name, k) else: name = '%s_at_k' % (name) if class_id is not None: name = '%s_class%d' % (name, class_id) return name @deprecated('2016-11-08', 'Please use `streaming_sparse_recall_at_k`, ' 'and reshape labels from [batch_size] to [batch_size, 1].') @deprecated_args(IGNORE_MASK_DATE, IGNORE_MASK_INSTRUCTIONS, 'ignore_mask') def streaming_recall_at_k(predictions, labels, k, ignore_mask=None, weights=None, metrics_collections=None, updates_collections=None, name=None): """Computes the recall@k of the predictions with respect to dense labels. The `streaming_recall_at_k` function creates two local variables, `total` and `count`, that are used to compute the recall@k frequency. This frequency is ultimately returned as `recall_at_<k>`: an idempotent operation that simply

tensorflow.contrib.framework.deprecated

import tensorflow as tf tf.logging.info("removing {}".format(src_ckpt)) tf.gfile.Remove(src_ckpt)

tensorflow.gfile.Remove

from tensorflow.python.platform import tf_logging as logging def every_n_step_begin(self, step): super(NanLoss, self).every_n_step_begin(step) return [self._loss_tensor] def every_n_step_end(self, step, outputs): super(NanLoss, self).every_n_step_end(step, outputs) if np.isnan(_extract_output(outputs, self._loss_tensor)): failure_message = "Model diverged with loss = NaN." if self._fail_on_nan_loss: logging.error(failure_message) raise NanLossDuringTrainingError else: logging.warning(failure_message) # We don't raise an error but we return "should stop" so we stop, but # without an exception. return True class RunHookAdapterForMonitors(session_run_hook.SessionRunHook):

tensorflow.python.platform.tf_logging.error

import tensorflow as tf save_dir = self._TestDir("abs_paths") abs_path = os.path.join(save_dir, "model-0") ckpt = tf.train.generate_checkpoint_state_proto(save_dir, abs_path) self.assertEqual(ckpt.model_checkpoint_path, abs_path) self.assertTrue(os.path.isabs(ckpt.model_checkpoint_path)) self.assertEqual(len(ckpt.all_model_checkpoint_paths), 1) self.assertEqual(ckpt.all_model_checkpoint_paths[-1], abs_path) def testRelPath(self): train_dir = "train" model = os.path.join(train_dir, "model-0") # model_checkpoint_path should have no "train" directory part. new_rel_path = "model-0" ckpt = tf.train.generate_checkpoint_state_proto(train_dir, model) self.assertEqual(ckpt.model_checkpoint_path, new_rel_path) self.assertEqual(len(ckpt.all_model_checkpoint_paths), 1) self.assertEqual(ckpt.all_model_checkpoint_paths[-1], new_rel_path) def testAllModelCheckpointPaths(self): save_dir = self._TestDir("all_models_test") abs_path = os.path.join(save_dir, "model-0") for paths in [None, [], ["model-2"]]: ckpt = tf.train.generate_checkpoint_state_proto( save_dir, abs_path, all_model_checkpoint_paths=paths)

tensorflow.train.generate_checkpoint_state_proto

import tensorflow as tf self.mu = self.mu * action_bound[1]; self.sigma = self.sigma + 1e-4 # get action from distribution self.normal_dist = tf.contrib.distributions.Normal(self.mu, self.sigma) self.action = tf.squeeze(self.normal_dist.sample(1),axis=0); self.action = tf.clip_by_value(self.action, action_bound[0], action_bound[1]) # Loss and train op self.loss = -self.normal_dist.log_prob(self.a_his) * self.target # Add cross entropy cost to encourage exploration self.loss -= entropy_beta * self.normal_dist.entropy() self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) self.grads_and_vars = self.optimizer.compute_gradients(self.loss) self.grads=[]; self.vars=[]; for i in range(len(self.grads_and_vars)): self.grads.append(self.grads_and_vars[i][0]); self.vars.append(self.grads_and_vars[i][1]); self.grads=self.grads[-1*NUM_VARS:]; self.vars=self.vars[-1*NUM_VARS:]; self.train_op = self.optimizer.apply_gradients( self.grads_and_vars, global_step=tf.contrib.framework.get_global_step())

tensorflow.train.AdamOptimizer

import tensorflow as tf self.assertTrue(os.path.isabs(ckpt.model_checkpoint_path)) self.assertEqual( len(ckpt.all_model_checkpoint_paths), len(paths) if paths else 1) self.assertEqual(ckpt.all_model_checkpoint_paths[-1], abs_path) def testUpdateCheckpointState(self): save_dir = self._TestDir("update_checkpoint_state") os.chdir(save_dir) # Make a temporary train directory. train_dir = "train" os.mkdir(train_dir) abs_path = os.path.join(save_dir, "model-0") rel_path = "train/model-2" tf.train.update_checkpoint_state( train_dir, rel_path, all_model_checkpoint_paths=[abs_path, rel_path]) ckpt = tf.train.get_checkpoint_state(train_dir) self.assertEqual(ckpt.model_checkpoint_path, rel_path) self.assertEqual(len(ckpt.all_model_checkpoint_paths), 2) self.assertEqual(ckpt.all_model_checkpoint_paths[-1], rel_path) self.assertEqual(ckpt.all_model_checkpoint_paths[0], abs_path) class MetaGraphTest(tf.test.TestCase):

tensorflow.train.update_checkpoint_state

import tensorflow as tf self._train_op = optimizer.apply_gradients( zip(grads, tvars), global_step=tf.contrib.framework.get_or_create_global_step())

tensorflow.contrib.framework.get_or_create_global_step

import tensorflow as tf Returns ------- A tensor. """ if axis < 0: dims = get_ndim(tensors[0]) if dims: axis = axis % dims else: axis = 0 try: return tf.concat_v2([x for x in tensors], axis) except AttributeError: return tf.concat(axis=axis, values=[x for x in tensors]) def _normalize_axis(axis, ndim): if isinstance(axis, tuple): axis = list(axis) if isinstance(axis, list): for i, a in enumerate(axis): if a is not None and a < 0: axis[i] = a % ndim

tensorflow.concat_v2

from tensorflow.contrib.layers.python.layers import utils def build_no_ops(): return (tf.no_op(), tf.no_op()) # Only make the ops if we know that `is_training=True`, or the value of # `is_training` is unknown. is_training_const = utils.constant_value(is_training) if is_training_const is None or is_training_const: update_mean_op, update_second_moment_op = utils.smart_cond( is_training, build_update_ops, build_no_ops,

tensorflow.contrib.layers.python.layers.utils.constant_value

from tensorflow.python.framework import ops default_name = _at_k_name('false_negative', k, class_id=class_id) with ops.name_scope(name, default_name, (predictions_idx, labels)) as scope:

tensorflow.python.framework.ops.name_scope