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import tensorflow as tf utils.add_activation_summary(relu7) relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob) W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8") b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8") conv8 = utils.conv2d_basic(relu_dropout7, W8, b8) # annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1") # now to upscale to actual image size deconv_shape1 = image_net["pool4"].get_shape() W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSESS], name="W_t1") b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1") conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape(image_net["pool4"])) fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1") deconv_shape2 = image_net["pool3"].get_shape() W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2") b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2") conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net["pool3"])) fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2") shape = tf.shape(image) deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSESS]) W_t3 = utils.weight_variable([16, 16, NUM_OF_CLASSESS, deconv_shape2[3].value], name="W_t3")

tensorflow.shape

from tensorflow.contrib.layers.python.layers import utils if layer.use_bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.name, outputs)

tensorflow.contrib.layers.python.layers.utils.collect_named_outputs

from tensorflow.contrib.metrics.python.ops import confusion_matrix_ops # Accumulate the prediction to current confusion matrix. current_cm = confusion_matrix_ops.confusion_matrix(

tensorflow.contrib.metrics.python.ops.confusion_matrix_ops.confusion_matrix

import tensorflow.contrib.graph_editor as ge op._set_device(origin_op.node_def.device) copied_ops = info._transformed_ops.values() debug_print("Copied %s to %s", ops_to_copy, copied_ops) ge.reroute_ts(checkpoints_disconnected_other, checkpoints_other, can_modify=copied_ops) debug_print("Rewired %s in place of %s restricted to %s", checkpoints_disconnected_other, checkpoints_other, copied_ops)

tensorflow.contrib.graph_editor.reroute_ts

import tensorflow as tf self.grads_and_vars, global_step=tf.contrib.framework.get_global_step()) def predict(self, state, sess=None): sess = sess or tf.get_default_session() state=featurize_state(state); return sess.run(self.action, { self.state: [state] })[0]

tensorflow.get_default_session

from tensorflow.python.ops import control_flow_ops # Create slots for the global solution. for v in var_list: self._zeros_slot(v, "vstar", self._name) self._zeros_slot(v, "gold", self._name) def _apply_dense(self, grad, var): lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) mu_t = math_ops.cast(self._mu_t, var.dtype.base_dtype) vstar = self.get_slot(var, "vstar") gold = self.get_slot(var, "gold") var_update = state_ops.assign_sub(var, lr_t*(grad + gold + mu_t*(var-vstar))) #Update 'ref' by subtracting 'value #Create an op that groups multiple operations. #When this op finishes, all ops in input have finished return control_flow_ops.group(*[var_update,]) def _apply_sparse_shared(self, grad, var, indices, scatter_add): lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) mu_t = math_ops.cast(self._mu_t, var.dtype.base_dtype) vstar = self.get_slot(var, "vstar") gold = self.get_slot(var, "gold") # glod is not sparse v_diff = state_ops.assign(vstar, mu_t * (var - vstar), use_locking=self._use_locking) with ops.control_dependencies([v_diff]): # run v_diff operation before scatter_add scaled_grad = scatter_add(vstar, indices, grad) var_update = state_ops.assign_sub(var, lr_t * (scaled_grad + gold))

tensorflow.python.ops.control_flow_ops.group

import tensorflow as tf print('\ntranspose(D)=') print(sess.run(tf.transpose(D))) print('\ninverse(D)=') print(sess.run(tf.matrix_inverse(D))) print('\ndeterminant(D)={:.1f}'.format(sess.run(tf.matrix_determinant(D)))) print('\ncholesky(D):') print(sess.run(tf.cholesky(identity_matrix))) print('\nselfAdjointEig(D):') print(sess.run(tf.self_adjoint_eig(D))) print(sess.run(tf.div(13, 4))) print(sess.run(tf.truediv(13, 4))) print(sess.run(tf.floordiv(13, 4))) print(sess.run(tf.mod(13.2, 4))) print(sess.run(tf.cross([1, 0, 0], [0, 1, 0]))) print(sess.run(tf.square([1, 2, 3]))) def custom_polynomial(local_tf, value):

tensorflow.self_adjoint_eig

import tensorflow as tf if activation == 'linear': cell_basic = tf.contrib.rnn.BasicRNNCell(state_size,activation=tf.identity)

tensorflow.contrib.rnn.BasicRNNCell

import tensorflow as tf sess.run(train_op) def testGraphExtension(self): self._testGraphExtensionSave() self._testGraphExtensionRestore() def testStrippedOpListDef(self): with self.test_session(): # Creates a graph. v0 = tf.Variable(0.0) var = tf.Variable(10.0) tf.add(v0, var) @function.Defun(x=tf.float32) def minus_one(x): return x - 1 minus_one(tf.identity(v0)) save = tf.train.Saver({"v0": v0}) tf.initialize_all_variables() # Generates MetaGraphDef. meta_graph_def = save.export_meta_graph() ops = [o.name for o in meta_graph_def.meta_info_def.stripped_op_list.op]

tensorflow.add

import tensorflow as tf expected_cross_terms - np.outer(expected_terms, expected_terms), self._numpy_dtype) ] def output_tensor_infos(self): return [ analyzer_nodes.TensorInfo( tf.as_dtype(self._numpy_dtype), self._output_shape, None) ] @common.log_api_use(common.ANALYZER_COLLECTION) def covariance(x: tf.Tensor, dtype: tf.DType,

tensorflow.as_dtype

from tensorflow.python.ops import math_ops where digamma(alpha) is the digamma function.""") def _entropy(self): return (self.alpha + math_ops.log(self.beta) + math_ops.lgamma(self.alpha) - (1. + self.alpha) * math_ops.digamma(self.alpha)) @distribution_util.AppendDocstring( """The mean of an inverse gamma distribution is `beta / (alpha - 1)`,

tensorflow.python.ops.math_ops.lgamma

from tensorflow.python.framework import ops if max_size is None: batch_size = values_size else: batch_size = math_ops.minimum(values_size, max_size - size) perm = [axis] + [n for n in range(ndim) if n != axis] batch_values = array_ops.transpose(values, perm)[:batch_size] def reallocate(): next_size = _next_array_size(new_size) next_shape = array_ops.pack([next_size] + fixed_shape) new_value = array_ops.zeros(next_shape, dtype=values.dtype) old_value = array.value() assign_op = state_ops.assign(array, new_value, validate_shape=False) with ops.control_dependencies([assign_op]): copy_op = array[:size].assign(old_value[:size]) # return value needs to be the same dtype as no_op() for cond with ops.control_dependencies([copy_op]): return control_flow_ops.no_op() new_size = size + batch_size array_size = array_ops.shape_internal(array, optimize=False)[0] maybe_reallocate_op = control_flow_ops.cond( new_size > array_size, reallocate, control_flow_ops.no_op) with ops.control_dependencies([maybe_reallocate_op]): append_values_op = array[size:new_size].assign(batch_values) with ops.control_dependencies([append_values_op]): update_op = size.assign(new_size)

tensorflow.python.framework.ops.control_dependencies

from tensorflow.python.ops import array_ops labels_2d = array_ops.reshape( math_ops.cast(labels, dtype=dtypes.bool), [1, -1]) # 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) # 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.transpose

from tensorflow.python.framework import ops grad_values = grad.values * multiplier grad = ops.IndexedSlices(grad_values, grad.indices, grad.dense_shape)

tensorflow.python.framework.ops.IndexedSlices

from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib eval_metric_ops = { GMM.SCORES: _streaming_sum(loss), } return model_fn_lib.ModelFnOps(mode=mode, predictions=predictions, eval_metric_ops=eval_metric_ops, loss=loss, train_op=training_op,

tensorflow.contrib.learn.python.learn.estimators.model_fn.ModelFnOps

import tensorflow as tf algo.optimize_policy() sampler.update_goals() """ with self.sess.as_default() as sess: # initialize uninitialized vars (only initialize vars that were not loaded) uninit_vars = [var for var in tf.global_variables() if not sess.run(tf.is_variable_initialized(var))] sess.run(tf.variables_initializer(uninit_vars)) start_time = time.time() for itr in range(self.start_itr, self.n_itr): itr_start_time = time.time() logger.log("\n ---------------- Iteration %d ----------------" % itr) logger.log("Sampling set of tasks/goals for this meta-batch...")

tensorflow.variables_initializer

from tensorflow.python.client import timeline lossval = 0. train_time = time.time() - start_time step_train_times.append(train_time) if step >= 0 and (step == 0 or (step + 1) % FLAGS.display_every == 0): log_fn('%i\t%s\t%.3f' % ( step + 1, get_perf_timing_str(batch_size, step_train_times), lossval)) if trace_filename is not None and step == -1: log_fn('Dumping trace to', trace_filename) trace = timeline.Timeline(step_stats=run_metadata.step_stats) with open(trace_filename, 'w') as trace_file: trace_file.write(trace.generate_chrome_trace_format(show_memory=True)) return summary_str def get_perf_timing_str(batch_size, step_train_times, scale=1): times = np.array(step_train_times) speeds = batch_size / times speed_mean = scale * batch_size / np.mean(times)

tensorflow.python.client.timeline.Timeline

from tensorflow.python.ops import array_ops with ops.device(None): if all(tensor.shape == tensor_shape.scalar() for tensor in tensors): with ops.device(tensors[0].device): values = array_ops.stack(tensors) with ops.device(device): return array_ops.unstack(values) else: with ops.device(tensors[0].device): sizes = array_ops.stack( [array_ops.shape(tensor)[0] for tensor in tensors]) values = array_ops.concat(tensors, axis=0) with ops.device(device): sizes = array_ops.unstack(sizes) return list(array_ops.split(values, sizes, axis=0)) def _scheduled_stamp_resource_op_runner(batch, stamp): """Runs a batch operation on a stamped resource.""" if not batch: return arg_keys = set(batch[0].args.keys()) grouped_args = collections.OrderedDict() resource_handles = [] # Check that the set of arguments is the same across all the scheduled ops. for op in batch: if set(op.args.keys()) != arg_keys:

tensorflow.python.ops.array_ops.split

from tensorflow.python.ops import clip_ops if "gradient_norm" in summaries: summary.scalar("gradient_norm/%s" % var_name, clip_ops.global_norm([grad_values]))

tensorflow.python.ops.clip_ops.global_norm

import tensorflow as tf fmean ** 2 + tf.matrix_diag_part(e_related_to_mean)

tensorflow.matrix_diag_part

import tensorflow as tf input_size_ = input_size if layer == 0 else 2 * num_units gru_fw = tf.contrib.rnn.GRUCell(num_units)

tensorflow.contrib.rnn.GRUCell

import tensorflow as tf input_image = np.concatenate([image, mask], axis=2) sess_config = tf.ConfigProto() sess_config.gpu_options.allow_growth = True with tf.Session(config=sess_config) as sess: input_image = tf.constant(input_image, dtype=tf.float32) output = model.build_server_graph(FLAGS, input_image) output = (output + 1.) * 127.5 output = tf.reverse(output, [-1]) output = tf.saturate_cast(output, tf.uint8) # load pretrained model vars_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) assign_ops = [] for var in vars_list: vname = var.name from_name = vname

tensorflow.reverse

import tensorflow as tf # 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) tf.compat.v2.summary.scalar( name="orig_q_vals", data=tf.reduce_mean(orig_q_vals), step=global_step, ) # What are the average Q values of the relabelled tasks? indices = tf.transpose( tf.stack([orig_indices, tf.squeeze(relabel_indices)], axis=0)) relabel_q_vals = tf.gather_nd(logits_vec, indices) tf.compat.v2.summary.scalar( name="relabel_q_vals", data=tf.reduce_mean(relabel_q_vals), step=global_step, ) max_q = tf.reduce_max(logits_vec, axis=1) tf.compat.v2.summary.scalar( name="max_q", data=tf.reduce_mean(max_q), step=global_step) ### End metrics # For both state-centric and goal-centric relabelling, the implementation of

tensorflow.gather_nd

from tensorflow.python.training import training_ops 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)

tensorflow.python.training.training_ops.apply_gradient_descent

import tensorflow as tf mapping_strings = self.load_tag_data() reverse_vocab_tags = tf.contrib.lookup.index_to_string_table_from_tensor( mapping_strings, name=name ) pred_strings = reverse_vocab_tags.lookup(tf.to_int64(pred_ids)) return pred_strings def id2word(self, word_ids, name=None): mapping_strings = self.load_word_data() reverse_vocab_tags = tf.contrib.lookup.index_to_string_table_from_tensor( mapping_strings, name=name ) word_strings = reverse_vocab_tags.lookup(tf.to_int64(word_ids)) return word_strings def loss_layer(self, preds, ground_true, nwords, crf_params): with tf.name_scope("CRF_log_likelihood"): log_likelihood, _ = tf.contrib.crf.crf_log_likelihood( preds, ground_true, nwords, crf_params ) loss = tf.reduce_mean(-log_likelihood) # regularizer = tf.contrib.layers.l2_regularizer(0.001) # reg = regularizer(embedding_variable) # loss += reg

tensorflow.to_int64

import tensorflow as tf e.g. for retarining set the epoch number you want to resume training from summary_every: int, epoch interval to write summary; higher value means lower frequency of summary writing """ with tf.Graph().as_default(), tf.device('/gpu:0'): self._setup_model_loss(num_classes=num_classes) if self.is_summary: self._setup_summaries(self.capped_d_grads, self.capped_g_grads)

tensorflow.device

import tensorflow as tf mask = tf.equal(mask, tf.ones_like(mask)) key_masks = tf.expand_dims(mask, 1) # [B, 1, T] paddings = tf.ones_like(scores) * (-2 ** 32 + 1) scores = tf.where(key_masks, scores, paddings) # [B, 1, T] # Activation if softmax_stag: scores = tf.nn.softmax(scores) # [B, 1, T] # Weighted sum if mode == 'SUM': output = tf.matmul(scores, facts) # [B, 1, H] # output = tf.reshape(output, [-1, tf.shape(facts)[-1]]) else: scores = tf.reshape(scores, [-1, tf.shape(facts)[1]]) output = facts * tf.expand_dims(scores, -1) output = tf.reshape(output, tf.shape(facts)) if return_alphas: return output, scores return output

tensorflow.matmul

from tensorflow.contrib import layers parent_scope = "dnn" input_layer_partitioner = (partitioned_variables.min_max_variable_partitioner( max_partitions=num_ps_replicas, min_slice_size=64 << 20)) input_layer_scope = parent_scope + "/input_from_feature_columns" with variable_scope.variable_scope( input_layer_scope, values=list(six.itervalues(features)), partitioner=input_layer_partitioner) as scope: net = layers.input_from_feature_columns( columns_to_tensors=features, feature_columns=feature_columns, weight_collections=[parent_scope], scope=scope) hidden_layer_partitioner = ( partitioned_variables.min_max_variable_partitioner( max_partitions=num_ps_replicas))

tensorflow.contrib.layers.input_from_feature_columns

from tensorflow.python.ops import math_ops grad = ops.IndexedSlices(grad_values, grad.indices, grad.dense_shape) else: grad *= math_ops.cast(multiplier, grad.dtype) multiplied_grads_and_vars.append((grad, var))

tensorflow.python.ops.math_ops.cast

import tensorflow as tf 1. full_cov: True and full_output_cov: True fvar N x P x N x P 2. full_cov: True and full_output_cov: False fvar P x N x N 3. full_cov: False and full_output_cov: True fvar N x P x P 4. full_cov: False and full_output_cov: False fvar N x P """ if full_cov and full_output_cov: fvar = tf.matrix_diag(tf.transpose(fvar)) # N x N x P x P fvar = tf.transpose(fvar, [0, 2, 1, 3]) # N x P x N x P if not full_cov and full_output_cov: fvar = tf.matrix_diag(fvar) # N x P x P if full_cov and not full_output_cov: pass # P x N x N if not full_cov and not full_output_cov: pass # N x P return fvar

tensorflow.matrix_diag

import tensorflow as tf Args: matrices: A list of Tensors with shape [..., N_i, M_i] (i.e. a list of matrices with the same batch dimension). dtype: Data type to use. The Tensors in `matrices` must match this dtype. Returns: A matrix with the input matrices stacked along its main diagonal, having shape [..., \sum_i N_i, \sum_i M_i]. """ matrices = [tf.convert_to_tensor(matrix, dtype=dtype) for matrix in matrices] blocked_rows = tf.Dimension(0) blocked_cols = tf.Dimension(0) batch_shape = tf.TensorShape(None) for matrix in matrices: full_matrix_shape = matrix.get_shape().with_rank_at_least(2) batch_shape = batch_shape.merge_with(full_matrix_shape[:-2]) blocked_rows += full_matrix_shape[-2] blocked_cols += full_matrix_shape[-1] ret_columns_list = [] for matrix in matrices:

tensorflow.Dimension

import tensorflow as tf (distance_kernel + '_b_initializer'), None), name=distance_kernel_kwargs.get((distance_kernel + '_name'), 'MatchingSigmoid')) return compute_l2_sigmoid_matching_distances if distance_kernel == common.DISTANCE_KERNEL_EXPECTED_LIKELIHOOD: def compute_gaussian_likelihoods(lhs, rhs): """Computes sample likelihoods.""" num_lhs_samples = lhs.shape.as_list()[-2] - 2 num_rhs_samples = rhs.shape.as_list()[-2] - 2 lhs_means, lhs_stddevs, lhs_samples = tf.split( lhs, [1, 1, num_lhs_samples], axis=-2) rhs_means, rhs_stddevs, rhs_samples = tf.split( rhs, [1, 1, num_rhs_samples], axis=-2) rhs_likelihoods = distance_utils.compute_gaussian_likelihoods( lhs_means, lhs_stddevs, rhs_samples, min_stddev=distance_kernel_kwargs.get( distance_kernel + '_min_stddev', None), max_squared_mahalanobis_distance=distance_kernel_kwargs.get( distance_kernel + '_max_squared_mahalanobis_distance', None),

tensorflow.split

from tensorflow.python.ops import array_ops with ops.name_scope( None, 'average_precision', (predictions, labels, k)) as scope: # Calculate top k indices to produce [D1, ... DN, k] tensor. _, predictions_idx = nn.top_k(predictions, k) predictions_idx = math_ops.to_int64(predictions_idx, name='predictions_idx') # Expand dims to produce [D1, ... DN, k, 1] tensor. This gives us a separate # prediction for each k, so we can calculate separate true positive values # for each k. predictions_idx_per_k = array_ops.expand_dims( predictions_idx, -1, name='predictions_idx_per_k') # Replicate labels k times to produce [D1, ... DN, k, num_labels] tensor. labels_per_k = expand_and_tile( labels, multiple=k, dim=-1, name='labels_per_k') # The following tensors are all of shape [D1, ... DN, k], containing values # per row, per k value.

tensorflow.python.ops.array_ops.expand_dims

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

from tensorflow.contrib.slim.python.slim.data import dataset 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) return dataset.Dataset( data_sources=data_sources, reader=io_ops.TFRecordReader, decoder=decoder, num_samples=100, items_to_descriptions=None) class DatasetDataProviderTest(test.TestCase): def testTFRecordDataset(self): dataset_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), 'tfrecord_dataset'))

tensorflow.contrib.slim.python.slim.data.dataset.Dataset

import tensorflow as tf # hss(s): eta * (\varphi(s)^T * K^T * \Sigma^{-1} * K * \varphi(s)) varphisKt = tf.matmul(varphis, Kt) hss = param_eta * tf.reduce_sum(tf.matmul(varphisKt, prec) * varphisKt, axis=1) Haa = param_eta * prec + Waa # Haa = 0.5 * (Haa + TT.transpose(Haa)) HaaInv = tf.matrix_inverse(Haa) # The two terms 'term1' and 'term2' which come from normalizers of the # 1. Original policy distribution # 2. The distribution after completing the square sigma = tf.matrix_inverse(prec) term1 = -0.5 * param_eta * tf.log(tf.matrix_determinant(2 * np.pi * sigma)) if self.beta == 0: term2 = 0.5 * param_eta * tf.log(tf.matrix_determinant(2 * np.pi * param_eta * HaaInv)) else: term2 = 0.5 * (param_eta + param_omega) * tf.log(tf.matrix_determinant(2 * np.pi * (param_eta + param_omega) * HaaInv)) dual = param_eta * self.epsilon - param_omega * beta + \ term1 + term2 + tf.reduce_mean( 0.5 * (tf.reduce_sum(tf.matmul(ha, HaaInv) * ha, axis=1) - hss))

tensorflow.matrix_inverse

import tensorflow as tf with tf.variable_scope(scope): if self._max_diffusion_step == 0: pass else: for support in self._supports: x1 = tf.sparse_tensor_dense_matmul(support, x0) x = self._concat(x, x1) for _ in range(2, self._max_diffusion_step + 1): x2 = 2 * tf.sparse_tensor_dense_matmul(support, x1) - x0

tensorflow.sparse_tensor_dense_matmul

import tensorflow as tf data_format=data_format, weights_initializer=trunc_normal(0.01), scope=scope + '/self_gating/transformer_W') tile_multiples = [1, t, w, h] tile_multiples.insert(index_c, 1) weights = tf.tile(weights, tile_multiples) weights = tf.nn.sigmoid(weights) return tf.multiply(weights, input_tensor)

tensorflow.tile

from tensorflow.python.ops import array_ops x = self._assert_valid_sample(x, check_integer=True) return x * math_ops.log(self.rate) - math_ops.lgamma(x + 1) def _mean(self): return array_ops.identity(self.rate) def _variance(self): return array_ops.identity(self.rate) @distribution_util.AppendDocstring( """Note: when `rate` is an integer, there are actually two modes: `rate` and `rate - 1`. In this case we return the larger, i.e., `rate`.""") def _mode(self): return math_ops.floor(self.rate)

tensorflow.python.ops.array_ops.identity

import tensorflow as tf Returns: 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:

tensorflow.DeviceSpec.from_string

from tensorflow.python.ops import parsing_ops return self._target_column.logits_to_predictions(logits, proba=True) def _get_feature_ops_from_example(self, examples_batch): column_types = layers.create_feature_spec_for_parsing(( self._get_linear_feature_columns() or []) + ( self._get_dnn_feature_columns() or [])) features = parsing_ops.parse_example(examples_batch, column_types) return features def _get_linear_feature_columns(self): if not self._linear_feature_columns: return None

tensorflow.python.ops.parsing_ops.parse_example

from tensorflow.python.ops import math_ops Args: numerator: A real `Tensor`. denominator: A real `Tensor`, with dtype matching `numerator`. name: Name for the returned op. Returns: 0 if `denominator` <= 0, else `numerator` / `denominator` """ return math_ops.select( math_ops.greater(denominator, 0), math_ops.truediv(numerator, denominator), 0, name=name) def _safe_scalar_div(numerator, denominator, name): """Divides two values, returning 0 if the denominator is 0. Args: numerator: A scalar `float64` `Tensor`. denominator: A scalar `float64` `Tensor`.

tensorflow.python.ops.math_ops.truediv

from tensorflow.python.framework import ops def _calc_conv_weight_params(graph, node): """Calculates the on-disk size of the weights for Conv2D.""" input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() filter_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) filter_in_depth = int(filter_shape[2]) filter_out_depth = int(filter_shape[3]) return ops.OpStats("weight_parameters", (filter_height * filter_width * filter_in_depth * filter_out_depth)) @ops.RegisterStatistics("BiasAdd", "flops") def _calc_bias_add_flops(graph, node): """Calculates the computing needed for BiasAdd.""" input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() input_count = np.prod(input_shape.as_list()) return ops.OpStats("flops", input_count)

tensorflow.python.framework.ops.OpStats

import tensorflow as tf for i in range (dim): dg_i = tf.gradients(flat_grads[i], par) #for each element of grads evaluate the gradients dg_i_flat = flatten(dg_i) #flatten the resulting hessian onto a 1 d array hess.append(dg_i_flat) #store row by row

tensorflow.gradients

import tensorflow as tf self.retrieve_indices = tf.make_template(

tensorflow.make_template

from tensorflow.python.training import device_setter # Create a run configuration self._config = BaseEstimator._Config() # Set device function depending if there are replicas or not. if self._config.num_ps_replicas > 0: ps_ops = ['Variable', 'AutoReloadVariable'] self._device_fn = device_setter.replica_device_setter( ps_tasks=self._config.num_ps_replicas, merge_devices=False, ps_ops=ps_ops) else: self._device_fn = None

tensorflow.python.training.device_setter.replica_device_setter

from tensorflow.python.ops import init_ops """Gated recurrent unit (GRU) with nunits cells.""" if self._gate_linear is None: bias_ones = self._bias_initializer if self._bias_initializer is None: bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype) with vs.variable_scope("gates"): # Reset gate and update gate. self._gate_linear = _Linear( [inputs, state],

tensorflow.python.ops.init_ops.constant_initializer

import tensorflow as tf max_axis = tf.reduce_max(target, axis, keep_dims=True) target_exp = tf.exp(target - max_axis)

tensorflow.exp

import tensorflow as tf initial_output = initial_state[:, -cell_output_size:] time = tf.constant(0, dtype=tf.int32, name='time') outputs = tf.TensorArray(dtype=tf.float32, size=time_steps) samples = tf.TensorArray(dtype=tf.int64, size=time_steps) inputs = tf.TensorArray(dtype=tf.int64, size=time_steps).unstack(tf.to_int64(tf.transpose(decoder_inputs)))

tensorflow.TensorArray

from tensorflow.python.ops import check_ops 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."""

tensorflow.python.ops.check_ops.assert_rank

import tensorflow as tf pred1, pred2 = tf.split(pred, 2, axis=0) tgt1, tgt2 = tf.split(tgt, 2, axis=0) geq = tf.cast((tgt1 - tgt2) > 0, tf.bool) tgt_larg = tf.where(geq, tgt1, tgt2) tgt_small = tf.where(geq, tgt2, tgt1) pred_larg = tf.where(geq, pred1, pred2) pred_small = tf.where(geq, pred2, pred1) loss = tf.maximum(0.0, (tgt_larg - tgt_small) - (pred_larg - pred_small)) loss = tf.reduce_mean(loss) return loss def contra_step_lossV3(pred, tgt, margin=1.0): # Step-wise contrastive loss pred1, pred2 = tf.split(pred, 2, axis=0)

tensorflow.maximum

import tensorflow as tf for matrix in matrices: matrix_shape = tf.shape(matrix) row_before_length = current_column current_column += matrix_shape[-1] row_after_length = ret_columns - current_column row_blocks.append(tf.pad( tensor=matrix, paddings=tf.concat( [tf.zeros([tf.rank(matrix) - 1, 2], dtype=tf.int32), [(row_before_length, row_after_length)]], axis=0))) blocked = tf.concat(row_blocks, -2) blocked.set_shape(batch_shape.concatenate((blocked_rows, blocked_cols))) return blocked

tensorflow.rank

from tensorflow.python.ops import variable_scope as vs def _adaptive_max_norm(norm, std_factor, decay, global_step, epsilon, name): """Find max_norm given norm and previous average.""" with vs.variable_scope(name, "AdaptiveMaxNorm", [norm]): log_norm = math_ops.log(norm + epsilon)

tensorflow.python.ops.variable_scope.variable_scope

import tensorflow as tf gru_fw, gru_bw = self.grus[layer] init_fw, init_bw = self.inits[layer] mask_fw, mask_bw = self.dropout_mask[layer] with tf.variable_scope("fw_{}".format(layer)): out_fw, _ = tf.nn.dynamic_rnn( gru_fw, outputs[-1] * mask_fw, seq_len, initial_state=init_fw, dtype=tf.float32) with tf.variable_scope("bw_{}".format(layer)): inputs_bw = tf.reverse_sequence( outputs[-1] * mask_bw, seq_lengths=seq_len, seq_dim=1, batch_dim=0) out_bw, _ = tf.nn.dynamic_rnn( gru_bw, inputs_bw, seq_len, initial_state=init_bw, dtype=tf.float32) out_bw = tf.reverse_sequence( out_bw, seq_lengths=seq_len, seq_dim=1, batch_dim=0) outputs.append(tf.concat([out_fw, out_bw], axis=2)) if concat_layers: res = tf.concat(outputs[1:], axis=2) else: res = outputs[-1] return res class ptr_net:

tensorflow.reverse_sequence

import tensorflow as tf class TranslateDistillProblem(TranslateProblem): """Base class for translation problems.""" def is_generate_per_split(self): return True def example_reading_spec(self): data_fields = {"dist_targets": tf.VarLenFeature(tf.int64)} if self.has_inputs: data_fields["inputs"] = tf.VarLenFeature(tf.int64) # hack: ignoring true targets and putting dist_targets in targets data_items_to_decoders = { "inputs": tf.contrib.slim.tfexample_decoder.Tensor("inputs"),

tensorflow.VarLenFeature

import tensorflow as tf # 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.

tensorflow.arg_min

from tensorflow.python.ops import array_ops denominator: A scalar `float64` `Tensor`. name: Name for the returned op. Returns: 0 if `denominator` == 0, else `numerator` / `denominator` """ numerator.get_shape().with_rank_at_most(1) denominator.get_shape().with_rank_at_most(1) return control_flow_ops.cond( math_ops.equal( array_ops.constant(0.0, dtype=dtypes.float64), denominator), lambda: array_ops.constant(0.0, dtype=dtypes.float64), lambda: math_ops.div(numerator, denominator), name=name) def _create_local(name, shape, collections=None, validate_shape=True, dtype=dtypes.float32): """Creates a new local variable. Args: name: The name of the new or existing variable.

tensorflow.python.ops.array_ops.constant

import tensorflow as tf gradvar = self._optimizer.compute_gradients(loss, replaced_list, *args, **kwargs) final_gradvar = [] for orig_var, (grad, var) in zip(var_list, gradvar): if var is not orig_var: grad = tf.cast(grad, orig_var.dtype) if self._scale != 1.0: grad = tf.scalar_mul(1. / self._scale, grad) final_gradvar.append((grad, orig_var)) return final_gradvar def apply_gradients(self, *args, **kwargs): return self._optimizer.apply_gradients(*args, **kwargs)

tensorflow.scalar_mul

from tensorflow.contrib.slim.python.slim.data import dataset_data_provider 'tfrecord_dataset')) height = 300 width = 280 with self.cached_session(): test_dataset = _create_tfrecord_dataset(dataset_dir) provider = dataset_data_provider.DatasetDataProvider(test_dataset) key, image, label = provider.get(['record_key', 'image', 'label']) image = _resize_image(image, height, width) with session.Session('') as sess: with queues.QueueRunners(sess): key, image, label = sess.run([key, image, label])

tensorflow.contrib.slim.python.slim.data.dataset_data_provider.DatasetDataProvider

import tensorflow as tf # The names are different and will work. tf.train.Saver({"vee1": v1, "other": [v2]}) def testBasicsWithListOfVariables(self): save_path = os.path.join(self.get_temp_dir(), "basics_with_list") with self.test_session(graph=tf.Graph()) as sess: # Build a graph with 2 parameter nodes, and Save and # Restore nodes for them. v0 = tf.Variable(10.0, name="v0") v1 = tf.Variable(20.0, name="v1") save = tf.train.Saver([v0, v1])

tensorflow.Graph

import tensorflow.contrib.slim as slim with tf.Graph().as_default() as graph, tf.device('/cpu:0'): num_gpu = len(cfgs.GPU_GROUP.strip().split(',')) global_step = slim.get_or_create_global_step() lr = self.warmup_lr(cfgs.LR, global_step, cfgs.WARM_SETP, num_gpu) tf.summary.scalar('lr', lr)

tensorflow.contrib.slim.get_or_create_global_step

import tensorflow as tf hparams["kwargs"]["clip_value_max"]) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) gn_grads_, gn_grads_true_, v_grads_, v_grads_true_ = sess.run( [gn_grads, gn_grads_true, v_grads, v_grads_true]) np.testing.assert_array_equal(gn_grads_, gn_grads_true_) np.testing.assert_array_equal(v_grads_, v_grads_true_) def test_get_train_op(self): """Tests get_train_op. """ var = tf.Variable(0.) loss = tf.nn.l2_loss(var) train_op = opt.get_train_op(loss) self.assertTrue(tf.contrib.framework.is_tensor(train_op)) if __name__ == "__main__": tf.test.main()

tensorflow.contrib.framework.is_tensor

from tensorflow.python.framework import dtypes `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into `[0, 1]`. """ dtype = dtypes.as_dtype(dtype).base_dtype if dtype not in (dtypes.uint8, dtypes.float32): raise TypeError('Invalid image dtype %r, expected uint8 or float32' %

tensorflow.python.framework.dtypes.as_dtype

import tensorflow as tf return h def minibatch_discrimination(x, n_kernels, dim_per_kernel, name): with tf.variable_scope(name): batch_size, nf = x.get_shape().as_list() h = linear(x, [nf, n_kernels*dim_per_kernel], 'h1') activation = tf.reshape(h, (batch_size, n_kernels, dim_per_kernel)) big = tf.eye(batch_size) big = tf.expand_dims(big, 1) abs_dif = tf.reduce_sum(tf.abs(tf.expand_dims(activation, 3) - tf.expand_dims(tf.transpose(activation, [1, 2, 0]), 0)), 2)

tensorflow.reshape

import tensorflow as tf # Calculate output indices when strides > 1. blk_indices_crop = tf.strided_slice(blk_indices, [0, 0, 0, 0], [

tensorflow.strided_slice

import tensorflow as tf # The two terms 'term1' and 'term2' which come from normalizers of the # 1. Original policy distribution # 2. The distribution after completing the square sigma = tf.matrix_inverse(prec) term1 = -0.5 * param_eta * tf.log(tf.matrix_determinant(2 * np.pi * sigma)) if self.beta == 0: term2 = 0.5 * param_eta * tf.log(tf.matrix_determinant(2 * np.pi * param_eta * HaaInv)) else: term2 = 0.5 * (param_eta + param_omega) * tf.log(tf.matrix_determinant(2 * np.pi * (param_eta + param_omega) * HaaInv)) dual = param_eta * self.epsilon - param_omega * beta + \ term1 + term2 + tf.reduce_mean( 0.5 * (tf.reduce_sum(tf.matmul(ha, HaaInv) * ha, axis=1) - hss)) # Symbolic dual gradient dual_grad = tf.gradients(xs=[param_eta, param_omega], ys=dual)

tensorflow.matrix_determinant

import tensorflow as tf return hparams def _terminate_eval(): tf.logging.info('Timeout passed with no new checkpoints ... terminating eval') return True def _get_next_checkpoint(): return tf.contrib.training.checkpoints_iterator( FLAGS.model_dir, timeout=FLAGS.eval_timeout, timeout_fn=_terminate_eval) def _set_or_add_hparam(hparams, name, value): if getattr(hparams, name, None) is None: hparams.add_hparam(name, value)

tensorflow.contrib.training.checkpoints_iterator

from tensorflow.python.ops import init_ops clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients, clip_gradients) return list(zip(clipped_gradients, variables)) def _adaptive_max_norm(norm, std_factor, decay, global_step, epsilon, name): """Find max_norm given norm and previous average.""" with vs.variable_scope(name, "AdaptiveMaxNorm", [norm]): log_norm = math_ops.log(norm + epsilon) def moving_average(name, value, decay): moving_average_variable = vs.get_variable( name, shape=value.get_shape(), dtype=value.dtype, initializer=init_ops.zeros_initializer(), trainable=False) return moving_averages.assign_moving_average( moving_average_variable, value, decay, zero_debias=False) # quicker adaptation at the beginning if global_step is not None: n = math_ops.cast(global_step, dtypes.float32) decay = math_ops.minimum(decay, n / (n + 1.)) # update averages mean = moving_average("mean", log_norm, decay) sq_mean = moving_average("sq_mean", math_ops.square(log_norm), decay)

tensorflow.python.ops.init_ops.zeros_initializer

from tensorflow.contrib.layers.python.ops import sparse_feature_cross_op cross_dense = sparse_ops.sparse_tensor_to_dense(cross) with session.Session(): values = cross_dense.eval() self.assertTrue(numpy.equal(values[0], values[1]).all()) def test_hashed_output_v2_has_no_collision(self): """Tests the new version of the fingerprint concatenation has no collisions. """ # Although the last 10 bits of 359 and 1024+359 are identical. # As a result, all the crosses shouldn't collide. t1 = constant_op.constant([[359], [359 + 1024]]) t2 = constant_op.constant([list(range(10)), list(range(10))]) cross = sparse_feature_cross_op.sparse_feature_cross( [t2, t1], hashed_output=True, num_buckets=1024, hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY) cross_dense = sparse_ops.sparse_tensor_to_dense(cross) with session.Session(): values = cross_dense.eval() self.assertTrue(numpy.not_equal(values[0], values[1]).all()) def test_hashed_3x1x2(self): """Tests 3x1x2 permutation with hashed output.

tensorflow.contrib.layers.python.ops.sparse_feature_cross_op.sparse_feature_cross

from tensorflow.python.ops import data_flow_ops nrof_preprocess_threads = 4 image_size = (args.image_size, args.image_size) eval_input_queue = data_flow_ops.FIFOQueue(capacity=2000000, dtypes=[tf.string, tf.int32, tf.int32],

tensorflow.python.ops.data_flow_ops.FIFOQueue

from tensorflow.python.ops import array_ops raise ValueError('Invalid k=%s.' % k) with ops.name_scope(None, 'num_relevant', (labels,)) as scope: # For SparseTensor, calculate separate count for each row. if isinstance(labels, (ops.SparseTensor, ops.SparseTensorValue)): labels_sizes = set_ops.set_size(labels) return math_ops.minimum(labels_sizes, k, name=scope) # For dense Tensor, calculate scalar count based on last dimension, and # tile across labels shape. labels_shape = array_ops.shape(labels) labels_size = labels_shape[-1] num_relevant_scalar = math_ops.minimum(labels_size, k) return array_ops.fill(labels_shape[0:-1], num_relevant_scalar, name=scope) def expand_and_tile(tensor, multiple, dim=0, name=None): """Slice `tensor` shape in 2, then tile along the sliced dimension. A new dimension is inserted in shape of `tensor` before `dim`, then values are tiled `multiple` times along the new dimension. Args: tensor: Input `Tensor` or `SparseTensor`. multiple: Integer, number of times to tile.

tensorflow.python.ops.array_ops.fill

from tensorflow.python.ops import logging_ops logit = layers.legacy_fully_connected( net, self._num_label_columns(), weight_collections=[self._dnn_weight_collection], bias_collections=[self._dnn_weight_collection], name="dnn_logit") self._add_hidden_layer_summary(logit, "dnn_logit") return logit def _add_hidden_layer_summary(self, value, tag): # TODO(zakaria): Move this code to tf.learn and add test. logging_ops.scalar_summary("%s:fraction_of_zero_values" % tag, nn.zero_fraction(value)) logging_ops.histogram_summary("%s:activation" % tag, value) def _linear_logits(self, features): logits, _, _ = layers.weighted_sum_from_feature_columns( columns_to_tensors=features, feature_columns=self._get_linear_feature_columns(), num_outputs=self._num_label_columns(), weight_collections=[self._linear_weight_collection], name="linear") return logits def _get_feature_dict(self, features): if isinstance(features, dict):

tensorflow.python.ops.logging_ops.histogram_summary

from tensorflow.contrib.distributions.python.ops import distribution_util @distribution_util.AppendDocstring(_poisson_sample_note) def _log_cdf(self, x): return math_ops.log(self.cdf(x)) @distribution_util.AppendDocstring(_poisson_sample_note) def _cdf(self, x): x = self._assert_valid_sample(x, check_integer=False) return math_ops.igammac(math_ops.floor(x + 1), self.rate)

tensorflow.contrib.distributions.python.ops.distribution_util.AppendDocstring

import tensorflow as tf """Returns dict of variables to restore from ImageNet-checkpoint.""" vars_to_restore_imagenet = {} ckpt_var_names = tf.contrib.framework.list_variables(imagenet_ckpt) ckpt_var_names = [name for (name, unused_shape) in ckpt_var_names]

tensorflow.contrib.framework.list_variables

import tensorflow as tf # applying maxnorm constraints easier rel_embedding_shape = [rel_cnt, self.embedding_size * self.embedding_size] entity_init = tf.truncated_normal(entity_embedding_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_embedding_shape, stddev=init_sd)

tensorflow.truncated_normal

import tensorflow as tf # where m(x) is the mean_function and \mu(x) is fmean e_mean_mean = expectation(pXnew, mean_function, mean_function) # N x D x D Lit_q_mu = tf.matrix_triangular_solve(Luu, q_mu, adjoint=True) e_mean_Kuf = expectation(pXnew, mean_function, (kern, feat)) # N x D x M # einsum isn't able to infer the rank of e_mean_Kuf, hence we explicitly set the rank of the tensor: e_mean_Kuf = tf.reshape(e_mean_Kuf, [num_data, num_func, num_ind]) e_fmean_mean = tf.einsum("nqm,mz->nqz", e_mean_Kuf, Lit_q_mu) # N x D x D e_related_to_mean = e_fmean_mean + tf.matrix_transpose(e_fmean_mean) + e_mean_mean if full_output_cov: fvar = ( tf.matrix_diag(tf.tile((eKff - tf.trace(Li_eKuffu_Lit))[:, None], [1, num_func])) + tf.matrix_diag(tf.einsum("nij,dji->nd", Li_eKuffu_Lit, cov)) + # tf.matrix_diag(tf.trace(tf.matmul(Li_eKuffu_Lit, cov))) + tf.einsum("ig,nij,jh->ngh", q_mu, Li_eKuffu_Lit, q_mu) - # tf.matmul(q_mu, tf.matmul(Li_eKuffu_Lit, q_mu), transpose_a=True) - fmean[:, :, None] * fmean[:, None, :] + e_related_to_mean ) else: fvar = ( (eKff - tf.trace(Li_eKuffu_Lit))[:, None] +

tensorflow.trace

from tensorflow.python.ops import math_ops random_tensor += random_ops.random_uniform( noise_shape, seed=seed, dtype=x.dtype) # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) binary_tensor = math_ops.floor(random_tensor) ret = x * math_ops.inv(keep_prob) * binary_tensor ret.set_shape(x.get_shape()) return ret

tensorflow.python.ops.math_ops.inv

import tensorflow as tf # rep_map_dp = dropout(rep_map, keep_prob, is_train) bn = block_num bl = block_len with tf.variable_scope('self_attention'): # @2.self-attention in block # mask generation sl_indices = tf.range(block_len, dtype=tf.int32) sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices) if direction == 'forward': direct_mask = tf.greater(sl_row, sl_col) # bl,bl else: direct_mask = tf.greater(sl_col, sl_row) # bl,bl direct_mask_tile = tf.tile( tf.expand_dims(tf.expand_dims(direct_mask, 0), 0), [bs, bn, 1, 1]) # bs,bn,bl,bl rep_mask_tile_1 = tf.tile(tf.expand_dims(rep_mask_split, 2), [1, 1, bl, 1]) # bs,bn,bl,bl

tensorflow.meshgrid

from tensorflow.python.ops import random_ops 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) naive_dt = timeit.timeit(lambda: sess.run(naive_op), number=num_iters) return param_dt, naive_dt

tensorflow.python.ops.random_ops.truncated_normal

from tensorflow.python.ops import nn_ops self.W_h = W_h encoder_features = nn_ops.conv2d(encoder_features, W_h, [1, 1, 1, 1], "SAME") # [batch_size, passage_len, 1, attention_vec_size]

tensorflow.python.ops.nn_ops.conv2d

import tensorflow as tf with tf.variable_scope(scope): init_w = tf.random_normal_initializer(0., 0.01)

tensorflow.random_normal_initializer

from tensorflow.python.training import ftrl model_dir=tempfile.mkdtemp(), linear_feature_columns=(bucketized_feature,), linear_optimizer=ftrl.FtrlOptimizer(learning_rate=0.1), dnn_feature_columns=(cont_feature,),

tensorflow.python.training.ftrl.FtrlOptimizer

import tensorflow as tf st_serialized = tf.serialize_many_sparse(st) st_deserialized = tf.deserialize_many_sparse( st_serialized, dtype=values.dtype)

tensorflow.deserialize_many_sparse

import tensorflow as tf tf.summary.histogram('advantage', adv) tf.summary.histogram('action_probability', self.mu_ph) if tf_util.is_image(self.observation_space): tf.summary.image('observation', train_model.obs_ph) else: tf.summary.histogram('observation', train_model.obs_ph) trainer = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate_ph, decay=self.rprop_alpha, epsilon=self.rprop_epsilon) _opt_op = trainer.apply_gradients(grads) # so when you call _train, you first do the gradient step, then you apply ema with tf.control_dependencies([_opt_op]): _train = tf.group(ema_apply_op) # Ops/Summaries to run, and their names for logging assert norm_grads is not None run_ops = [_train, loss, loss_q, entropy, loss_policy, loss_f, loss_bc, explained_variance, norm_grads] names_ops = ['loss', 'loss_q', 'entropy', 'loss_policy', 'loss_f', 'loss_bc', 'explained_variance', 'norm_grads'] if self.trust_region: self.run_ops = run_ops + [norm_grads_q, norm_grads_policy, avg_norm_grads_f, avg_norm_k, avg_norm_g, avg_norm_k_dot_g, avg_norm_adj] self.names_ops = names_ops + ['norm_grads_q', 'norm_grads_policy', 'avg_norm_grads_f', 'avg_norm_k', 'avg_norm_g', 'avg_norm_k_dot_g', 'avg_norm_adj']

tensorflow.group

from tensorflow.python.client import session expected_out = self._sparse_tensor([[83]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_hashed_output_v1_has_collision(self): """Tests the old version of the fingerprint concatenation has collisions. """ # The last 10 bits of 359 and 1024+359 are identical. # As a result, all the crosses collide. t1 = constant_op.constant([[359], [359 + 1024]]) t2 = constant_op.constant([list(range(10)), list(range(10))]) cross = sparse_feature_cross_op.sparse_feature_cross( [t2, t1], hashed_output=True, num_buckets=1024) cross_dense = sparse_ops.sparse_tensor_to_dense(cross) with session.Session(): values = cross_dense.eval() self.assertTrue(numpy.equal(values[0], values[1]).all()) def test_hashed_output_v2_has_no_collision(self): """Tests the new version of the fingerprint concatenation has no collisions. """ # Although the last 10 bits of 359 and 1024+359 are identical. # As a result, all the crosses shouldn't collide. t1 = constant_op.constant([[359], [359 + 1024]]) t2 = constant_op.constant([list(range(10)), list(range(10))]) cross = sparse_feature_cross_op.sparse_feature_cross( [t2, t1], hashed_output=True,

tensorflow.python.client.session.Session

import tensorflow as tf # Trainable parameters w1 = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1)) w2 = tf.Variable(tf.random_normal([input_size, attention_size], stddev=0.1)) b = tf.Variable(tf.random_normal([attention_size], stddev=0.1)) v = tf.Variable(tf.random_normal([attention_size], stddev=0.1)) with tf.name_scope('v'): # Applying fully connected layer with non-linear activation to each of the B*T timestamps;

tensorflow.random_normal

import tensorflow as tf else: filter_shape = [k_size, k_size] + [in_channel, out_dims] if w_init is None: w_init = tf.contrib.layers.variance_scaling_initializer() if b_init is None: b_init = tf.constant_initializer() w = tf.get_variable('W', filter_shape, initializer=w_init) b = None if use_bias: b = tf.get_variable('b', [out_dims], initializer=b_init) conv = tf.nn.atrous_conv2d(value=input_tensor, filters=w, rate=rate, padding=padding, name='dilation_conv')

tensorflow.get_variable

from tensorflow.contrib.learn.python.learn.estimators import composable_model self._linear_model = composable_model.LinearComposableModel( num_label_columns=target_column.num_label_columns, optimizer=linear_optimizer, gradient_clip_norm=gradient_clip_norm, num_ps_replicas=num_ps_replicas) self._dnn_model = composable_model.DNNComposableModel( num_label_columns=target_column.num_label_columns, hidden_units=dnn_hidden_units, optimizer=dnn_optimizer, activation_fn=dnn_activation_fn, dropout=dnn_dropout,

tensorflow.contrib.learn.python.learn.estimators.composable_model.DNNComposableModel

from tensorflow.contrib.layers.python.layers import feature_column constant_op.constant( iris.target, dtype=dtypes.int32), (-1, 1)) return features, labels iris = test_data.prepare_iris_data_for_logistic_regression() cont_features = [ feature_column.real_valued_column(str(i)) for i in range(4) ] linear_features = [ feature_column.bucketized_column( cont_features[i], test_data.get_quantile_based_buckets(iris.data[:, i], 10)) for i in range(4) ] linear_features.append( feature_column.sparse_column_with_hash_bucket( 'dummy_sparse_column', hash_bucket_size=100)) classifier = dnn_linear_combined.DNNLinearCombinedClassifier( model_dir=tempfile.mkdtemp(), linear_feature_columns=linear_features, dnn_feature_columns=cont_features, dnn_hidden_units=(3, 3)) metrics = classifier.fit(input_fn=_input_fn, steps=_ITERS).evaluate( input_fn=_input_fn, steps=100) self._assertSingleClassMetrics(metrics) def benchmarkCustomOptimizer(self): iris = test_data.prepare_iris_data_for_logistic_regression()

tensorflow.contrib.layers.python.layers.feature_column.sparse_column_with_hash_bucket

import tensorflow as tf tf.logging.info("Total trainable variables size: %d", total_size) learning_rate = get_learning_rate_decay(params.learning_rate, global_step, params) learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32) tf.summary.scalar("learning_rate", learning_rate) # Create optimizer opt = tf.train.AdamOptimizer(learning_rate, beta1=params.adam_beta1, beta2=params.adam_beta2, epsilon=params.adam_epsilon) if params.update_cycle == 1: train_op = tf.contrib.layers.optimize_loss( name="training", loss=loss, global_step=global_step, learning_rate=learning_rate, clip_gradients=params.clip_grad_norm or None, optimizer=opt, colocate_gradients_with_ops=True ) zero_op = tf.no_op("zero_op") collect_op = tf.no_op("collect_op") else: grads_and_vars = opt.compute_gradients( loss, colocate_gradients_with_ops=True)

tensorflow.contrib.layers.optimize_loss

import tensorflow as tf shape = x.get_shape().as_list() with tf.variable_scope(name): beta = tf.get_variable('beta', [shape[-1]], initializer=tf.constant_initializer(0.)) gamma = tf.get_variable('gamma', [shape[-1]], initializer=tf.random_normal_initializer(1., 0.02)) pop_mean = tf.get_variable('pop_mean', [shape[-1]], initializer=tf.constant_initializer(0.), trainable=False) pop_var = tf.get_variable('pop_var', [shape[-1]], initializer=tf.constant_initializer(1.), trainable=False) if pop_mean not in tf.moving_average_variables(): tf.add_to_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES, pop_mean) tf.add_to_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES, pop_var) def func1(): # execute at training time batch_mean, batch_var = tf.nn.moments(x, range(len(shape) - 1))

tensorflow.moving_average_variables

import tensorflow as tf self.pool3 = tf.layers.max_pooling2d(self.conv3, 2, 2) self.conv4 = tf.layers.conv2d(self.pool3, self.config.cifar10_cnn["num_filters"], self.config.cifar10_cnn["filter_size"], padding='same', activation=tf.nn.relu) self.drop3 = tf.layers.dropout(self.conv4, self.config.cifar10_cnn["keep_prob"], training=self.train) # b. Flatten input data self.flatten = tf.reshape(self.drop3, [-1, self.config.cifar10_cnn["fc1_nb_units"]]) # Create connected layers: fc1, fc2 with tf.contrib.framework.arg_scope([tf.contrib.layers.fully_connected], normalizer_fn=tf.contrib.layers.batch_norm, normalizer_params={"is_training": self.train}): self.fc1 = tf.contrib.layers.fully_connected(self.flatten, self.config.cifar10_cnn["fc1_nb_units"]) self.fc2 = tf.contrib.layers.fully_connected(self.fc1, self.config.data["num_categories"], activation_fn=None) # Compute loss with tf.name_scope("loss"): self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.fc2, labels=self.y)) # Optimizer with tf.name_scope("training_op"):

tensorflow.contrib.framework.arg_scope

from tensorflow.python.training import gradient_descent with self._maybeWithDevice("/job:worker" if is_distributed else None): grads = ops.IndexedSlices( constant_op.constant( [[0.1, 0.1], [0.1, 0.1]], dtype=dtype), [0, 2], [3, 2]) sgd = gradient_descent.GradientDescentOptimizer(3.0) clip_opt = variable_clipping_optimizer.VariableClippingOptimizer( sgd, {var0: [1], var1: [0]}, 2.0)

tensorflow.python.training.gradient_descent.GradientDescentOptimizer

import tensorflow as tf self.assertFalse(has_nan_or_inf.eval()) self.assertEqual(1.0, grad_scale.eval()) # The final gradient must be finite. self.assertFalse(tf.is_nan(final_var_grads.a[1]).eval()) self.assertTrue(tf.is_finite(final_var_grads.a[1]).eval())

tensorflow.is_nan

from tensorflow.contrib.learn.python.learn import ops def test_categorical_variable(self): random_seed.set_random_seed(42) with self.cached_session() as sess: cat_var_idx = array_ops.placeholder(dtypes.int64, [2, 2]) embeddings = ops.categorical_variable( cat_var_idx, n_classes=5, embedding_size=10, name="my_cat_var") sess.run(variables.global_variables_initializer()) emb1 = sess.run(embeddings, feed_dict={cat_var_idx.name: [[0, 1], [2, 3]]})

tensorflow.contrib.learn.python.learn.ops.categorical_variable

import tensorflow as tf num_func = tf.shape(q_mu)[1] # output dimension (D) q_sqrt_r = tf.matrix_band_part(q_sqrt, -1, 0) # D x M x M eKuf = tf.transpose(expectation(pXnew, (kern, feat))) # M x N (psi1) if Luu is None: Kuu = feat.Kuu(kern, jitter=settings.numerics.jitter_level) # M x M Luu = tf.cholesky(Kuu) # M x M if not white: q_mu = tf.matrix_triangular_solve(Luu, q_mu, lower=True) Luu_tiled = tf.tile(Luu[None, :, :], [num_func, 1, 1]) # remove line once issue 216 is fixed q_sqrt_r = tf.matrix_triangular_solve(Luu_tiled, q_sqrt_r, lower=True) Li_eKuf = tf.matrix_triangular_solve(Luu, eKuf, lower=True) # M x N fmean = tf.matmul(Li_eKuf, q_mu, transpose_a=True) eKff = expectation(pXnew, kern) # N (psi0) eKuffu = expectation(pXnew, (kern, feat), (kern, feat)) # N x M x M (psi2) Luu_tiled = tf.tile(Luu[None, :, :], [num_data, 1, 1]) # remove this line, once issue 216 is fixed

tensorflow.matrix_triangular_solve

from tensorflow.contrib.learn.python.learn.estimators import tensor_signature def _check_inputs(self, features, targets): if self._features_info is not None: if not tensor_signature.tensors_compatible(features, self._features_info): raise ValueError('Features are incompatible with given information. ' 'Given features: %s, required signatures: %s.' % (str(features), str(self._features_info))) else: self._features_info = tensor_signature.create_signatures(features) if self._targets_info is not None: if not tensor_signature.tensors_compatible(targets, self._targets_info): raise ValueError('Targets are incompatible with given information. ' 'Given targets: %s, required signatures: %s.' % (str(targets), str(self._targets_info))) else: self._targets_info = tensor_signature.create_signatures(targets) def _train_model(self, input_fn, steps, feed_fn=None, device_fn=None, monitor=None, log_every_steps=100, fail_on_nan_loss=True): if self._config.execution_mode not in ('all', 'train'): return # Stagger startup of worker sessions based on task id. sleep_secs = min(self._config.training_worker_max_startup_secs,

tensorflow.contrib.learn.python.learn.estimators.tensor_signature.create_signatures

from tensorflow.python.ops import partitioned_variables max_partitions=num_ps_replicas, min_slice_size=64 << 20)) input_layer_scope = parent_scope + "/input_from_feature_columns" with variable_scope.variable_scope( input_layer_scope, values=list(six.itervalues(features)), partitioner=input_layer_partitioner) as scope: net = layers.input_from_feature_columns( columns_to_tensors=features, feature_columns=feature_columns, weight_collections=[parent_scope], scope=scope) hidden_layer_partitioner = ( partitioned_variables.min_max_variable_partitioner( max_partitions=num_ps_replicas)) for layer_id, num_hidden_units in enumerate(hidden_units): with variable_scope.variable_scope( parent_scope + "/hiddenlayer_%d" % layer_id, values=[net], partitioner=hidden_layer_partitioner) as scope: net = layers.fully_connected( net, num_hidden_units, activation_fn=activation_fn, variables_collections=[parent_scope], scope=scope)

tensorflow.python.ops.partitioned_variables.min_max_variable_partitioner

import tensorflow as tf trainable: If `True`, the default, also adds the variable to the graph collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as the default list of variables to use by the `Optimizer` classes. dual_rate_factor: A floating point value or `Tensor`. The learning rate for the dual variable is scaled by this factor. Returns: dual_value: An op that computes the absolute value of the dual variable and reverses its gradient. dual_variable: The underlying variable itself. """ # We disable partitioning while constructing dual variables because they will # be updated with assign, which is not available for partitioned variables. partitioner = tf.get_variable_scope().partitioner try: tf.get_variable_scope().set_partitioner(None) dual_variable = tf.contrib.framework.model_variable( name=name, shape=shape, dtype=dtype, initializer=initializer, collections=collections, trainable=trainable) finally: tf.get_variable_scope().set_partitioner(partitioner) # Using the absolute value enforces nonnegativity. dual_value = tf.abs(dual_variable) if trainable: # To reverse the gradient on the dual variable, multiply the gradient by # -dual_rate_factor

tensorflow.contrib.framework.model_variable

from tensorflow.contrib import layers def default_input_fn(unused_estimator, examples): return layers.parse_feature_columns_from_examples(examples,

tensorflow.contrib.layers.parse_feature_columns_from_examples