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from tensorflow.contrib.metrics.python.ops import metric_ops def _streaming_auc(predictions, labels, weights=None): return metric_ops.streaming_auc( predictions, labels, weights=_float_weights_or_none(weights)) def _accuracy_at_threshold(threshold): def _accuracy_metric(predictions, labels, weights=None): threshold_predictions = math_ops.to_float( math_ops.greater_equal(predictions, threshold)) return metric_ops.streaming_accuracy( predictions=threshold_predictions, labels=labels, weights=weights) return _accuracy_metric def _streaming_at_threshold(streaming_metrics_fn, threshold): def _streaming_metrics(predictions, labels, weights=None): precision_tensor, update_op = streaming_metrics_fn( predictions, labels=labels,

tensorflow.contrib.metrics.python.ops.metric_ops.streaming_accuracy

import tensorflow as tf outputs = [] for i in xrange(200): outputs.append(f(tf.fill([1, 5], i), tf.fill([1, 5], i)))

tensorflow.fill

from tensorflow.python.keras.utils.generic_utils import register_keras_serializable return outputs @register_keras_serializable(package='Vitis', name='AveragePooling2D') class VitisAveragePooling2D(tf.keras.layers.AveragePooling2D): """Vitis version of AveragePooling2D layer.

tensorflow.python.keras.utils.generic_utils.register_keras_serializable

import tensorflow as tf def contra_traj_lossV7(pred, tgt, horizon=12, temp=100): horizon_pred, horizon_tgt = horizon_sumV1(pred, horizon), horizon_sumV1(tgt, horizon) # horizon_pred, horizon_tgt = horizon_sumV2(pred, tgt, horizon) pred_flat1, pred_flat2 = tf.reshape(horizon_pred, [-1, 1]), tf.reshape(horizon_pred, [1, -1]) tgt_flat1, tgt_flat2 = tf.reshape(horizon_tgt, [-1, 1]), tf.reshape(horizon_tgt, [1, -1]) tgt_dif = tgt_flat1 - tgt_flat2 pred_dif = pred_flat1 - pred_flat2 geq = tf.cast(tgt_dif > 0, tf.bool) tgt_posi_dif = tf.where(geq, tgt_dif, -tgt_dif) pred_posi_dif = tf.where(geq, pred_dif, -pred_dif) loss = tf.maximum(0., tgt_posi_dif - pred_posi_dif) cstr_pct = tf.math.count_nonzero(loss, dtype=tf.float32) / tf.cast(tf.reduce_prod(tf.shape(loss)), tf.float32) unorm_w = tf.exp((tgt_flat1 + tgt_flat2)/temp) loss = unorm_w * loss / (tf.reduce_sum(unorm_w)) a = tf.print(tf.reduce_sum(unorm_w)) with tf.control_dependencies([a]): final_loss = tf.reduce_sum(loss) return final_loss, cstr_pct def contra_traj_lossV8(pred, tgt, horizon=12): horizon_pred, horizon_tgt = horizon_sumV1(pred, horizon), horizon_sumV1(tgt, horizon) # horizon_pred, horizon_tgt = horizon_sumV2(pred, tgt, horizon)

tensorflow.math.count_nonzero

import tensorflow.contrib.eager as tfe # limitations under the License. # ============================================================================== from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf # pylint: disable=g-bad-import-order import tensorflow.contrib.eager as tfe # pylint: disable=g-bad-import-order from official.mnist import mnist from official.mnist import mnist_eager from official.utils.misc import keras_utils def device(): return "/device:GPU:0" if tfe.num_gpus() else "/device:CPU:0" def data_format(): return "channels_first" if tfe.num_gpus() else "channels_last" def random_dataset(): batch_size = 64 images = tf.random_normal([batch_size, 784]) labels = tf.random_uniform([batch_size], minval=0, maxval=10, dtype=tf.int32) return tf.data.Dataset.from_tensors((images, labels)) def train(defun=False):

tensorflow.contrib.eager.num_gpus

from tensorflow.contrib import slim depthwise_regularizer = regularizer else: depthwise_regularizer = None with slim.arg_scope( [slim.conv2d, slim.separable_conv2d], weights_initializer=weights_init,

tensorflow.contrib.slim.arg_scope

from tensorflow.python.platform import gfile self.assertTrue(gfile.Exists(save._MetaGraphFilename(s1))) def testSharded(self): save_dir = os.path.join(self.get_temp_dir(), "max_to_keep_sharded") try: gfile.DeleteRecursively(save_dir) except OSError: pass # Ignore gfile.MakeDirs(save_dir)

tensorflow.python.platform.gfile.DeleteRecursively

from tensorflow.contrib.layers.python.layers import initializers def simple_model(img_in, num_actions, scope, reuse=False, num_filters=64): with tf.variable_scope(scope, reuse=reuse): out = img_in gauss_initializer = initializers.xavier_initializer(uniform=False) # stddev = 1/n with tf.variable_scope("convnet"): out = layers.convolution2d( out, num_outputs=num_filters, kernel_size=8, stride=4,

tensorflow.contrib.layers.python.layers.initializers.xavier_initializer

from tensorflow.python.platform import gfile """ with gfile.Open(filename, 'wb') as f: f.write(pickle.dumps(self)) @classmethod def restore(cls, filename): """Restores vocabulary processor from given file. Args: filename: Path to file to load from. Returns: VocabularyProcessor object. """ with gfile.Open(filename, 'rb') as f: return pickle.loads(f.read())

tensorflow.python.platform.gfile.Open

import tensorflow as tf for p in warmup_vfn.parameters(): p.invalidate() for p in warmup_model.parameters(): p.invalidate() for p in policy.parameters(): p.invalidate() task.parameters().invalidate() pol_params, warm_params = tf.get_default_session().run([nn.utils.parameters_to_vector(policy.parameters()), nn.utils.parameters_to_vector(warmup_policy.parameters())]) print ("After WARMUP, pol_params_norm:", np.linalg.norm(pol_params), "warm_params_norm:", np.linalg.norm(warm_params)) mod, warm_mod = tf.get_default_session().run([nn.utils.parameters_to_vector(model.parameters()), nn.utils.parameters_to_vector(warmup_model.parameters())]) print ("mod_norm:", np.linalg.norm(mod), "warm_mod_norm:", np.linalg.norm(warm_mod)) eval_rollout(runners['train'], warmup_policy, 'Use warmup policy to collect data from virtual env') warmup_collect_virt = [] eval_rollout(runners['train'], policy, 'Use policy to collect data from virtual env')

tensorflow.get_default_session

from tensorflow.python.lib.io import file_io validation_steps=ceil(val_dataset_size/batch_size), initial_epoch=initial_epoch) model_name = "vgg19BNReLUmodel.h5" model.save(model_name) with file_io.FileIO(model_name, mode='rb') as input_f: with file_io.FileIO("gs://deeplearningteam11/" + model_name, mode='w+') as output_f: output_f.write(input_f.read())

tensorflow.python.lib.io.file_io.FileIO

import tensorflow as tf X = tf.contrib.layers.group_norm(X, groups=16, scope=scope, reuse=reuse) if dropout > 0.0: X = tf.layers.dropout(X, dropout, training=is_train) if slope < 1.0: X = tf.nn.leaky_relu(X, slope) if slope > 0.0 else tf.nn.relu(X)

tensorflow.layers.dropout

import tensorflow as tf as_text = filename.endswith('txt') log_fn('Writing GraphDef as %s to %s' % ( 'text' if as_text else 'binary', self.graph_file)) tf.train.write_graph(sess.graph_def, path, filename, as_text) log_fn('Running warm up')

tensorflow.train.write_graph

import tensorflow as tf tf.nn.rnn_cell.LSTMCell( size_layers, initializer=tf.orthogonal_initializer(), reuse=reuse

tensorflow.orthogonal_initializer

import tensorflow as tf if self.local_condition: self._placeholders.append(tf.placeholder(tf.float32, shape=(None, hparams.num_mels, None), name='local_condition_features')) queue_types.append(tf.float32) if self.global_condition: self._placeholders.append(tf.placeholder(tf.int32, shape=(None, 1), name='global_condition_features')) queue_types.append(tf.int32) # Create queue for buffering data queue = tf.FIFOQueue(8, queue_types, name='input_queue') self._enqueue_op = queue.enqueue(self._placeholders) variables = queue.dequeue() self.inputs = variables[0] self.inputs.set_shape(self._placeholders[0].shape) self.targets = variables[1] self.targets.set_shape(self._placeholders[1].shape)

tensorflow.FIFOQueue

import tensorflow as tf token_type_table = tf.get_variable( name=token_type_embedding_name, shape=[token_type_vocab_size, width], initializer=create_initializer(initializer_range)) # This vocab will be small so we always do one-hot here, since it is always # faster for a small vocabulary. flat_token_type_ids = tf.reshape(token_type_ids, [-1]) one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size) token_type_embeddings = tf.matmul(one_hot_ids, token_type_table) token_type_embeddings = tf.reshape(token_type_embeddings, [batch_size, seq_length, width]) output += token_type_embeddings if use_position_embeddings: assert_op = tf.assert_less_equal(seq_length, max_position_embeddings) with tf.control_dependencies([assert_op]): full_position_embeddings = tf.get_variable( name=position_embedding_name, shape=[max_position_embeddings, width], initializer=create_initializer(initializer_range)) # Since the position embedding table is a learned variable, we create it # using a (long) sequence length `max_position_embeddings`. The actual # sequence length might be shorter than this, for faster training of # tasks that do not have long sequences. # # So `full_position_embeddings` is effectively an embedding table # for position [0, 1, 2, ..., max_position_embeddings-1], and the current # sequence has positions [0, 1, 2, ... seq_length-1], so we can just

tensorflow.assert_less_equal

import tensorflow as tf kernel_size: a list of 2 ints stride: a list of 2 ints Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_h, kernel_w = kernel_size stride_h, stride_w = stride outputs = tf.nn.avg_pool(inputs, ksize=[1, kernel_h, kernel_w, 1], strides=[1, stride_h, stride_w, 1], padding=padding, name=sc.name) return outputs def max_pool3d(inputs,

tensorflow.nn.avg_pool

import tensorflow as tf initial_state = tf.contrib.layers.layer_norm(initial_state, activation_fn=activation_fn, scope='initial_state_layer_norm') else: initial_state = dense(initial_state, cell_state_size, use_bias=True, name='initial_state_projection', activation=activation_fn) if decoder.cell_type.lower() == 'lstm' and decoder.use_lstm_full_state: initial_output = initial_state else: # Last layer's state is the right-most part. Output is the left-most part of an LSTM's state. 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))) states = tf.TensorArray(dtype=tf.float32, size=time_steps) weights = tf.TensorArray(dtype=tf.float32, size=time_steps) attns = tf.TensorArray(dtype=tf.float32, size=time_steps) initial_symbol = inputs.read(0) # first symbol is BOS initial_input = embed(initial_symbol) initial_pos = tf.zeros([batch_size], tf.float32) initial_weights = tf.zeros(tf.shape(attention_states[align_encoder_id])[:2]) zero_context = tf.zeros(shape=tf.shape(attention_states[align_encoder_id][:,0])) # FIXME with tf.variable_scope('decoder_{}'.format(decoder.name)): initial_context, _ = look(0, initial_output, initial_input, pos=initial_pos, prev_weights=initial_weights, context=zero_context)

tensorflow.transpose

import tensorflow as tf features = {TIMESERIES_COL: inputs} return features, labels # Create list of files that match pattern file_list = tf.gfile.Glob(filename) # Create dataset from file list dataset = tf.data.TextLineDataset(file_list).map(decode_csv)

tensorflow.gfile.Glob

from tensorflow.python.framework import dtypes one_hot=False, dtype=dtypes.float32, reshape=True): """Construct a DataSet. one_hot arg is used only if fake_data is true. `dtype` can be either `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' % dtype) if fake_data: self._num_examples = 10000 self.one_hot = one_hot else:

tensorflow.python.framework.dtypes.as_dtype

import tensorflow as tf self.args = args self.kwargs = kwargs self.name = self.kwargs.get("name", self.func.__name__) self._template = tf.make_template(self.name, self.func, create_scope_now_=True) self._unique_name = self._template.variable_scope.name.split("/")[-1] self._summary_added = False

tensorflow.make_template

from tensorflow.python.ops import variables clip_opt = variable_clipping_optimizer.VariableClippingOptimizer( sgd, {var0: [1]}, 2.0) update_op = clip_opt.apply_gradients( list(zip([grads0, grads1], [var0, var1]))) variables.global_variables_initializer().run() return var0, var1, update_op def _assertDenseCorrect(self, var0, var1, update_op): # Fetch params to validate initial values

tensorflow.python.ops.variables.global_variables_initializer

import tensorflow as tf """ JPEG_OPT = {'fancy_upscaling': True, 'dct_method': 'INTEGER_ACCURATE'} def uint8_resize_bicubic(image, shape): ret = tf.image.resize_bicubic([image], shape) return tf.cast(tf.clip_by_value(ret, 0, 255), tf.uint8)[0] def resize_shortest_edge(image, image_shape, size):

tensorflow.image.resize_bicubic

import tensorflow as tf img = img - img.min() img *= 255.0/img.max() file_name = 'heatmap_{}_{}.jpg'.format(save_image_with_heatmap.counter, ind) imsave(os.path.join(config.DEBUG_DIR, file_name), img.astype(np.uint8)) return save_image_with_heatmap.counter def get_keypoint(image, targets, predictions, heatmap_size, height, width, category, clip_at_zero=True, data_format='channels_last', name=None): predictions = tf.reshape(predictions, [1, -1, heatmap_size*heatmap_size]) pred_max = tf.reduce_max(predictions, axis=-1) pred_indices = tf.argmax(predictions, axis=-1) pred_x, pred_y = tf.cast(tf.floormod(pred_indices, heatmap_size), tf.float32), tf.cast(tf.floordiv(pred_indices, heatmap_size), tf.float32) width, height = tf.cast(width, tf.float32), tf.cast(height, tf.float32) pred_x, pred_y = pred_x * width / tf.cast(heatmap_size, tf.float32), pred_y * height / tf.cast(heatmap_size, tf.float32) if clip_at_zero: pred_x, pred_y = pred_x * tf.cast(pred_max>0, tf.float32), pred_y * tf.cast(pred_max>0, tf.float32) pred_x = pred_x * tf.cast(pred_max>0, tf.float32) + tf.cast(pred_max<=0, tf.float32) * (width / 2.) pred_y = pred_y * tf.cast(pred_max>0, tf.float32) + tf.cast(pred_max<=0, tf.float32) * (height / 2.) if config.PRED_DEBUG:

tensorflow.floormod

import tensorflow as tf beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) if var.dtype.base_dtype == tf.float16: eps = 1e-7 # Can't use 1e-8 due to underflow -- not sure if it makes a big difference. else: eps = 1e-8 v = self.get_slot(var, "v") v_t = v.assign(beta2_t * v + (1. - beta2_t) * tf.square(grad)) m = self.get_slot(var, "m") m_t = m.assign(beta1_t * m + (1. - beta1_t) * grad) v_t_hat = tf.div(v_t, 1. - beta2_t) m_t_hat = tf.div(m_t, 1. - beta1_t) g_t = tf.div(m_t_hat, tf.sqrt(v_t_hat) + eps) g_t_1 = self.get_slot(var, "g")

tensorflow.square

import tensorflow as tf def _build_rnn_graph(self, inputs, config, is_training): if config.rnn_mode == CUDNN: return self._build_rnn_graph_cudnn(inputs, config, is_training) else: return self._build_rnn_graph_lstm(inputs, config, is_training) def _build_rnn_graph_cudnn(self, inputs, config, is_training): """Build the inference graph using CUDNN cell.""" inputs = tf.transpose(inputs, [1, 0, 2]) self._cell = tf.contrib.cudnn_rnn.CudnnLSTM( num_layers=config.num_layers, num_units=config.hidden_size, input_size=config.hidden_size, dropout=1 - config.keep_prob if is_training else 0) params_size_t = self._cell.params_size() self._rnn_params = tf.get_variable( "lstm_params", initializer=tf.random_uniform(

tensorflow.contrib.cudnn_rnn.CudnnLSTM

import tensorflow as tf model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, eval_batch_size=FLAGS.eval_batch_size) if FLAGS.do_train: tf.logging.info("***** Running training *****") tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) train_input_fn = input_fn_builder( input_files=input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=True) estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)

tensorflow.logging.info

import tensorflow as tf Input: vecs: A Tensor of shape (batch_size, vec_dim) segment_inds: A Tensor containing the segment index of each vec row, should agree with vecs in shape[0] Output: A tensor of shape (vec_dim) """ if reduction_mode == 'max': print('USING MAX POOLING FOR REDUCTION!') vecs_reduced = tf.segment_max(vecs, segment_inds) elif reduction_mode == 'mean': print('USING AVG POOLING FOR REDUCTION!') vecs_reduced = tf.segment_mean(vecs, segment_inds) vecs_reduced.set_shape([num_segments, vecs.get_shape()[1]]) return vecs_reduced

tensorflow.segment_max

import tensorflow as tf self._SaveAndLoad("var0", 0.0, 1.0, save_path) for use_tensor in [True, False]: with self.test_session() as sess: var = tf.Variable(1.0, name="var0") save = tf.train.Saver({var.op.name: var}) var.initializer.run() if use_tensor: global_step = tf.constant(global_step_int)

tensorflow.train.Saver

import tensorflow as tf weights=is_real_example) return {"pred": concat1, "label_ids": concat2, "pearson": pearson, "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)

tensorflow.metrics.true_positives

from tensorflow.python.ops import gen_nn_ops value: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`, `int16`, `int8`, or `complex64`. bias: A 1-D `Tensor` with size matching the last dimension of `value`. Must be the same type as `value` unless `value` is a quantized type, in which case a different quantized type may be used. name: A name for the operation (optional). Returns: A `Tensor` with the same type as `value`. """ with ops.op_scope([value, bias], name, "BiasAddV1") as name: value = ops.convert_to_tensor(value, name="input") bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") return gen_nn_ops._bias_add_v1(value, bias, name=name) ops.RegisterShape("BiasAddV1")(common_shapes.bias_add_shape) ops.RegisterShape("BiasAddGradV1")(common_shapes.bias_add_grad_shape) def relu6(features, name=None): """Computes Rectified Linear 6: `min(max(features, 0), 6)`.

tensorflow.python.ops.gen_nn_ops._bias_add_v1

from tensorflow.python.framework import ops ids, math_ops.equal(ids.values, selected_id)) # TODO(ptucker): Make this more efficient, maybe add a sparse version of # tf.equal and tf.reduce_any? # Shape of filled IDs is the same as `ids` with the last dim collapsed to 1. ids_shape = array_ops.shape(ids, out_type=dtypes.int64) ids_last_dim = array_ops.size(ids_shape) - 1 filled_selected_id_shape = math_ops.reduced_shape( ids_shape, array_ops.reshape(ids_last_dim, [1])) # Intersect `ids` with the selected ID. filled_selected_id = array_ops.fill( filled_selected_id_shape, math_ops.to_int64(selected_id)) result = set_ops.set_intersection(filled_selected_id, ids) return ops.SparseTensor( indices=result.indices, values=result.values, shape=ids_shape) def _maybe_select_class_id(labels, predictions_idx, selected_id=None): """If class ID is specified, filter all other classes. Args: labels: `int64` `Tensor` or `SparseTensor` with shape [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and `labels` has shape [batch_size, num_labels]. [D1, ... DN] must match `predictions_idx`. predictions_idx: `int64` `Tensor` of class IDs, with shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and `predictions_idx` has shape

tensorflow.python.framework.ops.SparseTensor

import tensorflow as tf # Note that this strips spaces from the end of the input as well. # We assume no inputs rely on the existence of trailing whitespace. txt = tf.strings.strip(txt) return txt

tensorflow.strings.strip

import tensorflow as tf # tf.Example only supports tf.int64, but the TPU only supports tf.int32. # So cast all int64 to int32. for name in list(example.keys()): t = example[name] if t.dtype == tf.int64: t = tf.to_int32(t) example[name] = t return example def input_fn(params):

tensorflow.to_int32

from tensorflow.python.platform import test 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]]}) emb2 = sess.run(embeddings, feed_dict={cat_var_idx.name: [[0, 2], [1, 3]]}) self.assertEqual(emb1.shape, emb2.shape) self.assertAllEqual(np.transpose(emb2, axes=[1, 0, 2]), emb1) if __name__ == "__main__": test.main()

tensorflow.python.platform.test.main

from tensorflow.contrib.metrics.python.ops import metric_ops def _predictions_streaming_mean(predictions, unused_labels, weights=None): return metric_ops.streaming_mean(predictions, weights=weights)

tensorflow.contrib.metrics.python.ops.metric_ops.streaming_mean

import tensorflow as tf # Generates a new MetaGraphDef. new_saver.export_meta_graph() # Restores from checkpoint. new_saver.restore(sess, saver0_ckpt) # Addes loss and train. labels = tf.constant(0, tf.int32, shape=[100], name="labels") batch_size = tf.size(labels) labels = tf.expand_dims(labels, 1) indices = tf.expand_dims(tf.range(0, batch_size), 1) concated = tf.concat(1, [indices, labels]) onehot_labels = tf.sparse_to_dense( concated, tf.pack([batch_size, 10]), 1.0, 0.0) logits = tf.get_collection("logits")[0] cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, onehot_labels, name="xentropy") loss = tf.reduce_mean(cross_entropy, name="xentropy_mean") tf.scalar_summary(loss.op.name, loss) # Creates the gradient descent optimizer with the given learning rate. optimizer = tf.train.GradientDescentOptimizer(0.01)

tensorflow.pack

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())

tensorflow.python.ops.math_ops.floor

import tensorflow as tf use_tpu=FLAGS.use_tpu, model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, eval_batch_size=FLAGS.eval_batch_size, ) else: estimator = tf.estimator.Estimator( model_fn=model_fn, config=run_config, params={ "batch_size": FLAGS.train_batch_size if FLAGS.do_train else FLAGS.eval_batch_size,

tensorflow.estimator.Estimator

from tensorflow.python.ops import array_ops with ops.Graph().as_default(), ops.device("/device:GPU:0"): model = cudnn_rnn_ops.CudnnLSTM(num_layers, num_units, num_units) params_size_t = model.params_size() input_data = variables.Variable( array_ops.ones([seq_length, batch_size, num_units])) input_h = variables.Variable( array_ops.ones([num_layers, batch_size, num_units])) input_c = variables.Variable( array_ops.ones([num_layers, batch_size, num_units])) params = variables.Variable( array_ops.ones([params_size_t]), validate_shape=False) output, output_h, output_c = model( is_training=True,

tensorflow.python.ops.array_ops.ones

import tensorflow as tf with tf.name_scope('AccumGradOptimizer'): ops = [] for s, gv in zip(slots, grads_and_vars): g, v = gv ops.append(s.assign_add(g)) update_counter = tf.assign_add(counter, 1, name='update_counter') update_slot_op = tf.group(update_counter, *ops, name='update_slot') def update_grad(): update_op = self._opt.apply_gradients(slots_and_vars) with tf.control_dependencies([update_op]): clear_ops = [tf.assign(s, tf.zeros_like(s)) for s in slots] return tf.group(*clear_ops, name='update_grad') pred = tf.equal(tf.mod(counter, self._niter), 0) with tf.control_dependencies([update_slot_op]): if name is None: name = 'cond_update_grad' op = tf.cond(pred, update_grad, tf.no_op, name=name).op return op if __name__ == '__main__': # run it with "python -m tensorpack.tfutils.optimizer" x = tf.get_variable('x', shape=[6]) cost = tf.reduce_sum(tf.abs(x), name='cost') opt = tf.train.GradientDescentOptimizer(0.01) opt = AccumGradOptimizer(opt, 5)

tensorflow.mod

import tensorflow as tf features['inputs'] = targets return (features, targets) def spc_tokenize(tokenizer, features, targets): del targets tokenized_text = tokenizer.tokenize(features['text']) features['targets'] = tf.cast(tokenized_text, tf.int64) features['inputs'] = features['targets'] return features, features['targets'] if tokenization == 'spc': spm_path = spm_path or t5_data().DEFAULT_SPM_PATH with tf.compat.v1.gfile.GFile(spm_path, 'rb') as f: spc_model = f.read() tokenizer = tf_text.SentencepieceTokenizer(model=spc_model) dataset = dataset.map(functools.partial(spc_tokenize, tokenizer)) else: dataset = dataset.map(unicode_decode_chars) def target_right_length(_, target): return tf.less(tf.shape(target)[0], max_target_length + 1) if max_target_length > 0: dataset = dataset.filter(target_right_length)

tensorflow.compat.v1.gfile.GFile

import tensorflow as tf # In each sequence, column index 0 to N_INPUTS - 1 are features, and column index N_INPUTS to SEQ_LEN are labels N_OUTPUTS = 1 N_INPUTS = SEQ_LEN - N_OUTPUTS LSTM_SIZE = 3 # number of hidden layers in each of the LSTM cells # Read data and convert to needed format def read_dataset(filename, mode, batch_size): def _input_fn(): # Provide the ability to decode a CSV def decode_csv(line): # all_data is a list of scalar tensors all_data = tf.decode_csv(line, record_defaults = DEFAULTS) inputs = all_data[:len(all_data) - N_OUTPUTS] # first N_INPUTS values labels = all_data[len(all_data) - N_OUTPUTS:] # last N_OUTPUTS values # Convert each list of rank R tensors to one rank R+1 tensor inputs = tf.stack(inputs, axis = 0) labels = tf.stack(labels, axis = 0) # Convert input R+1 tensor into a feature dictionary of one R+1 tensor features = {TIMESERIES_COL: inputs}

tensorflow.decode_csv

from tensorflow.python.ops import variable_scope if options.use_coverage: with variable_scope.variable_scope("coverage"): w_c = variable_scope.get_variable("w_c", [options.attention_vec_size]) w_c = tf.expand_dims(tf.expand_dims(w_c, axis=0), axis=0) # For each step, dec_input => lstm_output => vocab_score wordidx_t = decoder_inputs[0] # [batch_size] int32 for i in range(options.max_answer_len): if mode_gen in ('ce_train', 'loss',): wordidx_t = decoder_inputs[i] # the wordidx_t must from decoder_inputs for phrase model word_t = self.embedding_lookup(wordidx_t) if i > 0: variable_scope.get_variable_scope().reuse_variables() (state_t, context_t, coverage_t, attn_dist_t, p_gen_t, output_t) = self.one_step_decoder( state_t_1, context_t_1, coverage_t_1, word_t, encoder_states, self.encoder_features, passage_word_idx, passage_mask, v, w_c, vocab) coverages.append(coverage_t) attn_dists.append(attn_dist_t) p_gens.append(p_gen_t) vocab_scores.append(output_t) # The vocabulary distributions. state_t_1 = state_t

tensorflow.python.ops.variable_scope.get_variable_scope

import tensorflow as tf # These should hold all of the variables of the Q-function network and target network, # respectively. A convenient way to get these is to make use of TF's "scope" feature. # For example, you can create your Q-function network with the scope "q_func" like this: # <something> = q_func(obs_t_float, num_actions, scope="q_func", reuse=False) # And then you can obtain the variables like this: # q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_func') # Older versions of TensorFlow may require using "VARIABLES" instead of "GLOBAL_VARIABLES" ###### def q_online(obs_float): return q_func(obs_float,num_actions,scope="online_q_func",reuse=tf.AUTO_REUSE) # Q-function network and target network q_online_t = q_online(obs_t_float) q_online_tp1 = q_online(obs_tp1_float) q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,scope='online_q_func') q_target = q_func(obs_tp1_float,num_actions,scope="target_q_func",reuse=False) target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,scope='target_q_func') # Bellman training error if double_q: q_max = gather_2d(q_target,tf.argmax(q_online_tp1,axis=1,output_type=tf.int32)) else: q_max = tf.reduce_max(q_target,axis=1) target = rew_t_ph + gamma * q_max * (1.0 - done_mask_ph) q_t_act = gather_2d(q_online_t,act_t_ph) total_error = tf.reduce_mean(huber_loss(target - q_t_act)) ###### # construct optimization op (with gradient clipping)

tensorflow.get_collection

import tensorflow as tf if data_format_ == 'NHWC': inputs = tf.transpose(inputs, [0, 2, 3, 1]) ksize = int(6 * sigma + 1.) x = tf.expand_dims(tf.range(ksize, delta=1, dtype=tf.float32), axis=1) y = tf.transpose(x, [1, 0]) kernel_matrix = tf.exp(- ((x - ksize/2.) ** 2 + (y - ksize/2.) ** 2) / (2 * sigma ** 2)) #print(kernel_matrix) kernel_filter = tf.reshape(kernel_matrix, [ksize, ksize, 1, 1]) kernel_filter = tf.tile(kernel_filter, [1, 1, inputs_filters, 1]) #kernel_filter = tf.transpose(kernel_filter, [1, 0, 2, 3]) outputs = tf.nn.depthwise_conv2d(inputs, kernel_filter, strides=[1, 1, 1, 1], padding='SAME', data_format=data_format_, name='blur') if data_format_ == 'NHWC': outputs = tf.transpose(outputs, [0, 3, 1, 2]) return outputs cpn_backbone = cpn.cascaded_pyramid_net if 'seresnext50' in FLAGS.backbone: cpn_backbone = cpn.xt_cascaded_pyramid_net def keypoint_model_fn(features, labels, mode, params):

tensorflow.nn.depthwise_conv2d

import tensorflow as tf ranking_model_gradients = tf.gradients(self.rank_loss, ranking_model_params) if self.hparams.max_gradient_norm > 0: denoise_gradients, denoise_norm = tf.clip_by_global_norm(denoise_gradients, self.hparams.max_gradient_norm) ranking_model_gradients, ranking_model_norm = tf.clip_by_global_norm(ranking_model_gradients, self.hparams.max_gradient_norm * self.hparams.ranker_loss_weight) self.norm = tf.global_norm(denoise_gradients + ranking_model_gradients) opt_denoise = self.optimizer_func(self.hparams.learning_rate) opt_ranker = self.optimizer_func(self.ranker_learning_rate) denoise_updates = opt_denoise.apply_gradients(zip(denoise_gradients, denoise_params),

tensorflow.global_norm

from tensorflow.python.layers import core as core_layers if input_layer is None: input_layer = self.top_layer else: self.top_size = None name = 'dropout' + str(self.counts['dropout']) with tf.variable_scope(name): if not self.phase_train: keep_prob = 1.0 dropout = core_layers.dropout(input_layer, keep_prob) self.top_layer = dropout return dropout def batch_norm(self, input_layer=None, **kwargs): """Adds a Batch Normalization layer.""" if input_layer is None: input_layer = self.top_layer

tensorflow.python.layers.core.dropout

import tensorflow as tf print((sess.run(custom_polynomial(tf, 11)))) alpha = 0.1 val = tf.constant([[2, 3], [1, 4]], dtype=tf.float32) l1 = tf.contrib.layers.l1_regularizer(alpha)(val) l2 = tf.contrib.layers.l2_regularizer(alpha)(val) A = [[0.8, 0.6, 0.3], [0.1, 0.6, 0.4]]

tensorflow.contrib.layers.l1_regularizer

from tensorflow.core.framework import op_def_pb2 as _op_def_pb2 _ops.RegisterShape("TestStringOutput")(None) def _InitOpDefLibrary(): op_list = _op_def_pb2.OpList() _text_format.Merge(_InitOpDefLibrary.op_list_ascii, op_list) _op_def_registry.register_op_list(op_list) op_def_lib = _op_def_library.OpDefLibrary()

tensorflow.core.framework.op_def_pb2.OpList

import tensorflow as tf t = example[name] if t.dtype == tf.int64: t = tf.to_int32(t) example[name] = t return example def input_fn(params): """The actual input function.""" batch_size = params["batch_size"] # For training, we want a lot of parallel reading and shuffling. # For eval, we want no shuffling and parallel reading doesn't matter. d = tf.data.TFRecordDataset(input_file) if is_training: d = d.repeat() d = d.shuffle(buffer_size=100) d = d.apply( tf.data.experimental.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, drop_remainder=drop_remainder)) return d

tensorflow.data.TFRecordDataset

import tensorflow as tf vx_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1)) vz_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1)) x_t = tf.gather(x, l) x_t_len = tf.strings.length(x_t) x_t = tf.string_split([x_t], delimiter='').values z_t = tf.gather(y, m) z_t_len = tf.strings.length(z_t) z_t = tf.string_split([z_t], delimiter='').values for i in tf.range(start=0, limit=x_t_len - self._p + 1, delta=1, dtype=None, name='range'): u = tf.string_join(x_t[i:i + self._p], '') vx_keys, r = tf.cond( tf.greater(vx.lookup(u), -1), true_fn=lambda: (vx_keys, tf.add(vx.lookup(u), 1)), false_fn=lambda: (tf.concat([vx_keys, tf.reshape(u, (-1, 1))], axis=0),

tensorflow.strings.length

import tensorflow as tf """ context, sequence = tf.parse_single_sequence_example(

tensorflow.parse_single_sequence_example

import tensorflow as tf try: summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph) sess.run(tf.global_variables_initializer()) # saved model restoring if args.restore: # Restore saved model if the user requested it, default = True try: checkpoint_state = tf.train.get_checkpoint_state(save_dir) if checkpoint_state and checkpoint_state.model_checkpoint_path: log("Loading checkpoint {}".format(checkpoint_state.model_checkpoint_path), slack=True) saver.restore(sess, checkpoint_state.model_checkpoint_path) else: log("No model to load at {}".format(save_dir), slack=True)

tensorflow.train.get_checkpoint_state

from tensorflow.python.training import training ["centered_bias_%d" % cb for cb in range( self._target_column.num_label_columns)], array_ops.reshape(centered_bias, [-1])) return centered_bias def _centered_bias_step(self, targets, features): centered_bias = ops.get_collection(self._centered_bias_weight_collection) batch_size = array_ops.shape(targets)[0] logits = array_ops.reshape( array_ops.tile(centered_bias[0], [batch_size]), [batch_size, self._target_column.num_label_columns]) loss = self._target_column.loss(logits, targets, features) # Learn central bias by an optimizer. 0.1 is a convervative lr for a single # variable. return training.AdagradOptimizer(0.1).minimize(loss, var_list=centered_bias) def _logits(self, features, is_training=False): linear_feature_columns = self._get_linear_feature_columns() dnn_feature_columns = self._get_dnn_feature_columns() if not (linear_feature_columns or dnn_feature_columns): raise ValueError("Either linear_feature_columns or dnn_feature_columns " "should be defined.") if linear_feature_columns and dnn_feature_columns: logits = (self._linear_logits(features, is_training) + self._dnn_logits(features, is_training)) elif dnn_feature_columns: logits = self._dnn_logits(features, is_training)

tensorflow.python.training.training.AdagradOptimizer

from tensorflow.python.framework import ops Args: func: the operator op_name: name of the operator being overridden """ def binary_op_wrapper(x, y): with ops.op_scope([x, y], None, op_name) as name: assert isinstance(x, ops.Tensor) y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y") return func(x, y, name=name) ops.Tensor._override_operator("__%s__" % op_name, binary_op_wrapper) del binary_op_wrapper def r_binary_op_wrapper(y, x): with ops.op_scope([x, y], None, op_name) as name: assert isinstance(y, ops.Tensor) x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x") return func(x, y, name=name) ops.Tensor._override_operator("__r%s__" % op_name, r_binary_op_wrapper) del r_binary_op_wrapper # Conversion table for __truediv__. None entries mean no conversion required. _TRUEDIV_TABLE = { types.uint8: types.float32, types.int8: types.float32, types.int16: types.float32, types.int32: types.float64,

tensorflow.python.framework.ops.op_scope

import tensorflow as tf predict_batch_size=eval_batch_size, train_batch_size=hparams.train_batch_size, eval_batch_size=eval_batch_size, export_to_tpu=FLAGS.export_to_tpu, experimental_exported_model_uses_all_cores=FLAGS .inference_with_all_cores) else: save_checkpoints_steps = (FLAGS.save_checkpoints_steps or FLAGS.iterations_per_loop) run_config = tf.estimator.RunConfig( model_dir=FLAGS.model_dir, save_checkpoints_steps=save_checkpoints_steps) image_classifier = tf.estimator.Estimator( model_fn=model.model_fn, config=run_config, params=estimator_parmas) # Input pipelines are slightly different (with regards to shuffling and

tensorflow.estimator.RunConfig

import tensorflow as tf tf.summary.scalar("stop_token_loss", model.stop_token_loss) tf.summary.scalar("loss", model.loss) tf.summary.scalar("learning_rate", model.learning_rate) # Control learning rate decay speed if hparams.tacotron_teacher_forcing_mode == "scheduled": tf.summary.scalar("teacher_forcing_ratio", model.ratio) # Control teacher forcing # ratio decay when mode = "scheduled" gradient_norms = [tf.norm(grad) for grad in model.gradients] tf.summary.histogram("gradient_norm", gradient_norms) tf.summary.scalar("max_gradient_norm", tf.reduce_max(gradient_norms)) # visualize # gradients (in case of explosion) return tf.summary.merge_all()

tensorflow.norm

import tensorflow as tf ''' import keras.backend as K import tensorflow as tf from nvidia_info import get_memory_info memory_info = get_memory_info(0) total_memory = memory_info[1] memory_limit = int(fraction*total_memory) print(memory_info) if tf.version.VERSION[0]=="2": gpus = tf.config.experimental.list_physical_devices('GPU') tf.config.experimental.set_memory_growth(gpus[0], True) tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=memory_limit)]) else: gpu_options = tf.GPUOptions(allow_growth=allow_growth, per_process_gpu_memory_fraction=fraction) config = tf.ConfigProto(gpu_options=gpu_options) session = tf.Session(config=config) K.set_session(session)

tensorflow.config.experimental.list_physical_devices

import tensorflow as tf initializer=tf.random_normal_initializer()) alpha_logstd = tf.get_variable('alpha_logstd_layer'+str(h), shape=[1, 1, n_basis, n_out], initializer=tf.random_normal_initializer()) alpha_std = tf.exp(alpha_logstd) # Compute epsilon from {n_samples} standard Gaussian # epsilon = tf.random_normal([n_samples, 1, n_out*2, n_out]) epsilon = tf.random_uniform([n_samples, 1, n_basis, n_out]) hyp_params = tf.get_variable('hyp_params_layer'+str(h), shape=[2], initializer=tf.random_normal_initializer()) l1, l2 = tf.nn.sigmoid(hyp_params[0]), tf.exp(hyp_params[1]) epsilon = tf.sinh(epsilon*l2)/tf.cosh(epsilon*l2)**l1/l2 # Compute A_{h+1} A = tf.tile(alpha_mean+epsilon*alpha_std, [1, tf.shape(X)[0], 1, 1]) # Compute z_{h}A_{h+1} Z1 = tf.matmul(Z, A[:,:,:n_basis//2,:])/tf.sqrt(n_basis*.5) Z2 = tf.matmul(Z, A[:,:,n_basis//2:,:])/tf.sqrt(n_basis*.5) # Compute u_{h+1} and v_{h+1} U, V = tf.cos(Z1)+tf.cos(Z2), tf.sin(Z1)+tf.sin(Z2) Z = tf.concat([U, V], 3)/tf.sqrt(n_out*1.) KL += tf.reduce_mean(alpha_std**2+alpha_mean**2-2*alpha_logstd-1)/2. # Output layer else:

tensorflow.sinh

import tensorflow as tf # Implement an exponential learning rate decay every 1000 epochs #Implement a dynamical learning rate global_step = tf.Variable(0., trainable=False) rate = tf.train.exponential_decay(starter_learning, global_step, 500, 0.9) #exponential learning rate decay #rate = starter_learning tvars = tf.trainable_variables() #list of trainable variables

tensorflow.train.exponential_decay

from tensorflow.python.ops import init_ops padding='SAME', data_format=None, rate=1, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, use_spectral_norm=False, is_training=False,

tensorflow.python.ops.init_ops.zeros_initializer

import tensorflow as tf shape=[None], name='input_example_tensor') receiver_tensors = {'examples': serialized_tf_example} features = tf.parse_example(serialized_tf_example, feature_spec) return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)

tensorflow.parse_example

import tensorflow as tf # If the vocabulary is empty add a dummy value with count one so # the tensorflow index operations don't fail to initialize with empty # tensors downstream. dummy_value = (b'49d0cd50-04bb-48c0-bc6f-5b575dce351a' if tf.dtypes.as_dtype(dtype) == tf.string else b'-1') return (1, dummy_value)

tensorflow.dtypes.as_dtype

from tensorflow.python.ops import array_ops # Shape of filled IDs is the same as `ids` with the last dim collapsed to 1. ids_shape = array_ops.shape(ids, out_type=dtypes.int64) ids_last_dim = array_ops.size(ids_shape) - 1 filled_selected_id_shape = math_ops.reduced_shape(

tensorflow.python.ops.array_ops.size

import tensorflow as tf annotation = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="annotation") z = tf.placeholder(tf.float32, shape=[None, 4, 4, 128], name="z") # pred_annotation, logits = inference(image, keep_probability,z) # tf.summary.image("input_image", image, max_outputs=2) # tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2) # tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2) # loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, # labels=tf.squeeze(annotation, squeeze_dims=[3]), # name="entropy"))) mask_ = tf.ones([FLAGS.batch_size,64,64,3]) mask = tf.pad(mask_, [[0,0],[32,32],[32,32],[0,0]]) mask2__ = tf.ones([FLAGS.batch_size,78,78,3]) mask2_ = tf.pad(mask2__, [[0,0],[25,25],[25,25],[0,0]]) mask2 = mask2_ - mask pred_annotation, logits = inference((1-mask)*image + mask*255, keep_probability,z) tf.summary.image("input_image", image, max_outputs=2) tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2) tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2) # loss0 = tf.reduce_mean(tf.abs(z)) loss = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square((image - logits)),[1,2,3]))) # loss2 = tf.reduce_mean(tf.square((image - logits)*mask2)) # loss = loss1 + loss2 + loss0 # loss = tf.reduce_mean(tf.squared_difference(logits ,annotation ))

tensorflow.ones

import tensorflow as tf # In the demo, we are doing a simple classification task on the entire # segment. # # If you want to use the token-level output, use model_bak.get_sequence_output() # instead. output_layer = model.get_pooled_output() hidden_size = output_layer.shape[-1].value output_weights = tf.get_variable( "output_weights", [num_labels, hidden_size], initializer=tf.truncated_normal_initializer(stddev=0.02)) output_bias = tf.get_variable( "output_bias", [num_labels], initializer=tf.zeros_initializer()) with tf.variable_scope("loss"): if is_training: # I.e., 0.1 dropout output_layer = tf.nn.dropout(output_layer, keep_prob=0.9) logits = tf.matmul(output_layer, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) probabilities = tf.nn.softmax(logits, axis=-1) log_probs = tf.nn.log_softmax(logits, axis=-1) one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32) per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)

tensorflow.zeros_initializer

import tensorflow as tf 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): return local_tf.subtract(3 * local_tf.square(value), value) + 10 print((sess.run(custom_polynomial(tf, 11)))) alpha = 0.1 val = tf.constant([[2, 3], [1, 4]], dtype=tf.float32) l1 = tf.contrib.layers.l1_regularizer(alpha)(val)

tensorflow.cross

import tensorflow as tf a_mask = tf.matrix_band_part(ones, -1, 0) s_ex12 = tf.expand_dims(tf.expand_dims(mask_sequence, 1), 2) s_ex13 = tf.expand_dims(tf.expand_dims(mask_sequence, 1), 3) a_mask = (1 - s_ex13) * (1 - s_ex12) + s_ex13 * a_mask # generate mask of batch x seq_len x seq_len a_mask = tf.reshape(a_mask, (-1, seq_len, seq_len)) out_mask = attention_mask * a_mask else: ones = tf.ones_like(attention_mask[:1]) mask = (tf.matrix_band_part(ones, -1, 0)) out_mask = attention_mask * mask else: out_mask = attention_mask return out_mask

tensorflow.matrix_band_part

from tensorflow.python.ops import math_ops labels, labels, weights=weights, name='variance_labels') pearson_r = _safe_div( cov, math_ops.mul(math_ops.sqrt(var_predictions), math_ops.sqrt(var_labels)), 'pearson_r') with ops.control_dependencies( [update_cov, update_var_predictions, update_var_labels]):

tensorflow.python.ops.math_ops.sqrt

import tensorflow as tf epsilon = tf.random_uniform([n_samples, 1, n_basis, n_out]) hyp_params = tf.get_variable('hyp_params_layer'+str(h), shape=[2], initializer=tf.random_normal_initializer()) l1, l2 = tf.nn.sigmoid(hyp_params[0]), tf.exp(hyp_params[1]) epsilon = tf.sinh(epsilon*l2)/tf.cosh(epsilon*l2)**l1/l2 # Compute A_{h+1} A = tf.tile(alpha_mean+epsilon*alpha_std, [1, tf.shape(X)[0], 1, 1]) # Compute z_{h}A_{h+1} Z1 = tf.matmul(Z, A[:,:,:n_basis//2,:])/tf.sqrt(n_basis*.5) Z2 = tf.matmul(Z, A[:,:,n_basis//2:,:])/tf.sqrt(n_basis*.5) # Compute u_{h+1} and v_{h+1} U, V = tf.cos(Z1)+tf.cos(Z2), tf.sin(Z1)+tf.sin(Z2) Z = tf.concat([U, V], 3)/tf.sqrt(n_out*1.) KL += tf.reduce_mean(alpha_std**2+alpha_mean**2-2*alpha_logstd-1)/2. # Output layer else: F = tf.squeeze(tf.layers.dense(Z, n_out), [2]) return F, KL

tensorflow.sin

import tensorflow as tf stddev=0.02, data_format='NHWC',padding='SAME',epsilon=1e-9) : with tf.variable_scope(name) : assert data_format == 'NHWC' self.v = tf.get_variable('v', [k_h, k_w, input_dim, output_dim], initializer=tf.truncated_normal_initializer(stddev=stddev)) self.g = tf.get_variable('g',[output_dim], initializer=tf.constant_initializer(float('nan'))) self.b = tf.get_variable('b',[output_dim],

tensorflow.truncated_normal_initializer

import tensorflow as tf im = axs[fig_obj_count, 1].matshow(inter.numpy()) plt.colorbar(im, ax=axs[fig_obj_count, 1]) values = sdf_values inter = tf.reshape(values, [self.resolution, self.resolution, self.resolution]) inter = tf.transpose(tf.reduce_max(inter, axis=a)) im = axs[fig_obj_count, 2].matshow(inter.numpy()) plt.colorbar(im, ax=axs[fig_obj_count, 2]) fig_obj_count += 1 intersection = tf.reduce_sum(tf.math.sign(tf.nn.relu(sdf_values - 1))) union = tf.reduce_sum(tf.math.sign(sdf_values)) iou = intersection / union self.collisions.append(num_collisions) self.intersections.append(intersection) self.ious.append(iou) return num_collisions, intersection, iou def evaluate(self): """Evaluate.""" if self.slave: data = {'collisions': self.collisions, 'intersections': self.intersections, 'ious': self.ious} with gfile.Open(self.path, 'wb') as file:

tensorflow.math.sign

from tensorflow.python.framework import ops training_op = control_flow_ops.group(*all_grads) self._BenchmarkOp(training_op, "cudnn_lstm %s %s" % (config_name, self._GetConfigDesc(config))) def benchmarkTfRNNLSTMTraining(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) ] initializer = init_ops.random_uniform_initializer(-0.01, 0.01, seed=127) cell = rnn_cell.LSTMCell( num_units=num_units, initializer=initializer, state_is_tuple=True) multi_cell = rnn_cell.MultiRNNCell( [cell() for _ in range(num_layers)]) outputs, final_state = core_rnn.static_rnn( multi_cell, inputs, dtype=dtypes.float32)

tensorflow.python.framework.ops.Graph

from tensorflow.contrib.rnn.python.ops.core_rnn_cell import _Linear @property def output_size(self): return self._num_units def __call__(self, inputs, state, att_score): return self.call(inputs, state, att_score) def call(self, inputs, state, att_score=None): """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], 2 * self._num_units, True, bias_initializer=bias_ones, kernel_initializer=self._kernel_initializer) value = math_ops.sigmoid(self._gate_linear([inputs, state])) r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1) r_state = r * state if self._candidate_linear is None: with vs.variable_scope("candidate"): self._candidate_linear = _Linear( [inputs, r_state],

tensorflow.contrib.rnn.python.ops.core_rnn_cell._Linear

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

import tensorflow as tf 'worker': self.worker_hosts}) self.server = None if not self.server: self.server = tf.train.Server(self.cluster, job_name=self.job_name, task_index=self.task_index, config=create_config_proto(), protocol=FLAGS.server_protocol) worker_prefix = '/job:worker/task:%s' % self.task_index self.param_server_device = tf.train.replica_device_setter( worker_device=worker_prefix + '/cpu:0', cluster=self.cluster) # This device on which the queues for managing synchronization between # servers should be stored. num_ps = len(self.ps_hosts) self.sync_queue_devices = ['/job:ps/task:%s/cpu:0' % i for i in range(num_ps)] else: self.task_index = 0

tensorflow.train.replica_device_setter

import tensorflow as tf def create_optimizer(learning_rate, params): """Creates optimized based on the specified flags.""" if params['optimizer'] == 'momentum': optimizer = tf.train.MomentumOptimizer( learning_rate, momentum=params['momentum']) elif params['optimizer'] == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate) elif params['optimizer'] == 'adadelta': optimizer = tf.train.AdadeltaOptimizer(learning_rate) elif params['optimizer'] == 'adagrad': optimizer = tf.train.AdagradOptimizer(learning_rate) elif params['optimizer'] == 'rmsprop': optimizer = tf.train.RMSPropOptimizer( learning_rate, momentum=params['momentum']) elif params['optimizer'] == 'lars': optimizer = tf.contrib.opt.LARSOptimizer(

tensorflow.train.AdadeltaOptimizer

from tensorflow.python.training import moving_averages mean: The mean value to update with. variance: The variance value to update with. is_training: Boolean Tensor to indicate if we're currently in training mode. """ def build_update_ops(): """Builds the exponential moving average update ops.""" update_mean_op = moving_averages.assign_moving_average( variable=self._moving_mean, value=mean, decay=self._decay_rate, name="update_moving_mean").op update_variance_op = moving_averages.assign_moving_average( variable=self._moving_variance, value=variance, decay=self._decay_rate, name="update_moving_variance").op return update_mean_op, update_variance_op 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:

tensorflow.python.training.moving_averages.assign_moving_average

from tensorflow.python.ops import gen_state_ops container: An optional string. Defaults to "". If non-empty, this variable is placed in the given container. Otherwise, a default container is used. shared_name: An optional string. Defaults to "". If non-empty, this variable is named in the given bucket with this shared_name. Otherwise, the node name is used instead. Returns: A variable tensor. """ ret = gen_state_ops._variable(shape=shape, dtype=dtype, name=name, container=container, shared_name=shared_name) # TODO(mrry): Move this to where it is used, so we can get rid of this op # wrapper? if set_shape: ret.set_shape(shape) return ret # NOTE(mrry): Shapes are conditionally set in the Python wrapper.

tensorflow.python.ops.gen_state_ops._variable

from tensorflow.python.ops import state_ops def _apply_dense(self, grad, var): lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) g_t = grad g_t_1 = self.get_slot(var, "g") g_t = g_t_1.assign(g_t) var_update = state_ops.assign_sub(var, 2. * lr_t * g_t - lr_t * g_t_1) # Adam would be lr_t * g_t return control_flow_ops.group(*[var_update, g_t]) def _apply_sparse(self, grad, var): raise NotImplementedError("Sparse gradient updates are not supported.")

tensorflow.python.ops.state_ops.assign_sub

import tensorflow.contrib.layers as layers return out def simple_model(img_in, num_actions, scope, reuse=False, num_filters=64): with tf.variable_scope(scope, reuse=reuse): out = img_in gauss_initializer = initializers.xavier_initializer(uniform=False) # stddev = 1/n with tf.variable_scope("convnet"): out = layers.convolution2d( out, num_outputs=num_filters, kernel_size=8, stride=4, activation_fn=tf.nn.relu, weights_initializer=gauss_initializer, trainable=False) out = layers.flatten(out) with tf.variable_scope("action_value"): out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None) return out def simple_model_w_feat_eng(img_in, num_actions, scope, reuse=False): with tf.variable_scope(scope, reuse=reuse): out = img_in out = layers.flatten(out) # stddev = 1/n, where n = number of inputs gauss_initializer = initializers.xavier_initializer(uniform=False)

tensorflow.contrib.layers.flatten

import tensorflow as tf Returns: a `float` decov loss """ with tf.name_scope(name): x = tf.reshape(xs, [int(xs.get_shape()[0]), -1]) m = tf.reduce_mean(x, 0, True) z = tf.expand_dims(x - m, 2) corr = tf.reduce_mean(tf.matmul(z, tf.transpose(z, perm=[0, 2, 1])), 0) corr_frob_sqr = tf.reduce_sum(tf.square(corr)) corr_diag_sqr = tf.reduce_sum(tf.square(tf.diag_part(corr))) loss = 0.5 * (corr_frob_sqr - corr_diag_sqr) return loss def center_loss(features, label, alpha, num_classes, name='center_loss'): """Center loss based on the paper "A Discriminative Feature Learning Approach for Deep Face Recognition" (http://ydwen.github.io/papers/WenECCV16.pdf)

tensorflow.diag_part

from tensorflow.python.ops import init_ops 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) ] initializer = init_ops.random_uniform_initializer(-0.01, 0.01, seed=127) cell = rnn_cell.LSTMCell( num_units=num_units, initializer=initializer, state_is_tuple=True) 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],

tensorflow.python.ops.init_ops.random_uniform_initializer

import tensorflow as tf """ assert len(input_tensor.get_shape()) == 2 assert len(idx.get_shape()) == 1 idx_flattened = tf.range(0, input_tensor.shape[0], dtype=tf.int64) * input_tensor.shape[1] + idx offset_tensor = tf.gather(tf.reshape(input_tensor, [-1]), # flatten input idx_flattened) # use flattened indices

tensorflow.range

from tensorflow.python.framework import ops x: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`, or `qint32`. name: A name for the operation (optional). Returns: A Tensor with the same type as `x` if `x.dtype != qint32` otherwise the return type is `quint8`. """ with ops.op_scope([x], name, "Sigmoid") as name: x = ops.convert_to_tensor(x, name="x") return gen_math_ops._sigmoid(x, name=name) def tanh(x, name=None): """Computes hyperbolic tangent of `x` element-wise. Args: x: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`,

tensorflow.python.framework.ops.convert_to_tensor

from tensorflow.contrib.framework.python.framework import checkpoint_utils def weights(self): """Returns the cluster weights.""" return checkpoint_utils.load_variable( self.model_dir, gmm_ops.GmmAlgorithm.CLUSTERS_WEIGHT)

tensorflow.contrib.framework.python.framework.checkpoint_utils.load_variable

import tensorflow as tf for step in num_inner_grad_steps: sampler.sample() algo.compute_updated_dists() 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...") self.sampler.update_tasks() self.policy.switch_to_pre_update() # Switch to pre-update policy

tensorflow.variables_initializer

import tensorflow as tf params.learning_rate_boundaries, params.learning_rate_values) elif params.learning_rate_decay == "none": return learning_rate else: raise ValueError("Unknown learning_rate_decay") def session_config(params): optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L1, do_function_inlining=True) graph_options = tf.GraphOptions(optimizer_options=optimizer_options) config = tf.ConfigProto(allow_soft_placement=True, graph_options=graph_options) if params.device_list: device_str = ",".join([str(i) for i in params.device_list]) config.gpu_options.visible_device_list = device_str config.gpu_options.per_process_gpu_memory_fraction = params.gpu_memory_fraction config.gpu_options.allow_growth = True return config

tensorflow.GraphOptions

from tensorflow.contrib.eager.python.examples.l2hmc import l2hmc def benchmark_eager_defun(self): self._benchmark_eager(defun=True) def _benchmark_eager(self, defun=False): """Benchmark Eager performance.""" hparams = get_default_hparams() for sample_size in [10, 25, 50, 100, 200]: hparams.n_samples = sample_size energy_fn, _, _ = l2hmc.get_scg_energy_fn() dynamics = l2hmc.Dynamics( x_dim=hparams.x_dim, minus_loglikelihood_fn=energy_fn, n_steps=hparams.n_steps, eps=hparams.eps) optimizer = tf.train.AdamOptimizer(learning_rate=hparams.learning_rate) step_fn = tfe.defun(step) if defun else step # Warmup to reduce initialization effect when timing

tensorflow.contrib.eager.python.examples.l2hmc.l2hmc.get_scg_energy_fn

from tensorflow.python.training import device_setter self._model_dir = model_dir if self._model_dir is None: self._model_dir = tempfile.mkdtemp() logging.info('Using temporary folder as model directory: %s', self._model_dir) # 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 # Features and targets TensorSingature objects. self._features_info = None self._targets_info = None @abc.abstractproperty def _get_train_ops(self, features, targets):

tensorflow.python.training.device_setter.replica_device_setter

import tensorflow as tf # limitations under the License. import time import numpy as np import tensorflow as tf import random from tensorflow.contrib import slim from npu_bridge.estimator import npu_ops from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig tf.app.flags.DEFINE_integer('input_size', 512, '') tf.app.flags.DEFINE_integer('batch_size_per_gpu', 14, '') tf.app.flags.DEFINE_integer('num_readers', 16, '') tf.app.flags.DEFINE_float('learning_rate', 0.0001, '') tf.app.flags.DEFINE_integer('max_steps', 100000, '') tf.app.flags.DEFINE_integer('loss_scale', 1024, '') tf.app.flags.DEFINE_float('moving_average_decay', 0.997, '') tf.app.flags.DEFINE_string('gpu_list', '1', '') tf.app.flags.DEFINE_string('checkpoint_path', '/tmp/east_resnet_v1_50_rbox/', '') tf.app.flags.DEFINE_boolean('restore', False, 'whether to resotre from checkpoint') tf.app.flags.DEFINE_integer('save_checkpoint_steps', 1000, '') tf.app.flags.DEFINE_integer('save_summary_steps', 100, '') tf.app.flags.DEFINE_string('pretrained_model_path', None, '') tf.app.flags.DEFINE_boolean('allow_mix_precision', False, 'whether to allow mix precision') tf.app.flags.DEFINE_boolean('auto_tune', False, 'whether to autotune') tf.app.flags.DEFINE_boolean('use_processed_data', False, 'whether to use processed data') tf.app.flags.DEFINE_string('processed_data', './processed_dataset/', 'where to save preprocessed datasets')

tensorflow.app.flags.DEFINE_float

import tensorflow as tf # This is the node that will produce the output. output_nodes = tf.get_default_graph().get_operation_by_name('main_level/agent/main/online/' + \ 'network_1/ppo_head_0/policy') # Save the model as a servable model. tf.saved_model.simple_save(session=sess, export_dir='model', inputs={"observation": input_nodes}, outputs={"policy": output_nodes.outputs[0]})

tensorflow.saved_model.simple_save

import tensorflow as tf self.hparams.block_dim ], initializer=tf.initializers.variance_scaling(distribution="uniform"))

tensorflow.initializers.variance_scaling

from tensorflow.python.framework import ops self._saved_model_loader_value = None self._loaded_saved_model_graph = None # TODO(b/160294509): Use tf.compat.v1 when we stop supporting TF 1.15. if ops.executing_eagerly_outside_functions(): _check_tensorflow_version() # The model must be tracked by assigning to an attribute of the Keras

tensorflow.python.framework.ops.executing_eagerly_outside_functions

from tensorflow.contrib.learn.python.learn.estimators import tensor_signature predictions: `Tensor` or `dict` of `Tensor` objects. """ targets = tensor_signature.create_placeholders_from_signatures( self._targets_info)

tensorflow.contrib.learn.python.learn.estimators.tensor_signature.create_placeholders_from_signatures

import tensorflow as tf tf.less_equal(x0, max_x) & tf.greater_equal(x0, 0)) x1_valid = tf.to_float( tf.less_equal(x1, max_x) & tf.greater_equal(x1, 0)) y0_valid = tf.to_float( tf.less_equal(y0, max_y) & tf.greater_equal(y0, 0)) y1_valid = tf.to_float( tf.less_equal(y1, max_y) & tf.greater_equal(y1, 0)) z0_valid = tf.to_float( tf.less_equal(z0, max_z) & tf.greater_equal(z0, 0)) z1_valid = tf.to_float( tf.less_equal(z1, max_z) & tf.greater_equal(z1, 0)) w_z0_y0_x0 = tf.expand_dims(((x1_f - x) * (y1_f - y) * (z1_f - z) * x1_valid * y1_valid * z1_valid), 1) w_z0_y0_x1 = tf.expand_dims(((x - x0_f) * (y1_f - y) * (z1_f - z) * x0_valid * y1_valid * z1_valid), 1) w_z0_y1_x0 = tf.expand_dims(((x1_f - x) * (y - y0_f) * (z1_f - z) * x1_valid * y0_valid * z1_valid),

tensorflow.less_equal

from tensorflow.python.ops import array_ops parameters.pop("self") with ops.name_scope(name, values=[alpha, beta]) as ns: with ops.control_dependencies([ check_ops.assert_positive(alpha), check_ops.assert_positive(beta), ] if validate_args else []): self._alpha = array_ops.identity(alpha, name="alpha") self._beta = array_ops.identity(beta, name="beta") super(InverseGamma, self).__init__( dtype=self._alpha.dtype, validate_args=validate_args, allow_nan_stats=allow_nan_stats, is_continuous=True, is_reparameterized=False,

tensorflow.python.ops.array_ops.identity

import tensorflow as tf def get_assignment_map_from_checkpoint(tvars, init_checkpoint, num_of_group=0): """Compute the union of the current variables and checkpoint variables.""" assignment_map = {} initialized_variable_names = {} name_to_variable = collections.OrderedDict() for var in tvars: name = var.name m = re.match("^(.*):\\d+$", name) if m is not None: name = m.group(1) name_to_variable[name] = var init_vars = tf.train.list_variables(init_checkpoint) init_vars_name = [name for (name, _) in init_vars] if num_of_group > 0: assignment_map = [] for gid in range(num_of_group): assignment_map.append(collections.OrderedDict()) else: assignment_map = collections.OrderedDict() for name in name_to_variable: if name in init_vars_name: tvar_name = name elif (re.sub(r"/group_\d+/", "/group_0/",

tensorflow.train.list_variables

from tensorflow.python.ops import math_ops Args: required_size: number or tf.Tensor specifying required array capacity. growth_factor: optional number or tf.Tensor specifying the growth factor between subsequent allocations. Returns: tf.Tensor with dtype=int32 giving the next array size. """ exponent = math_ops.ceil( math_ops.log(math_ops.cast(required_size, dtypes.float32)) / math_ops.log(math_ops.cast(growth_factor, dtypes.float32))) return math_ops.cast(math_ops.ceil(growth_factor ** exponent), dtypes.int32) def streaming_concat(values, axis=0, max_size=None, metrics_collections=None, updates_collections=None, name=None): """Concatenate values along an axis across batches. The function `streaming_concat` creates two local variables, `array` and

tensorflow.python.ops.math_ops.ceil