def dense_make_stats_update(is_active, are_buckets_ready, float_column, quantile_buckets, example_partition_ids, gradients, hessians, weights, empty_gradients, empty_hessians): """Updates the state for dense split handler.""" empty_float = constant_op.constant_v1([], dtype=dtypes.float32) quantile_values, quantile_weights = control_flow_ops.cond( is_active[1], # For the next layer, this handler is inactive. lambda: (float_column, weights), lambda: (empty_float, empty_float)) def ready_inputs_fn(): """Branch to execute when quantiles are ready.""" quantized_feature = quantile_ops.quantiles([float_column], [], [quantile_buckets], [], []) quantized_feature = math_ops.cast(quantized_feature[0], dtypes.int64) quantized_feature = array_ops.squeeze(quantized_feature, axis=0) return (example_partition_ids, quantized_feature, gradients, hessians) def not_ready_inputs_fn(): return (constant_op.constant_v1([], dtype=dtypes.int32), constant_op.constant_v1([[]], dtype=dtypes.int64, shape=[1, 2]), empty_gradients, empty_hessians) example_partition_ids, feature_ids, gradients, hessians = ( control_flow_ops.cond( math_ops.logical_and( math_ops.logical_and(are_buckets_ready, array_ops.size(quantile_buckets) > 0), is_active[0]), ready_inputs_fn, not_ready_inputs_fn)) return (quantile_values, quantile_weights, example_partition_ids, feature_ids, gradients, hessians)
def is_initialized(self, name=None): # We have to cast the self._index.values() to a `list` because when we # use `model_to_estimator` to run tf.keras models, self._index.values() is # of type `dict_values` and not `list`. values_list = list(self._index.values()) result = values_list[0].is_initialized() # We iterate through the list of values except the last one to allow us to # name the final `logical_and` op the same name that is passed by the user # to the `is_initialized` op. For tower local variables, the # `is_initialized` op is a `logical_and` op. for v in values_list[1:-1]: result = math_ops.logical_and(result, v.is_initialized()) result = math_ops.logical_and(result, values_list[-1].is_initialized(), name=name) return result
def maybe_update_masks(): with ops.name_scope(self._spec.name): is_step_within_pruning_range = math_ops.logical_and( math_ops.greater_equal(self._global_step, self._spec.begin_pruning_step), # If end_pruning_step is negative, keep pruning forever! math_ops.logical_or( math_ops.less_equal(self._global_step, self._spec.end_pruning_step), math_ops.less(self._spec.end_pruning_step, 0))) is_pruning_step = math_ops.less_equal( math_ops.add(self._last_update_step, self._spec.pruning_frequency), self._global_step) return math_ops.logical_and(is_step_within_pruning_range, is_pruning_step)
def _filter_input(input_tensor, vocab_freq_table, vocab_min_count, vocab_subsampling, corpus_size, seed): """Filters input tensor based on vocab freq, threshold, and subsampling.""" if vocab_freq_table is None: return input_tensor if not isinstance(vocab_freq_table, lookup.InitializableLookupTableBase): raise ValueError( "vocab_freq_table must be a subclass of " "InitializableLookupTableBase (such as HashTable) instead of type " "{}.".format(type(vocab_freq_table))) with ops.name_scope( "filter_vocab", values=[vocab_freq_table, input_tensor, vocab_min_count]): freq = vocab_freq_table.lookup(input_tensor) # Filters out elements in input_tensor that are not found in # vocab_freq_table (table returns a default value of -1 specified above when # an element is not found). mask = math_ops.not_equal(freq, vocab_freq_table.default_value) # Filters out elements whose vocab frequencies are less than the threshold. if vocab_min_count is not None: cast_threshold = math_ops.cast(vocab_min_count, freq.dtype) mask = math_ops.logical_and(mask, math_ops.greater_equal(freq, cast_threshold)) input_tensor = array_ops.boolean_mask(input_tensor, mask) freq = array_ops.boolean_mask(freq, mask) if not vocab_subsampling: return input_tensor if vocab_subsampling < 0 or vocab_subsampling > 1: raise ValueError( "Invalid vocab_subsampling={} - it should be within range [0, 1].". format(vocab_subsampling)) # Subsamples the input tokens based on vocabulary frequency and # vocab_subsampling threshold (ie randomly discard commonly appearing # tokens). with ops.name_scope( "subsample_vocab", values=[input_tensor, freq, vocab_subsampling]): corpus_size = math_ops.cast(corpus_size, dtypes.float64) freq = math_ops.cast(freq, dtypes.float64) vocab_subsampling = math_ops.cast(vocab_subsampling, dtypes.float64) # From tensorflow_models/tutorials/embedding/word2vec_kernels.cc, which is # suppose to correlate with Eq. 5 in http://arxiv.org/abs/1310.4546. keep_prob = ((math_ops.sqrt(freq / (vocab_subsampling * corpus_size)) + 1.0) * (vocab_subsampling * corpus_size / freq)) random_prob = random_ops.random_uniform( array_ops.shape(freq), minval=0, maxval=1, dtype=dtypes.float64, seed=seed) mask = math_ops.less_equal(random_prob, keep_prob) return array_ops.boolean_mask(input_tensor, mask)
def mode(self, name="mode"): """Mode of the distribution. Note that the mode for the Beta distribution is only defined when `a > 1`, `b > 1`. This returns the mode when `a > 1` and `b > 1`, and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. Args: name: The name for this op. Returns: Mode of the Beta distribution. """ with ops.name_scope(self.name): with ops.op_scope([self._a, self._b, self._a_b_sum], name): a = self._a b = self._b a_b_sum = self._a_b_sum one = constant_op.constant(1, self.dtype) mode = (a - 1)/ (a_b_sum - 2) if self.allow_nan_stats: return math_ops.select( math_ops.logical_and( math_ops.greater(a, 1), math_ops.greater(b, 1)), mode, (constant_op.constant(float("NaN"), dtype=self.dtype) * array_ops.ones_like(a_b_sum, dtype=self.dtype))) else: return control_flow_ops.with_dependencies([ check_ops.assert_less(one, a), check_ops.assert_less(one, b)], mode)
def _mode(self): mode = (self.a - 1.0) / (self.a_b_sum - 2.0) if self.allow_nan_stats: nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype()) return math_ops.select( math_ops.logical_and(math_ops.greater(self.a, 1.0), math_ops.greater(self.b, 1.0)), mode, array_ops.fill(self.batch_shape(), nan, name="nan"), ) else: return control_flow_ops.with_dependencies( [ check_ops.assert_less( array_ops.ones((), dtype=self.dtype), self.a, message="Mode not defined for components of a <= 1.", ), check_ops.assert_less( array_ops.ones((), dtype=self.dtype), self.b, message="Mode not defined for components of b <= 1.", ), ], mode, )
def get_seed(seed): """Returns the local seeds an operation should use given an op-specific seed. See `tf.compat.v1.get_seed` for more details. This wrapper adds support for the case where `seed` may be a tensor. Args: seed: An integer or a `tf.int64` scalar tensor. Returns: A tuple of two `tf.int64` scalar tensors that should be used for the local seed of the calling dataset. """ seed, seed2 = random_seed.get_seed(seed) if seed is None: seed = constant_op.constant(0, dtype=dtypes.int64, name="seed") else: seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed") if seed2 is None: seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2") else: with ops.name_scope("seed2") as scope: seed2 = ops.convert_to_tensor(seed2, dtype=dtypes.int64) seed2 = array_ops.where( math_ops.logical_and( math_ops.equal(seed, 0), math_ops.equal(seed2, 0)), constant_op.constant(2**31 - 1, dtype=dtypes.int64), seed2, name=scope) return seed, seed2
def body(time, outputs_ta, state, inputs, finished, sequence_lengths): """Internal while_loop body. Args: time: scalar int32 tensor. outputs_ta: structure of TensorArray. state: (structure of) state tensors and TensorArrays. inputs: (structure of) input tensors. finished: bool tensor (keeping track of what's finished). sequence_lengths: int32 tensor (keeping track of time of finish). Returns: `(time + 1, outputs_ta, next_state, next_inputs, next_finished, next_sequence_lengths)`. ``` """ (next_outputs, decoder_state, next_inputs, decoder_finished) = decoder.step(time, inputs, state) next_finished = math_ops.logical_or(decoder_finished, finished) if maximum_iterations is not None: next_finished = math_ops.logical_or( next_finished, time + 1 >= maximum_iterations) next_sequence_lengths = array_ops.where( math_ops.logical_and(math_ops.logical_not(finished), next_finished), array_ops.fill(array_ops.shape(sequence_lengths), time + 1), sequence_lengths) nest.assert_same_structure(state, decoder_state) nest.assert_same_structure(outputs_ta, next_outputs) nest.assert_same_structure(inputs, next_inputs) # Zero out output values past finish if impute_finished: emit = nest.map_structure( lambda out, zero: array_ops.where(finished, zero, out), next_outputs, zero_outputs) else: emit = next_outputs # Copy through states past finish def _maybe_copy_state(new, cur): # TensorArrays and scalar states get passed through. if isinstance(cur, tensor_array_ops.TensorArray): pass_through = True else: new.set_shape(cur.shape) pass_through = (new.shape.ndims == 0) return new if pass_through else array_ops.where(finished, cur, new) if impute_finished: next_state = nest.map_structure( _maybe_copy_state, decoder_state, state) else: next_state = decoder_state outputs_ta = nest.map_structure(lambda ta, out: ta.write(time, out), outputs_ta, emit) return (time + 1, outputs_ta, next_state, next_inputs, next_finished, next_sequence_lengths)
def collapse_repeated(labels, seq_length, name=None): """Merge repeated labels into single labels. Args: labels: Tensor of shape [batch, max value in seq_length] seq_length: Tensor of shape [batch], sequence length of each batch element. name: A name for this `Op`. Defaults to "collapse_repeated_labels". Returns: A tuple `(collapsed_labels, new_seq_length)` where collapsed_labels: Tensor of shape [batch, max_seq_length] with repeated labels collapsed and padded to max_seq_length, eg: `[[A, A, B, B, A], [A, B, C, D, E]] => [[A, B, A, 0, 0], [A, B, C, D, E]]` new_seq_length: int tensor of shape [batch] with new sequence lengths. """ with ops.name_scope(name, "collapse_repeated_labels", [labels, seq_length]): labels = ops.convert_to_tensor(labels, name="labels") seq_length = ops.convert_to_tensor(seq_length, name="seq_length") # Mask labels that don't equal previous label. label_mask = array_ops.concat([ array_ops.ones_like(labels[:, :1], dtypes.bool), math_ops.not_equal(labels[:, 1:], labels[:, :-1]) ], axis=1) # Filter labels that aren't in the original sequence. maxlen = _get_dim(labels, 1) seq_mask = array_ops.sequence_mask(seq_length, maxlen=maxlen) label_mask = math_ops.logical_and(label_mask, seq_mask) # Count masks for new sequence lengths. new_seq_len = math_ops.reduce_sum( math_ops.cast(label_mask, dtypes.int32), axis=1) # Mask indexes based on sequence length mask. new_maxlen = math_ops.reduce_max(new_seq_len) idx_mask = array_ops.sequence_mask(new_seq_len, maxlen=new_maxlen) # Flatten everything and mask out labels to keep and sparse indices. flat_labels = array_ops.reshape(labels, [-1]) flat_label_mask = array_ops.reshape(label_mask, [-1]) flat_idx_mask = array_ops.reshape(idx_mask, [-1]) idx = math_ops.range(_get_dim(flat_idx_mask, 0)) # Scatter to flat shape. flat = array_ops.scatter_nd( indices=array_ops.expand_dims( array_ops.boolean_mask(idx, flat_idx_mask), axis=1), updates=array_ops.boolean_mask(flat_labels, flat_label_mask), shape=array_ops.shape(flat_idx_mask)) # Reshape back to square batch. batch_size = _get_dim(labels, 0) new_shape = [batch_size, new_maxlen] return (array_ops.reshape(flat, new_shape), math_ops.cast(new_seq_len, seq_length.dtype))
def _prune_invalid_ids(sparse_ids, sparse_weights): """Prune invalid IDs (< 0) from the input ids and weights.""" is_id_valid = math_ops.greater_equal(sparse_ids.values, 0) if sparse_weights is not None: is_id_valid = math_ops.logical_and(is_id_valid, math_ops.greater(sparse_weights.values, 0)) sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid) if sparse_weights is not None: sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid) return sparse_ids, sparse_weights
def _logical_and(*args): """Convenience function which attempts to statically `reduce_all`.""" args_ = [_static_value(x) for x in args] if any(x is not None and not bool(x) for x in args_): return constant_op.constant(False) if all(x is not None and bool(x) for x in args_): return constant_op.constant(True) if len(args) == 2: return math_ops.logical_and(*args) return math_ops.reduce_all(args)
def undo_make_batch_of_event_sample_matrices( self, x, sample_shape, expand_batch_dim=True, name="undo_make_batch_of_event_sample_matrices"): """Reshapes/transposes `Distribution` `Tensor` from B_+E_+S_ to S+B+E. Where: - `B_ = B if B or not expand_batch_dim else [1]`, - `E_ = E if E else [1]`, - `S_ = [tf.reduce_prod(S)]`. This function "reverses" `make_batch_of_event_sample_matrices`. Args: x: `Tensor` of shape `B_+E_+S_`. sample_shape: `Tensor` (1D, `int32`). expand_batch_dim: Python `bool`. If `True` the batch dims will be expanded such that `batch_ndims>=1`. name: Python `str`. The name to give this op. Returns: x: `Tensor`. Input transposed/reshaped to `S+B+E`. """ with self._name_scope(name, values=[x, sample_shape]): x = ops.convert_to_tensor(x, name="x") # x.shape: _B+_E+[prod(S)] sample_shape = ops.convert_to_tensor(sample_shape, name="sample_shape") x = distribution_util.rotate_transpose(x, shift=1) # x.shape: [prod(S)]+_B+_E if self._is_all_constant_helper(self.batch_ndims, self.event_ndims): if self._batch_ndims_is_0 or self._event_ndims_is_0: squeeze_dims = [] if self._event_ndims_is_0: squeeze_dims += [-1] if self._batch_ndims_is_0 and expand_batch_dim: squeeze_dims += [1] if squeeze_dims: x = array_ops.squeeze(x, axis=squeeze_dims) # x.shape: [prod(S)]+B+E _, batch_shape, event_shape = self.get_shape(x) else: s = (x.get_shape().as_list() if x.get_shape().is_fully_defined() else array_ops.shape(x)) batch_shape = s[1:1+self.batch_ndims] # Since sample_dims=1 and is left-most, we add 1 to the number of # batch_ndims to get the event start dim. event_start = array_ops.where( math_ops.logical_and(expand_batch_dim, self._batch_ndims_is_0), 2, 1 + self.batch_ndims) event_shape = s[event_start:event_start+self.event_ndims] new_shape = array_ops.concat([sample_shape, batch_shape, event_shape], 0) x = array_ops.reshape(x, shape=new_shape) # x.shape: S+B+E return x
def wrapped_cond(loop_counter, *args): # Convert the flow variables in `args` to TensorArrays. `args` should # already have the same structure as `orig_loop_vars` but currently there # is no nest.zip so we call `_pack_sequence_as` which flattens both # `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays # and packs it into the structure of `orig_loop_vars`. if maximum_iterations is None: return cond(*_pack_sequence_as(orig_loop_vars, args)) else: return math_ops.logical_and( loop_counter < maximum_iterations, cond(*_pack_sequence_as(orig_loop_vars, args)))
def element_to_bucket_id(*args): """Return int64 id of the length bucket for this element.""" seq_length = element_length_func(*args) boundaries = list(bucket_boundaries) buckets_min = [np.iinfo(np.int32).min] + boundaries buckets_max = boundaries + [np.iinfo(np.int32).max] conditions_c = math_ops.logical_and( math_ops.less_equal(buckets_min, seq_length), math_ops.less(seq_length, buckets_max)) bucket_id = math_ops.reduce_min(array_ops.where(conditions_c)) return bucket_id
def _get_training_value(self, training=None): if training is None: training = K.learning_phase() if self._USE_V2_BEHAVIOR: if isinstance(training, int): training = bool(training) if base_layer_utils.is_in_keras_graph(): training = math_ops.logical_and(training, self._get_trainable_var()) elif not self.trainable: # When the layer is not trainable, it overrides the value passed from # model. training = self.trainable return training
def element_to_bucket_id(*args): """Return int64 id of the length bucket for this element.""" seq_length = element_length_func(*args) boundaries = list(bucket_boundaries) buckets_min = [np.iinfo(np.int32).min] + boundaries buckets_max = boundaries + [np.iinfo(np.int32).max] conditions_c = math_ops.logical_and( math_ops.less_equal(buckets_min, seq_length), math_ops.less(seq_length, buckets_max)) bucket_id = math_ops.reduce_min(array_ops.where(conditions_c)) return bucket_id
def fn_with_cond(*inner_args, **inner_kwds): """Conditionally runs initialization if it's needed.""" condition = True for wr in self._created_variables: variable = wr() if variable is None: raise ValueError( "A tf.Variable created inside your tf.function has been" " garbage-collected. Your code needs to keep Python references" " to variables created inside `tf.function`s.\n" "\n" "A common way to raise this error is to create and return a" " variable only referenced inside your function:\n" "\n" "@tf.function\n" "def f():\n" " v = tf.Variable(1.0)\n" " return v\n" "\n" "v = f() # Crashes with this error message!\n" "\n" "The reason this crashes is that @tf.function annotated" " function returns a **`tf.Tensor`** with the **value** of the" " variable when the function is called rather than the" " variable instance itself. As such there is no code holding a" " reference to the `v` created inside the function and Python" " garbage collects it.\n" "\n" "The simplest way to fix this issue is to create variables" " outside the function and capture them:\n" "\n" "v = tf.Variable(1.0)\n" "\n" "@tf.function\n" "def f():\n" " return v\n" "\n" "f() # <tf.Tensor: ... numpy=1.>\n" "v.assign_add(1.)\n" "f() # <tf.Tensor: ... numpy=2.>") condition = math_ops.logical_and( condition, resource_variable_ops.var_is_initialized_op( variable.handle)) # We want to call stateless_fn if possible because it avoids recomputing # potentially expensive initializers. return control_flow_ops.cond( condition, lambda: self._stateless_fn(*inner_args, **inner_kwds), _call_concrete(self._concrete_stateful_fn, inner_args, inner_kwds))
def body(time, outputs_ta, state, inputs, history_masking, hit_ta, finished, sequence_lengths): (next_outputs, decoder_state, next_inputs, next_history_masking, next_hit, decoder_finished) = \ decoder.step(time, inputs, state, history_masking) if decoder.tracks_own_finished: next_finished = decoder_finished else: next_finished = math_ops.logical_or(decoder_finished, finished) if maximum_iterations is not None: next_finished = math_ops.logical_or(next_finished, time + 1 >= maximum_iterations) next_sequence_lengths = array_ops.where(math_ops.logical_and(math_ops.logical_not(finished), next_finished), array_ops.fill(array_ops.shape(sequence_lengths), time + 1), sequence_lengths) nest.assert_same_structure(state, decoder_state) nest.assert_same_structure(outputs_ta, next_outputs) nest.assert_same_structure(inputs, next_inputs) nest.assert_same_structure(history_masking, next_history_masking) nest.assert_same_structure(hit_ta, next_hit) if impute_finished: emit = nest.map_structure(lambda out, zero: array_ops.where(finished, zero, out), next_outputs, zero_outputs) else: emit = next_outputs def _maybe_copy_state(new, cur): if isinstance(cur, tensor_array_ops.TensorArray): pass_through = True else: new.set_shape(cur.shape) pass_through = (new.shape.ndims == 0) return new if pass_through else array_ops.where(finished, cur, new) if impute_finished: next_state = nest.map_structure( _maybe_copy_state, decoder_state, state) else: next_state = decoder_state outputs_ta = nest.map_structure(lambda ta, out: ta.write(time, out), outputs_ta, emit) hit_ta = nest.map_structure(lambda ta, out: ta.write(time, out), hit_ta, next_hit) return (time + 1, outputs_ta, next_state, next_inputs, next_history_masking, hit_ta, next_finished, next_sequence_lengths)
def is_initialized(self, name=None): if context.executing_eagerly(): result = self._distributed_variables[0].is_initialized() for v in self._distributed_variables[1:-1]: result = math_ops.logical_and(result, v.is_initialized()) result = math_ops.logical_and( result, self._distributed_variables[-1].is_initialized(), name=name) else: with ops.device(self._devices[0]): result = super(PackedDistributedVariable, self).is_initialized(name) for d in self._devices[1:-1]: with ops.device(d): initialized = super(PackedDistributedVariable, self).is_initialized(name) result = math_ops.logical_and(result, initialized) with ops.device(self._devices[-1]): initialized = super(PackedDistributedVariable, self).is_initialized(name) result = math_ops.logical_and(result, initialized, name=name) return result
def fn_with_cond(*inner_args, **inner_kwds): """Conditionally runs initialization if it's needed.""" condition = True for wr in self._created_variables: variable = wr() if variable is None: raise ValueError( "A tf.Variable created inside your tf.function has been" " garbage-collected. Your code needs to keep Python references" " to variables created inside `tf.function`s.\n" "\n" "A common way to raise this error is to create and return a" " variable only referenced inside your function:\n" "\n" "@tf.function\n" "def f():\n" " v = tf.Variable(1.0)\n" " return v\n" "\n" "v = f() # Crashes with this error message!\n" "\n" "The reason this crashes is that @tf.function annotated" " function returns a **`tf.Tensor`** with the **value** of the" " variable when the function is called rather than the" " variable instance itself. As such there is no code holding a" " reference to the `v` created inside the function and Python" " garbage collects it.\n" "\n" "The simplest way to fix this issue is to create variables" " outside the function and capture them:\n" "\n" "v = tf.Variable(1.0)\n" "\n" "@tf.function\n" "def f():\n" " return v\n" "\n" "f() # <tf.Tensor: ... numpy=1.>\n" "v.assign_add(1.)\n" "f() # <tf.Tensor: ... numpy=2.>") condition = math_ops.logical_and( condition, resource_variable_ops.var_is_initialized_op( variable.handle)) # We want to call stateless_fn if possible because it avoids recomputing # potentially expensive initializers. return control_flow_ops.cond( condition, lambda: self._stateless_fn(*inner_args, **inner_kwds), functools.partial(self._concrete_stateful_fn._filtered_call, # pylint: disable=protected-access inner_args, inner_kwds))
def sparsemax_loss(logits, sparsemax, labels, name=None): """Computes sparsemax loss function [1]. [1]: https://arxiv.org/abs/1602.02068 Args: logits: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. sparsemax: A `Tensor`. Must have the same type as `logits`. labels: A `Tensor`. Must have the same type as `logits`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `logits`. """ with ops.name_scope(name, "sparsemax_loss", [logits, sparsemax, labels]) as name: logits = ops.convert_to_tensor(logits, name="logits") sparsemax = ops.convert_to_tensor(sparsemax, name="sparsemax") labels = ops.convert_to_tensor(labels, name="labels") # In the paper, they call the logits z. # A constant can be substracted from logits to make the algorithm # more numerically stable in theory. However, there are really no major # source numerical instability in this algorithm. z = logits # sum over support # Use a conditional where instead of a multiplication to support z = -inf. # If z = -inf, and there is no support (sparsemax = 0), a multiplication # would cause 0 * -inf = nan, which is not correct in this case. sum_s = array_ops.where( math_ops.logical_or(sparsemax > 0, math_ops.is_nan(sparsemax)), sparsemax * (z - 0.5 * sparsemax), array_ops.zeros_like(sparsemax)) # - z_k + ||q||^2 q_part = labels * (0.5 * labels - z) # Fix the case where labels = 0 and z = -inf, where q_part would # otherwise be 0 * -inf = nan. But since the lables = 0, no cost for # z = -inf should be consideredself. # The code below also coveres the case where z = inf. Howeverm in this # caose the sparsemax will be nan, which means the sum_s will also be nan, # therefor this case doesn't need addtional special treatment. q_part_safe = array_ops.where( math_ops.logical_and(math_ops.equal(labels, 0), math_ops.is_inf(z)), array_ops.zeros_like(z), q_part) return math_ops.reduce_sum(sum_s + q_part_safe, axis=1)
def interpolate_pr_auc(self): """Add option to remove summary.""" dtp = self.true_positives[:self.num_thresholds - 1] - self.true_positives[1:] p = self.true_positives + self.false_positives dp = p[:self.num_thresholds - 1] - p[1:] prec_slope = math_ops.div_no_nan(dtp, math_ops.maximum(dp, 0), name='prec_slope') intercept = self.true_positives[1:] - math_ops.multiply( prec_slope, p[1:]) safe_p_ratio = array_ops.where( math_ops.logical_and(p[:self.num_thresholds - 1] > 0, p[1:] > 0), math_ops.div_no_nan(p[:self.num_thresholds - 1], math_ops.maximum(p[1:], 0), name='recall_relative_ratio'), array_ops.ones_like(p[1:])) pr_auc_increment = math_ops.div_no_nan( prec_slope * (dtp + intercept * math_ops.log(safe_p_ratio)), math_ops.maximum( self.true_positives[1:] + self.false_negatives[1:], 0), name='pr_auc_increment') if self.multi_label: by_label_auc = math_ops.reduce_sum(pr_auc_increment, name=self.name + '_by_label', axis=0) if self._summarize: if self.label_weights is None: # Evenly weighted average of the label AUCs. return math_ops.reduce_mean(by_label_auc, name=self.name) else: # Weighted average of the label AUCs. return math_ops.div_no_nan(math_ops.reduce_sum( math_ops.multiply(by_label_auc, self.label_weights)), math_ops.reduce_sum( self.label_weights), name=self.name) else: return by_label_auc else: if self._summarize: return math_ops.reduce_sum(pr_auc_increment, name='interpolate_pr_auc') else: return pr_auc_increment
def sparsemax_loss(logits, sparsemax, labels, name=None): """Computes sparsemax loss function [1]. [1]: https://arxiv.org/abs/1602.02068 Args: logits: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. sparsemax: A `Tensor`. Must have the same type as `logits`. labels: A `Tensor`. Must have the same type as `logits`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `logits`. """ with ops.name_scope(name, "sparsemax_loss", [logits, sparsemax, labels]) as name: logits = ops.convert_to_tensor(logits, name="logits") sparsemax = ops.convert_to_tensor(sparsemax, name="sparsemax") labels = ops.convert_to_tensor(labels, name="labels") # In the paper, they call the logits z. # A constant can be substracted from logits to make the algorithm # more numerically stable in theory. However, there are really no major # source numerical instability in this algorithm. z = logits # sum over support # Use a conditional where instead of a multiplication to support z = -inf. # If z = -inf, and there is no support (sparsemax = 0), a multiplication # would cause 0 * -inf = nan, which is not correct in this case. sum_s = array_ops.where( math_ops.logical_or(sparsemax > 0, math_ops.is_nan(sparsemax)), sparsemax * (z - 0.5 * sparsemax), array_ops.zeros_like(sparsemax)) # - z_k + ||q||^2 q_part = labels * (0.5 * labels - z) # Fix the case where labels = 0 and z = -inf, where q_part would # otherwise be 0 * -inf = nan. But since the lables = 0, no cost for # z = -inf should be consideredself. # The code below also coveres the case where z = inf. Howeverm in this # caose the sparsemax will be nan, which means the sum_s will also be nan, # therefor this case doesn't need addtional special treatment. q_part_safe = array_ops.where( math_ops.logical_and(math_ops.equal(labels, 0), math_ops.is_inf(z)), array_ops.zeros_like(z), q_part) return math_ops.reduce_sum(sum_s + q_part_safe, axis=1)
def is_initialized(self, name=None): """Identifies if all the component variables are initialized. Args: name: Name of the final `logical_and` op. Returns: The op that evaluates to True or False depending on if all the component variables are initialized. """ # We have to cast the self._index.values() to a `list` because when we # use `model_to_estimator` to run tf.keras models, self._index.values() is # of type `dict_values` and not `list`. values_list = list(self._index.values()) result = values_list[0].is_initialized() # We iterate through the list of values except the last one to allow us to # name the final `logical_and` op the same name that is passed by the user # to the `is_initialized` op. For distributed variables, the # `is_initialized` op is a `logical_and` op. for v in values_list[1:-1]: result = math_ops.logical_and(result, v.is_initialized()) result = math_ops.logical_and(result, values_list[-1].is_initialized(), name=name) return result
def is_initialized(self, name=None): """Identifies if all the component variables are initialized. Args: name: Name of the final `logical_and` op. Returns: The op that evaluates to True or False depending on if all the component variables are initialized. """ # We have to cast the self._index.values() to a `list` because when we # use `model_to_estimator` to run tf.keras models, self._index.values() is # of type `dict_values` and not `list`. values_list = list(self._index.values()) result = values_list[0].is_initialized() # We iterate through the list of values except the last one to allow us to # name the final `logical_and` op the same name that is passed by the user # to the `is_initialized` op. For distributed variables, the # `is_initialized` op is a `logical_and` op. for v in values_list[1:-1]: result = math_ops.logical_and(result, v.is_initialized()) result = math_ops.logical_and(result, values_list[-1].is_initialized(), name=name) return result
def segment_iou(predicts, labels): """ :param predicts: shape [batch_size, h, w, c] :param labels: shape [batch_size, h, w] """ num_classes = predicts.shape[-1] labels = array_ops.one_hot(labels, depth=num_classes) predicts = engine.bool(predicts) labels = engine.bool(labels) inter = engine.float32(math_ops.logical_and(predicts, labels)) union = engine.float32(math_ops.logical_or(predicts, labels)) inter = math_ops.reduce_sum(inter, axis=[0, 1, 2]) union = math_ops.reduce_sum(union, axis=[0, 1, 2]) classes_iou = inter / union mean_iou = math_ops.reduce_mean(classes_iou) return mean_iou, classes_iou
def _process_matrix(self, matrix, min_rank, event_ndims): """Helper to __init__ which gets matrix in batch-ready form.""" # Pad the matrix so that matmul works in the case of a matrix and vector # input. Keep track if the matrix was padded, to distinguish between a # rank 3 tensor and a padded rank 2 tensor. # TODO(srvasude): Remove side-effects from functions. Its currently unbroken # but error-prone since the function call order may change in the future. self._rank_two_event_ndims_one = math_ops.logical_and( math_ops.equal(array_ops.rank(matrix), min_rank), math_ops.equal(event_ndims, 1)) left = array_ops.where(self._rank_two_event_ndims_one, 1, 0) pad = array_ops.concat( [array_ops.ones( [left], dtype=dtypes.int32), array_ops.shape(matrix)], 0) return array_ops.reshape(matrix, pad)
def func(sliced_table, indices, min_idx, max_idx): # Do a serialized embedding lookup by adjusting the indices. adjusted_indices = indices - min_idx x = gen_popops_ops.ipu_multi_slice(sliced_table, adjusted_indices, name=name) # Mask out any outputs which are not in range [min_idx, max_idx). mask_max = indices < max_idx mask_min = indices >= min_idx mask = math_ops.logical_and(mask_max, mask_min) mask = array_ops.expand_dims(mask, 1) return array_ops.where_v2(mask, x, array_ops.constant(0, x.dtype), name=name + "/Mask")
def _process_matrix(self, matrix, min_rank, event_ndims): """Helper to __init__ which gets matrix in batch-ready form.""" # Pad the matrix so that matmul works in the case of a matrix and vector # input. Keep track if the matrix was padded, to distinguish between a # rank 3 tensor and a padded rank 2 tensor. # TODO(srvasude): Remove side-effects from functions. Its currently unbroken # but error-prone since the function call order may change in the future. self._rank_two_event_ndims_one = math_ops.logical_and( math_ops.equal(array_ops.rank(matrix), min_rank), math_ops.equal(event_ndims, 1)) left = array_ops.where(self._rank_two_event_ndims_one, 1, 0) pad = array_ops.concat([ array_ops.ones([left], dtype=dtypes.int32), array_ops.shape(matrix) ], 0) return array_ops.reshape(matrix, pad)
def _is_shape(expected_shape, actual_tensor, actual_shape=None): """Returns whether actual_tensor's shape is expected_shape. Args: expected_shape: Integer list defining the expected shape, or tensor of same. actual_tensor: Tensor to test. actual_shape: Shape of actual_tensor, if we already have it. Returns: New tensor. """ with ops.op_scope([actual_tensor], "is_shape") as scope: is_rank = _is_rank(array_ops.size(expected_shape), actual_tensor) if actual_shape is None: actual_shape = array_ops.shape(actual_tensor, name="actual") shape_equal = _all_equal(ops.convert_to_tensor(expected_shape, name="expected"), actual_shape) return math_ops.logical_and(is_rank, shape_equal, name=scope)
def _UnsortedSegmentMinOrMaxGrad(op, grad): """ Gradient for UnsortedSegmentMin and UnsortedSegmentMax. """ # Get the number of selected (minimum or maximum) elements in each segment. gathered_outputs, zero_clipped_indices, is_positive = \ _GatherDropNegatives(op.outputs[0], op.inputs[1]) is_selected = math_ops.equal(op.inputs[0], gathered_outputs) is_selected = math_ops.logical_and(is_selected, is_positive) num_selected = math_ops.unsorted_segment_sum( math_ops.cast(is_selected, grad.dtype), op.inputs[1], op.inputs[2]) # Compute the gradient for each segment. The gradient for the ith segment is # divided evenly among the selected elements in that segment. weighted_grads = math_ops.div(grad, num_selected) gathered_grads, _, _ = _GatherDropNegatives(weighted_grads, None, zero_clipped_indices, is_positive) zeros = array_ops.zeros_like(gathered_grads) return array_ops.where(is_selected, gathered_grads, zeros), None, None
def wrapped_cond(loop_counter, maximum_iterations_arg, *args): """Extra `cond` wrapper that can handle the extra counter loop_var.""" # Convert the flow variables in `args` to TensorArrays. `args` should # already have the same structure as `orig_loop_vars` but currently there # is no nest.zip so we call `_pack_sequence_as` which flattens both # `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays # and packs it into the structure of `orig_loop_vars`. pred = cond(*_pack_sequence_as(orig_loop_vars, args)) if (tensor_util.is_tensor(pred) and (pred.shape.dims is None or pred.shape.dims)): pred = array_ops.squeeze_v2(pred) if maximum_iterations is None: return pred else: return math_ops.logical_and( loop_counter < maximum_iterations_arg, pred)
def _UnsortedSegmentMinOrMaxGrad(op, grad): """ Gradient for UnsortedSegmentMin and UnsortedSegmentMax. """ # Get the number of selected (minimum or maximum) elements in each segment. gathered_outputs, zero_clipped_indices, is_positive = \ _GatherDropNegatives(op.outputs[0], op.inputs[1]) is_selected = math_ops.equal(op.inputs[0], gathered_outputs) is_selected = math_ops.logical_and(is_selected, is_positive) num_selected = math_ops.unsorted_segment_sum( math_ops.cast(is_selected, grad.dtype), op.inputs[1], op.inputs[2]) # Compute the gradient for each segment. The gradient for the ith segment is # divided evenly among the selected elements in that segment. weighted_grads = math_ops.div(grad, num_selected) gathered_grads, _, _ = _GatherDropNegatives(weighted_grads, None, zero_clipped_indices, is_positive) zeros = array_ops.zeros_like(gathered_grads) return array_ops.where(is_selected, gathered_grads, zeros), None, None
def _is_shape(expected_shape, actual_tensor, actual_shape=None): """Returns whether actual_tensor's shape is expected_shape. Note that -1 in `expected_shape` is recognized as unknown dimension. Args: expected_shape: Integer list defining the expected shape, or tensor of same. actual_tensor: Tensor to test. actual_shape: Shape of actual_tensor, if we already have it. Returns: New tensor. """ with ops.name_scope('is_shape', values=[actual_tensor]) as scope: is_rank = _is_rank(array_ops.size(expected_shape), actual_tensor) if actual_shape is None: actual_shape = array_ops.shape(actual_tensor, name='actual') shape_equal = _shape_tensor_compatible(expected_shape, actual_shape) return math_ops.logical_and(is_rank, shape_equal, name=scope)
def _is_shape(expected_shape, actual_tensor, actual_shape=None): """Returns whether actual_tensor's shape is expected_shape. Note that -1 in `expected_shape` is recognized as unknown dimension. Args: expected_shape: Integer list defining the expected shape, or tensor of same. actual_tensor: Tensor to test. actual_shape: Shape of actual_tensor, if we already have it. Returns: New tensor. """ with ops.name_scope('is_shape', values=[actual_tensor]) as scope: is_rank = _is_rank(array_ops.size(expected_shape), actual_tensor) if actual_shape is None: actual_shape = array_ops.shape(actual_tensor, name='actual') shape_equal = _shape_tensor_compatible(expected_shape, actual_shape) return math_ops.logical_and(is_rank, shape_equal, name=scope)
def _tf_dataset_len(s): l = cardinality.cardinality(s) msg = gen_string_ops.string_join([ 'len requires dataset with definitive cardinality, got ', gen_string_ops.as_string(l) ]) # TODO (yongtang): UNKNOWN is treated as an error. # In case there are more UNKNOWN cases for dataset, we could # use dataset.reduce() to find out the length (in an expensive way). with ops.control_dependencies([ control_flow_ops.Assert( math_ops.logical_and( math_ops.not_equal(l, cardinality.INFINITE), math_ops.not_equal(l, cardinality.UNKNOWN)), [msg]) ]): l = array_ops.identity(l) return l
def predicting_decode(i, input_data, hidden_state, predicted_ids, parent_ids, sequence_lengths, finished, scores): outputs, next_state, next_inputs, decoder_finished = beam_search_decoder.step( i, input_data, hidden_state ) next_finished = math_ops.logical_or(decoder_finished, finished) next_finished = math_ops.logical_or( next_finished, i + 1 >= num_decoder_output) next_sequence_lengths = array_ops.where( math_ops.logical_and(math_ops.logical_not(finished), next_finished), array_ops.fill(array_ops.shape(sequence_lengths), i + 1), sequence_lengths) states = get_final_state(next_state) predicted_ids = predicted_ids.write(i, outputs.predicted_ids) parent_ids = parent_ids.write(i, outputs.parent_ids) scores = scores.write(i, outputs.scores) return i + 1, next_inputs, states, predicted_ids, parent_ids, \ next_sequence_lengths, next_finished, scores
def fn_with_cond(*inner_args, **inner_kwds): """Conditionally runs initialization if it's needed.""" condition = True for wr in self._created_variables: variable = wr() if variable is None: raise ValueError( "Variable created in a tf.function garbage-collected. Code needs" " to keep python references to variables created in a" " tf.function.") condition = math_ops.logical_and( condition, resource_variable_ops.var_is_initialized_op( variable.handle)) # We want to call stateless_fn if possible because it avoids recomputing # potentially expensive initializers. return control_flow_ops.cond( condition, lambda: self._stateless_fn(*inner_args, **inner_kwds), _call_concrete(self._concrete_stateful_fn, inner_args, inner_kwds))
def fn_with_cond(*inner_args, **inner_kwds): """Conditionally runs initialization if it's needed.""" condition = True for wr in self._created_variables: variable = wr() if variable is None: raise ValueError( "Variable created in a tf.function garbage-collected. Code needs" " to keep python references to variables created in a" " tf.function.") condition = math_ops.logical_and( condition, resource_variable_ops.var_is_initialized_op( variable.handle)) # We want to call stateless_fn if possible because it avoids recomputing # potentially expensive initializers. return control_flow_ops.cond( condition, lambda: self._stateless_fn(*inner_args, **inner_kwds), _call_concrete(self._concrete_stateful_fn, inner_args, inner_kwds))
def _mode(self): mode = (self.a - 1.)/ (self.a_b_sum - 2.) if self.allow_nan_stats: nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype()) return array_ops.where( math_ops.logical_and( math_ops.greater(self.a, 1.), math_ops.greater(self.b, 1.)), mode, array_ops.fill(self.batch_shape(), nan, name="nan")) else: return control_flow_ops.with_dependencies([ check_ops.assert_less( array_ops.ones((), dtype=self.dtype), self.a, message="Mode not defined for components of a <= 1."), check_ops.assert_less( array_ops.ones((), dtype=self.dtype), self.b, message="Mode not defined for components of b <= 1."), ], mode)
def t_power(logu, t, self_normalized=False, name=None): """The T-Power Csiszar-function in log-space. A Csiszar-function is a member of, ```none F = { f:R_+ to R : f convex }. ``` When `self_normalized = True` the T-Power Csiszar-function is: ```none f(u) = s [ u**t - 1 - t(u - 1) ] s = { -1 0 < t < 1 { +1 otherwise ``` When `self_normalized = False` the `- t(u - 1)` term is omitted. This is similar to the `amari_alpha` Csiszar-function, with the associated divergence being the same up to factors depending only on `t`. Args: logu: `float`-like `Tensor` representing `log(u)` from above. t: `Tensor` of same `dtype` as `logu` and broadcastable shape. self_normalized: Python `bool` indicating whether `f'(u=1)=0`. name: Python `str` name prefixed to Ops created by this function. Returns: t_power_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at `u = exp(logu)`. """ with ops.name_scope(name, "t_power", [logu, t]): logu = ops.convert_to_tensor(logu, name="logu") t = ops.convert_to_tensor(t, dtype=logu.dtype.base_dtype, name="t") fu = math_ops.expm1(t * logu) if self_normalized: fu -= t * math_ops.expm1(logu) fu *= array_ops.where(math_ops.logical_and(0. < t, t < 1.), -array_ops.ones_like(t), array_ops.ones_like(t)) return fu
def ordered_pair_accuracy(labels, predictions, weights=None, name=None): """Computes the percentage of correctedly ordered pair. For any pair of examples, we compare their orders determined by `labels` and `predictions`. They are correctly ordered if the two orders are compatible. That is, labels l_i > l_j and predictions s_i > s_j and the weight for this pair is the weight from the l_i. Args: labels: A `Tensor` of the same shape as `predictions`. predictions: A `Tensor` with shape [batch_size, list_size]. Each value is the ranking score of the corresponding example. weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The former case is per-example and the latter case is per-list. name: A string used as the name for this metric. Returns: A metric for the accuracy or ordered pairs. """ with ops.name_scope(name, 'ordered_pair_accuracy', (labels, predictions, weights)): clean_labels, predictions, weights, _ = _prepare_and_validate_params( labels, predictions, weights) label_valid = math_ops.equal(clean_labels, labels) valid_pair = math_ops.logical_and( array_ops.expand_dims(label_valid, 2), array_ops.expand_dims(label_valid, 1)) pair_label_diff = array_ops.expand_dims( clean_labels, 2) - array_ops.expand_dims(clean_labels, 1) pair_pred_diff = array_ops.expand_dims( predictions, 2) - array_ops.expand_dims(predictions, 1) # Correct pairs are represented twice in the above pair difference tensors. # We only take one copy for each pair. # correct_pairs = math_ops.to_float(pair_label_diff > 0) * math_ops.to_float(pair_pred_diff > 0) correct_pairs = tf.cast(pair_label_diff > 0, tf.float32) * tf.cast( pair_pred_diff > 0, tf.float32) # pair_weights = math_ops.to_float(pair_label_diff > 0) * array_ops.expand_dims( # weights, 2) * math_ops.to_float(valid_pair) pair_weights = tf.cast( pair_label_diff > 0, tf.float32) * array_ops.expand_dims( weights, 2) * tf.cast(valid_pair, tf.float32) return math_ops.reduce_mean(correct_pairs * pair_weights)
def mode(self, name="mode"): """Mode of the distribution. Note that the mode for the Beta distribution is only defined when `a > 1`, `b > 1`. This returns the mode when `a > 1` and `b > 1`, and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. Args: name: The name for this op. Returns: Mode of the Beta distribution. """ with ops.name_scope(self.name): with ops.name_scope(name, values=[self._a, self._b, self._a_b_sum]): a = self._a b = self._b a_b_sum = self._a_b_sum one = constant_op.constant(1, self.dtype) mode = (a - 1) / (a_b_sum - 2) if self.allow_nan_stats: return math_ops.select( math_ops.logical_and(math_ops.greater(a, 1), math_ops.greater(b, 1)), mode, (constant_op.constant(float("NaN"), dtype=self.dtype) * array_ops.ones_like(a_b_sum, dtype=self.dtype))) else: return control_flow_ops.with_dependencies([ check_ops.assert_less( one, a, message="mode not defined for components of a <= 1" ), check_ops.assert_less( one, b, message="mode not defined for components of b <= 1" ) ], mode)
def _should_record_summaries_internal(default_state): """Returns boolean Tensor if summaries should/shouldn't be recorded. Now the summary condition is decided by logical "and" of two conditions: ctx.summary_recording and ctx.summary_recording_distribution_strategy. The former one is usually set by user, and the latter one is controlled by DistributionStrategy (tf.distribute.ReplicaContext). Args: default_state: can be True or False. The default summary behavior when user does not specify ctx.summary_recording and ctx.summary_recording_distribution_strategy is True. """ ctx = context.context() resolve = lambda x: x() if callable(x) else x cond_distributed = resolve(ctx.summary_recording_distribution_strategy) cond = resolve(ctx.summary_recording) if cond is None: cond = default_state return math_ops.logical_and(cond_distributed, cond)
def _mode(self): mode = (self.concentration1 - 1.) / (self.total_concentration - 2.) if self.allow_nan_stats: nan = array_ops.fill(self.batch_shape_tensor(), np.array(np.nan, dtype=self.dtype.as_numpy_dtype()), name="nan") is_defined = math_ops.logical_and(self.concentration1 > 1., self.concentration0 > 1.) return array_ops.where(is_defined, mode, nan) return control_flow_ops.with_dependencies([ check_ops.assert_less( array_ops.ones([], dtype=self.dtype), self.concentration1, message="Mode undefined for concentration1 <= 1."), check_ops.assert_less( array_ops.ones([], dtype=self.dtype), self.concentration0, message="Mode undefined for concentration0 <= 1.") ], mode)
def _should_record_summaries_internal(default_state): """Returns boolean Tensor if summaries should/shouldn't be recorded. Now the summary condition is decided by logical "and" of two conditions: ctx.summary_recording and ctx.summary_recording_distribution_strategy. The former one is usually set by user, and the latter one is controlled by DistributionStrategy (tf.distribute.ReplicaContext). Args: default_state: can be True or False. The default summary behavior when user does not specify ctx.summary_recording and ctx.summary_recording_distribution_strategy is True. """ ctx = context.context() resolve = lambda x: x() if callable(x) else x cond_distributed = resolve(ctx.summary_recording_distribution_strategy) cond = resolve(ctx.summary_recording) if cond is None: cond = default_state return math_ops.logical_and(cond_distributed, cond)
def _mode(self): mode = (self.concentration1 - 1.) / (self.total_concentration - 2.) if self.allow_nan_stats: nan = array_ops.fill( self.batch_shape_tensor(), np.array(np.nan, dtype=self.dtype.as_numpy_dtype()), name="nan") is_defined = math_ops.logical_and(self.concentration1 > 1., self.concentration0 > 1.) return array_ops.where(is_defined, mode, nan) return control_flow_ops.with_dependencies([ check_ops.assert_less( array_ops.ones([], dtype=self.dtype), self.concentration1, message="Mode undefined for concentration1 <= 1."), check_ops.assert_less( array_ops.ones([], dtype=self.dtype), self.concentration0, message="Mode undefined for concentration0 <= 1.") ], mode)
def t_power(logu, t, self_normalized=False, name=None): """The T-Power Csiszar-function in log-space. A Csiszar-function is a member of, ```none F = { f:R_+ to R : f convex }. ``` When `self_normalized = True` the T-Power Csiszar-function is: ```none f(u) = s [ u**t - 1 - t(u - 1) ] s = { -1 0 < t < 1 { +1 otherwise ``` When `self_normalized = False` the `- t(u - 1)` term is omitted. This is similar to the `amari_alpha` Csiszar-function, with the associated divergence being the same up to factors depending only on `t`. Args: logu: `float`-like `Tensor` representing `log(u)` from above. t: `Tensor` of same `dtype` as `logu` and broadcastable shape. self_normalized: Python `bool` indicating whether `f'(u=1)=0`. name: Python `str` name prefixed to Ops created by this function. Returns: t_power_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at `u = exp(logu)`. """ with ops.name_scope(name, "t_power", [logu, t]): logu = ops.convert_to_tensor(logu, name="logu") t = ops.convert_to_tensor(t, dtype=logu.dtype.base_dtype, name="t") fu = math_ops.expm1(t * logu) if self_normalized: fu -= t * math_ops.expm1(logu) fu *= array_ops.where(math_ops.logical_and(0. < t, t < 1.), -array_ops.ones_like(t), array_ops.ones_like(t)) return fu
def _check_batch_beam(t, batch_size, beam_width): """Returns an Assert operation checking that the elements of the stacked TensorArray can be reshaped to [batch_size, beam_size, -1]. At this point, the TensorArray elements have a known rank of at least 1. """ error_message = ( "TensorArray reordering expects elements to be " "reshapable to [batch_size, beam_size, -1] which is " "incompatible with the dynamic shape of %s elements. " "Consider setting reorder_tensor_arrays to False to disable " "TensorArray reordering during the beam search." % (t.name)) rank = t.shape.ndims shape = array_ops.shape(t) if rank == 2: condition = math_ops.equal(shape[1], batch_size * beam_width) else: condition = math_ops.logical_or( math_ops.equal(shape[1], batch_size * beam_width), math_ops.logical_and(math_ops.equal(shape[1], batch_size), math_ops.equal(shape[2], beam_width))) return control_flow_ops.Assert(condition, [error_message])
def _assert_sparse_indices_are_ragged_right(indices): """Checks that the given SparseTensor.indices tensor is ragged-right. Example: `indices = [[0, 0], [0, 1], [2, 0], [3, 1]]` is not ragged right because the entry `[3, 1]` skips a cell. Args: indices: The SparseTensor indices to check. Returns: A list of control dependency op tensors. """ index_prefix = indices[:, :-1] index_suffix = indices[:, -1] # Check whether each index is starting a new row in the innermost dimension # (prefix[i] != prefix[i-1]) or continuing a row (prefix[i] == prefix[i-1]). # (Note: this skips the first index; we will check that separately below.) index_prefix_changed = math_ops.reduce_any( math_ops.not_equal(index_prefix[1:], index_prefix[:-1]), axis=1) # Check two cases: # * For indices that start a new row: index_suffix[i] must be zero. # * For indices that continue a row: index_suffix[i] must be equal to # index_suffix[i-1]+1. index_ok = array_ops.where( index_prefix_changed, math_ops.equal(index_suffix[1:], 0), math_ops.equal(index_suffix[1:], index_suffix[:-1] + 1)) # Also check that the very first index didn't skip any cells. The first # index starts a new row (by definition), so its suffix should be zero. sparse_indices_are_ragged_right = math_ops.logical_and( math_ops.reduce_all(math_ops.equal(index_suffix[:1], 0)), math_ops.reduce_all(index_ok)) message = [ 'SparseTensor is not right-ragged', 'SparseTensor.indices =', indices ] return [control_flow_ops.Assert(sparse_indices_are_ragged_right, message)]
def _assert_sparse_indices_are_ragged_right(indices): """Checks that the given SparseTensor.indices tensor is ragged-right. Example: `indices = [[0, 0], [0, 1], [2, 0], [3, 1]]` is not ragged right because the entry `[3, 1]` skips a cell. Args: indices: The SparseTensor indices to check. Returns: A list of control dependency op tensors. """ index_prefix = indices[:, :-1] index_suffix = indices[:, -1] # Check whether each index is starting a new row in the innermost dimension # (prefix[i] != prefix[i-1]) or continuing a row (prefix[i] == prefix[i-1]). # (Note: this skips the first index; we will check that separately below.) index_prefix_changed = math_ops.reduce_any(math_ops.not_equal( index_prefix[1:], index_prefix[:-1]), axis=1) # Check two cases: # * For indices that start a new row: index_suffix[i] must be zero. # * For indices that continue a row: index_suffix[i] must be equal to # index_suffix[i-1]+1. index_ok = array_ops.where( index_prefix_changed, math_ops.equal(index_suffix[1:], 0), math_ops.equal(index_suffix[1:], index_suffix[:-1] + 1)) # Also check that the very first index didn't skip any cells. The first # index starts a new row (by definition), so its suffix should be zero. sparse_indices_are_ragged_right = math_ops.logical_and( math_ops.reduce_all(math_ops.equal(index_suffix[:1], 0)), math_ops.reduce_all(index_ok)) message = [ 'SparseTensor is not right-ragged', 'SparseTensor.indices =', indices ] return [control_flow_ops.Assert(sparse_indices_are_ragged_right, message)]
def _check_batch_beam(t, batch_size, beam_width): """Returns an Assert operation checking that the elements of the stacked TensorArray can be reshaped to [batch_size, beam_size, -1]. At this point, the TensorArray elements have a known rank of at least 1. """ error_message = ("TensorArray reordering expects elements to be " "reshapable to [batch_size, beam_size, -1] which is " "incompatible with the dynamic shape of %s elements. " "Consider setting reorder_tensor_arrays to False to disable " "TensorArray reordering during the beam search." % (t.name)) rank = t.shape.ndims shape = array_ops.shape(t) if rank == 2: condition = math_ops.equal(shape[1], batch_size * beam_width) else: condition = math_ops.logical_or( math_ops.equal(shape[1], batch_size * beam_width), math_ops.logical_and( math_ops.equal(shape[1], batch_size), math_ops.equal(shape[2], beam_width))) return control_flow_ops.Assert(condition, [error_message])
def _should_record_summaries_internal(default_state): """Returns boolean Tensor if summaries should/shouldn't be recorded. Now the summary condition is decided by logical "and" of below conditions: First, summary writer must be set. Given this constraint is met, ctx.summary_recording and ctx.summary_recording_distribution_strategy. The former one is usually set by user, and the latter one is controlled by DistributionStrategy (tf.distribute.ReplicaContext). Args: default_state: can be True or False. The default summary behavior when summary writer is set and the user does not specify ctx.summary_recording and ctx.summary_recording_distribution_strategy is True. """ if _summary_state.writer is None: return constant_op.constant(False) resolve = lambda x: x() if callable(x) else x cond_distributed = resolve(_summary_state.is_recording_distribution_strategy) cond = resolve(_summary_state.is_recording) if cond is None: cond = default_state return math_ops.logical_and(cond_distributed, cond)
def IterCondition(i, mat_m, _): return math_ops.logical_and( i < iter_count, math_ops.reduce_max(math_ops.abs(mat_m - identity)) > epsilon)
def stopping_criterion(i, state): return math_ops.logical_and(i < max_iter, linalg_ops.norm(state.r) > tol)
def _ConcatGrad(op, grad): """Gradient for concat op.""" def _CreateDenseMaskAndBegin(sizes, concat_dim): """Create variables for iteratively slicing a dense gradients tensor.""" # Since shape is 1-D, shape_of_shape = [rank-of-inputs] shape_of_shape = array_ops.shape(sizes[0]) # Make a vector of length equal to the input's dimensions, # with 0's everywhere and 1 in the concat dim position. # Note: Can't use sparse_to_dense since it isn't GPU-capable (for now) mask = array_ops.concat(0, [array_ops.fill( array_ops.expand_dims(concat_dim, 0), 0), [1], array_ops.fill( shape_of_shape - concat_dim - 1, 0)]) begin = array_ops.fill(shape_of_shape, 0) return mask, begin # Degenerate concatenation, just return grad. if len(op.inputs) == 2: return [None, grad] concat_dim = op.inputs[0] out_grads = [] if isinstance(grad, ops.Tensor): # Get the inputs' tensor shapes sizes = array_ops.shape_n(op.inputs[1:]) # pylint: disable=protected-access offset = gen_array_ops._concat_offset(concat_dim, sizes) # pylint: enable=protected-access for (begin, size) in zip(offset, sizes): out_grads.append(array_ops.slice(grad, begin, size)) elif isinstance(grad, ops.IndexedSlices): concat_dim_static = tensor_util.constant_value(concat_dim) if concat_dim_static is None: raise ValueError("Can only compute IndexedSlices gradient with " "statically-known concat_dim") # Get the inputs' tensor shapes sizes = [array_ops.shape(x) for x in op.inputs[1:]] if concat_dim_static > 0: # IndexedSlices, concat_dim > 0. Each input gets IndexedSlices gradients # with all the indices, but with grad.values sliced accordingly. This # is like the Tensor case, except shape(grad.values)[0] is not equal to # shape(sizes[i])[0], since only a subset of the dim-0 values are stored. mask, begin = _CreateDenseMaskAndBegin(sizes, concat_dim) for size in sizes: new_values = array_ops.slice( grad.values, begin, array_ops.concat(0, [[-1], array_ops.slice(size, [1], [-1])])) out_grads.append( ops.IndexedSlices(new_values, grad.indices, size)) # Lint complains begin = begin + ... begin = math_ops.add(begin, size * mask) else: # IndexedSlices, concat_dim == 0. Each input gets IndexedSlices gradients # only for the relevant indices. start = constant_op.constant(0, dtype=grad.indices.dtype) for size in sizes: size_concat_dim = array_ops.gather(size, concat_dim) if size_concat_dim.dtype != grad.indices.dtype: size_concat_dim = math_ops.cast(size_concat_dim, dtype=grad.indices.dtype) end = start + size_concat_dim # Compute the 1-D Tensor of indices relevant for this input. indices_to_select = array_ops.squeeze( array_ops.where(math_ops.logical_and(grad.indices >= start, grad.indices < end)), squeeze_dims=[1]) new_indices = array_ops.gather(grad.indices, indices_to_select) - start new_values = array_ops.gather(grad.values, indices_to_select) out_grads.append( ops.IndexedSlices(new_values, new_indices, size)) start = end else: raise TypeError("Expected Tensor or IndexedSlices, got %s" % type(grad)) return [None] + out_grads
def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): """Gradient for concat op. Args: op: An operation. grad: `Tensor` or `IndexedSlices` representing the gradients with respect to each output of the op. start_value_index: An integer index of the first value in the op.inputs. end_value_index: An integer index of the last value in the op.inputs. dim_index: An interger index of concat_dim or axis parameter in op.inputs. Returns: Tensors represending the partial gradients with respect to each input of the op. Raises: ValueError: if concat_dim/axis is not statically known. """ def _CreateDenseMaskAndBegin(sizes, concat_dim): """Create variables for iteratively slicing a dense gradients tensor.""" # Since shape is 1-D, shape_of_shape = [rank-of-inputs] shape_of_shape = array_ops.shape(sizes[0]) # Make a vector of length equal to the input's dimensions, # with 0's everywhere and 1 in the concat dim position. # Note: Can't use sparse_to_dense since it isn't GPU-capable (for now) mask = array_ops.concat([ array_ops.fill(array_ops.expand_dims(concat_dim, 0), 0), [1], array_ops.fill(shape_of_shape - concat_dim - 1, 0) ], 0) begin = array_ops.fill(shape_of_shape, 0) return mask, begin def _ExtractInputShapes(inputs): """Extract the shapes of a set of input tensors.""" sizes = [] fully_known = True for x in inputs: input_shape = array_ops.shape(x) if not isinstance(input_shape, ops.Tensor) or input_shape.op.type != "Const": fully_known = False break else: sizes.append(input_shape) if fully_known: return sizes else: return array_ops.shape_n(inputs) # Degenerate concatenation, just return grad. if len(op.inputs) == 2: return grad + [None] if end_value_index <= dim_index else [None] + grad concat_dim = op.inputs[dim_index] input_values = op.inputs[start_value_index:end_value_index] # Using mod here for convenience since concat_dim is already verified # in concat implementation to be within the allowed [-rank, rank) range. non_neg_concat_dim = concat_dim % array_ops.rank(input_values[0]) out_grads = [] if isinstance(grad, ops.Tensor): # Get the inputs' tensor shapes sizes = _ExtractInputShapes(input_values) # The magic number of 16 was found through benchmarking a range of sizes # on CPUs and a Maxwell TitanX. A speedup was seen in a large majority of # cases when switching implementations at N=16, but it is possible that # there will be a small number of performance regressions. # pylint: disable=protected-access if len(sizes) > 16: # extract the size of each input along the concat dimension sizes = array_ops.squeeze( array_ops.slice( array_ops.stack( sizes, axis=1), [non_neg_concat_dim, 0], [1, -1])) out_grads = array_ops.split(grad, sizes, non_neg_concat_dim) else: offset = gen_array_ops._concat_offset(non_neg_concat_dim, sizes) for (begin, size) in zip(offset, sizes): out_grads.append(array_ops.slice(grad, begin, size)) # pylint: enable=protected-access elif isinstance(grad, ops.IndexedSlices): concat_dim_static = tensor_util.constant_value(concat_dim) if concat_dim_static is None: raise ValueError("Can only compute IndexedSlices gradient with " "statically-known concat_dim") if concat_dim_static < 0: rank = tensor_util.constant_value(array_ops.rank(input_values[0])) if rank is None: raise ValueError("Can only compute IndexedSlices gradient with " "negative concat_dim when first value rank is " "statically-known.") concat_dim_static %= rank # Get the inputs' tensor shapes sizes = [array_ops.shape(x) for x in input_values] if concat_dim_static > 0: # IndexedSlices, non_neg_concat_dim > 0. Each input gets IndexedSlices # gradients with all the indices, but with grad.values sliced accordingly. # This is like the Tensor case, except shape(grad.values)[0] is not equal # to shape(sizes[i])[0], since only a subset of the dim-0 values are # stored. mask, begin = _CreateDenseMaskAndBegin(sizes, non_neg_concat_dim) for size in sizes: new_values = array_ops.slice( grad.values, begin, array_ops.concat([[-1], array_ops.slice(size, [1], [-1])], 0)) out_grads.append( ops.IndexedSlices(new_values, grad.indices, size)) # Lint complains begin = begin + ... begin = math_ops.add(begin, size * mask) else: # IndexedSlices, concat_dim == 0. Each input gets IndexedSlices gradients # only for the relevant indices. start = constant_op.constant(0, dtype=grad.indices.dtype) for size in sizes: size_concat_dim = array_ops.gather(size, non_neg_concat_dim) if size_concat_dim.dtype != grad.indices.dtype: size_concat_dim = math_ops.cast(size_concat_dim, dtype=grad.indices.dtype) end = start + size_concat_dim # Compute the 1-D Tensor of indices relevant for this input. indices_to_select = array_ops.squeeze( array_ops.where(math_ops.logical_and(grad.indices >= start, grad.indices < end)), squeeze_dims=[1]) new_indices = array_ops.gather(grad.indices, indices_to_select) - start new_values = array_ops.gather(grad.values, indices_to_select) out_grads.append( ops.IndexedSlices(new_values, new_indices, size)) start = end else: raise TypeError("Expected Tensor or IndexedSlices, got %s" % type(grad)) return (out_grads + [None] if end_value_index <= dim_index else [None] + out_grads)