本文整理汇总了Python中tensorflow.compat.v1.to_int32方法的典型用法代码示例。如果您正苦于以下问题:Python v1.to_int32方法的具体用法?Python v1.to_int32怎么用?Python v1.to_int32使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.compat.v1
的用法示例。
在下文中一共展示了v1.to_int32方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: padded_accuracy_topk
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def padded_accuracy_topk(predictions,
labels,
k,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that top-k predictions matches labels on non-0s."""
with tf.variable_scope("padded_accuracy_topk", values=[predictions, labels]):
padded_predictions, padded_labels = common_layers.pad_with_zeros(
predictions, labels)
weights = weights_fn(padded_labels)
effective_k = tf.minimum(k,
common_layers.shape_list(padded_predictions)[-1])
_, outputs = tf.nn.top_k(padded_predictions, k=effective_k)
outputs = tf.to_int32(outputs)
padded_labels = tf.to_int32(padded_labels)
padded_labels = tf.expand_dims(padded_labels, axis=-1)
padded_labels += tf.zeros_like(outputs) # Pad to same shape.
same = tf.to_float(tf.equal(outputs, padded_labels))
same_topk = tf.reduce_sum(same, axis=-1)
return same_topk, weights
示例2: rouge_l_fscore
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def rouge_l_fscore(predictions, labels, **unused_kwargs):
"""ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge_l_fscore: approx rouge-l f1 score.
"""
outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
# Convert the outputs and labels to a [batch_size, input_length] tensor.
outputs = tf.squeeze(outputs, axis=[-1, -2])
labels = tf.squeeze(labels, axis=[-1, -2])
rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (outputs, labels),
tf.float32)
return rouge_l_f_score, tf.constant(1.0)
示例3: rouge_2_fscore
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def rouge_2_fscore(predictions, labels, **unused_kwargs):
"""ROUGE-2 F1 score computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge2_fscore: approx rouge-2 f1 score.
"""
outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
# Convert the outputs and labels to a [batch_size, input_length] tensor.
outputs = tf.squeeze(outputs, axis=[-1, -2])
labels = tf.squeeze(labels, axis=[-1, -2])
rouge_2_f_score = tf.py_func(rouge_n, (outputs, labels), tf.float32)
return rouge_2_f_score, tf.constant(1.0)
示例4: __init__
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def __init__(self, pad_mask):
"""Compute and store the location of the padding.
Args:
pad_mask (tf.Tensor): Reference padding tensor of shape
[batch_size,length] or [dim_origin] (dim_origin=batch_size*length)
containing non-zeros positive values to indicate padding location.
"""
self.nonpad_ids = None
self.dim_origin = None
with tf.name_scope("pad_reduce/get_ids"):
pad_mask = tf.reshape(pad_mask, [-1]) # Flatten the batch
# nonpad_ids contains coordinates of zeros rows (as pad_mask is
# float32, checking zero equality is done with |x| < epsilon, with
# epsilon=1e-9 as standard, here pad_mask only contains positive values
# so tf.abs would be redundant)
self.nonpad_ids = tf.to_int32(tf.where(pad_mask < 1e-9))
self.dim_origin = tf.shape(pad_mask)[:1]
示例5: bleu_score
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def bleu_score(predictions, labels, **unused_kwargs):
"""BLEU score computation between labels and predictions.
An approximate BLEU scoring method since we do not glue word pieces or
decode the ids and tokenize the output. By default, we use ngram order of 4
and use brevity penalty. Also, this does not have beam search.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
bleu: int, approx bleu score
"""
outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
# Convert the outputs and labels to a [batch_size, input_length] tensor.
outputs = tf.squeeze(outputs, axis=[-1, -2])
labels = tf.squeeze(labels, axis=[-1, -2])
bleu = tf.py_func(compute_bleu, (labels, outputs), tf.float32)
return bleu, tf.constant(1.0)
示例6: noise_from_step_num
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def noise_from_step_num():
"""Quantization noise equal to (phi * (step_num + 1)) mod 1.0.
Not using random_uniform here due to a problem on TPU in that random seeds
are not respected, which may cause the parameters on different replicas
to go out-of-sync.
Returns:
a float32 scalar
"""
step = tf.to_int32(tf.train.get_or_create_global_step()) + 1
phi = ((5 ** 0.5) - 1) / 2
# Naive computation tf.mod(phi * step, 1.0) in float32 would be disastrous
# due to loss of precision when the step number gets large.
# Computation in doubles does not work on TPU, so we use this complicated
# alternative computation which does not suffer from these roundoff errors.
ret = 0.0
for i in range(30):
ret += (((phi * (2 ** i)) % 1.0) # double-precision computation in python
* tf.to_float(tf.mod(step // (2 ** i), 2)))
return tf.mod(ret, 1.0)
示例7: xception_exit
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def xception_exit(inputs):
"""Xception exit flow."""
with tf.variable_scope("xception_exit"):
x = inputs
x_shape = x.get_shape().as_list()
if x_shape[1] is None or x_shape[2] is None:
length_float = tf.to_float(tf.shape(x)[1])
length_float *= tf.to_float(tf.shape(x)[2])
spatial_dim_float = tf.sqrt(length_float)
spatial_dim = tf.to_int32(spatial_dim_float)
x_depth = x_shape[3]
x = tf.reshape(x, [-1, spatial_dim, spatial_dim, x_depth])
elif x_shape[1] != x_shape[2]:
spatial_dim = int(math.sqrt(float(x_shape[1] * x_shape[2])))
if spatial_dim * spatial_dim != x_shape[1] * x_shape[2]:
raise ValueError("Assumed inputs were square-able but they were "
"not. Shape: %s" % x_shape)
x = tf.reshape(x, [-1, spatial_dim, spatial_dim, x_depth])
x = common_layers.conv_block_downsample(x, (3, 3), (2, 2), "SAME")
return tf.nn.relu(x)
示例8: bytenet_internal
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def bytenet_internal(inputs, targets, hparams):
"""ByteNet, main step used for training."""
with tf.variable_scope("bytenet"):
# Flatten inputs and extend length by 50%.
inputs = tf.expand_dims(common_layers.flatten4d3d(inputs), axis=2)
extend_length = tf.to_int32(0.5 * tf.to_float(tf.shape(inputs)[1]))
inputs_shape = inputs.shape.as_list()
inputs = tf.pad(inputs, [[0, 0], [0, extend_length], [0, 0], [0, 0]])
inputs_shape[1] = None
inputs.set_shape(inputs_shape) # Don't lose the other shapes when padding.
# Pad inputs and targets to be the same length, divisible by 50.
inputs, targets = common_layers.pad_to_same_length(
inputs, targets, final_length_divisible_by=50)
final_encoder = residual_dilated_conv(inputs, hparams.num_block_repeat,
"SAME", "encoder", hparams)
shifted_targets = common_layers.shift_right(targets)
kernel = (hparams.kernel_height, hparams.kernel_width)
decoder_start = common_layers.conv_block(
tf.concat([final_encoder, shifted_targets], axis=3),
hparams.hidden_size, [((1, 1), kernel)],
padding="LEFT")
return residual_dilated_conv(decoder_start, hparams.num_block_repeat,
"LEFT", "decoder", hparams)
示例9: _import_feature
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def _import_feature(self, features, mesh, key):
"""Import a feature from the features dictionary into a mtf.Tensor.
Args:
features: a features dictionary
mesh: a Mesh
key: a string
Returns:
a mtf.Tensor with dtype int32 and shape self.batch_dims + self.length_dim
"""
if key not in features:
return None
x = tf.to_int32(features[key])
x = common_layers.expand_squeeze_to_nd(x, 2)
batch_size = mtf.Shape(self.batch_dims).size
x = x[:, :self.length_dim.size]
extra_length = self.length_dim.size - tf.shape(x)[1]
extra_batch = batch_size - tf.shape(x)[0]
x = tf.pad(x, [[0, extra_batch], [0, extra_length]])
mtf_shape = mtf.Shape(self.batch_dims + [self.length_dim])
x = tf.reshape(x, mtf_shape.to_integer_list)
return mtf.import_fully_replicated(mesh, x, mtf_shape, name=key)
示例10: ctc_symbol_loss
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def ctc_symbol_loss(top_out, targets, model_hparams, vocab_size, weight_fn):
"""Compute the CTC loss."""
del model_hparams, vocab_size # unused arg
logits = top_out
with tf.name_scope("ctc_loss", values=[logits, targets]):
# For CTC we assume targets are 1d, [batch, length, 1, 1] here.
targets_shape = targets.get_shape().as_list()
assert len(targets_shape) == 4
assert targets_shape[2] == 1
assert targets_shape[3] == 1
targets = tf.squeeze(targets, axis=[2, 3])
logits = tf.squeeze(logits, axis=[2, 3])
targets_mask = 1 - tf.to_int32(tf.equal(targets, 0))
targets_lengths = tf.reduce_sum(targets_mask, axis=1)
sparse_targets = tf.keras.backend.ctc_label_dense_to_sparse(
targets, targets_lengths)
xent = tf.nn.ctc_loss(
sparse_targets,
logits,
targets_lengths,
time_major=False,
preprocess_collapse_repeated=False,
ctc_merge_repeated=False)
weights = weight_fn(targets)
return tf.reduce_sum(xent), tf.reduce_sum(weights)
示例11: top_1_tpu
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def top_1_tpu(inputs):
"""find max and argmax over the last dimension.
Works well on TPU
Args:
inputs: A tensor with shape [..., depth]
Returns:
values: a Tensor with shape [...]
indices: a Tensor with shape [...]
"""
inputs_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
mask = tf.to_int32(tf.equal(inputs_max, inputs))
index = tf.range(tf.shape(inputs)[-1]) * mask
return tf.squeeze(inputs_max, -1), tf.reduce_max(index, axis=-1)
示例12: bit_to_int
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def bit_to_int(self, x_bit, num_bits, base=2):
"""Turn x_bit representing numbers bitwise (lower-endian) to int tensor.
Args:
x_bit: Tensor containing numbers in a particular base to be
converted to
int.
num_bits: Number of bits in the representation.
base: Base of the representation.
Returns:
Integer representation of this number.
"""
x_l = tf.stop_gradient(tf.to_int32(tf.reshape(x_bit, [-1, num_bits])))
# pylint: disable=g-complex-comprehension
x_labels = [
x_l[:, i] * tf.to_int32(base)**tf.to_int32(i) for i in range(num_bits)]
res = sum(x_labels)
return tf.to_int32(tf.reshape(res, common_layers.shape_list(x_bit)[:-1]))
示例13: int_to_bit
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def int_to_bit(self, x_int, num_bits, base=2):
"""Turn x_int representing numbers into a bitwise (lower-endian) tensor.
Args:
x_int: Tensor containing integer to be converted into base
notation.
num_bits: Number of bits in the representation.
base: Base of the representation.
Returns:
Corresponding number expressed in base.
"""
x_l = tf.to_int32(tf.expand_dims(x_int, axis=-1))
# pylint: disable=g-complex-comprehension
x_labels = [
tf.floormod(
tf.floordiv(tf.to_int32(x_l),
tf.to_int32(base)**i), tf.to_int32(base))
for i in range(num_bits)]
res = tf.concat(x_labels, axis=-1)
return tf.to_float(res)
示例14: int_to_bit
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def int_to_bit(x_int, num_bits, base=2):
"""Turn x_int representing numbers into a bitwise (lower-endian) tensor.
Args:
x_int: Tensor containing integer to be converted into base notation.
num_bits: Number of bits in the representation.
base: Base of the representation.
Returns:
Corresponding number expressed in base.
"""
x_l = tf.to_int32(tf.expand_dims(x_int, axis=-1))
x_labels = [tf.floormod(
tf.floordiv(tf.to_int32(x_l), tf.to_int32(base)**i), tf.to_int32(base))
for i in range(num_bits)]
res = tf.concat(x_labels, axis=-1)
return tf.to_float(res)
示例15: serving_input_receiver_fn
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import to_int32 [as 别名]
def serving_input_receiver_fn():
"""Creates an input function for serving."""
seq_len = FLAGS.max_seq_length
serialized_example = tf.placeholder(
dtype=tf.string, shape=[None], name="serialized_example")
features = {
"input_ids": tf.FixedLenFeature([seq_len], dtype=tf.int64),
"input_mask": tf.FixedLenFeature([seq_len], dtype=tf.int64),
"segment_ids": tf.FixedLenFeature([seq_len], dtype=tf.int64),
}
feature_map = tf.parse_example(serialized_example, features=features)
feature_map["is_real_example"] = tf.constant(1, dtype=tf.int32)
feature_map["label_ids"] = tf.constant(0, dtype=tf.int32)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in feature_map.keys():
t = feature_map[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
feature_map[name] = t
return tf.estimator.export.ServingInputReceiver(
features=feature_map, receiver_tensors=serialized_example)