tensorflow用法记录

使用 embedding 变量

import tensorflow as tf
import numpy as np

sess = tf.InteractiveSession()

M = list('ABCD')
table = tf.contrib.lookup.index_table_from_tensor(
    mapping=tf.constant(M), num_oov_buckets=1, default_value=-1)

# 包含多个ID
IDs = tf.Variable(["A|B|C", "C|D|A|B","C|A|A|B|E|E"] )
embedding_mat = tf.constant(np.arange(20, dtype=float).reshape((20,1)))

# 查找embedding变量，并聚合
hs = tf.string_split(IDs, '|')
sp_ID = tf.SparseTensor(hs.indices, table.lookup(hs.values),hs.dense_shape)
AB = tf.nn.embedding_lookup_sparse(embedding_mat, sp_ID, sp_weights=None, combiner='mean')
AC = tf.nn.embedding_lookup_sparse(embedding_mat, sp_ID, sp_weights=None, combiner='sum')


tf.tables_initializer().run()
tf.global_variables_initializer().run()

print(AB.eval() )
print()
print(AC.eval())

结果

[[ 1.        ]
 [ 1.5       ]
 [ 1.83333333]]

[[  3.]
 [  6.]
 [ 11.]]

修改learning_rate

开始用 feed_dict 方式传入，但通过tensorboard发现实际上没改成。搜索了一通发现，下面方式最简单。

self.learning_rate /= 2.0
_ = sess.run(self.learning_rate_var.assign(self.learning_rate))

---

tf.keras的用法

• Layer 自动生成mask 张量，并在下一个layer中继承，通过attribute属性_keras_mask 实现。 在序列模型上非常有用。

from tensorflow.python.keras.layers import Embedding
e = Embedding(100, 3, mask_zero=True)(tf.constant([1,3,4,0]))
e._keras_mask.eval()
# 输出 array([ True,  True,  True, False])

• 属性的用法

setattr(e, '_keras_mask0', 1)
getattr(e, '_keras_mask0')

• mask 举例说明

import tensorflow as tf
from tensorflow.python.keras.layers import Input
from tensorflow.python.keras.layers import Embedding
from tensorflow.compat.v1 import logging
logging.set_verbosity(logging.ERROR)


class NoMask(tf.keras.layers.Layer):
    def __init__(self, **kwargs):
        super(NoMask, self).__init__(**kwargs)

    def build(self, input_shape):
        # Be sure to call this somewhere!
        super(NoMask, self).build(input_shape)

    def call(self, x, mask=None, **kwargs):
        return x

    def compute_mask(self, inputs, mask):
        return None


class hasMask(tf.keras.layers.Layer):
    def __init__(self, **kwargs):
        super(hasMask, self).__init__(**kwargs)

    def build(self, input_shape):
        super(hasMask, self).build(input_shape)

    def call(self, x, mask=None, **kwargs):
        return x

    def compute_mask(self, inputs, mask):
        return mask


class hasMask2(tf.keras.layers.Layer):
    def __init__(self, supports_masking=False, **kwargs):
        super(hasMask2, self).__init__(**kwargs)
        self.supports_masking = supports_masking
        
    def build(self, input_shape):
        super(hasMask2, self).build(input_shape)

    def call(self, x, mask=None, **kwargs):
        return x


if __name__ == "__main__":
    e = Embedding(100, 3, mask_zero=True)(Input(shape=(1,), name='cate_id', dtype='int32'))
    print(e._keras_mask, hasMask()(e)._keras_mask, hasMask2(True)(e)._keras_mask, hasMask2(False)(e)._keras_mask, NoMask()(e)._keras_mask)
    print("*" * 50)
    e = Embedding(100, 3, mask_zero=False)(Input(shape=(1,), name='cate_id', dtype='int32'))
    print(e._keras_mask, hasMask()(e)._keras_mask, hasMask2(True)(e)._keras_mask, hasMask2(False)(e)._keras_mask, NoMask()(e)._keras_mask)

输出

Tensor("embedding/NotEqual:0", shape=(?, 1), dtype=bool) Tensor("embedding/NotEqual:0", shape=(?, 1), dtype=bool) Tensor("embedding/NotEqual:0", shape=(?, 1), dtype=bool) None None
**************************************************
None None None None None