函数一：tf.nn.embedding_lookup（）
ERROR：
I get this error: TypeError: Tensors in list passed to 'values' of 'ConcatV2' Op have types [float32, float64] that don't all match. is it because deprecated function in Tensorflow 1.0, or this is a problem with the script or a problem of deprecation can someone help
解决办法：
#之前的：tf.nn.embedding_lookup(embeddings, encoder_inputs)#修改后的：#will cast you're embeddings to tf.float64, which was what was caussing the concat between float32 and float64 error. I solved it by replacing above withtf.nn.embedding_lookup(embeddings, encoder_inputs)tf.cast(encoder_inputs_embedded,tf.float32)#and also casting the embeddings variable to float32 (assuming its a numpy array).
tf.nn.embedding_lookup函数的用法主要是选取一个张量里面索引对应的元素。tf.nn.embedding_lookup（tensor, id）:tensor就是输入张量，id就是张量对应的索引，其他的参数不介绍。
例如：
import tensorflow as tf; import numpy as np; c = np.random.random([10,1]) b = tf.nn.embedding_lookup(c, [1, 3]) with tf.Session() as sess: sess.run(tf.initialize_all_variables()) print sess.run(b) print c
输出：
[[ 0.77505197]
[ 0.20635818]][[ 0.23976515] [ 0.77505197] [ 0.08798201] [ 0.20635818] [ 0.37183035] [ 0.24753178] [ 0.17718483] [ 0.38533808] [ 0.93345168] [ 0.02634772]]分析：输出为张量的第一和第三个元素。
样例2：
- 原型：tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None, validate_indices=True, max_norm=None)
- 在网上搜会发现基本都是假设ids只有一行，但是假如ids有若干行，会怎样？
- 直接上代码：
# -*- coding= utf-8 -*-import tensorflow as tfimport numpy as npa = [[0.1, 0.2, 0.3], [1.1, 1.2, 1.3], [2.1, 2.2, 2.3], [3.1, 3.2, 3.3], [4.1, 4.2, 4.3]]a = np.asarray(a)idx1 = tf.Variable([0, 2, 3, 1], tf.int32)idx2 = tf.Variable([[0, 2, 3, 1], [4, 0, 2, 2]], tf.int32)out1 = tf.nn.embedding_lookup(a, idx1)out2 = tf.nn.embedding_lookup(a, idx2)init = tf.global_variables_initializer()with tf.Session() as sess: sess.run(init) print sess.run(out1) print out1 print '==================' print sess.run(out2) print out2
输出：
[[ 0.1 0.2 0.3] [ 2.1 2.2 2.3] [ 3.1 3.2 3.3] [ 1.1 1.2 1.3]]Tensor("embedding_lookup:0", shape=(4, 3), dtype=float64)==================[[[ 0.1 0.2 0.3] [ 2.1 2.2 2.3] [ 3.1 3.2 3.3] [ 1.1 1.2 1.3]] [[ 4.1 4.2 4.3] [ 0.1 0.2 0.3] [ 2.1 2.2 2.3] [ 2.1 2.2 2.3]]]Tensor("embedding_lookup_1:0", shape=(2, 4, 3), dtype=float64)