以书名为“Learning TensorFlow。建立深度学习系统指南（Tom Hope，Yehezkel S. Reshe和Itay Lieder）”为指导“我正在尝试用word2vec方法实现简单的RNN网络。
在第101页（第6章，文本II：单词向量，高级RNN和嵌入可视化）上，作者给出了RNN实现的示例（下面的代码），但是在sess.run方法中，我得到了TypeError: 'NoneType' object is not iterable
ENV：
-
import os
import math
import numpy as np
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
batch_size = 64
embedding_dimension = 5
negative_samples = 8
LOG_DIR = "/opt/notebooks/tlog/word2vec_intro"
digit_to_word_map = { 1:"One",2:"Two", 3:"Three", 4:"Four", 5:"Five",
6:"Six",7:"Seven",8:"Eight",9:"Nine" }
sentences = []
# Create two kinds of sentences - sequences of odd and even digits
for i in range(10000):
rand_odd_ints = np.random.choice(range(1,10,2),3)
sentences.append(" ".join([digit_to_word_map[r] for r in rand_odd_ints]))
rand_even_ints = np.random.choice(range(2,10,2),3)
sentences.append(" ".join([digit_to_word_map[r] for r in rand_even_ints]))
# Map words to indices
word2index_map = {}
index = 0
for sent in sentences:
for word in sent.lower().split():
if word not in word2index_map:
word2index_map[word] = index
index += 1
index2word_map = { index: word for word, index in word2index_map.items() }
vocabulary_size = len(index2word_map)
# Generate skip-gram pairs
skip_gram_pairs = []
for sent in sentences:
tokenized_sent = sent.lower().split()
for i in range(1, len(tokenized_sent)-1):
word_context_pair = [[word2index_map[tokenized_sent[i-1]],
word2index_map[tokenized_sent[i+1]]],
word2index_map[tokenized_sent[i]]]
skip_gram_pairs.append([word_context_pair[1],
word_context_pair[0][0]])
skip_gram_pairs.append([word_context_pair[1],
word_context_pair[0][1]])
def get_skipgram_batch(batch_size):
instance_indices = list(range(len(skip_gram_pairs)))
np.random.shuffle(instance_indices)
batch = instance_indices[:batch_size]
x = [skip_gram_pairs[i][0] for i in batch]
y = [[skip_gram_pairs[i][1]] for i in batch]
return x, y
# Batch example
# x_batch, y_batch = get_skipgram_batch(8)
# x_batch
# y_batch
# [index2word_map[word] for word in x_batch]
# [index2word_map[word[0]] for word in y_batch]
# Input data, labels
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
with tf.name_scope("embeddings"):
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_dimension], -1.0, 1.0), name='embedding'
)
# This is essentially a lookup table
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
# Create variables for the NCE loss
nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_dimension],
stddev = 1.0 / math.sqrt(embedding_dimension))
)
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
loss = tf.reduce_mean(
tf.nn.nce_loss(weights = nce_weights, biases = nce_biases, inputs = embed,
labels = train_labels, num_sampled = negative_samples, num_classes =
vocabulary_size
)
)
# Learning rate decay
global_step = tf.Variable(0, trainable = False)
learningRate = tf.train.exponential_decay(learning_rate = 0.1,
global_step = global_step,
decay_steps = 1000,
decay_rate = 0.95,
staircase = True)
train_step = tf.train.GradientDescentOptimizer(learningRate).minimize(loss)
# Merge all summary ops
merged = tf.summary.merge_all()
with tf.Session() as sess:
train_writer = tf.summary.FileWriter(LOG_DIR, graph = tf.get_default_graph())
saver = tf.train.Saver()
with open(os.path.join(LOG_DIR,'metadata.tsv'), "w") as metadata:
metadata.write('Name\tClass\n')
for k, v in index2word_map.items():
metadata.write('%s\t%d\n' % (v, k))
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embeddings.name
# Link embedding to its metadata file
embedding.metadata_path = os.path.join(LOG_DIR,'metadata.tsv')
projector.visualize_embeddings(train_writer, config)
tf.global_variables_initializer().run()
for step in range(1000):
x_batch, y_batch = get_skipgram_batch(batch_size)
summary, _ = sess.run(
train_step,
feed_dict = {
train_inputs: x_batch,
train_labels: y_batch
}
)
# train_writer.add_summary(summary, step)
if step % 100 == 0:
saver.save(sess, os.path.join(LOG_DIR, "w2v_model.ckpt"), step)
loss_value = sess.run(loss,
feed_dict = {train_inputs:x_batch,
train_labels:y_batch})
print("Loss at %d: %.5f" % (step, loss_value))
# Normalize embeddings before using
norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
normalized_embeddings = embeddings / norm
normalized_embeddings_matrix = sess.run(normalized_embeddings)
ref_word = normalized_embeddings_matrix[word2index_map["one"]]
cosine_dists = np.dot(normalized_embeddings_matrix,ref_word)
ff = np.argsort(cosine_dists)[::-1][1:10]
for f in ff:
print(index2word_map[f])
print(cosine_dists[f])
结果
TypeErrorTraceback (most recent call last)
<ipython-input-7-e91d4f09d595> in <module>()
143 feed_dict = {
144 train_inputs: x_batch,
--> 145 train_labels: y_batch
146 }
147 )
TypeError: 'NoneType' object is not iterable
但在sess.run print(y_batch)给我们之前（如书中所示）
[[4], [6], [0], [4], [7], [3], [2], [3], [3], [8], [4], [2], [2], [1], [6], [1], [0], [3], [5], [6], [5], [8], [0], [7], [7], [0], [0], [8], [2], [2], [4], [6], [3], [7], [1], [1], [4], [8], [6], [5], [4], [7], [8], [6], [1], [3], [4], [2], [2], [4], [5], [6], [3], [0], [5], [2], [2], [2], [0], [4], [5], [3], [0], [4]]
我应该怎么做才能正确运行这个例子？
答案 0 :(得分：0)
您应该在运行函数中添加合并值：
summary, _ = sess.run(
[merged, train_step],
feed_dict = {
train_inputs: x_batch,
train_labels: y_batch
}
)
如果您不想要此信息，请将其删除：
loss = sess.run(
train_step,
feed_dict = {
train_inputs: x_batch,
train_labels: y_batch
}
)