尺寸必须相等，但对于'sampled_softmax_loss / MatMul'（op：'MatMul'），输入形状为[128,1]，[64,128]的尺寸必须为1和128。
# -*- coding: utf-8 -*-
from __future__ import print_function
import collections
import math
import numpy as np
import random
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from sklearn.manifold import TSNE
import pickle
sample = open("/Users/henry/Desktop/Cor.txt")
words = sample.read()
print('Data size %d' % len(words))
sample.close()
vocabulary_size = 50000
def build_dataset(words):
count = [['UNK', -1]]
count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
dictionary = dict()
for word, _ in count:
dictionary[word] = len(dictionary)
data = list()
unk_count = 0
for word in words:
if word in dictionary:
index = dictionary[word]
else:
index = 0 # dictionary['UNK']
unk_count = unk_count + 1
data.append(index)
count[0][1] = unk_count
reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
return data, count, dictionary, reverse_dictionary
data, count, dictionary, reverse_dictionary = build_dataset(words)
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10])
del words # Hint to reduce memory.
data_index = 0
def generate_batch(batch_size, num_skips, skip_window):
global data_index
assert batch_size % num_skips == 0 # each word pair is a batch, so a training data [context target context] would increase batch number of 2.
assert num_skips <= 2 * skip_window
batch = np.ndarray(shape=(batch_size), dtype=np.int32)
labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
span = 2 * skip_window + 1 # [ skip_window target skip_window ]
buffer = collections.deque(maxlen=span)
for _ in range(span):
buffer.append(data[data_index])
data_index = (data_index + 1) % len(data)
for i in range(batch_size // num_skips):
target = skip_window # target label at the center of the buffer
targets_to_avoid = [ skip_window ]
for j in range(num_skips):
while target in targets_to_avoid:
target = random.randint(0, span - 1)
targets_to_avoid.append(target)
batch[i * num_skips + j] = buffer[skip_window]
labels[i * num_skips + j, 0] = buffer[target]
buffer.append(data[data_index])
data_index = (data_index + 1) % len(data)
return batch, labels
print('data:', [reverse_dictionary[di] for di in data[:8]])
for num_skips, skip_window in [(2, 1), (4, 2)]:
data_index = 0
batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window)
print('\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))
print(' batch:', [reverse_dictionary[bi] for bi in batch])
print(' labels:', [reverse_dictionary[li] for li in labels.reshape(8)])
batch_size = 128
embedding_size = 128 # Dimension of the embedding vector.
skip_window = 1 # How many words to consider left and right.
num_skips = 2 # How many times to reuse an input to generate a label.
# We pick a random validation set to sample nearest neighbors. here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent.
valid_size = 16 # Random set of words to evaluate similarity on.
valid_window = 100 # Only pick dev samples in the head of the distribution.
valid_examples = np.array(random.sample(range(valid_window), valid_size))
num_sampled = 64 # Number of negative examples to sample.
graph = tf.Graph()
with graph.as_default():
# Input data.
train_dataset = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
# Variables.
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
softmax_weights = tf.Variable(
tf.truncated_normal([vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))
# Model.
# Look up embeddings for inputs.
embed = tf.nn.embedding_lookup(embeddings, train_dataset)
# Compute the softmax loss, using a sample of the negative labels each time.
loss = tf.reduce_mean(
tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed,
train_labels, num_sampled, vocabulary_size))
# Optimizer.
# Note: The optimizer will optimize the softmax_weights AND the embeddings.
# This is because the embeddings are defined as a variable quantity and the
# optimizer's `minimize` method will by default modify all variable quantities
# that contribute to the tensor it is passed.
# See docs on `tf.train.Optimizer.minimize()` for more details.
optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)
# Compute the similarity between minibatch examples and all embeddings.
# We use the cosine distance:
norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
normalized_embeddings = embeddings / norm
valid_embeddings = tf.nn.embedding_lookup(
normalized_embeddings, valid_dataset)
similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))
embeddings_2 = (normalized_embeddings + softmax_weights)/2.0
norm_ = tf.sqrt(tf.reduce_sum(tf.square(embeddings_2), 1, keep_dims=True))
normalized_embeddings_2 = embeddings_2 / norm_
num_steps = 100001
with tf.Session(graph=graph) as session:
if int(tf.VERSION.split('.')[1]) > 11:
tf.global_variables_initializer().run()
else:
tf.initialize_all_variables().run()
print('Initialized')
average_loss = 0
for step in range(num_steps):
batch_data, batch_labels = generate_batch(batch_size, num_skips, skip_window)
feed_dict = {train_dataset : batch_data, train_labels : batch_labels}
_, l = session.run([optimizer, loss], feed_dict=feed_dict)
average_loss += l
if step % 2000 == 0:
if step > 0:
average_loss = average_loss / 2000
# The average loss is an estimate of the loss over the last 2000 batches.
print('Average loss at step %d: %f' % (step, average_loss))
average_loss = 0
# note that this is expensive (~20% slowdown if computed every 500 steps)
if step % 10000 == 0:
sim = similarity.eval()
for i in range(valid_size):
valid_word = reverse_dictionary[valid_examples[i]]
top_k = 8 # number of nearest neighbors
nearest = (-sim[i, :]).argsort()[1:top_k+1] # let alone itself, so begin with 1
log = 'Nearest to %s:' % valid_word
for k in range(top_k):
close_word = reverse_dictionary[nearest[k]]
log = '%s %s,' % (log, close_word)
print(log)
final_embeddings = normalized_embeddings.eval()
final_embeddings_2 = normalized_embeddings_2.eval() # this is better
num_points = 400
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])
two_d_embeddings_2 = tsne.fit_transform(final_embeddings_2[1:num_points+1, :])
with open('2d_embedding_skip_gram.pkl', 'wb') as f:
pickle.dump([two_d_embeddings, two_d_embeddings_2, reverse_dictionary], f)
错误：
Traceback (most recent call last):
File "<ipython-input-11-e08f5a40ae32>", line 1, in <module>
runfile('/Users/liuyang/Desktop/3333sk.py', wdir='/Users/liuyang/Desktop')
File "/anaconda3/lib/python3.6/site-packages/spyder_kernels/customize/spydercustomize.py", line 827, in runfile
execfile(filename, namespace)
File "/anaconda3/lib/python3.6/site-packages/spyder_kernels/customize/spydercustomize.py", line 110, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "/Users/liuyang/Desktop/3333sk.py", line 129, in <module>
train_labels, num_sampled, vocabulary_size))
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py", line 1901, in sampled_softmax_loss
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py", line 1429, in _compute_sampled_logits
acc_ids_2d_int32 = array_ops.reshape(
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 2580, in matmul
"""Convert 'x' to IndexedSlices.
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 5763, in mat_mul
`tf.truncatediv(x, y) * y + truncate_mod(x, y) = x`.
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 800, in _apply_op_helper
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3479, in create_op
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1983, in __init__
This property will return a dictionary for which the keys are nodes with
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1822, in _create_c_op
self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
ValueError: Dimensions must be equal, but are 1 and 128 for 'sampled_softmax_loss/MatMul' (op: 'MatMul') with input shapes: [128,1], [64,128].
如何解释此错误？
答案 0 :(得分：0)
根据该错误，问题在于矩阵的维数彼此相乘。为了解决这个问题，矩阵的维数应该为[1, 128], [128, 64]。
答案 1 :(得分：0)
我收到以下错误，当我查看API时，我发现“输入”之前的“标签”，在代码输入中首先出现
tf.nn.nce_loss(
weights, biases, labels, inputs, num_sampled, num_classes, num_true=1,
sampled_values=None, remove_accidental_hits=False, name='nce_loss'
)