Dimensions must be equal, but are 1 and 128 for 'sampled_softmax_loss/MatMul' (op: 'MatMul') with input shapes: [128,1], [64,128].
# -*- 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)
Error:
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].
How do I interpret this error?
According to the error, the problem is about the dimension of the matrices which are multiplied each other. In order to solve this, the dimensions of the matrices should be [1, 128], [128, 64]
.
I got the below error, when I look at the API, I find that the "labels" before the "inputs", In code inputs first
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'
)
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.