ValueError: Incompatible shapes for broadcasting
ValueError: Incompatible shapes for broadcasting
我是 tensorflow 的新手，我正在为所有 36 个字符（0-9 和 a-z）训练一个神经网络。
我使用 tfrecords 转换了一些图像：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import os
import cv2
import tensorflow as tf
tf.app.flags.DEFINE_string('directory', '/root/data2',
'Directory to download data files and write the '
'converted result')
FLAGS = tf.app.flags.FLAGS
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def le_imagens(aux_folder):
cont=0
img=np.empty([1,28,28,1])
for letter in os.listdir(aux_folder):
folder=aux_folder+letter+"/"
for imagem in os.listdir(folder):
os.chdir(folder)
img_temp=cv2.imread(imagem)
img_temp = cv2.cvtColor(img_temp,cv2.COLOR_BGR2GRAY)
img_temp= np.expand_dims(img_temp, axis=0)
img_temp= np.expand_dims(img_temp, axis=3)
img=np.vstack((img,img_temp))
cont=cont+1
print (cont)
print (img.shape)
return img
def calcula_label(letter):
aux_label=["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z","1","2","3","4","5","6","7","8","9","0"]
label= np.zeros([1,36])
cont=0
for let in aux_label:
if let==letter:
label[0,cont]=1
else:
label[0,cont]=0
cont=cont+1
return label
def cria_array_labels(aux_folder):
cont=0
lab=np.empty([1,36])
for letter in os.listdir(aux_folder):
lab_temp=calcula_label(letter)
folder=aux_folder+letter+"/"
for imagem in os.listdir(folder):
lab=np.vstack((lab,lab_temp))
cont=cont+1
print (cont)
print (lab.shape)
return lab
def convert_to(images, labels, name):
#identifica quantidade de imagens e labels
num_examples = labels.shape[0]
if images.shape[0] != num_examples:
raise ValueError("Images size %d does not match label size %d." %
(images.shape[0], num_examples))
#pega parametros da imagem
rows = images.shape[1]
cols = images.shape[2]
depth = 1
#cria nome do arquivo de saida-acho que todas as imagens vao ser escritas aqui
filename = os.path.join(FLAGS.directory, name + '.tfrecords')
print('Writing', filename)
writer = tf.python_io.TFRecordWriter(filename)
#faz um loop para cada uma das imagens
for index in range(num_examples):
#converte a imagem para string
image_raw = images[index].tostring()
labels_raw = labels[index].tostring()
#aloca no exemplo as dimensoes da img, o label e a imagem convertida
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(rows),
'width': _int64_feature(cols),
'depth': _int64_feature(depth),
'label': _bytes_feature(labels_raw),
'image_raw': _bytes_feature(image_raw)}))
#escreve o exemplo
writer.write(example.SerializeToString())
writer.close()
def main(argv):
#Train
aux_folder="/root/captchas/captchas_lft/"
img_treino=le_imagens(aux_folder)
lab_treino=cria_array_labels(aux_folder)
print ("Base de Treino Preparada")
#Cross Validation
aux_folder="/root/captchas/captchas_lfcv/"
img_cv=le_imagens(aux_folder)
lab_cv=cria_array_labels(aux_folder)
print ("Base de CV Preparada")
#Test Set
aux_folder="/root/captchas/captchas_lfts/"
img_ts=le_imagens(aux_folder)
lab_ts=cria_array_labels(aux_folder)
print ("Base de Teste Preparada")
convert_to(img_treino, lab_treino, 'train')
convert_to(img_cv, lab_cv, 'validation')
convert_to(img_ts, lab_ts, 'test')
if __name__ == '__main__':
tf.app.run()
我正在提供以下网络（改编自 MNIST2 Tensorflow 教程）：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import time
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import mnist
# Basic model parameters as external flags.
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_integer('num_epochs', 2, 'Number of epochs to run trainer.')
flags.DEFINE_integer('batch_size', 100, 'Batch size.')
flags.DEFINE_string('train_dir', '/root/data', 'Directory with the training data.')
#flags.DEFINE_string('train_dir', '/root/data2', 'Directory with the training data.')
# Constants used for dealing with the files, matches convert_to_records.
TRAIN_FILE = 'train.tfrecords'
VALIDATION_FILE = 'validation.tfrecords'
# Set-up dos pacotes
sess = tf.InteractiveSession()
def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
dense_keys=['image_raw', 'label'],
# Defaults are not specified since both keys are required.
dense_types=[tf.string, tf.string])
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape([784])
#print (mnist.IMAGE_PIXELS)
# OPTIONAL: Could reshape into a 28x28 image and apply distortions
# here. Since we are not applying any distortions in this
# example, and the next step expects the image to be flattened
# into a vector, we don't bother.
# Convert from [0, 255] -> [-0.5, 0.5] floats.
image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['label'], tf.float32)
#print (label)
#label.set_shape([1])
#print (label)
return image, label
def inputs(train, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, 30,26,1]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, char letras).
Note that an tf.train.QueueRunner is added to the graph, which
must be run using e.g. tf.train.start_queue_runners().
"""
if not num_epochs: num_epochs = None
filename = os.path.join(FLAGS.train_dir,
TRAIN_FILE if train else VALIDATION_FILE)
with tf.name_scope('input'):
filename_queue = tf.train.string_input_producer(
[filename], num_epochs=num_epochs)
# Even when reading in multiple threads, share the filename
# queue.
image, label = read_and_decode(filename_queue)
# Shuffle the examples and collect them into batch_size batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
images, sparse_labels = tf.train.shuffle_batch(
[image, label], batch_size=batch_size, num_threads=2,
capacity=1000 + 3 * batch_size,
# Ensures a minimum amount of shuffling of examples.
min_after_dequeue=1000)
return images, sparse_labels
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
#Variaveis
x, y_ = inputs(train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs)
#y_ = tf.string_to_number(y_, out_type=tf.int32)
teste=tf.convert_to_tensor(y_)
#Layer 1
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#Layer 2
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
#Densely Connected Layer
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
#Dropout - reduz overfitting
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
#Readout layer
W_fc2 = weight_variable([1024, 36])
b_fc2 = bias_variable([36])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
#y_conv=tf.cast(y_conv, tf.int32)
#Train and evaluate
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(20000):
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={keep_prob: 0.5})
x, y_ = inputs(train=True, batch_size=2000)
#y_ = tf.string_to_number(y_, out_type=tf.int32)
print("test accuracy %g"%accuracy.eval(feed_dict={keep_prob: 1.0}))
coord.join(threads)
sess.close()
但是说到
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
出现以下错误：
ValueError: Incompatible shapes for broadcasting: TensorShape([Dimension(100)]) and TensorShape([Dimension(100), Dimension(36)])
我认为问题出在 label 张量上，但我不确定如何修复它以获得 (None,36) 维度。有谁知道如何解决这个问题？
谢谢
马塞洛
完成此工作的最简单方法是使用 tf.one_hot():
将 y_ 替换为单热编码
onehot_y_ = tf.one_hot(y_, 36, dtype=tf.float32)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(onehot_y_ * tf.log(y_conv),
reduction_indices=[1]))
另一种方法是切换到使用专门的运算符 tf.nn.sparse_softmax_cross_entropy_with_logits()，它更高效且数值更稳定。要使用它，您必须在 y_conv:
的定义中删除对 tf.nn.softmax() 的调用
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y_conv, y_))
我是 tensorflow 的新手，我正在为所有 36 个字符（0-9 和 a-z）训练一个神经网络。
我使用 tfrecords 转换了一些图像：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import os
import cv2
import tensorflow as tf
tf.app.flags.DEFINE_string('directory', '/root/data2',
'Directory to download data files and write the '
'converted result')
FLAGS = tf.app.flags.FLAGS
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def le_imagens(aux_folder):
cont=0
img=np.empty([1,28,28,1])
for letter in os.listdir(aux_folder):
folder=aux_folder+letter+"/"
for imagem in os.listdir(folder):
os.chdir(folder)
img_temp=cv2.imread(imagem)
img_temp = cv2.cvtColor(img_temp,cv2.COLOR_BGR2GRAY)
img_temp= np.expand_dims(img_temp, axis=0)
img_temp= np.expand_dims(img_temp, axis=3)
img=np.vstack((img,img_temp))
cont=cont+1
print (cont)
print (img.shape)
return img
def calcula_label(letter):
aux_label=["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z","1","2","3","4","5","6","7","8","9","0"]
label= np.zeros([1,36])
cont=0
for let in aux_label:
if let==letter:
label[0,cont]=1
else:
label[0,cont]=0
cont=cont+1
return label
def cria_array_labels(aux_folder):
cont=0
lab=np.empty([1,36])
for letter in os.listdir(aux_folder):
lab_temp=calcula_label(letter)
folder=aux_folder+letter+"/"
for imagem in os.listdir(folder):
lab=np.vstack((lab,lab_temp))
cont=cont+1
print (cont)
print (lab.shape)
return lab
def convert_to(images, labels, name):
#identifica quantidade de imagens e labels
num_examples = labels.shape[0]
if images.shape[0] != num_examples:
raise ValueError("Images size %d does not match label size %d." %
(images.shape[0], num_examples))
#pega parametros da imagem
rows = images.shape[1]
cols = images.shape[2]
depth = 1
#cria nome do arquivo de saida-acho que todas as imagens vao ser escritas aqui
filename = os.path.join(FLAGS.directory, name + '.tfrecords')
print('Writing', filename)
writer = tf.python_io.TFRecordWriter(filename)
#faz um loop para cada uma das imagens
for index in range(num_examples):
#converte a imagem para string
image_raw = images[index].tostring()
labels_raw = labels[index].tostring()
#aloca no exemplo as dimensoes da img, o label e a imagem convertida
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(rows),
'width': _int64_feature(cols),
'depth': _int64_feature(depth),
'label': _bytes_feature(labels_raw),
'image_raw': _bytes_feature(image_raw)}))
#escreve o exemplo
writer.write(example.SerializeToString())
writer.close()
def main(argv):
#Train
aux_folder="/root/captchas/captchas_lft/"
img_treino=le_imagens(aux_folder)
lab_treino=cria_array_labels(aux_folder)
print ("Base de Treino Preparada")
#Cross Validation
aux_folder="/root/captchas/captchas_lfcv/"
img_cv=le_imagens(aux_folder)
lab_cv=cria_array_labels(aux_folder)
print ("Base de CV Preparada")
#Test Set
aux_folder="/root/captchas/captchas_lfts/"
img_ts=le_imagens(aux_folder)
lab_ts=cria_array_labels(aux_folder)
print ("Base de Teste Preparada")
convert_to(img_treino, lab_treino, 'train')
convert_to(img_cv, lab_cv, 'validation')
convert_to(img_ts, lab_ts, 'test')
if __name__ == '__main__':
tf.app.run()
我正在提供以下网络（改编自 MNIST2 Tensorflow 教程）：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import time
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import mnist
# Basic model parameters as external flags.
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_integer('num_epochs', 2, 'Number of epochs to run trainer.')
flags.DEFINE_integer('batch_size', 100, 'Batch size.')
flags.DEFINE_string('train_dir', '/root/data', 'Directory with the training data.')
#flags.DEFINE_string('train_dir', '/root/data2', 'Directory with the training data.')
# Constants used for dealing with the files, matches convert_to_records.
TRAIN_FILE = 'train.tfrecords'
VALIDATION_FILE = 'validation.tfrecords'
# Set-up dos pacotes
sess = tf.InteractiveSession()
def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
dense_keys=['image_raw', 'label'],
# Defaults are not specified since both keys are required.
dense_types=[tf.string, tf.string])
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape([784])
#print (mnist.IMAGE_PIXELS)
# OPTIONAL: Could reshape into a 28x28 image and apply distortions
# here. Since we are not applying any distortions in this
# example, and the next step expects the image to be flattened
# into a vector, we don't bother.
# Convert from [0, 255] -> [-0.5, 0.5] floats.
image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['label'], tf.float32)
#print (label)
#label.set_shape([1])
#print (label)
return image, label
def inputs(train, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, 30,26,1]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, char letras).
Note that an tf.train.QueueRunner is added to the graph, which
must be run using e.g. tf.train.start_queue_runners().
"""
if not num_epochs: num_epochs = None
filename = os.path.join(FLAGS.train_dir,
TRAIN_FILE if train else VALIDATION_FILE)
with tf.name_scope('input'):
filename_queue = tf.train.string_input_producer(
[filename], num_epochs=num_epochs)
# Even when reading in multiple threads, share the filename
# queue.
image, label = read_and_decode(filename_queue)
# Shuffle the examples and collect them into batch_size batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
images, sparse_labels = tf.train.shuffle_batch(
[image, label], batch_size=batch_size, num_threads=2,
capacity=1000 + 3 * batch_size,
# Ensures a minimum amount of shuffling of examples.
min_after_dequeue=1000)
return images, sparse_labels
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
#Variaveis
x, y_ = inputs(train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs)
#y_ = tf.string_to_number(y_, out_type=tf.int32)
teste=tf.convert_to_tensor(y_)
#Layer 1
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#Layer 2
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
#Densely Connected Layer
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
#Dropout - reduz overfitting
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
#Readout layer
W_fc2 = weight_variable([1024, 36])
b_fc2 = bias_variable([36])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
#y_conv=tf.cast(y_conv, tf.int32)
#Train and evaluate
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(20000):
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={keep_prob: 0.5})
x, y_ = inputs(train=True, batch_size=2000)
#y_ = tf.string_to_number(y_, out_type=tf.int32)
print("test accuracy %g"%accuracy.eval(feed_dict={keep_prob: 1.0}))
coord.join(threads)
sess.close()
但是说到
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
出现以下错误：
ValueError: Incompatible shapes for broadcasting: TensorShape([Dimension(100)]) and TensorShape([Dimension(100), Dimension(36)])
我认为问题出在 label 张量上，但我不确定如何修复它以获得 (None,36) 维度。有谁知道如何解决这个问题？
谢谢 马塞洛
完成此工作的最简单方法是使用 tf.one_hot():
y_ 替换为单热编码
onehot_y_ = tf.one_hot(y_, 36, dtype=tf.float32)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(onehot_y_ * tf.log(y_conv),
reduction_indices=[1]))
另一种方法是切换到使用专门的运算符 tf.nn.sparse_softmax_cross_entropy_with_logits()，它更高效且数值更稳定。要使用它，您必须在 y_conv:
tf.nn.softmax() 的调用
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y_conv, y_))