我对tensorflow和python很新，目前正在尝试修改MNIST以获得240x320x3图像的专家教程。我有2个.py脚本
tfrecord_reeader.py
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
data_path = 'train.tfrecords' # address to save the hdf5 file
def read_data():
with tf.Session() as sess:
feature = {'train/image': tf.FixedLenFeature([], tf.string),
'train/label': tf.FixedLenFeature([], tf.int64)}
# Create a list of filenames and pass it to a queue
filename_queue = tf.train.string_input_producer([data_path], num_epochs=1)
# Define a reader and read the next record
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
# Decode the record read by the reader
features = tf.parse_single_example(serialized_example, features=feature)
# Convert the image data from string back to the numbers
image = tf.decode_raw(features['train/image'], tf.float32)
# Cast label data into int32
label = tf.cast(features['train/label'], tf.int32)
# Reshape image data into the original shape
image = tf.reshape(image, [240, 320, 3])
sess.close()
return image, label
def next_batch(image, label, batchSize):
imageBatch, labelBatch = tf.train.shuffle_batch([image, label], batch_size=batchSize, capacity=30, num_threads=1,
min_after_dequeue=10)
return imageBatch, labelBatch
train.py
import tensorflow as tf
from random import shuffle
import glob
import sys
#import cv2
from tfrecord_reader import read_data, next_batch
import argparse # For passing arguments
import numpy as np
import math
import time
IMAGE_WIDTH = 240
IMAGE_HEIGHT = 320
IMAGE_DEPTH = 3
IMAGE_SIZE = 240*320*3
NUM_CLASSES = 5
BATCH_SIZE = 50
# Creates a weight tensor sized by shape
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
# Creates a bias tensor sized by shape
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')
def main(argv):
# Perform training
x = tf.placeholder(tf.float32, [None, IMAGE_SIZE]) # 240*320=76800
W = tf.Variable(tf.zeros([IMAGE_SIZE, NUM_CLASSES]))
b = tf.Variable(tf.zeros([NUM_CLASSES]))
y = tf.matmul(x, W) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, NUM_CLASSES]) # Desired output
# First convolutional layer
W_conv1 = weight_variable([5, 5, IMAGE_DEPTH, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_DEPTH])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
# Second convolutional layer
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)
# First fully connected layer
W_fc1 = weight_variable([60 * 80 * 64, 1024])
b_fc1 = bias_variable([1024])
# Flatten the layer
h_pool2_flat = tf.reshape(h_pool2, [-1, 60 * 80 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# Drop out layer
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# Second fully connected layer
W_fc2 = weight_variable([1024, NUM_CLASSES])
b_fc2 = bias_variable([NUM_CLASSES])
# Output layer
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
# print(y_conv.shape)
# print(y_conv.get_shape)
# Get the loss
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
# Minimize the loss
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# Read all data from tfrecord file
imageList, labelList = read_data()
imageBatch, labelBatch = next_batch(imageList, labelList, BATCH_SIZE)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
train_images, train_labels = sess.run([imageBatch, labelBatch])
train_images = np.reshape(train_images, (-1, IMAGE_SIZE))
train_labels = np.reshape(train_labels, (-1, NUM_CLASSES))
sess.run(train_step, feed_dict = {x: train_images, y_: train_labels, keep_prob: 1.0})
coord.request_stop()
coord.join(threads)
sess.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
当我运行程序时，我正在
InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[50,5] labels_size=[10,5]
[[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape_2, Reshape_3)]]
我已经对这个问题进行了几个小时的搜索，但是看不出为什么logits与标签大小不匹配。如果我将batchsize更改为10，则标签大小将变为[2,5]，就像它总是被除以5一样。有人可以帮助我吗？
答案 0 :(得分：2)
您的标签很可能是单个整数值而不是单热矢量，因此您的labelBatch是一个大小为[50]的向量，包含单个数字，如“1”或“4”。现在，当您使用train_labels = np.reshape(train_labels, (-1, NUM_CLASSES))重塑它们时
你正在改变形状[10,5]。
tf.nn.softmax_cross_entropy_with_logits函数期望标签是标签的“一热”编码（这意味着标签3转换为大小为5的向量，其中1位置为3，其他位置为零）。您可以使用tf.nn.one_hot函数实现此目的，但更简单的方法是使用旨在处理这些单值标签的tf.nn.sparse_softmax_cross_entropy_with_logits函数。为此，您需要更改以下行：
y_ = tf.placeholder(tf.float32, [None]) # Desired output
cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
摆脱train_labels = np.reshape(train_labels, (-1, NUM_CLASSES))行。
（顺便说一句，在以这种方式读取数据时，实际上并不需要使用占位符 - 您可以直接使用输出张量。）