以下代码是我从文件中读取图像和标签的管道：
import tensorflow as tf
import numpy as np
import tflearn.data_utils
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
import sys
#process labels in the input file
def process_label(label):
info=np.zeros(6)
...
return info
def read_label_file(file):
f = open(file, "r")
filepaths = []
labels = []
lines = []
for line in f:
tokens = line.split(",")
filepaths.append([tokens[0],tokens[1],tokens[2]])
labels.append(process_label(tokens[3:]))
lines.append(line)
return filepaths, np.vstack(labels), lines
def get_data_batches(params):
# reading labels and file path
train_filepaths, train_labels, train_line = read_label_file(params.train_info)
test_filepaths, test_labels, test_line = read_label_file(params.test_info)
# convert string into tensors
train_images = ops.convert_to_tensor(train_filepaths)
train_labels = ops.convert_to_tensor(train_labels)
train_line = ops.convert_to_tensor(train_line)
test_images = ops.convert_to_tensor(test_filepaths)
test_labels = ops.convert_to_tensor(test_labels)
test_line = ops.convert_to_tensor(test_line)
# create input queues
train_input_queue = tf.train.slice_input_producer([train_images, train_labels, train_line], shuffle=params.shuffle)
test_input_queue = tf.train.slice_input_producer([test_images, test_labels, test_line],shuffle=False)
# process path and string tensor into an image and a label
train_image=None
for i in range(train_input_queue[0].get_shape()[0]):
file_content = tf.read_file(params.path_prefix+train_input_queue[0][i])
train_imageT = (tf.to_float(tf.image.decode_jpeg(file_content, channels=params.num_channels)))*(1.0/255)
train_imageT = tf.image.resize_images(train_imageT,[params.load_size[0],params.load_size[1]])
train_imageT = tf.random_crop(train_imageT,size=[params.crop_size[0],params.crop_size[1],params.num_channels])
train_imageT = tf.image.random_flip_up_down(train_imageT)
train_imageT = tf.image.per_image_standardization(train_imageT)
if(i==0):
train_image = train_imageT
else:
train_image = tf.concat([train_image, train_imageT], 2)
train_label = train_input_queue[1]
train_lineInfo = train_input_queue[2]
test_image=None
for i in range(test_input_queue[0].get_shape()[0]):
file_content = tf.read_file(params.path_prefix+test_input_queue[0][i])
test_imageT = tf.to_float(tf.image.decode_jpeg(file_content, channels=params.num_channels))*(1.0/255)
test_imageT = tf.image.resize_images(test_imageT,[params.load_size[0],params.load_size[1]])
test_imageT = tf.image.central_crop(test_imageT, (params.crop_size[0]+0.0)/params.load_size[0])
test_imageT = tf.image.per_image_standardization(test_imageT)
if(i==0):
test_image = test_imageT
else:
test_image = tf.concat([test_image, test_imageT],2)
test_label = test_input_queue[1]
test_lineInfo = test_input_queue[2]
# define tensor shape
train_image.set_shape([params.crop_size[0], params.crop_size[1], params.num_channels*3])
train_label.set_shape([66])
test_image.set_shape( [params.crop_size[0], params.crop_size[1], params.num_channels*3])
test_label.set_shape([66])
# collect batches of images before processing
train_image_batch, train_label_batch, train_lineno = tf.train.batch([train_image, train_label, train_lineInfo],batch_size=params.batch_size,num_threads=params.num_threads,allow_smaller_final_batch=True)
test_image_batch, test_label_batch, test_lineno = tf.train.batch([test_image, test_label, test_lineInfo],batch_size=params.test_size,num_threads=params.num_threads,allow_smaller_final_batch=True)
if(params.loadSlice=='all'):
return train_image_batch, train_label_batch, train_lineno, test_image_batch, test_label_batch, test_lineno
elif params.loadSlice=='train':
return train_image_batch, train_label_batch
elif params.loadSlice=='test':
return test_image_batch, test_label_batch
elif params.loadSlice=='train_info':
return train_image_batch, train_label_batch, train_lineno
elif params.loadSlice=='test_info':
return test_image_batch, test_label_batch, test_lineno
else:
return train_image_batch, train_label_batch, test_image_batch, test_label_batch
我想使用相同的管道来加载测试数据。我的测试数据的大小很大，我无法一次加载所有这些数据。 我有20453个测试示例，它不是批量大小的整数倍（这里是512）。
如何通过此管道只读一次我的所有测试示例，然后测量它们的性能？
目前，我正在使用此代码批量处理我的测试数据，但它不起作用。即使我将 allow_smaller_final_batch 设置为True
，它也始终从队列中读取完整批次with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess,"checkpoints2/snapshot-16")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
more = True
num_examples=0
while(more):
img_test, lbl_test, lbl_line=sess.run([test_image_batch,test_label_batch,test_lineno])
print(lbl_test.shape)
size=lbl_test.shape[0]
num_examples += size
if size<args.batch_size:
more = False
sess.close()
这是我模特的代码：
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.normalization import batch_normalization
from tflearn.layers.estimator import regression
from tflearn.activations import relu
def get_alexnet(x,num_output):
network = conv_2d(x, 64, 11, strides=4)
network = batch_normalization(network,epsilon=0.001)
network = relu (network)
network = max_pool_2d(network, 3, strides=2)
network = conv_2d(network, 192, 5)
network = batch_normalization(network,epsilon=0.001)
network = relu(network)
network = max_pool_2d(network, 3, strides=2)
network = conv_2d(network, 384, 3)
network = batch_normalization(network,epsilon=0.0001)
network = relu(network)
network = conv_2d(network, 256, 3)
network = batch_normalization(network,epsilon=0.001)
network = relu(network)
network = conv_2d(network, 256, 3)
network = batch_normalization(network,epsilon=0.001)
network = relu(network)
network = max_pool_2d(network, 3, strides=2)
network = fully_connected(network, 4096)
network = batch_normalization(network,epsilon=0.001)
network = relu(network)
network = dropout(network, 0.5)
network = fully_connected(network, 4096)
network = batch_normalization(network,epsilon=0.001)
network = relu(network)
network = dropout(network, 0.5)
network1 = fully_connected(network, num_output)
network2 = fully_connected(network, 12)
network3 = fully_connected(network,6)
return network1,network2,network3
答案 0 :(得分：0)
这可以通过设置num_epochs = 1和allow_smaller_final_batch = True来实现！
答案 1 :(得分：0)
设置一个解决方案batch_size =测试集的大小