首先，我试图按照人们的指示恢复该模型，但是我找不到任何线索。 以下是我保存模型的代码，并且模型已成功保存。
import tensorflow as tf
from sklearn.utils import shuffle
EPOCHS = 10
BATCH_SIZE = 128
x = tf.placeholder(tf.float32, (None, 32, 32, 3),name='x')
y = tf.placeholder(tf.int32, (None),name='y')
one_hot_y = tf.one_hot(y, 43)
rate = 0.001
logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
def evaluate(TrainX, trainLabels):
num_examples = len(TrainX)
total_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = TrainX[offset:offset+BATCH_SIZE], trainLabels[offset:offset+BATCH_SIZE]
accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(trainImages)
print("Training...")
print()
for i in range(EPOCHS):
TrainX, trainLabels = shuffle(TrainX, trainLabels)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = TrainX[offset:end], trainLabels[offset:end]
sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})
validation_accuracy = evaluate(TrainX, trainLabels)
print("EPOCH {} ...".format(i+1))
print("Validation Accuracy = {:.3f}".format(validation_accuracy))
print()
saver.save(sess, './lenet')
print("Model saved")
培训...
EPOCH 1 ... 验证准确性= 0.765
EPOCH 2 ... 验证精度= 0.911
EPOCH 3 ... 验证准确性= 0.933
EPOCH 4 ... 验证准确度= 0.958
EPOCH 5 ... 验证准确度= 0.965
EPOCH 6 ... 验证准确性= 0.973
EPOCH 7 ... 验证准确性= 0.978
EPOCH 8 ... 验证准确性= 0.986
EPOCH 9 ... 验证精度= 0.985
EPOCH 10 ... 验证准确度= 0.980
模型已保存
我的问题是，假设我放置了5个测试数据以查看通过训练后的模型时它们的准确性，那么当我放置新的测试数据时如何使用该模型。我想看看测试数据和标签的正确性，它们将正确地适应训练后的模型。感谢您的宝贵时间，如果您有不了解的内容，我愿意为您提供更多详细信息。
答案 0 :(得分：0)
简而言之，我建议使用tf.data和tf.saved_model API。有2种机制：tf.train.Saver()或基于先前机制的更高级别的API tf.saved_model。您可以在其他帖子link1 link2中找到不同之处。我试图为您编写后一版本的伪代码，但最终我想到了以下所有摘要。希望对您有所帮助：
培训部分：
# create list of file names: ['0.csv', '1.csv', ...]
totrain_features = [os.path.join('./TrainX/', f) for f in os.listdir('./TrainX/') if f.endswith('.csv')]
totrain_img = [os.path.join('./TrainLabels/', f) for f in os.listdir('./TrainLabels/') if f.endswith('.csv')]
epoch_length = len(totrain_features)
# model starts here
file_names_ph = tf.placeholder(tf.string, shape=(None), name='file_name_ph') #create a ph to put list of file paths
in_pipeline = input_pipeline(file_names_ph) # check standalone code below
nodes = model(in_pipeline['img_next_op'], in_pipeline['label_next_op'])
with tf.Session() as sess:
sess.run([tf.global_variables_initializer(), in_pipeline['iter_init_op']], feed_dict={file_names_ph: totrain_files})
for step in tqdm(range(epoch_length)):
# run train_op
_ = sess.run(nodes['train_op'])
# use saver to save weights
if step % epoch_length == epoch_length - 1: #save last step
# prepare args for simple_save
in_dict = {
'file_names': file_names_ph,
}
out_dict = {
'predict': nodes['predict_op'],
'diff_op': nodes['diff_op']
}
tf.saved_model.simple_save(sess, './savedmodel', in_dict, out_dict) # This is what you need, the pb file of the graph and variables are saved in savedmodel folder
预测部分：
# input pipeline for predict
# create list of file names: ['0.csv', '1.csv', ...]
topredict_files = [os.path.join('./predict/', f) for f in os.listdir('./predict/') if f.endswith('.csv')]
epoch_length = len(topredict_files)
# save prediction images to /results folder
if not os.path.exists('./results'):
os.makedirs('./results')
# reset another graph as the default graph
graph2 = tf.Graph()
with graph2.as_default():
with tf.Session() as sess:
tf.saved_model.loader.load(
sess,
[tf.saved_model.tag_constants.SERVING], './savedmodel'
) # here's what you need
# get operation and so on
file_names_ph = graph2.get_tensor_by_name('file_name_ph:0')
predict_tensor = graph2.get_tensor_by_name('Conv1/prediction:0')
diff_tensor = graph2.get_tensor_by_name('Operations/difference:0')
iter_init_op = graph2.get_operation_by_name('iter_init_op')
sess.run(iter_init_op, feed_dict={file_names_ph: topredict_files})
for step in tqdm(range(epoch_length)):
predict, difference = sess.run([predict_tensor, diff_tensor])
# then save your prediction and comparison
...
看看我如何定义管道和模型：
def input_pipeline(file_names_ph):
# create new dataset for predict
dataset = tf.data.Dataset.from_tensor_slices(file_names_ph)
# apply list of file names to the py function wrapper for reading files
dataset = dataset.map(_pyfn_wrapper, num_parallel_calls=mp.cpu_count()) # use the tf built-in csv reader or take a look how to use py_func:https://stackoverflow.com/questions/55363728/how-to-feed-h5-files-in-tf-data-pipeline-in-tensorflow-model
# construct batch size
dataset = dataset.batch(1).prefetch(mp.cpu_count())
# initialize iterator
iterator = tf.data.Iterator.from_structure(dataset.output_types, dataset.output_shapes)
iterator_initialize_op = iterator.make_initializer(dataset, name='iter_init_op')
# get image and labels
image_getnext_op, label_getnext_op = iterator.get_next()
return {'img_next_op': image_getnext_op, 'label_next_op': label_getnext_op, 'iter_init_op': iterator_initialize_op}
def model(in_ds, out_ds):
# a simple model
with tf.name_scope("Conv1"):
W = tf.get_variable("W", shape=[3, 3, 1, 1],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable("b", shape=[1], initializer=tf.contrib.layers.xavier_initializer())
layer1 = tf.nn.conv2d(in_ds, W, strides=[1, 1, 1, 1], padding='SAME') + b
prediction = tf.nn.relu(layer1, name='prediction')
with tf.name_scope("Operations"):
global_step = tf.Variable(0, name='global_step', trainable=False)
loss = tf.reduce_mean(tf.losses.mean_squared_error(labels=out_ds, predictions=prediction), name='loss')
train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss, name='train_op', global_step=global_step)
difference_op = tf.cast(tf.equal(prediction, out_ds), dtype=tf.int32, name='difference')
# I really like dictionary, it's easy to handle
return {'global_step': global_step, 'loss': loss, 'train_op': train_op, 'diff_op': difference_op, 'predict_op': prediction}