如何解决为什么模型预测的准确性和训练的准确性之间有如此大的差异？
这是我的代码：
数据集：mnist;
型号：resnet
基于tensorflow 1.15.0
在resnet模型训练期间，我在实验中注意到了这种行为。训练损失减少，并且训练精度在0.99左右增加。但是在测试过程中，测试数据的精度为0.06，与训练精度有很大差异。
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
np.set_printoptions(threshold=np.inf)
np.random.seed(2020)
tf.set_random_seed(2020)
class ModelVGG11(object):
def __init__(self,data_train,data_test,session):
self.data_train = data_train
self.data_test = data_test
self.sess = session
self.num_epochs = 10
self.num_class = 10
self.batch_size = 256
self.learning_rate = 0.01
self.__def_placeholders()
self.__initial_dataset()
self.__def_model()
self.__def_loss()
self.__def_optimizer()
self.__def_metrics()
def __def_placeholders(self):
self.inputs = tf.placeholder(tf.float32,shape=(None,224,1),name='model_input')
self.label = tf.placeholder(tf.int64,),name='model_label')
self.is_training = tf.placeholder(tf.bool,shape=(),name='is_training')
self.handle = tf.placeholder(tf.string,shape=())
self.global_step = tf.Variable(0,trainable=False)
def __initial_dataset(self):
def __image_resize(data):
image = tf.cast(data['image'],tf.float32)
# image = image / 255
image = tf.image.resize_image_with_pad(image,224)
return image,data['label']
self.data_train = self.data_train.map(__image_resize).shuffle(buffer_size=1000).batch(self.batch_size).prefetch(1)
self.data_test = self.data_test.map(__image_resize).shuffle(buffer_size=1000).batch(self.batch_size).prefetch(1)
self.iterator_train = self.data_train.make_initializable_iterator()
self.iterator_test = self.data_test.make_initializable_iterator()
self.handle_train = self.iterator_train.string_handle()
self.handle_test = self.iterator_test.string_handle()
iterator = tf.data.Iterator.from_string_handle(self.handle,self.data_train.output_types,self.data_train.output_shapes)
self.next_batch = iterator.get_next()
def __res_block(self,inputs,out_channels,conv1x1=False,strides=1,name=None):
with tf.variable_scope(name):
outputs = tf.layers.Conv2D(out_channels,kernel_size=3,strides=strides,padding='same',activation=None,kernel_initializer=tf.glorot_normal_initializer)(inputs)
outputs = tf.layers.BatchNormalization()(outputs,training=self.is_training)
outputs = tf.nn.relu(outputs)
outputs = tf.layers.Conv2D(out_channels,kernel_initializer=tf.glorot_normal_initializer)(outputs)
outputs = tf.layers.BatchNormalization()(outputs,training=self.is_training)
# change input channels equal to out_channels
if conv1x1:
inputs = tf.layers.Conv2D(out_channels,kernel_size=1,strides=strides)(inputs)
return tf.nn.relu(outputs + inputs)
def __def_model(self):
with tf.variable_scope('resnet'):
# [batch,1]
inputs = tf.layers.Conv2D(filters=64,kernel_size=7,strides=2,kernel_initializer=tf.glorot_normal_initializer)(self.inputs) # [batch,112,64]
inputs = tf.layers.BatchNormalization()(inputs,training=self.is_training)
inputs = tf.nn.relu(inputs)
inputs = tf.layers.MaxPooling2D(pool_size=3,padding='same')(inputs) # [batch,56,64]
inputs = self.__res_block(inputs,64,False,1,'res_bolock_1') # [batch,128,True,2,'res_bolock_2') # [batch,28,128]
inputs = self.__res_block(inputs,256,'res_bolock_3') # [batch,14,256]
inputs = self.__res_block(inputs,512,'res_bolock_4') # [batch,7,512]
inputs = tf.reduce_mean(inputs,axis=[1,2])
outputs = tf.layers.Dense(units=10,activation=None)(inputs)
self.outputs = outputs
def __def_loss(self):
y_true = tf.cast(tf.one_hot(self.label,self.num_class),tf.float32)
loss = tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=self.outputs)
self.loss = tf.reduce_mean(loss)
tf.summary.scalar('loss',self.loss)
def __def_optimizer(self):
self.optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss,global_step=self.global_step)
def __def_metrics(self):
y_pred_label = tf.arg_max(tf.nn.softmax(self.outputs),1)
# self.acc = tf.metrics.accuracy(labels=self.label,predictions=y_pred_label,name='acc')
self.acc = tf.reduce_mean(tf.cast(tf.equal(y_pred_label,self.label),tf.float32))
tf.summary.scalar('acc',self.acc)
def train_and_evaluate(self):
# merge_summary = tf.summary.merge_all()
# summary_writer = tf.summary.FileWriter(self.summary_file,self.sess.graph)
# summary_writer.add_summary(train_summary,step)
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.local_variables_initializer())
handle_train,handle_test = self.sess.run([self.handle_train,self.handle_test])
for i in range(self.num_epochs):
epoch_train_metrics = []
try:
self.sess.run(self.iterator_train.initializer)
while True:
batch_x,batch_y = self.sess.run(self.next_batch,feed_dict={self.handle: handle_train})
loss,acc,_ = self.sess.run([self.loss,self.acc,self.optimizer],feed_dict={self.inputs: batch_x,self.label: batch_y,self.is_training: True})
epoch_train_metrics.append((loss,acc))
except tf.errors.OutOfRangeError:
try:
self.sess.run(self.iterator_test.initializer)
while True:
batch_x_test,batch_y_test = self.sess.run(self.next_batch,feed_dict={self.handle: handle_test})
acc = self.sess.run(self.acc,feed_dict={self.inputs: batch_x_test,self.label: batch_y_test,self.is_training: False})
except tf.errors.OutOfRangeError:
print('epoch {} train minibatch loss and acc: {},test minibatch acc: {}'.format(i + 1,np.mean(epoch_train_metrics,axis=0),acc))
if __name__ == "__main__":
mnist_data = tfds.load('mnist')
mnist_train,mnist_test = mnist_data['train'],mnist_data['test']
with tf.Session() as sess:
model = ModelVGG11(mnist_train,mnist_test,sess)
model.train_and_evaluate()
结果如下：
epoch 1 train minibatch loss and acc: [0.45032835 0.8764905 ],test minibatch acc: 0.0
epoch 2 train minibatch loss and acc: [0.06525008 0.9811669 ],test minibatch acc: 0.0
epoch 3 train minibatch loss and acc: [0.04049642 0.9874501 ],test minibatch acc: 0.125
epoch 4 train minibatch loss and acc: [0.02956291 0.99075246],test minibatch acc: 0.0
epoch 5 train minibatch loss and acc: [0.02403079 0.99252546],test minibatch acc: 0.0625
epoch 6 train minibatch loss and acc: [0.02128655 0.9933344 ],test minibatch acc: 0.0625
epoch 7 train minibatch loss and acc: [0.01614667 0.9947141 ],test minibatch acc: 0.0625
epoch 8 train minibatch loss and acc: [0.01534516 0.99461436],test minibatch acc: 0.0625
epoch 9 train minibatch loss and acc: [0.01119067 0.9964262 ],test minibatch acc: 0.125
epoch 10 train minibatch loss and acc: [0.0108306 0.9965314],test minibatch acc: 0.0625
我只想知道为什么会这样？我的代码有任何错误吗？
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