问题描述
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
解决方法
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