尝试使用cnn对图片识别
新手，不知道为什么结果像是随机的。最不能理解的是每次loss输出值一样，而accuracy值却不一样。调试了几天，依然没有头绪，大神救救我
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')
class network(object):
def __init__(self):
with tf.variable_scope("weights") as scope_weight:
self.weights={
"conv1":tf.get_variable("conv1",[3,2,1,32],
initializer=tf.contrib.layers.xavier_initializer_conv2d()),
"conv2":tf.get_variable("conv2",[3,2,32,64],
initializer=tf.contrib.layers.xavier_initializer_conv2d()),
"fc1":tf.get_variable("fc1",[6*4*64,1024],
initializer=tf.contrib.layers.xavier_initializer_conv2d()),
"fc2":tf.get_variable("fc2",[1024,34],
initializer=tf.contrib.layers.xavier_initializer_conv2d()),
}
tf.summary.histogram("conv1_weight",self.weights["conv1"])
tf.summary.histogram("conv2_weight",self.weights["conv2"])
tf.summary.histogram("fc1_weight",self.weights["fc1"])
tf.summary.histogram("fc2_weight",self.weights["fc2"])
scope_weight.reuse_variables()
with tf.variable_scope("bias")as scope_bias:
self.bias={
"conv1":tf.get_variable("conv1",[32],
initializer=tf.constant_initializer(0.1)),
"conv2":tf.get_variable("conv2",[64],
initializer=tf.constant_initializer(0.1)),
"fc1":tf.get_variable("fc1",[1024],
initializer=tf.constant_initializer(0.1)),
"fc2":tf.get_variable("fc2",[34],
initializer=tf.constant_initializer(0.1))
}
tf.summary.histogram("conv1_bias",self.bias["conv1"])
tf.summary.histogram("conv2_bias",self.bias["conv2"])
tf.summary.histogram("fc1_bias",self.bias["fc1"])
tf.summary.histogram("fc2_bias",self.bias["fc2"])
scope_bias.reuse_variables()
def inference(self,image):
h_conv1=tf.nn.relu(conv2d(image,self.weights["conv1"])+self.bias["conv1"])
h_pool1=max_pool_2x2(h_conv1)
h_conv2=tf.nn.relu(conv2d(h_pool1,self.weights["conv2"])+self.bias["conv2"])
h_pool2=max_pool_2x2(h_conv2)
h_pool2_flat=tf.reshape(h_pool2,[-1,6*4*64])
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,self.weights["fc1"])+self.bias["fc1"])
y_conv=tf.nn.relu(tf.matmul(h_fc1,self.weights["fc2"])+self.bias["fc2"])
return y_conv
def softmax_loss(self,predicts,labels):
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=predicts,labels=labels))
self.cost=loss
return self.cost
def optimizer(self,loss,lr=0.01):
train_optimizer=tf.train.AdamOptimizer(lr).minimize(loss)
return train_optimizer
def train(batch_size):
#testnamequeue=tr.generate_filenamequeue(['test24_16.tfrecord'])
#[testimages,testlabels]=tr.get_batch(testnamequeue,2000)
trainnamequeue=tr.generate_filenamequeue(['train24_16.tfrecord'])
[trainimages,trainlabels]=tr.get_batch(trainnamequeue,batch_size)
x=tf.reshape(trainimages,[-1,24,16,1])
img=tf.summary.image('input',x,batch_size)
y_=tf.cast(trainlabels,tf.float32)
y_=tf.reshape(y_,[batch_size,34])
with tf.variable_scope("train")as scope_train:
net=network()
y_train=net.inference(x)
loss=net.softmax_loss(y_train,y_)
opt=net.optimizer(loss)
tf.summary.scalar("loss",loss)
correct_prediction=tf.equal(tf.argmax(y_,1),tf.argmax(y_train,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess=tf.InteractiveSession()
sess.run(init_op)
coord=tf.train.Coordinator()
threads=tf.train.start_queue_runners(sess=sess,coord=coord)
train_writer=tf.summary.FileWriter('train',sess.graph)
i=0
#train_summary=tf.summary.merge([img,loss,w_conv1_summ])
train_summary=tf.summary.merge_all()
try:
while not coord.should_stop():
[_,train_summ]=sess.run([opt,train_summary])
train_writer.add_summary(train_summ,i)
if i%100==0:
[ac,loss_num]=sess.run([accuracy,loss])
print loss_num
print("step %d, training accuracy %g"%(i, ac))
i=i+1
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
train_writer.close()
# Wait for threads to finish.
coord.join(threads)
train(50)
回答：
楼主请问你的tr里边引用的变量是什么
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