求助！我想模仿vgg16的結構跑一下mnist，結果出現了這個錯誤：
InvalidArgumentError (see above for traceback): Incompatible shapes: [200] vs. [50]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]]
下面是我的代碼：
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist=input_data.read_data_sets("E:/MDLearning/mnist",one_hot=True)
sess=tf.InteractiveSession()
#定義變數
x=tf.placeholder("float",shape=[None,784])
y_=tf.placeholder("float",shape=[None,10])
#定義權重
def weight_variable(shape):
initial=tf.truncated_normal(shape,stddev=0.1)
return tf.Variable(initial)
#定義偏置
def bias_variable(shape):
initial=tf.constant(0.1,shape=shape)
return tf.Variable(initial)
#定義卷積函式
def myConv(x,W):
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding="SAME")
#定義池化函式
def myMaxPool1(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,1,1,1],padding="SAME")
def myMaxPool2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding="VALID")
x_image=tf.reshape(x,[-1,28,28,1])
#第一層卷積
W_conv11=weight_variable([3,3,1,64])
b_conv1=bias_variable([64])
h_conv11=tf.nn.relu(myConv(x_image,W_conv11)+b_conv1)
W_conv12=weight_variable([3,3,64,64])
h_conv12=tf.nn.relu(myConv(h_conv11,W_conv12)+b_conv1)
h_pool1=myMaxPool1(h_conv12)
#第二層卷積
W_conv21=weight_variable([3,3,64,128])
b_conv2=bias_variable([128])
h_conv21=tf.nn.relu(myConv(h_pool1,W_conv21)+b_conv2)
W_conv22=weight_variable([3,3,128,128])
h_conv22=tf.nn.relu(myConv(h_conv21,W_conv22)+b_conv2)
h_pool2=myMaxPool1(h_conv22)
#第三層卷積
W_conv31=weight_variable([3,3,128,256])
b_conv3=bias_variable([256])
h_conv31=tf.nn.relu(myConv(h_pool2,W_conv31)+b_conv3)
W_conv32=weight_variable([3,3,256,256])
h_conv32=tf.nn.relu(myConv(h_conv31,W_conv32)+b_conv3)
W_conv33=weight_variable([3,3,256,256])
h_conv33=tf.nn.relu(myConv(h_conv32,W_conv33)+b_conv3)
h_pool3=myMaxPool1(h_conv33)
#第四層卷積
W_conv41=weight_variable([3,3,256,512])
b_conv4=bias_variable([512])
h_conv41=tf.nn.relu(myConv(h_pool3,W_conv41)+b_conv4)
W_conv42=weight_variable([3,3,512,512])
h_conv42=tf.nn.relu(myConv(h_conv41,W_conv42)+b_conv4)
W_conv43=weight_variable([3,3,512,512])
h_conv43=tf.nn.relu(myConv(h_conv42,W_conv43)+b_conv4)
h_pool4 =myMaxPool2(h_conv43)
#第五層卷積
W_conv51=weight_variable([3,3,512,512])
b_conv5=bias_variable([512])
h_conv51=tf.nn.relu(myConv(h_pool4,W_conv51)+b_conv5)
W_conv52=weight_variable([3,3,512,512])
h_conv52=tf.nn.relu(myConv(h_conv51,W_conv52)+b_conv5)
W_conv53=weight_variable([3,3,512,512])
h_conv53=tf.nn.relu(myConv(h_conv52,W_conv53)+b_conv5)
h_pool5=myMaxPool2(h_conv53)
#第一層全連接
W_fc1=weight_variable([7*7*512,4096])
b_fc1=bias_variable([4096])
h_pool5_flat=tf.reshape(h_pool2,[-1,7*7*512])
h_fc1=tf.nn.relu(tf.matmul(h_pool5_flat,W_fc1)+b_fc1)
#Dropout
keep_prob=tf.placeholder("float")
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)
#第二層全連接
W_fc2=weight_variable([4096,4096])
b_fc2=bias_variable([4096])
h_fc2=tf.nn.relu(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
#隨機變數失活
h_fc2_drop=tf.nn.dropout(h_fc2,keep_prob)
#第三層全連接
W_fc3=weight_variable([4096,10])
b_fc3=bias_variable([10])
y=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc3)+b_fc3)
#類別預測
cross_entropy=-tf.reduce_sum(y_*tf.log(y))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
#初始化變數
sess.run(tf.initialize_all_variables())
#迭代輸出
for i in range(10000):
batch=mnist.train.next_batch(50)
if i%50==0:
train_accuracy=accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
print("step:%d,training accuracy:%g"%(i,train_accuracy))
train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})
print("test accuracy is %g"%accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))
應該是tf.equal()那里出問題了，是維數對應不起來嘛，但沒看出來哪里有問題呀
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