I'm training a CNN with tensorflow for recognizing handwriting digits. First code trains model with mnist dataset. After that saves the model.
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("data/mnist",one_hot=True,reshape=False)
X=tf.placeholder(tf.float32,[None,28,28,1])
Y=tf.placeholder(tf.float32,[None,10])
wc1=tf.Variable(tf.random.truncated_normal([6,6,1,16],stddev=0.2))
bc1=tf.Variable(tf.random.truncated_normal([16],stddev=0.2))
wc2=tf.Variable(tf.random.truncated_normal([5,5,16,32],stddev=0.2))
bc2=tf.Variable(tf.random.truncated_normal([32],stddev=0.2))
wd1=tf.Variable(tf.random.truncated_normal([1568,256],stddev=0.2))
bd1=tf.Variable(tf.random.truncated_normal([256],stddev=0.2))
wd2=tf.Variable(tf.random.truncated_normal([256,64],stddev=0.2))
bd2=tf.Variable(tf.random.truncated_normal([64],stddev=0.2))
wdo=tf.Variable(tf.random.truncated_normal([64,10],stddev=0.2))
bdo=tf.Variable(tf.random.truncated_normal([10],stddev=0.2))
y=tf.nn.relu(tf.nn.conv2d(X,wc1,strides=[1,1,1,1],padding="SAME")+bc1)
y=tf.nn.max_pool(y,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")
y=tf.nn.relu(tf.nn.conv2d(y,wc2,strides=[1,1,1,1],padding="SAME")+bc2)
y=tf.nn.max_pool(y,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")
y=tf.reshape(y,(-1,1568))
y=tf.nn.tanh(tf.linalg.matmul(y,wd1)+bd1)
y=tf.nn.tanh(tf.linalg.matmul(y,wd2)+bd2)
y_pred=tf.nn.softmax(tf.linalg.matmul(y,wdo)+bdo)
xent=-tf.reduce_sum(Y*tf.math.log(y_pred))
l2=tf.reduce_sum(tf.math.square(Y-y_pred))
correct_pred=tf.equal(tf.argmax(Y,1),tf.argmax(y_pred,1))
accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))
optimizer=tf.train.AdamOptimizer(1e-3).minimize(xent)
images=[]
saver=tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(3001):
bx,by=mnist.train.next_batch(50)
sess.run(optimizer,feed_dict={X:bx,Y:by})
print("Model is trained")
acc,x_l,l2_l=sess.run([accuracy,xent,l2],feed_dict={X:bx,Y:by})
print("Iteration",i,"Accuracy="+str(acc),"Cross Entropy Loss="+str(x_l),"Mean Squared Error="+str(l2_l))
test_acc,test_x,test_l=sess.run([accuracy,xent,l2],feed_dict={X:mnist.test.images,Y:mnist.test.labels})
print("Train Accuracy="+str(acc),"Cross Entropy Loss="+str(x_l),"Mean Squared Error="+str(l2_l),"\n\n")
save_path = saver.save(sess, "tmp/model.ckpt")
print("Model saved in path: %s" % save_path)
Second code restores model. It predicts image with restored model.
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("data/mnist",one_hot=True,reshape=False)
X=tf.placeholder(tf.float32,[None,28,28,1])
Y=tf.placeholder(tf.float32,[None,10])
wc1=tf.Variable(tf.random.truncated_normal([6,6,1,16],stddev=0.2))
bc1=tf.Variable(tf.random.truncated_normal([16],stddev=0.2))
wc2=tf.Variable(tf.random.truncated_normal([5,5,16,32],stddev=0.2))
bc2=tf.Variable(tf.random.truncated_normal([32],stddev=0.2))
wd1=tf.Variable(tf.random.truncated_normal([1568,256],stddev=0.2))
bd1=tf.Variable(tf.random.truncated_normal([256],stddev=0.2))
wd2=tf.Variable(tf.random.truncated_normal([256,64],stddev=0.2))
bd2=tf.Variable(tf.random.truncated_normal([64],stddev=0.2))
wdo=tf.Variable(tf.random.truncated_normal([64,10],stddev=0.2))
bdo=tf.Variable(tf.random.truncated_normal([10],stddev=0.2))
y=tf.nn.relu(tf.nn.conv2d(X,wc1,strides=[1,1,1,1],padding="SAME")+bc1)
y=tf.nn.max_pool(y,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")
y=tf.nn.relu(tf.nn.conv2d(y,wc2,strides=[1,1,1,1],padding="SAME")+bc2)
y=tf.nn.max_pool(y,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")
y=tf.reshape(y,(-1,1568))
y=tf.nn.tanh(tf.linalg.matmul(y,wd1)+bd1)
y=tf.nn.tanh(tf.linalg.matmul(y,wd2)+bd2)
y_pred=tf.nn.softmax(tf.linalg.matmul(y,wdo)+bdo)
xent=-tf.reduce_sum(Y*tf.math.log(y_pred))
l2=tf.reduce_sum(tf.math.square(Y-y_pred))
correct_pred=tf.equal(tf.argmax(Y,1),tf.argmax(y_pred,1))
accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))
optimizer=tf.train.AdamOptimizer(1e-3).minimize(xent)
saver=tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, "tmp/model.ckpt")
print("Acc is",sess.run(accuracy,feed_dict={X:mnist.test.images[0:59],Y:mnist.train.labels[0:59]}))
while(True):
path=input("file:")
image=cv2.imread(path,0)
cv2.imshow("Image",image)
image=cv2.resize(image,(28,28)).reshape(28,28,1)
val_=sess.run(y_pred,feed_dict={X:(image,)})
val=sess.run(tf.argmax(y_pred,1),feed_dict={X:(image,)})
print(val_)
print(val)
print("Value=",val)
When I test the restored model, accuracy is under 0.1 .But model trained with 1.0 accuracy. How to fix this problem?
