Thanks for your help. This question has bothered me for 2 days. I searched many sites and did not solve.
Background:
I am learning mnist, and it goes right at first, but when I save the model and restore, there is an Error, told me placeholder_1 must be fed. I am confused.
Code: Below code goes right.
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("mnist data/", one_hot=True)
import tensorflow as tf
# 操作符号变量来描述这些可交互的操作单元
x = tf.placeholder(tf.float32, [None, 784])
# 权重值和偏置量
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
# 实现模型
y = tf.nn.softmax(tf.matmul(x,W) + b)
# 添加一个新的占位符用于输入正确值
y_ = tf.placeholder("float", [None,10])
# 计算交叉熵:
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
# 要求TensorFlow用梯度下降算法（gradient descent algorithm）以0.01的学习速率最小化交叉熵
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
# 添加一个操作来初始化我们创建的变量
init = tf.initialize_all_variables()
saver = tf.train.Saver()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
save_path = saver.save(sess, "./model_mnist.ckpt",write_meta_graph=False)
print("Model saved in life:", save_path)
import cv2
import numpy as np
img = cv2.imread('lena.png')
img = cv2.resize(img, (28,28))
img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
arr = []
for i in range(28):
for j in range(28):
gray = 1 - img[i,j]/255
arr.append(gray)
arr_mnist = np.array([arr])
#print(arr_mnist)
result = sess.run(y, feed_dict={x:arr_mnist})
print(result)
#print(np.argmax(result[0]))
#print(np.sum(result[0]))
print("预测值为：",np.argmax(result[0]),"；概率为：",np.max(result[0])/np.sum(result[0]))
#print(tf.argmax(result,1))
But, when I want to use the model to restore. it goes wrong.
import cv2
import numpy as np
import tensorflow as tf
img = cv2.imread('lena.png')
img = cv2.resize(img, (28,28))
img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
arr = []
for i in range(28):
for j in range(28):
gray = 1 - img[i,j]/255
arr.append(gray)
arr_mnist = np.array([arr])
#print(arr_mnist)
tf.reset_default_graph()
x = tf.placeholder("float", shape=[None, 784])
y = tf.placeholder("float", shape=[None, 10])
#keep_prob = tf.placeholder("float")
sess = tf.Session()
saver = tf.train.import_meta_graph('./model_mnist.ckpt.meta')
saver.restore(sess, './model_mnist.ckpt')
result = sess.run(y, feed_dict={x:arr_mnist})
print(result)
print("预测值为：",np.argmax(result[0]),"；概率为：",np.max(result[0])/np.sum(result[0]))
the Error is :
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,10]
[[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
so, I suppose the problem should be in the model save or restore process, but I cannot figure out. how can I correct the code? thank you!
