无法将数据输入tensorflow图

我已经在计算机上训练了一个神经网络模型 MNIST 使用脚本的数据集 mnist_3.1_convolutional_bigger_dropout.py 本条规定 tutorial 。

我想在自定义数据集上测试经过训练的模型,因此我编写了一个小脚本 predict.py 加载经过训练的模型并向其提供数据。我尝试了两种预处理图像的方法,以便它们与MNIST格式兼容。

• 方法1 :将图像大小调整为28x28
• 方法2 :提到的技巧 here 使用

这两种方法都会导致错误

InvalidArgumentError(回溯见上文):必须为带有dtype float的占位符张量“占位符_2”输入一个值

预测py

# Importing libraries
from scipy.misc import imread
import tensorflow as tf
import numpy as np
import cv2 as cv
import glob

from test import imageprepare

files = glob.glob('data2/*.*')
#print(files)

# Method 1
'''
img_data = []
for fl in files:
    img = imageprepare(fl)
    img = img.reshape(img.shape[0], img.shape[1], 1)
    img_data.append(img)
'''

# Method 2
dig_cont = [cv.imread(fl, 0) for fl in files]
#print(len(dig_cont))

img_data = []
for i in range(len(dig_cont)):
    img = cv.resize(dig_cont[i], (28, 28))
    img = img.reshape(img.shape[0], img.shape[1], 1)
    img_data.append(img)


print("Restoring Model ...")

sess = tf.Session()

# Step-1: Recreate the network graph. At this step only graph is created.
tf_saver = tf.train.import_meta_graph('model/model.meta')

# Step-2: Now let's load the weights saved using the restore method.
tf_saver.restore(sess, tf.train.latest_checkpoint('model'))

print("Model restored")

x = tf.get_default_graph().get_tensor_by_name('X:0')
print('x :', x.shape)
y = tf.get_default_graph().get_tensor_by_name('Y:0')
print('y :', y.shape)

dict_data = {x: img_data}

result = sess.run(y, feed_dict=dict_data)
print(result)
print(result.shape)

sess.close()