我有import pickle
import numpy as np
import os
import tensorflow as tf
from tensorflow.python.framework import tensor_util
import math
#imports data
def unpickle(file):
import pickle
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
cifar100_test = {}
cifar100_train = {}
labelMap = {}
labelNames = {}
# Load the raw CIFAR-10 data.
cifar100_test = unpickle('dataset/cifar-100-python/test')
cifar100_train = unpickle('dataset/cifar-100-python/train')
labelMap = unpickle('dataset/cifar-100-python/meta')
#tr for training data and te for testing data, X is data, Y is label
Xtr = cifar100_train[b'data']
Yr = cifar100_train[b'fine_labels']
Xte = cifar100_test[b'data']
Ye = cifar100_test[b'fine_labels']
classNames = labelMap[b'fine_label_names']
num_train = Xtr.shape[0]
num_test = Xte.shape[0]
num_class = len(classNames)
Ytr = np.zeros([num_train, num_class])
Yte = np.zeros([num_test, num_class])
Ytr[0:num_train, Yr[0:num_train]] = 1
Yte[0:num_test, Ye[0:num_test]] = 1
# As a sanity check, we print out the size of the training and test data.
print('Train data shape:', Xtr.shape)
print('Train Label shape:', Ytr.shape)
print('Test data shape:', Xte.shape)
print('Test Label shape:', Yte.shape)
print('Name of Predicted Class:', classNames[0]) #indice of the label name is the indice of the class.
Xtrain = Xtr#[:1000]
Xtest = Xte#[:100]
Ytrain = Ytr#[:1000]
Ytest = Yte#[:100]
print('Train data shape:', Xtrain.shape)
print('Train Label shape:', Ytrain.shape)
print('Test data shape:', Xtest.shape)
print('Test Label shape:', Ytest.shape)
Xtrain = np.reshape(Xtrain,(50000, 32, 32, 3)).transpose(0,1,2,3).astype(float)
Xtest = np.reshape(Xtest,(10000, 32, 32, 3)).transpose(0,1,2,3).astype(float)
Xbatches = np.split(Xtrain, 500); #second number is # of batches
Ybatches = np.split(np.asarray(Ytrain), 500);
XtestB = np.split(Xtest, 100);
YtestB = np.split(Ytest, 100);
print('X # of batches:', len(Xbatches))
print('Y # of batches:', len(Ybatches))
# input X: 28x28 grayscale images, the first dimension (None) will index the images in the mini-batch
X = tf.placeholder(tf.float32, [100, 32, 32, 3])
# correct answers will go here
Y_ = tf.placeholder(tf.float32, [100, 100])
# weights W[784, 10] 784=28*28
W = tf.Variable(tf.zeros([3072, 100]))
# biases b[10]
b = tf.Variable(tf.zeros([100]))
# flatten the images into a single line of pixels
# -1 in the shape definition means "the only possible dimension that will preserve the number of elements"
XX = tf.reshape(X, [-1, 3072])
# The model
Y = tf.nn.softmax(tf.matmul(XX, W) + b)
# loss function: cross-entropy = - sum( Y_i * log(Yi) )
# Y: the computed output vector
# Y_: the desired output vector
# cross-entropy
# log takes the log of each element, * multiplies the tensors element by element
# reduce_mean will add all the components in the tensor
# so here we end up with the total cross-entropy for all images in the batch
cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y)) * 1000.0 # normalized for batches of 100 images,
# *10 because "mean" included an unwanted division by 10
# accuracy of the trained model, between 0 (worst) and 1 (best)
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# training, learning rate = 0.005
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
# init
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for i in range(500):
# the backpropagation training step
t, Loss = sess.run([train_step, cross_entropy], feed_dict={X: Xbatches[i], Y_: Ybatches[i]})
print(Loss)
print(i)
for i in range(100):
print('accuracy:', sess.run(accuracy, feed_dict={X: XtestB[i], Y_: YtestB[i]}))，这是我的形式。我创建了2个数据库：2 dropdown menus和category，其结构如下：
类别数据库：
item
项目数据库：
id, name。
我在其中手动存储数据（在id, category_id, name, price我存储category，Computers和Cars，并根据类别ID我手动存储的项目。
所以我有2 Phones，一个是dropDownMenus，另一个是category name。这是我需要做的：
如果用户选择f.e item name类别，则自动computer下拉菜单应显示计算机项目，依此类推。有人可以解释一下它是如何解决的吗？我想我应该显示项目下拉菜单应该根据items显示值，但我不确定。
这是我的category_id文件，其中有2个下拉菜单：
view我希望你理解我的问题，谢谢你的帮助。