唐宇迪-TensorFlow創建CNN網絡

#數據導入

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import input_data

mnist = input_data.read_data_sets('data/', one_hot=true)
#以下四行普通雙層神經網絡沒有，爲什麼？
trainimg   = mnist.train.images
trainlabel = mnist.train.labels
testimg    = mnist.test.images
testlabel  = mnist.test.labels
print ("MNIST ready")

 

Extracting data/train-images-idx3-ubyte.gz

此處顯示以後可能出錯，原視頻無  ？
C:\Users\xujs\Anaconda3\lib\gzip.py:274: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
  return self._buffer.read(size)
C:\Users\xujs\Documents\input_data.py:52: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
  data = data.reshape(num_images, rows, cols, 1) 

Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
MNIST ready

#參數初始化

n_input  = 784
n_output = 10
#權重項初始化
weights  = {
        #兩個池化層
        'wc1' : tf.variable(tf.random_normal([3, 3, 1, 64],stddev=0.1))
        'wc2' : tf.variable(tf.random_normal([3, 3, 64, 128],stddev=0.1))
        #兩個全連接層
        'wc1' : tf.variable(tf.random_normal([7*7*128, 1024],stddev=0.1))
        'wc1' : tf.variable(tf.random_normal([1024, n_output],stddev=0.1))
#偏置項初始化
bias     = {
        'bc1': tf.variable(tf.random_normal([64], stddev=0.1)),
        'bc2': tf.variable(tf.random_normal([128], stddev=0.1))
        'bd1': tf.variable(tf.random_normal([1024], stddev=0.1))
        'bd2': tf.variable(tf.random_normal([n_output], stddev=0.1))
    }

#卷積和池化實際操作