n_input  = 784        # 28*28的灰度图n_output = 10         # 完成一个10分类的操作weights  = {    #‘权重参数‘: tf.Variable(tf.高期([feature的H, feature的W, 当前feature连接的输入的深度, 最终想得到多少个特征图], 标准差=0.1)),    ‘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)),　　 #‘全连接层参数‘: tf.Variable(tf.高斯([特征图H*特征图W*深度, 最终想得到多少个特征图], 标准差=0.1)),    ‘wd1‘: tf.Variable(tf.random_normal([7*7*128, 1024], stddev=0.1)),    ‘wd2‘: tf.Variable(tf.random_normal([1024, n_output], stddev=0.1))}biases   = {　　 #‘偏置参数‘: tf.Variable(tf.高斯([第1层有多少个偏置项], 标准差=0.1)),    ‘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))}#卷积神经网络def conv_basic(_input, _w, _b, _keepratio):    #将输入数据转化成一个四维的[n, h, w, c]tensorflow格式数据    #_input_r = tf.将输入数据转化成tensorflow格式(输入, shape=[batch_size大小, H, W, 深度])    _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])    #第1层卷积        #_conv1 = tf.nn.卷积(输入, 权重参数, 步长=[batch_size大小, H, W, 深度], padding=‘建议选择SAME‘)    _conv1 = tf.nn.conv2d(_input_r, _w[‘wc1‘], strides=[1, 1, 1, 1], padding=‘SAME‘)    #_conv1 = tf.nn.非线性激活函数(tf.nn.加法(_conv1, _b[‘bc1‘]))    _conv1 = tf.nn.relu(tf.nn.bias_add(_conv1, _b[‘bc1‘]))    #第1层池化    #_pool1 = tf.nn.池化函数(_conv1, 指定池化窗口的大小=[batch_size大小, H, W, 深度], strides=[1, 2, 2, 1], padding=‘SAME‘)    _pool1 = tf.nn.max_pool(_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘)    #随机杀死一些节点，不让所有神经元都加入到训练中    #_pool_dr1 = tf.nn.dropout(_pool1, 保留比例)    _pool_dr1 = tf.nn.dropout(_pool1, _keepratio)        #第2层卷积    _conv2 = tf.nn.conv2d(_pool_dr1, _w[‘wc2‘], strides=[1, 1, 1, 1], padding=‘SAME‘)    _conv2 = tf.nn.relu(tf.nn.bias_add(_conv2, _b[‘bc2‘]))    _pool2 = tf.nn.max_pool(_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘)    _pool_dr2 = tf.nn.dropout(_pool2, _keepratio)        #全连接层    #转化成tensorflow格式    _dense1 = tf.reshape(_pool_dr2, [-1, _w[‘wd1‘].get_shape().as_list()[0]])    #第1层全连接层    _fc1 = tf.nn.relu(tf.add(tf.matmul(_dense1, _w[‘wd1‘]), _b[‘bd1‘]))    _fc_dr1 = tf.nn.dropout(_fc1, _keepratio)    #第2层全连接层    _out = tf.add(tf.matmul(_fc_dr1, _w[‘wd2‘]), _b[‘bd2‘])    #返回值    out = { ‘input_r‘: _input_r, ‘conv1‘: _conv1, ‘pool1‘: _pool1, ‘pool1_dr1‘: _pool_dr1,        ‘conv2‘: _conv2, ‘pool2‘: _pool2, ‘pool_dr2‘: _pool_dr2, ‘dense1‘: _dense1,        ‘fc1‘: _fc1, ‘fc_dr1‘: _fc_dr1, ‘out‘: _out    }    return outprint ("CNN READY")#设置损失函数&优化器（代码说明：略 请看前面文档）learning_rate = 0.001x      = tf.placeholder("float", [None, nsteps, diminput])y      = tf.placeholder("float", [None, dimoutput])myrnn  = _RNN(x, weights, biases, nsteps, ‘basic‘)pred   = myrnn[‘O‘]cost   = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) optm   = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Adam Optimizeraccr   = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(pred,1), tf.argmax(y,1)), tf.float32))init   = tf.global_variables_initializer()print ("Network Ready!")#训练（代码说明：略 请看前面文档）training_epochs = 5batch_size      = 16display_step    = 1sess = tf.Session()sess.run(init)print ("Start optimization")for epoch in range(training_epochs):    avg_cost = 0.    #total_batch = int(mnist.train.num_examples/batch_size)    total_batch = 100    # Loop over all batches    for i in range(total_batch):        batch_xs, batch_ys = mnist.train.next_batch(batch_size)        batch_xs = batch_xs.reshape((batch_size, nsteps, diminput))        # Fit training using batch data        feeds = {x: batch_xs, y: batch_ys}        sess.run(optm, feed_dict=feeds)        # Compute average loss        avg_cost += sess.run(cost, feed_dict=feeds)/total_batch    # Display logs per epoch step    if epoch % display_step == 0:         print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))        feeds = {x: batch_xs, y: batch_ys}        train_acc = sess.run(accr, feed_dict=feeds)        print (" Training accuracy: %.3f" % (train_acc))        testimgs = testimgs.reshape((ntest, nsteps, diminput))        feeds = {x: testimgs, y: testlabels, istate: np.zeros((ntest, 2*dimhidden))}        test_acc = sess.run(accr, feed_dict=feeds)        print (" Test accuracy: %.3f" % (test_acc))print ("Optimization Finished.")