フラスコの関数make_response pythonファイルapp.pyの引数rvが0に等しいのはなぜですか？

Question

私はフラスコのウェブサイトから入力番号を取り出し、それらの数字をニューラルネットワークに差し込み、出力を辞書に保存しようとしています。次に、結果ページにはすべての出力を含むテーブルがあります。ここ
は、関連するすべてのフラスココードです：また

{% extends "header.html" %} 
{% block body %} 

<h1 class="text-center">Results of Convolutional Neural Network</h1> 
<table> 
{% for key, value in ConvResult.iteritems() %} 
    <tr> 
     <th> {{ key }} </th> 
     <td> {{ value }} </td> 
    </tr> 
{% endfor %} 
</table> 
{% endblock %} 

：結果テンプレートから（テンプレート）

{% extends "header.html" %} 
{% block body %} 
<h1 class="text-center">Convolutional Neural Network Site</h1> 
<div class="title"> 
    <h3>Post Hyper Parameter Values</h3> 
</div> 
<div id="content"> 
    <form method="post" action="{{ url_for('ConvNetForm')}}"> 
     <label for="learning_rate">Please enter a value for the hyper parameter learning rate:</label> 
     <input type="number" name="learning_rate" value="0.001" step="0.001" min="0"max="1"/><br /> 
     <label for="training_iters">Please enter a value for the hyper parameter training iterations:</label> 
     <input type="number" name="training_iters" value="200000" min="1000" max="500000"/><br /> 
     <label for="batch_size">Please enter a value for the hyper parameter batch size:</label> 
     <input type="number" name="batch_size" value="128" min="1" max="1000"/><br /> 
     <label for="display_step">Please enter a value for the hyper parameter display step:</label> 
     <input type="number" name="display_step" value="10" min="1" max="100"/><br /> 
     <input type="submit" /> 
     <input type="reset" /> 
    </form> 
</div> 
{% endblock %} 

し、関連するコード：ここで

@app.route('/ConvNet', methods=["GET", "POST"]) 

def ConvNet(): 
    return render_template("ConvNet.html") 

@app.route('/ConvNetForm', methods=["GET", "POST"]) 

def ConvNetForm(): 
    if request.form == "POST": 
     #hyperparameters 
     learning_rate = request.form['learning_rate'] 
     training_iters= request.form['training_iters'] 
     batch_size= request.form['batch_size'] 
     display_step = request.form['display_step'] 

     #network parameters 
     #28 * 28 image 
     n_input = 784 
     n_classes = 10 
     dropout = 0.75 

     x = tf.placeholder(tf.float32,[None, n_input]) 
     y = tf.placeholder(tf.float32,[None, n_classes]) 
     keep_prob = tf.placeholder(tf.float32) 

     def conv2d(x, W, b, strides=1): 
      x = tf.nn.conv2d(x,W,strides=[1, strides, strides, 1], padding = 'SAME') 
      x = tf.nn.bias_add(x, b) 
      return tf.nn.relu(x) 

     def maxpool2d(x, k=2): 
      return tf.nn.max_pool(x, ksize=[1,k,k,1], strides=[1,k,k,1], padding='SAME') 

     #create model 
     def conv_net(x, weights, biases, dropout): 
      #reshape input 
      x = tf.reshape(x, shape=[-1, 28, 28, 1]) 

      #convolutional layer 
      conv1 = conv2d(x, weights['wc1'], biases['bc1']) 
      #maxpooling 
      conv1 = maxpool2d(conv1, k=2) 

      conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) 
      conv2 = maxpool2d(conv2, k=2) 

      fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) 
      fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) 
      fc1 = tf.nn.relu(fc1) 
      #apply dropout 
      fc1 = tf.nn.dropout(fc1, dropout) 

      #outpout, class predication 
      out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) 
      return out 

     #create weights 
     weights = { 
       'wc1': tf.Variable(tf.random_normal([5,5,1,32])), 
       'wc2': tf.Variable(tf.random_normal([5,5,32,64])), 
       'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), 
       'out': tf.Variable(tf.random_normal([1024, n_classes])) 
     } 

     biases = { 
       'bc1': tf.Variable(tf.random_normal([32])), 
       'bc2': tf.Variable(tf.random_normal([64])), 
       'bd1': tf.Variable(tf.random_normal([1024])), 
       'out': tf.Variable(tf.random_normal([n_classes])) 
      } 

     #construct model 
     pred = conv_net(x, weights, biases, keep_prob) 

     #define optimizer and loss 
     cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = pred,labels = y)) 
     optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 

     #Evaluate model 
     correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 
     accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) 

     #initializing the variables 
     init = tf.global_variables_initializer() 

     style.use('fivethirtyeight') 

     #Creating Graph 
     #fig = plt.figure(edgecolor='c') 
     #ax1=fig.add_subplot(1,1,1) 

     ConvResult=dict() 
     # Launch the graph 
     with tf.Session() as sess: 
      sess.run(init) 
      step = 1 
      # Keep training until reach max iterations 
      while step * batch_size < training_iters: 
       images_seen = step * batch_size 
       batch_x, batch_y = mnist.train.next_batch(batch_size) 
       # Run optimization op (backprop) 
       sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, 
               keep_prob: dropout}) 
       if step % display_step == 0: 
        # Calculate batch loss and accuracy 
        loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, 
                     y: batch_y, 
                     keep_prob: 1.}) 
        print("Iter " + str(images_seen) + ", Minibatch Loss= " + \ 
          "{:.6f}".format(loss) + ", Training Accuracy= " + \ 
          "{:.5f}".format(acc)) 
        ConvResult[images_seen] = acc 
       step += 1 
      print("Optimization Finished!") 

      # Calculate accuracy for 256 mnist test images 
      print("Testing Accuracy:", \ 
       sess.run(accuracy, feed_dict={x: mnist.test.images[:256], 
               y: mnist.test.labels[:256], 
               keep_prob: 1.})) 
     return render_template("ConvNetForm.html", ConvResult=ConvResult) 

は、関連するすべてのhtmlコードであります、コンピュータの実際のエラーは値エラーでした：ビュー関数は応答を返しませんでした。

Answer 1

フォームに投稿するだけの方法を変更し、convnet関数のメソッドについては言及していないはずです。