我正在尝试从烧瓶网站获取一些输入数字，然后将这些数字插入神经网络，该网络将输出存储在字典中。然后，我有一个结果页面，其中包含一个包含所有输出的表格 以下是所有相关的烧瓶代码：
@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)
以下是所有相关的html代码:(模板）
{% 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 %}
结果模板中的相关代码：
{% 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 %}
此外，计算机上的实际错误是值错误：视图功能未返回响应。
答案 0 :(得分：0)
您应该更改允许仅在表单上发布的方法，甚至不要在convnet函数中提及方法。