我对Flask完全不熟悉,并试图弄清楚如何使用d3js强制布局显示networkx图数据.这是相关的Python代码:
@app.route("/")
def index():
"""
When you request the root path, you'll get the index.html template.
"""
return flask.render_template("index.html")
@app.route("/thread")
def get_graph_data(thread_id: int=3532967):
"""
returns json of a network graph for the specified thread
:param thread_id:
:return:
"""
pqdict, userdict = graphs.get_post_quote_dict(thread_id)
G = graphs.create_graph(pqdict)
s = graphs.graph_to_node_link(G, remove_singlets=True) # returns dict
return flask.jsonify(s)
这是index.html文件:
<!DOCTYPE html>
<html>
<head>
<title>Index thing</title>
<script type="text/javascript" src="http://d3js.org/d3.v2.js"></script>
<link type="text/css" rel="stylesheet" href="templates/graph.css"/>
</head>
<body>
<div id="chart"></div>
<script>
var w = 1500,
h = 1500, …我有一个卷积神经网络,我最近重构使用Tensorflow的Estimator API,很大程度上遵循本教程.但是,在训练期间,我添加到EstimatorSpec的度量标准没有显示在Tensorboard上,并且似乎没有在tfdbg中进行评估,尽管名称范围和度量标准存在于写入Tensorboard的图表中.
相关位model_fn如下:
...
predictions = tf.placeholder(tf.float32, [num_classes], name="predictions")
...
with tf.name_scope("metrics"):
predictions_rounded = tf.round(predictions)
accuracy = tf.metrics.accuracy(input_y, predictions_rounded, name='accuracy')
precision = tf.metrics.precision(input_y, predictions_rounded, name='precision')
recall = tf.metrics.recall(input_y, predictions_rounded, name='recall')
if mode == tf.estimator.ModeKeys.PREDICT:
spec = tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions)
elif mode == tf.estimator.ModeKeys.TRAIN:
...
# if we're doing softmax vs sigmoid, we have different metrics
if cross_entropy == CrossEntropyType.SOFTMAX:
metrics = {
'accuracy': accuracy,
'precision': precision,
'recall': recall
}
elif cross_entropy == CrossEntropyType.SIGMOID:
metrics = {
'precision': …