小编Ric*_*ich的帖子

我正在研究ML / Theano，最近遇到了这个脚本：https ://gist.github.com/notmatthancock/68d52af2e8cde7fbff1c9225b2790a7f 玩起来很酷。和所有ML研究人员一样，我最近升级到了服务器，虽然功能更强大，但它也给我带来了问题。

该脚本很长，但是以以下代码结尾：

def plot_stuff(inputs, outputs, losses, net_func, n_hidden):
fig,axes = plt.subplots(1,2,figsize=(12,6))

    axes[0].plot(np.arange(losses.shape[0])+1, losses)
    axes[0].set_xlabel('iteration')
    axes[0].set_ylabel('loss')
    axes[0].set_xscale('log')
    axes[0].set_yscale('log')

    x,y = np.mgrid[inputs[:,0].min():inputs[:,0].max():51j, inputs[:,1].min():inputs[:,1].max():51j]
    z = net_func( np.c_[x.flatten(), y.flatten()] ).reshape(x.shape)

    axes[1].contourf(x,y,z, cmap=plt.cm.RdBu, alpha=0.6)
    axes[1].plot(inputs[outputs==0,0], inputs[outputs==0,1], 'or') 
    axes[1].plot(inputs[outputs==1,0], inputs[outputs==1,1], 'sb') 
    axes[1].set_title('Percent missclassified: %0.2f%%' % (((net_func(inputs)>0.5) != outputs.astype(np.bool)).mean()*100))

    fig.suptitle('Shallow net with %d hidden units'%n_hidden)
    plt.show()

if __name__=='__main__':
    n_hidden = 40
    inputs, outputs = gen_data(n_samples_per_class=100)
    losses, net_func = train_neural_network(inputs=inputs, outputs=outputs, n_hidden=n_hidden, n_iters=int(2000), learning_rate=0.1)
    plot_stuff(inputs, outputs, losses, net_func, n_hidden)

生成以下图表：

当我尝试在服务器上运行它时，它是一个只有命令行而没有屏幕的服务器，我可以预料地收到此错误：

fedora@ip-173-33-18-911:~/scripting/spiral$ …

我正在开发一个程序,用python中的大图像切割面部.但是,即使看到它们,我也遇到了问题.

我正在使用的图像可能超过2000x2000,并且它们不适合我的屏幕.这是代码:

import cv2
import sys

# Get user supplied values
imagePath = sys.argv[1]
cascPath = sys.argv[2]

# Create the haar cascade
faceCascade = cv2.CascadeClassifier(cascPath)

# Read the image
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces in the image
faces = faceCascade.detectMultiScale(
    gray,
    scaleFactor=1.2,
    minNeighbors=5,
    minSize=(100, 100),
    flags = cv2.cv.CV_HAAR_SCALE_IMAGE
 )

print "Found {0} faces!".format(len(faces))

# Draw a rectangle around the faces
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), …

我正在学习 networkX 教程，第 9 页是这样的：

>>> g = nx . Graph ()
>>> g . add_node (1 , name = ‘ Obrian ’)
>>> g . add_nodes_from ([2] , name = ‘ Quintana ’ ])
>>> g [1][ ‘ name ’]
‘ Obrian ’

我在代码中重建：

import networkx as nx

g = nx.Graph() 

g.add_node(1,name='Obrian')
g.add_nodes_from([2],name='Quintana')

print  "Node 1 name: " + g[1]['name']

然而，出于某种原因，这个简单的 5 行脚本无法运行：

Traceback (most recent call last):
  File "NetTest[nx_tut]--[P09].py", line 9, in <module>
    print  "Node 1 name: " …

我正在研究包含以下部分的教程：

>>> import numpy as np
>>> import pandas as pd
>>> from sklearn.feature_extraction.text import TfidfVectorizer
>>> from sklearn.linear_model.logistic import LogisticRegression
>>> from sklearn.cross_validation import train_test_split, cross_val_score
>>> df = pd.read_csv('data/sms.csv')
>>> X_train_raw, X_test_raw, y_train, y_test = train_test_split(df['message'], df['label'])
>>> vectorizer = TfidfVectorizer()
>>> X_train = vectorizer.fit_transform(X_train_raw)
>>> X_test = vectorizer.transform(X_test_raw)
>>> classifier = LogisticRegression()
>>> classifier.fit(X_train, y_train)
>>> precisions = cross_val_score(classifier, X_train, y_train, cv=5, scoring='precision')
>>> print 'Precision', np.mean(precisions), precisions
>>> recalls = cross_val_score(classifier, X_train, y_train, cv=5, scoring='recall')
>>> …