小编Eng*_*ero的帖子

在这个问题,有人建议意见,我应该不会投的结果malloc,即

int *sieve = malloc(sizeof(int) * length);

而不是:

int *sieve = (int *) malloc(sizeof(int) * length);

为什么会这样呢？

我遇到过这个:

item = someSortOfSelection()
if item in myList:
    doMySpecialFunction(item)

但有时它不适用于我的所有项目,就好像它们在列表中未被识别一样(当它是一个字符串列表时).

这是在列表中找到一个项目的最"Python化"的方式:if x in l:？

When plotting heatmaps with seaborn (and correlation matrices with matplotlib) the first and the last row is cut in halve. This happens also when I run this minimal code example which I found online.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = pd.read_csv('https://raw.githubusercontent.com/resbaz/r-novice-gapminder-files/master/data/gapminder-FiveYearData.csv')
plt.figure(figsize=(10,5))
sns.heatmap(data.corr())
plt.show()

The labels at the y axis are on the correct spot, but the rows aren't completely there.

A few days ago, it work as intended. Since then, I …

我按照CPU的指示在我的Ubuntu 15.10机器上安装了TensorFlow:

$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

然后当我运行Python REPL并导入tensorflow时,我得到:

$ python
Python 2.7.10 (default, Oct 14 2015, 16:09:02) 
[GCC 5.2.1 20151010] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/phil/.local/lib/python2.7/site-packages/tensorflow/__init__.py", line 4, in <module>
   from tensorflow.python import *
  File "/home/phil/.local/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 13, in <module>
    from tensorflow.core.framework.graph_pb2 import *
  File "/home/phil/.local/lib/python2.7/site-packages/tensorflow/core/framework/graph_pb2.py", line 16, in <module>
    from tensorflow.core.framework import attr_value_pb2 as     tensorflow_dot_core_dot_framework_dot_attr__value__pb2
  File …

我正在尝试使用该函数可视化tensorflow中卷积层的输出tf.image_summary.我已经在其他情况下成功使用它(例如可视化输入图像),但是在这里正确地重塑输出有一些困难.我有以下转换层:

img_size = 256
x_image = tf.reshape(x, [-1,img_size, img_size,1], "sketch_image")

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

所以输出h_conv1会有形状[-1, img_size, img_size, 32].只是使用tf.image_summary("first_conv", tf.reshape(h_conv1, [-1, img_size, img_size, 1]))不考虑32个不同的内核,所以我基本上在这里切换不同的功能图.

我怎样才能正确地重塑它们？或者是否有另一个帮助函数可用于在摘要中包含此输出？

该MNIST For ML Beginners教程是给我一个错误,当我运行print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})).其他一切都很好.

错误和跟踪:

InternalErrorTraceback (most recent call last)
<ipython-input-16-219711f7d235> in <module>()
----> 1 print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    338     try:
    339       result = self._run(None, fetches, feed_dict, options_ptr,
--> 340                          run_metadata_ptr)
    341       if run_metadata:
    342         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
    562     try:
    563       results = self._do_run(handle, target_list, unique_fetches,
--> 564                              feed_dict_string, options, run_metadata)
    565     finally:
    566       # The …

我已经设法使用命令行sklearn将图像加载到一个文件夹中: load_sample_images()

我现在想将其转换为numpy.ndarray具有float32数据类型的格式

我能够将它转换为np.ndarray使用:np.array(X),np.array(X, dtype=np.float32)然后np.asarray(X).astype('float32')给我错误:

ValueError: setting an array element with a sequence.

Run Code Online (Sandbox Code Playgroud)

有办法解决这个问题吗？

from sklearn_theano.datasets import load_sample_images
import numpy as np  

kinect_images = load_sample_images()
X = kinect_images.images

X_new = np.array(X)  # works
X_new = np.array(X[1], dtype=np.float32)  # works

X_new = np.array(X, dtype=np.float32)  # does not work

假设我有一个非常简单的神经网络,如多层感知器.对于每一层,激活功能是S形的,并且网络是完全连接的.

在TensorFlow中,这可能是这样定义的:

    sess = tf.InteractiveSession()

    # Training Tensor
    x = tf.placeholder(tf.float32, shape = [None, n_fft])
    # Label Tensor
    y_ = tf.placeholder(tf.float32, shape = [None, n_fft])

    # Declaring variable buffer for weights W and bias b
    # Layer structure [n_fft, n_fft, n_fft, n_fft]
    # Input -> Layer 1
    struct_w = [n_fft, n_fft]
    struct_b = [n_fft]
    W1 = weight_variable(struct_w, 'W1')
    b1 = bias_variable(struct_b, 'b1')
    h1 = tf.nn.sigmoid(tf.matmul(x, W1) + b1)

    # Layer1 -> Layer 2
    W2 = weight_variable(struct_w, 'W2')
    b2 = bias_variable(struct_b, …

我在sklearn中使用MinMaxScaler模型来规范化模型的功能.

training_set = np.random.rand(4,4)*10
training_set

       [[ 6.01144787,  0.59753007,  2.0014852 ,  3.45433657],
       [ 6.03041646,  5.15589559,  6.64992437,  2.63440202],
       [ 2.27733136,  9.29927394,  0.03718093,  7.7679183 ],
       [ 9.86934288,  7.59003904,  6.02363739,  2.78294206]]


scaler = MinMaxScaler()
scaler.fit(training_set)    
scaler.transform(training_set)


   [[ 0.49184811,  0.        ,  0.29704831,  0.15972182],
   [ 0.4943466 ,  0.52384506,  1.        ,  0.        ],
   [ 0.        ,  1.        ,  0.        ,  1.        ],
   [ 1.        ,  0.80357559,  0.9052909 ,  0.02893534]]

现在我想使用相同的缩放器来规范化测试集:

   [[ 8.31263467,  7.99782295,  0.02031658,  9.43249727],
   [ 1.03761228,  9.53173021,  5.99539478,  4.81456067],
   [ 0.19715961,  5.97702519,  0.53347403,  5.58747666],
   [ 9.67505429, …

我有一个非常简单的输入管道,from_generator非常适合......

dataset = tf.data.Dataset.from_generator(complex_img_label_generator,
                                        (tf.int32, tf.string))
dataset = dataset.batch(64)
iter = dataset.make_one_shot_iterator()
imgs, labels = iter.get_next()

其中complex_img_label_generator动态生成图像,并返回表示一个numpy的阵列(H, W, 3)图像和一个简单的string标签.处理不是我可以表示从文件和tf.image操作中读取的内容.

我的问题是关于如何平衡发电机？我如何让N个这些生成器在自己的线程中运行.

一个想法是使用dataset.map与num_parallel_calls处理线程; 但是地图在张量上运行......另一个想法是创建多个生成器,每个生成器都有自己的,prefetch并以某种方式加入它们,但我看不出我如何加入N个生成器流？

我可以遵循任何规范的例子吗？