theano

really finding it hard to understand the input dimensions to the convolutional 1d layer in keras: Input shape 3D tensor with shape: (samples, steps, input_dim). Output shape 3D tensor with shape: (samples, new_steps, nb_filter). steps value might have changed due to padding. I want my network to take in a time series of prices (101, in order) and output 4 probabilities. My current non-convolutional network which does this fairly well (with a training set of 28000) looks like this: standardModel = Sequential() standardModel.add(Dense(input_dim=101, output_dim=100, W_regularizer=l2(0.5),

来源 ：《暗知识-机器认知如何颠覆商业和社会》 　　TensorFlow是由谷歌大脑团队开发的，主要用于机器学习和深度神经网络的研究。2016年5月，谷歌从Torch（一种编程框架）转移到TensorFlow，这对其他编程框架造成了打击，特别是torch和theanoo许多人将TensorFlow描述成一个比theano更现代化的版本，吸取了这些年在新领域/技术的许多重要的经验教训。 　　TensorFlow以智能、灵活的方式而闻名，是一种高度可扩展的机器学习系统，使其更容易适应不同的新旧产品和研究，并且比较容易安装，还针对初学者提供了教程，涵盖神经网络的理论基础和实际应用。TensorFlow比theano和torch慢，但谷歌和开源社区正在解决这个问题。TensorBoard是TensorFlow的可视化模块，它提供了一个计算路径的直观视图。深度学习库Keras被移植到TensorFlow上运行，这意味着任何用Keras编写的模型现在都可以运行在TensorFlow上。最后，值得一提的是TensorFlow可以在各种硬件上运行，其特点如下： （1）GPU加速：支持 （2）语言/界面：Python、Numpy、C++ （3）平台：跨平台 （4）维护者：谷歌 　　theano起源于2007年在蒙特利尔大学的知名MILA（学习算法研究所），是用Python编写的CPU

I want to use tensordot to compute the dot product of a specific dim of two tensors. Like: A is a tensor, whose shape is (3, 4, 5) B is a tensor, whose shape is (3, 5) I want to do a dot use A's third dim and B's second dim, and get a output whose dims is (3, 4) Like below: for i in range(3): C[i] = dot(A[i], B[i]) How to do it by tensordot? Well, do you want this in numpy or in Theano? In the case, where, as you state, you would like to contract axis 3 of A against axis 2 of B, both are straightforward: import numpy as np a = np.arange(3 * 4 * 5).reshape(3, 4, 5).astype('float32') b = np

越努力，越幸运。 欢迎访问我的博客 Sky’s blog 一些基本概念 在开始学习Keras之前，一些基础知识是必备的，关于深度学习的基本概念和技术，在使用Keras之前大体了解一下基础知识，这将减少你学习中的困惑。 1.符号计算 Keras的底层库使用Theano或TensorFlow，这两个库也称为Keras的后端。无论是Theano还是TensorFlow，都是一个“符号式”的库。 因此，这也使得Keras的编程与传统的Python代码有所差别。笼统的说，符号主义的计算首先定义各种变量，然后建立一个“计算图”，计算图规定了各个变量之间的计算关系。建立好的计算图需要编译以确定其内部细节，然而，此时的计算图还是一个“空壳子”，里面没有任何实际的数据，只有当你把需要运算的输入放进去后，才能在整个模型中形成数据流，从而形成输出值。 就像用管道搭建供水系统，当你在拼水管的时候，里面是没有水的。只有所有的管子都接完了，才能送水。 Keras的模型搭建形式就是这种方法，在你搭建Keras模型完毕后，你的模型就是一个空壳子，只有实际生成可调用的函数后（K.function），输入数据，才会形成真正的数据流。 2.张量 张量是什么，一上来我也一脸懵逼，看了解释之后，嗯嗯。 张量可以看作是向量、矩阵的自然推广，用张量来表示广泛的数据类型。 规模最小的张量是0阶张量，即标量，也就是一个数。

I'm using Anaconda Python 2.7 on windows 10 I was planning on doing Keras visualization so (whilst spyder was open) I opened the Anaconda command prompt and pip installed graphviz and pydot. Now when I try run the following: from keras.models import Sequential or any sort of "from keras." , I get the error: ImportError: cannot import name gof I have uninstalled and reinstalled Keras, Graphviz and pydot. i am using the development version of theano. I cannot find a fix. P.S If I uninstall graphviz and pydot, keras works again EDIT After uninstalling anaconda and reinstalling it including theano

I installed theano with Spyder 2.3.8 on Windows 7 64-bit by running "pip install theano". It worked well. But then, when I tried to run "import theano", I got the following error: Traceback (most recent call last): File "", line 1, in import theano File "D:\Anaconda3\lib\site-packages\theano\__init__.py", line 55, in from theano.compile import \ File "D:\Anaconda3\lib\site-packages\theano\compile\__init__.py", line 9, in from theano.compile.function_module import * File "D:\Anaconda3\lib\site-packages\theano\compile\function_module.py", line 18, in import theano.compile.mode File "D:\Anaconda3

I wonder how to install Theano on Anaconda Python 2.7 x64 on Windows 7 x64. The Theano website provides some instructions but is not clear as to what is specific to Anaconda. I'm not 100% certain but this may be a minimal set of instructions, but only if you don't want to use a GPU. Getting Theano to use a GPU on Windows is quite a bit more difficult. Install TDM GCC x64. Install Anaconda x64. run conda update conda . run conda update --all . run conda install mingw libpython . Install Theano (how you do this depends on whether you want to interact with the Theano source code or not, and