I'm trying to exponentiate a complex matrix in Python and am running into some trouble. I'm using the scipy.linalg.expm function, and am having a rather strange error message when I try the following code: import numpy as np from scipy import linalg hamiltonian = np.mat('[1,0,0,0;0,-1,0,0;0,0,-1,0;0,0,0,1]') # This works t_list = np.linspace(0,1,10) unitary = [linalg.expm(-(1j)*t*hami
我试图在Python中取代复杂的矩阵，并且遇到了一些麻烦。 我使用scipy.linalg.expm函数，当我尝试下面的代码时，出现了一个相当奇怪的错误消息： import numpy as np from scipy import linalg hamiltonian = np.mat('[1,0,0,0;0,-1,0,0;0,0,-1,0;0,0,0,1]') # This works t_list = np.linspace(0,1,10) unitary = [linalg.expm(-(1j)*t*hamiltonian) for t in t_list] # This doesn't t_list = np.linspace(0,10,100) unitar
I had a working LSTM model that had one weight/bias layer from its recurrent state to the output. I then also coded up the same system, but with two layers. This means that I would have the LSTM, then a hidden layer, and then the output. I wrote the lines to define this double layer model, but did not use them a single time. But, now that those layers exist but are not used at all, it wouldn&
我有一个有效的LSTM模型，它具有从其经常状态到输出的一个权重/偏置层。 然后我也编写了相同的系统，但有两层。 这意味着我将拥有LSTM，然后是隐藏层，然后是输出。 我写了几行来定义这个双层模型，但并没有单独使用它们。 但是，现在这些图层存在但完全没有使用，它不会学习！ 所以我的权重和偏见是这样定义的： weights = {
I'm trying to create a DNN in Python 3.5 with Tensorflow for classifying a tuple into one of 3 classes. # define initial hyperparameters batch_size = 100 train_steps = 5000 hidden_units=[10,20,10] # build model dnn = tf.contrib.learn.DNNClassifier(hidden_units=hidden_units, feature_columns=feature_cols, n_classes=3) input_fn = tf.estimator.inputs.pandas_input_fn(x=X_train, y=y_train,
我试图用Tensorflow在Python 3.5中创建一个DNN，将元组分类到3个类中的一个。 # define initial hyperparameters batch_size = 100 train_steps = 5000 hidden_units=[10,20,10] # build model dnn = tf.contrib.learn.DNNClassifier(hidden_units=hidden_units, feature_columns=feature_cols, n_classes=3) input_fn = tf.estimator.inputs.pandas_input_fn(x=X_train, y=y_train,
I am using the following code to make a Neural netowrk for classification of some data. (https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py) I want to compare the output of my prediction to the labels to better visualize how the NN works. So I am using this piece of code: #y : Labels tf_y = y [yp] = sess.run([tf_y], feed_dict
我正在使用下面的代码来创建一个用于分类某些数据的神经网络。 （https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py） 我想将我的预测结果与标签进行比较，以便更好地了解NN的工作方式。 所以我使用这段代码： #y : Labels tf_y = y [yp] = sess.run([tf_y], feed_dict = { x : test_input, y : test_output } ) ypp = tf.argm
I try to understand how the Tensorflow computation graph operates, more deeply. Assume that we have the following code: A = tf.truncated_normal(shape=(1, ), stddev=0.1) B = tf.Variable([0.3], dtype=tf.float32) C = A * B grads = tf.gradients(C, [A, B]) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) for i in range(1000): results = sess.run([C, grads], {A: [2], B:
我试图更深入地了解Tensorflow计算图如何操作。 假设我们有以下代码： A = tf.truncated_normal(shape=(1, ), stddev=0.1) B = tf.Variable([0.3], dtype=tf.float32) C = A * B grads = tf.gradients(C, [A, B]) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) for i in range(1000): results = sess.run([C, grads], {A: [2], B:[5]}) 如预期的那样，我得到A和B的结果10和梯度5。 我
I found in many available neural network code implemented using TensorFlow that regularization terms are often implemented by manually adding an additional term to loss value. My questions are: Is there a more elegant or recommended way of regularization than doing it manually? I also find that get_variable has an argument regularizer . How should it be used? According to my observation,
我发现在使用TensorFlow实现的许多可用的神经网络代码中，正则化术语通常是通过手动添加额外的损失值来实现的。 我的问题是： 有没有一种更优雅或推荐的正规化方式比手动方式？ 我还发现， get_variable有一个参数regularizer 。 它应该如何使用？ 根据我的观察，如果我们通过正规化器（例如tf.contrib.layers.l2_regularizer ，将计算一个表示正则化术语的张量并将其添加到一个名为tf.GraphKeys.REGULARIZATOIN_LOSSES
This question is related to this one: Export Tensorflow graphs from Python for use in C++ I'm trying to export a Tensorflow model from Python to C++. The problem is, my neural net starts with a placeholder to receive input, which requires a feed_dict. I cannot find any c++ API to supply a feed_dict for my model. What can I do? If there's no API for supplying feed_dicts, how should
这个问题与这个问题有关：从Python导出Tensorflow图以用于C ++ 我试图从Python导出一个Tensorflow模型到C ++。 问题是，我的神经网络以一个占位符开始接收输入，这需要一个feed_dict。 我无法找到任何c ++ API为我的模型提供feed_dict。 我能做什么？ 如果没有提供feed_dicts的API，我应该如何更改我的模型，以便可以在没有占位符的情况下为c ++目的而训练和导出它？ tensorflow::Session::Run()方法是Python tf.Sessio
I have been given a trained neural network in torch and I need to rebuild it exactly in tensorflow. I believe I have correctly defined the network's architecture in tensorflow but I am having trouble transferring the weight and bias tensors. Using a third party package, I converted all the weight and bias tensors from the torch network to numpy arrays then wrote them to disk. I can load th
我已经在火炬中获得了训练有素的神经网络，并且我需要以张量流形式重建它。 我相信我已经在tensorflow中正确定义了网络的体系结构，但我无法转移权重和偏置张量。 使用第三方软件包，我将火炬网络中的所有重量和偏差张量转换为numpy数组，然后将它们写入磁盘。 我可以将它们加载回我的python程序中，但我无法找到一种方法将它们分配到我的tensorflow网络中的相应图层。 例如，我有一个在tensorflow中定义的卷积层 kernel_1
I'm totally new to TensorFlow and Python, so please excuse me for posting such a basic question, but I'm a bit overwhelmed with learning both things at once. EDIT: I found a solution myself and posted it below, however, more efficient solutions are wellcome Short version of the question: How can I extract every weight and bias at any point from a neural network using TensorFlow and sto
我对TensorFlow和Python完全不熟悉，所以请原谅我发布这样一个基本问题，但是我有点不知所措，一次学习两件事。 编辑：我自己找到了一个解决方案，并在下面发布，但是，更有效的解决方案是很好的 问题的简短摘要：如何使用TensorFlow从神经网络的任何点提取每个重量和偏差，并将其存储到形状为[图层] [神经元 - 上一层] [神经元 - 当前层]的Python数组中， 。 目标不是存储在硬盘上，而是存储在与最后一个代码snipet下面解释
I want to define a operation in tensorflow that calculates something like: x is provided by a tensor. Finally the operation should be compared to a known value and parameters alpha, beta i and b should be learned. (I guess) The product of all inputs causes trouble. This is one version that I tried to deploy, with no success. # input X = tf.placeholder(tf.float32, [None, 2], name="X&qu
我想在tensorflow中定义一个计算如下的操作： x由张量提供。 最后，应该将操作与已知值进行比较，并且应该学习参数α，βi和b。 （我猜）所有输入的产品都会造成麻烦。 这是我试图部署的一个版本，但没有成功。 ＃input X = tf.placeholder（tf.float32，[None，2]，name =“X”）Y = tf.placeholder（tf.float32，[None，1]，name =“Y”） # hidden beta = tf.get_variable("beta", shape=[2], initializer=tf.contrib.layers