mlp

How to translate the neural network of MLP from tensorflow to pytorch

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问题 I have built up an MLP neural network using 'Tensorflow', which is stated as follow: model_mlp=Sequential() model_mlp.add(Dense(units=35, input_dim=train_X.shape[1], kernel_initializer='normal', activation='relu')) model_mlp.add(Dense(units=86, kernel_initializer='normal', activation='relu')) model_mlp.add(Dense(units=86, kernel_initializer='normal', activation='relu')) model_mlp.add(Dense(units=10, kernel_initializer='normal', activation='relu')) model_mlp.add(Dense(units=1)) I want to

How to feed Bert embeddings to LSTM

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来源： https://stackoverflow.com/questions/55669695/how-to-feed-bert-embeddings-to-lstm

TensorFlow人工智能引擎入门教程之八接着补充一章MLP多层感知器网络原理以及使用

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【推荐】2019 Java 开发者跳槽指南.pdf(吐血整理) >>> 这一章我们讲MLP 多层感知器的使用，多层感知器，常用来做分类，效果非常好，比如文本分类，效果比SVM 贝叶斯好多了，这些以前的机器学习很有名的算法，我现在基本不用它们，现在是深度学习的AI时代。多层感知器的介绍 MLP（多层感知器）神经网络是常见的ANN算法，它由一个输入层，一个输出层和一个或多个隐藏层组成。在MLP中的所有神经元都差不多，每个神经元都有几个输入（连接前一层）神经元和输出（连接后一层）神经元，该神经元会将相同值传递给与之相连的多个输出神经元一个神经网络训练网将一个特征向量作为输入，将该向量传递到隐藏层，然后通过权重和激励函数来计算结果，并将结果传递给下一层，直到最后传递给输出层才结束首先我们来下面是一个2层的多层感知器其中 relu可以换成 tanh或者sigmoid 比如 tf.nn.sigmoid(tf.matmul(X, w_h)) #WX+B def multilayer_perceptron(_X, _weights, _biases): layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) #Hidden layer with RELU activation

Spark MultilayerPerceptronClassifier Class Probabilities

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问题 I am an experienced Python programmer trying to transition some Python code to Spark for a classification task. This is my first time working in Spark/Scala. In Python, both Keras/tensorflow and sci-kit Learn neural networks do a great job on the multi-class classification and I'm able to easily return the top 3 most probable classes along with probabilities which are key to this project. I have been generally successful in moving the code to Spark (Scala) and I'm able to generate the correct

SkikitLearn learning curve strongly dependent on batch size of MLPClassifier ??? Or: how to diagnose bias/ variance for NN?

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问题 I am currently working on a classification problem with two classes in ScikitLearn with the solver adam and activation relu. To explore if my classifier suffers from high bias or high variance, I plotted the learning curve with Scikitlearns build-in function: https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html I am using a Group-K_Fold crossvalidation with 8 splits. However, I found that my learning curve is strongly dependent on the batch size of my

Does Dlib's MLP implementation have any restriction?

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问题 I am training a simple MLP (8 units in the input layer, 50 units in the hidden layer) using C++ DLib with the following 89 samples as training data: float template_user[89][8]={ {0.083651,0.281587,0.370476,0.704444,0.253865,0.056415,0.002344,0.465187}, {0.142540,0.272857,0.376032,0.740952,0.591501,0.227614,0.000000,0.832224}, {0.095625,0.258750,0.447500,0.779792,0.449932,0.000964,0.035104,0.606591}, {0.115208,0.181250,0.478750,0.797083,0.491855,0.015824,0.011652,0.649632}, {0.107436,0.166026