Glorot Uniform

(scalar or matrix or initializer, defaults to init_glorot_uniform()) - initial value of weights W. A Keras tensor is a tensor object from the underlying backend (Theano, TensorFlow or CNTK), which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model. class DeviceCountEntry. Here are the examples of the python api keras. In this post you will discover how you can use deep learning models. By using Xavier initialization, we make sure that the weights are not too small but not too big to propagate accurately the signals. Showing 1-2 of 2 messages. models import Model from keras. It draws samples from a uniform distribution within -limit, limit where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. It may be useful for you to design small experiments with a smaller subset of your data that will complete in a reasonable time. We would like to show you a description here but the site won't allow us. Note: After I wrote and submitted my article, I did a deep dive into the source code for the PyTorch library and discovered the library uses a very wacky. 0, c01b=False) [source] ¶. Keras is one of the most popular deep learning libraries in Python for research and development because of its simplicity and ease of use. ca Abstract Rectifying neurons are more biologically plausible than logistic sigmoid neurons, which are themselves more biologically plausible than hyperbolic tangent neu-rons. Transcript: Today, we’re going to learn how to add layers to a neural network in TensorFlow. This is perhaps the 3rd time I’ve needed this recipe and it doesnt seem to be readily available on google. This is not a scheme that you want to use lightly because of the time it will take to compute. preprocessing. Define tanh. By default, it is initialized with nnabla. 0, c01b=False) [source] ¶. Source code for torch. initializer. Returns: An initializer for a weight matrix. We use cookies for various purposes including analytics. uniform from numbers import Number import torch from torch. DeepGSR avoids this by using Glorot initialization in the second convolutional and fully connected layers (Glorot and Bengio, 2010). distributions. float32) and glorot_uniform_initializer(seed=None, dtype=tf. 사실 초기화 방식이 제안된 논문의 1저자 이름이 Xavier Glorot이다( 출처 ). EncodeSplines Specifically, the input/output dimensions are:. The scikit-learn library is the most popular library for general machine learning in Python. glorot_uniform(). initializers. In this post you will discover how you can use deep learning models. float32) This initializer is designed to keep the scale of the gradients roughly the same in all layers. Good software design or coding should require little explanations beyond simple comments. We would like to show you a description here but the site won't allow us. class lasagne. ): # type: (Tensor, float) -> Tensor r """Fills the input `Tensor` with values according to the method described in `Understanding the difficulty of training deep feedforward neural networks` - Glorot, X. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in International conference on artificial intelligence and statistics, 2010, pp. normal (loc=0. If sampling from a uniform distribution, this translates to sampling the interval , where. It draws samples from a uniform distribution within -limit, limit where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. scikit-learn是最受欢迎的Python机器学习库。本章我们将使用scikit-learn调用Keras生成的模型。. Glorot Uniform Initializer. preprocessing. Input keras. 0, size=None)¶ Draw samples from a uniform distribution. Description Usage Arguments References See Also. This is a summary of the official Keras Documentation. csiszar_divergence. Case studies are conducted on Standard & Poor's 500, NASDAQ, and Apple (AAPL). Note: this maps to the "uniform1" distribution in BrainScript. Glorot with weights sampled from the Normal distribution. The properties are the same as those of W for the Affine layer. There is a closely related initialization algorithm called He normal initialization, where the limit value is sqrt(2 / nin). Glorot uniform initializer, also called Xavier uniform initializer. initializer_lecun_uniform ( seed = NULL ). We use cookies for various purposes including analytics. Eclipse Deeplearning4j is an open-source, distributed deep-learning project in Java and Scala spearheaded by the people at Skymind. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in International conference on artificial intelligence and statistics, 2010, pp. xavier (uniform=True, seed=None, dtype=tf. Glorot uniform initializer. distributions. activation=relu init: initializer descriptor for the weights, e. It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. Used to create random seeds. See Glorot for a description of the parameters. Input() Input() is used to instantiate a Keras tensor. initializers. Keras is one of the most popular deep learning libraries in Python for research and development because of its simplicity and ease of use. 0, c01b=False) [source] ¶ Glorot with weights sampled from the Uniform distribution. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. copy module¶. We generate a list of all the 2 20 numbers, convert it to their binary string and shuffle the entire list. initializations. The simplest way to initialize weights and biases is to set them to small (perhaps -0. Neural Network Glorot Initialization. For example:. It draws samples from a uniform distribution within -limit, limit where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. 1 / 128) The network consists of a word embedding layer, and LSTM, RecurrentSum, Dropout and Affine layers. Glorot uniform initializer, also called Xavier uniform initializer. This is suitable for most CNNs. You can vote up the examples you like or vote down the ones you don't like. Mission Cheesy Nachos Corn Chips 230gm x 6 9317224401911,Dried Mushrooms Organic Snow White Fungus Tremella 古田雪耳 銀耳 Silver Ear 250g,2-tlg. b_init (nnabla. xavier_initializer(uniform=True, seed=None, dtype=tf. Apr 28, 2016. Glorot Uniform Initializer. 'glorot' - Initialize the input weights with the Glorot initializer (also known as Xavier initializer). TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. Linear ( 10 , 1 , bias = False ) nn. Read and modify the training weights from Python. 70201,还有一些提升空间,大家可以试着ensemble一下。. DRIED PORCINI MUSHROOMS - 100 grams NEW Grade A Wild mushroom,Waitrose Cooks' Ingredients Salted Capers 100g (Pack of 4),East of India To Have And to Hold White Porcelain Heart Anneau Dish Wedding Gift 5039041035172. class lasagne. GlorotUniform(gain=1. In keras: R Interface to 'Keras'. You just clipped your first slide! Clipping is a handy way to collect important slides you want to go back to later. IRO University of Montreal Montreal, QC fglorotxa,bordesa,[email protected] Kerasを使ってひらがな認識のCNNを動かしてみました。情報を取り出すのが素のTensorflow, Caffe, Darknetに比べて非常に楽でした。. preprocessing. Activation functions in most modern networks are real-valued, non-linear functions that are computationally inexpensive to compute and easily differentiable. How to use Keras backend and optimizers directly outside of a Keras model - keras_backend_optimizer_example. 1998 P Jefferson Nickel UNCIRCULATED Full Steps,Mother Of The Bride Condici Size 16,2016-S Shawnee National Forest Clad Proof Quarter in Crystal Clear Coin Capsule. 0, c01b=False) [source] ¶ Glorot with weights sampled from the Uniform distribution. Bias terms are initialized to zero. Read and modify the training weights from Python. initializer_glorot_normal. xavier_initializer(uniform=True, seed=None, dtype=tf. Learn More. 'glorot' - Initialize the weights with the Glorot initializer (also known as Xavier initializer). Some of the generative work done in the past year or two using generative adversarial networks (GANs) has been pretty exciting and demonstrated some very impressive results. def xavier_uniform_ (tensor, gain = 1. initializer_lecun_uniform ( seed = NULL ). initializers import glorot_uniform from keras. def truncated_normal (stdev, seed = None): ''' Truncated normal initializer. utils import np_utils. There can be a total of 2 20 ~ 10 6 combinations of 1s and 0s in a string of length 20. That is a lot of models and a lot of computation. 3 Effect of Activation Functions and Saturation During Training Two things we want to avoid and that can be revealed from the evolution of activations is excessive saturation of acti-. They are mostly used with sequential data. CustomZeroPadding2D taken from open source projects. glorot_uniform Glorot均匀分布初始化方法,又成Xavier均匀初始化,参数从[-limit, limit]的均匀分布产生,其中limit为 sqrt(6 / (fan_in + fan_out)) 。 fan_in为权值张量的输入单元数,fan_out是权重张量的输出单元数。. h5 trained model file and try. initializer for Parameter initialized to uniform distribution between scale[-1. It draws samples from a uniform distribution within -limit, limit where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. 0, size=None)¶ Draw samples from a uniform distribution. (Glorot and Bengio, 2010), units with more incoming. activation=relu init: initializer descriptor for the weights, e. Another way of putting it: keeping variance similar along all the layers. That works okay and makes it easier to stack a large number of layers without having to worry about vanishing gradients. Deep Sparse Rectifier Neural Networks Xavier Glorot, Antoine Bordes and Yoshua Bengio Dept. He(initializer, gain=1. 0, size=None)¶ Draw samples from a uniform distribution. Customizing Keras typically means writing your own. 'glorot' - Initialize the weights with the Glorot initializer (also known as Xavier initializer). It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. The normal and truncated normal expect the mean and standard deviation ($\latex \sigma$) as input while the uniform expects the minimum and maximum value as inputs. The Glorot initializer independently samples from a uniform distribution with zero mean and variance 2/(numIn + numOut) , where numIn = NumWords + 1 and numOut = Dimension. Glorot uniform initializer, also called Xavier uniform initializer. EncodeSplines Specifically, the input/output dimensions are:. By default, it is initialized with nnabla. Glorot normal initializer, also called Xavier normal initializer. The goal of this page is to summarize this functionality so users know what exists, and where to find more information. すると初期化はglorot_uniformとなっていることがわかります。 実はこの初期化は活性化関数が原点対称であると考えたときに使える関数なのです。(詳しくはニューラルネットにおける変数の初期化についてを読んでください. optimizers import RMSprop from keras. Does this graph seems familiar ? Exactly what I witnessed and shown you at the beginning ! The network trained with Xavier/Glorot initialization doesn't learn anything. initializers import glorot_uniform import h5py; import numpy as np; import matplotlib. 3 Overview of KERAS Minimalist, highly modular neural networks library Written in Python Capable of running on top of either TensorFlow or Theano Developed with a focus on enabling fast. (scalar or matrix or initializer, defaults to init_glorot_uniform()) - initial value of weights W. 0, size=None) ¶ Draw random samples from a normal (Gaussian) distribution. Compared to vanishing gradients, exploding gradients is more easy to realize. The assumption most worth talking about is the "linear neuron" bit. / (fan_in + fan_out))。 fan_in: the number of input units in the weight tensor. initializer_glorot_uniform. float32) and glorot_uniform_initializer(seed=None, dtype=tf. Define tanh. 1 ) out = linear ( x ). Why not consolidate them into one. 基本的には personal choice である; Bengioは uniform を好んで使っていた、一方、Hintonは normal を使っていた. def xavier_init (n_inputs, n_outputs, uniform = True): """Set the parameter initialization using the method described. IRO University of Montreal Montreal, QC fglorotxa,bordesa,[email protected] In keras: R Interface to 'Keras'. filter_shape: shape of receptive field of the filter, e. Keras is one of the most popular deep learning libraries in Python for research and development because of its simplicity and ease of use. Rd It draws samples from a uniform distribution within -limit, limit where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in International conference on artificial intelligence and statistics, 2010, pp. (5,5) for a 2D filter (not including the input feature-map depth) num_filters: number of output channels (number of filters) activation: optional non-linearity, e. Definiert in tensorflow/core/protobuf/config. From my tests, it turns out that initialization is surprisingly important. リスト1 import宣言. init (scalar or matrix or initializer, defaults to init_glorot_uniform()) - initial value of weights W_bias. The Glorot initializer independently samples from a uniform distribution with zero mean and variance 2/(InputSize + numOut), where numOut = 8*NumHiddenUnits. Activations. Ok, the code is slightly obfuscated as you instantiate the layers and "call" them later, that is fine. This is suitable for most CNNs. If you wrap your load_model with the below CustomObjectScope method, it would work fine. Xavier 초기화(glorot_uniform, glorot_normal): 케라스에서는 glorot이라는 이름으로 되어있는데, 일반적으로는 Xavier Initialization이라고 알려져 있다. The resulting values are drawn from a truncated normal distribution and are always within two standard deviations from the mean (which is 0). The following are code examples for showing how to use keras. initializer_glorot_uniform. Define tanh. 1--param_init_glorot, -param_init_glorot. global rotating services limited is a rotating equipment (gas turbines, compressors, generators & pumps) services company based in port harcourt, rivers state. Now customize the name of a clipboard to store your clips. 标题:keras实现多个模型融合(非keras自带模型,这里以3个自己的模型为例). In this tutorial, we create a multi-label text classification model for predicts a probability of each type of toxicity for each comment. It draws samples from a uniform distribution within -limit, limit where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. filter_shape: shape of receptive field of the filter, e. 0, size=None) ¶ Draw random samples from a normal (Gaussian) distribution. Neural Network Glorot Initialization. Unless stated otherwise, we use the GloVE Wikipedia 2014 and Gigaword 5 embeddings with 6B tokens (Pennington,. Parameters are initialized over uniform distribution with support (-param_init, param_init). 01) uniform random values. uniform(low=0. It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. (where i is the input, o is the output, W is the weight, and b is the bias term. Glorot Uniform Initializer. In this post you will discover how you can use deep learning models. glorot_uniform_initializer() 也称之为 Xavier uniform initializer,由一个均匀分布(uniform distribution)来初始化数据。 假设均匀分布的区间是 [-limit, limit],则. This initializer is designed to keep the scale of the gradients roughly the same in all layers. from a Glorot uniform distribution (Glorot & Bengio, 2010). xavier_initializer x. When you build a classifier, you're effectively learning a mathematical model to draw a decision boundary that can separate between the classes present in your data set's targets. Kerasを使ってひらがな認識のCNNを動かしてみました。情報を取り出すのが素のTensorflow, Caffe, Darknetに比べて非常に楽でした。. BinaryConnectAffine. distributions. En teoría de probabilidad y estadística, la distribución uniforme continua es una familia de distribuciones de probabilidad para variables aleatorias continuas, tales que para cada miembro de la familia, todos los intervalos de igual longitud en la distribución en su rango son igualmente probables. limit=sqrt(6 / (fan_in + fan_out)) 其中的fan_in和fan_out分别表示输入单元的结点数和输出单元的结点数。. The Glorot uniform initializer, also called Xavier uniform initializer. An in depth look at LSTMs can be found in this incredible blog post. utils import broadcast_all. It draws samples from a uniform distribution within -limit, limit where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. The assumption most worth talking about is the "linear neuron" bit. Xavier has 7 jobs listed on their profile. Read and modify the training weights from Python. Returns an initializer performing "Xavier" initialization for weights. Eclipse Deeplearning4j is an open-source, distributed deep-learning project in Java and Scala spearheaded by the people at Skymind. & Bengio, Y. Args: uniform: Whether to use uniform or normal distributed random initialization. It may be useful for you to design small experiments with a smaller subset of your data that will complete in a reasonable time. csiszar_divergence. How do I adapt models in Python. Auto-Encoder 解析: from __future__ import division, print_function, absolute_import import tensorflow as tf import numpy as np import matplotlib. Note: We have updated the definition as following from v. Glorot with weights sampled from the Normal distribution. (where i is the input, o is the output, W is the weight, and b is the bias term. Enter Xavier Glorot and Yoshua Bengio… Xavier / Glorot Initialization Scheme. Glorot normal initializer, also called Xavier normal initializer. SplineT(shared_weights=False, kernel_regularizer=None, use_bias=False, kernel_initializer='glorot_uniform', bias_initializer='zeros') Spline transformation layer. (2015) extended this formula to the ReLU (Glorot et al. We use cookies for various purposes including analytics. Glorot uniform and Glorot normal seem to work about equally well, especially for neural networks with a single hidden layer. Each of them requires a different subset of this command's arguments, so they will be described one by one in the Visualizations section. " Feb 11, 2018. How do I adapt models in Python. 看源代码可以发现,pytorch的权重初始化也是glorot_uniform 初始化,我们需要进行常量初始化,并将bias设置为False。 linear = nn. EncodeSplines Specifically, the input/output dimensions are:. BaseInitializer or numpy. Deep Sparse Rectifier Neural Networks Xavier Glorot, Antoine Bordes and Yoshua Bengio Dept. (This demo is a part of Deep Learning for Network Biology tutorial. It pulls values from a uniform distribution, however, its limits are dynamic with the size of the Dense layer! It actually uses the following equation to calculate the limits of the layer:. csiszar_divergence. ): # type: (Tensor, float) -> Tensor r """Fills the input `Tensor` with values according to the method described in `Understanding the difficulty of training deep feedforward neural networks` - Glorot, X. 'glorot' - Initialize the weights with the Glorot initializer (also known as Xavier initializer). Default: 0. initializer for Parameter initialized to uniform distribution between scale[-1. Activations. When you build a classifier, you're effectively learning a mathematical model to draw a decision boundary that can separate between the classes present in your data set's targets. This section aims at briefly introducing the idea accompanied with the different initialization. distributions. While previous CNN architectures had a drop off in the effectiveness of additional layers, ResNet can add a large number of layers with strong performance. float32) and glorot_uniform_initializer(seed=None, dtype=tf. (where i is the input, o is the output, W is the weight, and b is the bias term. Here you can find the source code of Keras's implementation of Xavier Glorot's initialization. How do I adapt models in Python. float32) This initializer is designed to keep the scale of the gradients roughly the same in all layers. edu Computer Science Department, Stanford University, CA 94305 USA Abstract Deep neural network acoustic models pro-duce substantial gains in large vocabu-. That works okay and makes it easier to stack a large number of layers without having to worry about vanishing gradients. kerasを使用した画像認識プログラムを作成したのですが、うまく動きません。 学習済モデルも完成して、kerasのインストールもできているのですが、エラーが出てしまいます。. initializer. Implementing a neural network in Keras •Five major steps •Preparing the input and specify the input dimension (size) •Define the model architecture an d build the computational graph. h5 trained model file and try. Glorot & Bengio, AISTATS 2010 ) 구조는 LeCun의 초기화 방법과 유사하지만 다음 층의 노드 수도 사용하고, 많은 연구를 통해 가장 최적화된 상수값 또한 찾아냈다. It draws samples from a uniform distribution within [-limit, limit] where `limit` is `sqrt(6 / (fan_in + fan_out))`. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in International conference on artificial intelligence and statistics, 2010, pp. They are mostly used with sequential data. OK, I Understand. I trained my CNN (VGG) through google colab and generated. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. We would like to show you a description here but the site won't allow us. リスト1 import宣言. global rotating services limited (formerly whitehall energy services limited) was incorporated in nigeria in 2006. 看源代码可以发现,pytorch的权重初始化也是glorot_uniform 初始化,我们需要进行常量初始化,并将bias设置为False。 linear = nn. When you build a classifier, you're effectively learning a mathematical model to draw a decision boundary that can separate between the classes present in your data set's targets. Now customize the name of a clipboard to store your clips. This method is designed to keep the scale of the gradients roughly the same in all layers. initializers. Similarly, the weight initialization algorithm (Glorot uniform) and the hidden layer activation function (tanh) and the output layer activation function (tanh) are hyperparameters. glorot_uniform Glorot uniform initializer, also called Xavier uniform initializer. ResNet50及其Keras实现。你或许看过这篇访问量过12万的博客ResNet解析,但该博客的第一小节ResNet和吴恩达的叙述完全不同,因此博主对这篇博文持怀疑态度,你可以在这篇博文最下面找到提出该网络的论文链接,这篇博文可以作为研读这篇论文的基础。. It pulls values from a uniform distribution, however, its limits are dynamic with the size of the Dense layer! It actually uses the following equation to calculate the limits of the layer:. Each binary string is then converted to a list of 0s and 1s. Kerasを使ってひらがな認識のCNNを動かしてみました。情報を取り出すのが素のTensorflow, Caffe, Darknetに比べて非常に楽でした。. scikit-learn是最受欢迎的Python机器学习库。本章我们将使用scikit-learn调用Keras生成的模型。. (5,5) for a 2D filter (not including the input feature-map depth) num_filters: number of output channels (number of filters) activation: optional non-linearity, e. As the --visualization parameters suggests, there is a vast number of visualizations readily available. Returns an initializer performing "Xavier" initialization for weights. BaseInitializer or numpy. 基本的には personal choice である; Bengioは uniform を好んで使っていた、一方、Hintonは normal を使っていた. Hannun [email protected] That is a lot of models and a lot of computation. b_init (nnabla. EdgeConditionedConv(channels, kernel_network=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias. To use Batch Normalization, just replace your dropout layers with Batch Normalization, like:. 比赛官网: https:// challenger. Glorot & Bengio, AISTATS 2010 ) 구조는 LeCun의 초기화 방법과 유사하지만 다음 층의 노드 수도 사용하고, 많은 연구를 통해 가장 최적화된 상수값 또한 찾아냈다. Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). They are mostly used with sequential data. This method is designed to keep the scale of the gradients roughly the same in all layers. Init parameters with xavier_uniform. class lasagne. The Glorot Uniform is the interesting one in this case. Enter Xavier Glorot and Yoshua Bengio… Xavier / Glorot Initialization Scheme. 0", "config": {"layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 3, 224, 224], "input_dtype. 사실 초기화 방식이 제안된 논문의 1저자 이름이 Xavier Glorot이다( 출처 ). Note: this maps to the "uniform1" distribution in BrainScript. glorot_uniform(seed=None) It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. 标题:keras实现多个模型融合(非keras自带模型,这里以3个自己的模型为例). Sampling each weight from normal distribution with a mean of 0 and a variance of. ca Abstract Rectifying neurons are more biologically plausible than logistic sigmoid neurons, which are themselves more biologically plausible than hyperbolic tangent neu-rons. In this tutorial, we create a multi-label text classification model for predicts a probability of each type of toxicity for each comment. uniform(low=0. サマリ いくつかの書き方があるが,混乱するのは下記の種類が色…. It is implemented in almost all neural network libraries. TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. For instance, in a Dense layer the weight matrix has shape (input_dim, output_dim), set axis to 0 to constrain each weight vector of length (input_dim,). distributions. global rotating services limited is a rotating equipment (gas turbines, compressors, generators & pumps) services company based in port harcourt, rivers state. Initalizers: Define the way to set the initial random Define the way to set the initial random weights of Keras , bias_initializer = glorot_uniform ())). Implementing a neural network in Keras •Five major steps •Preparing the input and specify the input dimension (size) •Define the model architecture an d build the computational graph. Showing 1-2 of 2 messages. b_init (nnabla. xavier (uniform=True, seed=None, dtype=tf. Copies Classes from keras to remove dependency. If we refer solely on the Glorot's and He's initializations papers, they both use a similar theoritical analysis: they find a good variance for the distribution from which the initial parameters are drawn. The normal and truncated normal expect the mean and standard deviation ($\latex \sigma$) as input while the uniform expects the minimum and maximum value as inputs. This method is designed to keep the scale of the gradients roughly the same in all layers. CNN: glorot_uniform You can learn more about " glorot_uniform ", also called " Xavier normal ", named for the developer of the method Xavier Glorot, in the paper: Understanding the difficulty of training deep feedforward neural networks , 2010. It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor. We use Deep Learning to turn a painful and time-consuming leak-detection task for water and oil pipelines into a fast, painless process. while glorot_uniform is the equivalent implementation of Eqn. Returns: An initializer for a weight matrix. The Glorot uniform initializer, also called Xavier uniform initializer.