conv2d keras

Keras Chinese DocumentKeras English Document 1. Brief introduction 2. Keras Basic Flow Take handwritten digit recognition as an example 1. Define Network structure 2. Set the form of loss function 3. Model Fitting When batch_size=1, it is a random gradient descent stochastic gradient descentWe know that stochastic gradient descent is a lot faster than 50,000 data. However, when batch_size>1, it a

(' X_test shape: ', X_test.shape) # (412L, 50L, 1L) print (' Y_test shape: ', Y_test.shape) # (412 L,) return [X_train, Y_train, X_test, Y_test] (3) LSTM model This article uses the Keras depth study frame, the reader may use is other, like Theano, TensorFlow and so on, the similar. Keras LSTM Official Document LSTM's structure can be customized, Stack lstm or bidirectional lstm def build_model (layers):

In order to amplify the data set, 2 ways are used to amplify the data. 1. Data enhancement processing using Keras 2. Data enhancement processing using Skimage Keras includes processing, there is featurewise visual image will be slightly dimmed, samplewise visual image will become class X-ray image form, ZCA processing visual image will become gray image, rotation range randomly rotated image, horizonta

This paper describes how to apply the deep learning-based target detection algorithm to the specific project development, which embodies the value of deep learning technology in actual production, and is considered as a landing realization of AI algorithm. The algorithms section of this article can be found in the previous blogs: [AI Development] Python+tensorflow to build its own computer Vision API Service [AI development] Video Multi-object tracking implementation based on deep learning [AI d

Wunda Automatic driving target detection data set: Automatic driving target detection autonomous Driving-car detection Welcome to your Week 3 programming assignment. You'll learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook is described in the YOLO Papers:redmon et al., (https://arxiv.org/abs/1506.0 2640) and Redmon and Farhadi, (https://arxiv.org/abs/1612.08242). You'll learnto:-use object detection on a car detection dataset-Deal with bounding b

people that still seem t O think that's the import keras leap for every hurdle, and so they, in knowing it, with some tremendous advantage over their C Ompetition. It can be seen that deep learning spreads the fanatics are not popular. Even the experts, who stand on top of science, now lose a great deal of enthusiasm for using the term, with only a bit of frustration, preferring to downplay the power of modern neural networks and avoid lett

== ' conv2d ' and ' import/' I N Op.name] layers = [op.name for op in Self.graph.get_operations ()] feature_nums = [Int (self.graph.get_tenso R_by_name (name+ ': 0 '). Get_shape () [-1]) for name in layers] for feature in Feature_nums:print (feature) ' Restore tensor from model ' w_out = self.graph.get_tensor_by_name (' w:0 ') b_out = Self.graph.get_tensor_ By_name (' b:0 ') _inpUT = self.graph.get_tensor_by_name (' x:0 ') _out = Self.graph.get_

layers, that is, a pixel is associated with the surrounding 5x5 pixels, which can be said to feel that the wild is 5x5. At the same time, the concatenation of 3 Series 3x3 convolution layers is equivalent to a 7x7 convolution layer. In addition, 3 series of 3x3 convolutional layers have fewer parameters than one 7x7, only the latter (3x3x3)/(7x7) = 55%. Most importantly, 3 3x3 convolution layers have more non-linear transformations than a 7x7 convolution layer (the former can be activated with

, kw kernel width, convolution kernel width, n_out Number of convolution kernels, number of output channels, dh step height, dw step width, the p parameter list. Get_shape () [-1]. value gets the number of input_op channels. Tf. name_scope (name) sets scope. Tf. get_variable creates kernel (convolution kernel), shape [kh, kw, n_in, n_out], convolution kernel height and width, and input and output channels. Tf. contrib. layers. xavier_initializer_conv2d () parameter initialization. Tf. nn.

the trend of symbolic diagrams used in programming networks. Theano's symbolic API supports loop control, making the RNN implementation easier and more efficient.Torch Support for convolutional networks is very good. The time domain convolution in tensorflow and Theano can be achieved by conv2d, but this is a bit trickery; The local interface of the time domain convolution makes it very intuitive to use. Torch supports a large number of RNN through m

from Theano, which leads to the trend of symbolic diagrams used in programming networks. Theano's symbolic API supports loop control, making the RNN implementation easier and more efficient.Torch Support for convolutional networks is very good. The time domain convolution in tensorflow and Theano can be achieved by conv2d, but this is a bit trickery; The local interface of the time domain convolution makes it very intuitive to use. Torch supports a l

,lower=0.2, upper=1.8)#Contrast Variation #Generate Batch #Shuffle_batch Parameters: capacity is used to define the scope of the shuttle, and if it is for the entire training data set, then capacity should be large enough to get batch #Make sure the data hits the big enough messImages, Label_batch = Tf.train.shuffle_batch ([Distorted_image, label],batch_size=batch_size, Num_threads=1,capacity=2000,min_after_dequeue=1000) returnimages, Label_batchclassNetwork (object):#constructor

be represented by the sum of the original input and all residuals output to it, whereas the previous network is the product representation of the layer and layer(3) Derivative discovery of loss　　Sum that will not always be-1, so the gradient will not diffuse vanish, even when weights arbitrarily smallCompared with different shortcut ways, it is found that simple identity is better, 1x1conv effect is worse, can be used to deal with the data of different dimensionsComparing the different combinat

the parameter initialization. From Mxnet.gluon.model_zoo import Vision pretrained = Vision.get_model (' resnet18_v1 ', pretrained=true). Features net = nn. Hybridsequential () for I in range (len (pretrained)-2): Net.add (Pretrained[i]) # anchor Scales, try Adjust It yourself scales = [[3.3004, 3.59034], [9.84923, 8.23783]] # use 2 classes, 1 as dummy class, otherwise soft Max won ' t work predictor = Yolo2output (2, scales) predictor.initialize () Net.add (Predictor) the Yolo2

First to import Slim: From Tensorflow.contrib Import Slim Tf-slim mainly consists of the following: Arg_scopeDataEvluationLayersLearningLossesMetricsNetsQueuesRegularizersVariables Layer The most commonly used is the slim Layers, the creation of Layer is very convenient: Input = ... NET = slim.conv2d (input, 128, [3, 3], scope= ' conv1_1 ') net = slim.max_pool2d (NET, kernel_size=[2,2], stride=2, scope= ' Pool1 ') # generally (inputs=, kernel_size=, stride=, padding=?, ....) NET = Slim.repeat

term that adds weights, most of the weights of the boot model tend to be 0). After completing the training, cut off these 0 of the filters. Advantages:Simple. Disadvantages:Not cut clean. Back-End compression It will change the compression technology of the original network structure to a large extent and not be reversible. 1. Low Rank approximation Use structured matrices to perform low rank decomposition. Advantages:This method has a good effect on small and medium sized network models. Disad

", shape= (+, +, +), Dtype=float32)It can be seen that there is a multiple relationship between the size of the output size and the stride of the convolution. Second, the realization of the function of the process For a given shape for [batch, In_height, In_width, In_channels], the tensor variable input, and the shape for [Filter_height, Filter_width, In_channels, Out_ Channels] The convolution kernel filter, the function tensorflow::ops::conv2d (

],initializer=tf.constant_ Initializer (1.0)) with Tf.variable_scope ("Bar"): v1=tf.get_variable ("V", [1],initializer=tf.constant_ Initializer (1.0)) v2=tf.get_variable ("V", [1],initializer=tf.constant_initializer (1.0)) This is because tf.get_variable () does not handle naming conflicts, The complete code implementation: import TensorFlow as TF def conv_relu (input, Kernel_shape, Bias_shape): # Create variable nam Ed "weights". With Tf.variable_scope ("H1") as Scope:weights = tf

: /job:localhost/replica:0/task:0/gpu:0a: /job:localhost/replica:0/task:0/gpu:0MatMul: /job:localhost/replica:0/task:0/gpu:0[[ 22. 28.] [ 49. 64.]] (2). Example TestDownload the TensorFlow source on GitHub with a lot of sample codeRun Example:python mnist_with_summaries.py..............................The results just started to stall ...couldn‘t open CUDA library cupti64_80.dllCheck, this DLL in NVIDIA GPU Computing Toolkit\CUDA\v8.0\extras\CUPTI\libx64 , because this also did not add to

Python1. Theano is a Python class library that uses array vectors to define and calculate mathematical expressions. It makes it easy to write deep learning algorithms in a python environment. On top of it, many classes of libraries have been built.1.Keras is a compact, highly modular neural network library that is designed to reference torch, written in Python, to support the invocation of GPU and CPU-optimized Theano operations.2.PYLEARN2 is a librar