keras conv2d

Recently paid attention to a burst of keras, feeling this thing quite convenient, today tried to find it really quite convenient. Not only provide the commonly used algorithms such as layers, normalization, regularation, activation, but also include several commonly used databases such as cifar-10 and mnist, etc. The following code is Keras HelloWorld bar. Mnist handwritten digit recognition with MLP implem

The model saved with H5py has very little space to take up. Before you can use H5py to save Keras trained models, you need to install h5py, and the specific installation process will refer to my blog post about H5py installation: http://blog.csdn.net/linmingan/article/details/50736300 the code to save and read the Keras model using H5py is as follows: Import h5py from keras.models import model_from_json

RNN model of deep learning--keras training RNN principle: (Recurrent neural Networks) cyclic neural network. It interacts with each neuron in the hidden layer and is able to handle the problems associated with the input and back. In RNN, the output from the previous moment is passed along with the input of the next moment, which is equivalent to a stream of data over time. Unlike Feedforward neural networks, RNN can receive serialized data as input,

) Reducelronplateau when the indicator becomes Reduce learning rate Reducelronplateau (monitor= ' Val_loss ', factor=0.1, patience=10, mode= ' auto ', epsilon=0.0001, CoolD Own=0, min_lr=0) modelcheckpoint Example: From keras.callbacks import modelcheckpoint model = sequential () model.add (Dense, input_dim=784, kernel_ initializer= ' uniform ')) Model.add (Activation (' Softmax ')) model.compile (loss= ' categorical_crossentropy ') , optimizer= ' R

index is to assign an integer ID to each word in turn. Traversing all the news texts, we keep only the 20,000 words we see most, and each news text retains a maximum of 1000 words. Generates a word vector matrix. Column I is a word vector that represents the word index for I. Load the word vector matrix into the Keras embedding layer, set the weight of the layer can not be trained (that is, in the course of network training, the word vector will no l

Deeplearning library is quite a lot of, now GitHub on the most hot should be caffe. However, I personally think that the Caffe package is too dead, many things are packaged into a library, to learn the principle, or to see the Theano version.My personal use of the library is recommended by Friends Keras, is based on Theano, the advantage is easy to use, can be developed quickly.Network frameworkThe network framework references Caffe's CIFAR-10 framew

The following error occurred while running the Keras code:Traceback (most recent):File "segnet_train.py", line 254, in Train (args)File "segnet_train.py", line-up, in trainModel = Segnet ()File "segnet_train.py", line 134, in SegnetModel.add (Maxpooling2d (pool_size= (2,2)))File "/usr/local/lib/python2.7/dist-packages/keras/engine/sequential.py", line 181, in AddOutput_tensor = Layer (Self.outputs[0])File "

Before I have been using Theano, the previous five deeplearning related articles are also learning Theano some notes, at that time already feel Theano use up a little trouble, sometimes want to achieve a new structure, it will take a lot of time to programming, so think about the code modularity, Easy to reuse, but because it's too busy to do it. Recently discovered a framework called Keras, which coincides with my ideas, is particularly simple to use

Nowadays, AI is getting more and more attention, and this is largely attributed to the rapid development of deep learning. The successful cross-border between AI and different industries has a profound impact on traditional industries.Recently, I also began to keep in touch with deep learning, before I read a lot of articles, the history of deep learning and related theoretical knowledge also have a general understanding.But as the saying goes: The end of the paper is shallow, it is known that t

Recently in the study of data mining related knowledge, the class has mentioned keras related knowledge, under the class would like to build their own keras, helpless related information too little. So he wrote this blog, for small white installation learning. Keras is a deep learning framework based on Theano, designed to refer to torch, written in Python, is a

The content of a simple experiment lesson.First, the size of the given sample material is 32*32, which can be done in Python batch and OpenCV function resize (), where I do not list the code.List some of the pictures that are well-shrunk.Then in the use of Keras CV convolutional neural network model, before doing this experiment, the computer should be configured Python+theano+keras environment.#生成一个modelde

1. Installing Anacondahttps://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Conda info to query installation informationConda list can query which libraries you have installed now2. CPU version of TensorFlowPip Install--upgrade--ignore-installed tensorflowWhether the test was successfulPython import tensorflow as TF hello=tf.constant ("hello!") SESS=TF. Session () print (Sess.run (hello))3. Installing Keraspip install keras -U --preTest：import ker

International-airline-passengers.csv is less, roughly as follows"Month","International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60""1949-01",112"1949-02",118"1949-03",132"1949-04",129"1949-05",121"1949-06",135"1949-07",148"1949-08",148"1949-09",136"1949-10",119"1949-11",104"1949-12",118"1950-01",115"1950-02",126"1950-03",141"1950-04",135"1950-05",125"1950-06",149"1950-07",170"1950-08",170"1950-09",158"1950-10",133"1950-11",114"1950-12",140"1951-01",145"1951-02",150"1951-03"

Full Stack Engineer Development Manual (author: Shangpeng) Python Tutorial Full Solution Keras uses a depth network to achieve the encoding, that is, the n-dimensional characteristics of each sample, using K as a feature to achieve the function of coding compression. The feature selection function is also realized. For example, the handwriting contains 754 pixels, and it contains 754 features, if you want to represent them with two features. How do yo

Keras Framework Training Model preservation and re-loading Experimental data mnist The Initial training model and save Import NumPy as NP from keras.datasets import mnist from keras.utils import np_utils from keras.models import sequential F Rom keras.layers import dense from keras.optimizers import SGD # Load data (X_train,y_train), (x_test,y_test) = Mnist.load_data () # (60000,28,28) print (' X_shape: ', X_train.shape) # (60000) print (' Y_shape: ',

. I've told you before, not to repeat.Try another optimizer (optimizer) before you've talked about it.Keras's callback function earlystopping () has been said before, no more 3.7.5 regularization method Regularization method means that when the objective function or cost function is optimized, a regular term is added after the objective function or the cost function, usually with L1 regular and L2 regular. The code snippet illustrates: From Keras impo

first, the initialization of variables # for each filter, generate the dimension of the image Img_width = Img_height = + # We want to go to the visual layer name # (see Model definition in keras/applications/vgg16.py ) layer_name = ' block5_conv1 ' convert the tensor to a valid image def deprocess_image (x): # Normalize tensor x-= X.mean () x/= (X.STD () + 1e-5) x *= 0.1 # clip to [0, 1] x + = 0.5 x = np.clip (x, 0, 1)

Recently in the study of using Keras to implement a lstm to train their own data (lstm the basic principles of self-tuition), the first of their own data with the DNN to train, and then to the LSTM, because the input is not the same, so some burn, DNN input format is input: (Samples,dim), is a two-dimensional data, and the input format of lstm: (Samples,time_step,dim) is three-dimensional, so, first understand how to convert DNN input into lstm input,

Objective function Objectives The objective function, or loss function, is one of the two parameters that must be compiled for a model: Model.compile (loss= ' mean_squared_error ', optimizer= ' SGD ')You can specify a target function by passing a predefined target function name, or you can pass a Theano/tensroflow symbolic function as the target function, which should return only a scalar value for each data point, with the following two parameters as parameters: Y_true: Real data labels, theano

Tags: caff href tps medium mode line DAO use UDAToday use Anaconda3 to install TensorFlow and Caffe, the main reference blogNow the computer environment:ubuntu16.04cuda8.0cudnn6.0Anaconda31. From Scipy.misc import imread,imresize errorHint error importerror:cannot import name ImreadBut import scipy is displayed correctly.Solution: Pip install Pillow. 2. Libcublas.so.9.0:cannot open Shared object file:no such file or directoryCause: The new version of TensorFlow (after 1.5) does not support CUDA8