It is better to have a comparison of these lasagne,keras,pylearn2,nolearn, tensor and symbolic calculation framework I have chosen to use Theano, the top of the library with which good?
First of all, the document is as detailed as possible, its secondary structure is clear, the inheritance and the invocation is convenient.
Reply content:Python-based libraries personal favorite is the Keras, for a variety of
Let's spit it out. This is based on the Theano Keras how difficult to install, anyway, I am under Windows toss to not, so I installed a dual system. This just feel the powerful Linux system at the beginning, no wonder big companies are using this to do development, sister, who knows ah ....Let's start by introducing the framework: We all know the depth of the neural network, Python started with Theano this framework to write the neural network, but la
Deep learning Keras Frame Notes Autoencoder class use notes This is a very common auto-coding model for building. If the parameter is Output_reconstruction=true, then Dim (input) =dim (output), otherwise dim (output) =dim (hidden).Inputshape: Depends on the definition of encoderOutputshape: Depends on the definition of decoderParameters:
Encoder: Encoder, which is a layer type or layer container type.
Decoder: Decoder, which is a layer t
contain a fully connected layer (the first run automatically downloads the weights contained in the model, and the function is downloaded directly from GitHub, which may be slower).
From keras.models import sequential, Model from
keras import applications from
keras.layers import dropout, Flatt En, dense from
keras.optimizers import SGD
Img_shape = (224, 224, 3)
Base_model = applications. VGG16 (weights=
In Keras, a neural network visualization function plot is provided, and the visualization results can be saved locally. Plot use is as follows:
From Keras.utils.visualize_util import plot
plot (model, to_file= ' model.png ')
Note: The author uses the Keras version is 1.0.6, if is python3.5
From
keras.utils
import
plot_model
plot_model (model,to_file= ' model.png ')
However, this feature relies on the
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
(modules):
Import NumPy as NP
np.random.seed (1337) # for reproducibility from
keras.datasets import mnist
from Keras.utils import np_utils from
keras.models import sequential from
keras.layers import Simplernn, Activation, Dense from
keras.optimizers import Adam
Initialization of parameters:
Time_steps = Same as the height of the image
Input_size = same as the width of the image
batch_s IZE =
Batch_index = 0
output_size =
cell_size = +
LR
Installing OPENCV on the server encountered a problem with CUDA8.0, and had to see if other machines could be preinstalled and used..First, python+opencv3.2 installationOpenCV Why is it so easy to install in Windows?Installation process:1. Download OpenCV file Opencv-3.2.0-vc14.exe2, click to download, in fact, is the decompression process, casually placed in a plate inside.3, the Python deployment phase,Go to OPENCV installation directory to find + copy: \build\python\2.7\x64\cv2.pydCopy Cv2.py
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
This weekend, I decided it is time:i is going to update my Python environment and get Keras and TensorFlow installed So I could the start doing tutorials (particularly for deep learning) using R. Although I used to is a systems administrator (about years ago), I don ' t do much installing or configuring so I guess T Hat ' s why I ' ve put the this task off for so long. And it wasn ' t unwarranted:it took me the whole weekend to get the install working
In recent months in order to write a small paper, the topic is about using the depth of learning face search, so you need to choose a suitable depth learning framework, Caffe I learned after the use of the feeling is not very convenient, after someone recommended to me Keras, its simple style attracted me, After four months I have been using the Keras framework, because I use the time, the TensorFlow tutori
Problem:When you run the sample program MNIST_CNN with Keras, the following error occurs: ' Keras.backend ' has no attribute ' Image_data_format 'Program Path https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.pyThe Python Conda environment used is the carnd-term1 of the Udacity autopilot courseFault Program segment:if ' Channels_first ' : = X_train.reshape (x_train.shape[0], 1, Img_rows,
Reference: Keras Chinese Handbook
Note: This installation has only a CPU-accelerated process and no GPU acceleration. 1. First install Linux recommended Ubuntu, version can choose 16.04. 2. Ubuntu Initial environment Settings (1) First system upgrade
>>>sudo APT Update
>>>sudo apt Upgrade (2) to install a Python-based development package
>>>sudo apt install-y python-dev python-pip python-nose gcc g++ git gfortran vim 3. Install Operation Acceleratio
Usually, we use deep learning to classify, but sometimes it is used to do regression. Original source: Regression Tutorial with the Keras Deep Learning Library in Python 1. Here the author uses keras and Python's Scikit-learn machine learning Library To achieve the return of housing prices forecast. About Scikit-learn and Keras Federated Reference Scikit-learn
seed value to a integer.Seed = 7np.random.seed (Seed)#Loading The data set (PIMA diabetes Dataset)DataSet = Pd.read_csv (r'C:/users/administrator/desktop/pima-indians-diabetes.csv') Dataset.head () Dataset.shape#Loading the input values to X and Label values Y using slicing.X = Np.mat (dataset.iloc[:, 0:8]) Y= Np.mat (dataset.iloc[:,8]). Reshape ( -1,1)#Initializing the sequential model from KERAS.Model =Sequenti
The Keras has many advantages, and building a model is quick and easy, but it is recommended to understand the basic principles of neural networks.
Backend suggested using TensorFlow, much faster than Theano.
From sklearn.datasets import Load_iris from sklearn.model_selection import train_test_split import Keras from Keras.model s import sequential from keras.
In the previous TensorFlow Exercise 1 I mentioned a high-level library using TensorFlow as the backend, called Keras, which is a high-level neural network Python library. In TensorFlow Exercise 1, I was manually defining a neural network, with a few lines of code to take care of it.
The first Keras use Theano as the back end, TensorFlow after the fire, Keras adde
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