Tag: keras

tensorflow.keras six-step method to build neural network

Tensorflow six-step method for deep learning to build a neural network Use tensorflow.kears to build a neural network to implement iris and mnist data set classification Article directory Deep learning tensorflow six-step method to build a neural network Preface 1. Sequential six-step method to build neural network 1.tf.keras.models.Sequential() 2.model.compile() 3.model.fit() 3.model.summary() 2. Class six-step method […]

Keras convolutional neural network (CNN) implementation

Python Deep Learning 03 – Keras Convolutional Neural Network (CNN) Implementation_keras cnn_Qianzhi Chenchen’s Blog-CSDN Blog convolutional neural network Convolutional neural networks actually perform cross-correlation operations, which are not the same thing as the convolution formula in probability theory. I won’t introduce too much about the theory. Remember that convolutional neural networks are used for image […]

Use Keras to build cnn+rnn, BRNN, DRNN and other models

Keras api knows ahead of time: BatchNormalization, used to speed up training in each iteration Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1. TimeDistributed, in general, the TimeDistributed layer operates Dense at each […]

Resolved ImportError: cannot import name relu from keras.layers

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CNN classification model — (1) VGG: tf.keras.applications.vgg16.VGG16

1. Introduction to VGG A model trained in 2014 by the Oxford University Visual Geometry Group team to solve 100 categories of image classification in ImageNet. VGG consists of 5-layer convolutional layer, 3-layer fully connected layer, and 1-layer softmax output layer. Maxpool is used between layers ( Maximization pooling) is separated, and the activation units […]

Data ScienceKeras

Write the directory title here 1. Keras 2. Data 2.1 Keras Dataset 2.2 Others 3. Model Architecture 3.1 Sequential model 3.2 Multilayer Perceptron (MLP) 3.2.1 Binary classification 3.2.2 Multi-level classification 3.2.3 Return 3.3 Convolutional Neural Network (CNN) 3.4 Recurrent Neural Network (RNN) 4. Preprocessing 4.1 Sequence filling 4.2 One-hot encoding 4.3 Training and test sets […]

TensorFlow 03(Keras)

1. tf.keras tf.keras is the high-order API interface of TensorFlow 2.0. It provides a new style and design pattern for TensorFlow code, which greatly improves the simplicity and reusability of TF code. The official recommendation is to use tf .keras for model design and development. 1.1 Commonly used modules in tf.keras As shown in the […]

Model parallelism: Keras currently has a distributed training solution;

Author: Zen and the Art of Computer Programming 1. Introduction Machine learning (ML) is the science of letting computers “learn”. The ability of a well-trained machine learning model to solve practical problems depends on how to use the least data, train for a long enough time, and select an appropriate model structure. In recent years, […]

On-device large language models using Keras and TensorFlow Lite

Deploying large language models to Android devices is a complex task as these models often require extensive computing resources. First, you need to load and optimize the model, then choose a suitable inference engine, and finally integrate it into your Android application. Here are the general steps: 1. Load and optimize the model: If you […]