AutoEncoder=keras.models.Sequential([ encoder, decoder ]) AutoEncoder.compile(optimizer='adam', loss='mse') AutoEncoder.fit(x_train, x_train, epochs=10, batch_size=256) predict=encoder.predict(x_test) plt.scatter(predict[:,0], predict[:,1], c=y_test) plt.show() 将数据降到两维以后...
是指在使用Keras深度学习库进行图像分类任务时,使用手写数字识别数据集MNIST。 MNIST(Modified National Institute of Standards and Technology)是一个常用的机器学习数据集,包含了一系列的手写数字图像样本。它由60000张训练样本和10000张测试样本组成,每张图像都是28x28像素的灰度图像,标记了对应的数字类别(0到9之间)...
Trains a Stacked What-Where AutoEncoder built on residual blocks on the MNIST dataset.[mnist_transfer_cnn.py](mnist_transfer_cnn.py)Transfer learning toy example on the MNIST dataset.[mnist_denoising_autoencoder.py](mnist_denoising_autoencoder.py)Trains a denoising autoencoder on the MNIST datase...
Trains a Stacked What-Where AutoEncoder built on residual blocks on the MNIST dataset.[mnist_trans...
autoencoder, the encoder can be used to generate latent vectors of input data for low-dim visualization like PCA or TSNE. 好,我们在训练完成后绘制降维效果。 (x_train, _), (x_test, y_test) = mnist.load_data() 1. 修改源代码,添加y_test方便我们绘图。 随机取100个样本点,对latent和原训练...
官网实例详解4.41(variational_autoencoder_deconv.py) 官网实例详解4.42(imdb.py) Vision models examplesion models examples 视觉模型实例 mnist_mlp.pyTrains a simple deep multi-layer perceptron on the MNIST dataset. 基于MINIST数据集训练简单的深度多层感知机 ...
rcppDL7thImplementation of basic machine learning methods with many layers (deep learning), including dA (Denoising Autoencoder), SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and DBN (Deep Belief Nets) deepr??*Package to streamline the training, fine-tuning and predicting...
mnist_swwae.py 列出了一个堆栈,其中AutoEncoder在MNIST数据集上的剩余块上构建。 mnist_transfer_cnn.py 转移学习玩具的例子。 neural_doodle.py 神经涂鸦。 neural_style_transfer.py 神经样式转移。 pretrained_word_embeddings.py 将预训练的词嵌入(GloVe embeddings)加载到冻结的Keras嵌入层中,并使用它在20个新...
(LSTM)networks,autoencoders,andgenerativeadversarialnetworks(GANs)usingreal-worldtrainingdatasets.WewillexaminehowtouseCNNsforimagerecognition,howtousereinforcementlearningagents,andmanymore.Wewilldiveintothespecificarchitecturesofvariousnetworksandthenimplementeachoftheminahands-onmannerusingindustry-gradeframeworks.By...
Basic Convnet for MNIST Convolutional Variational Autoencoder, trained on MNIST Auxiliary Classifier Generative Adversarial Networks (AC-GAN) on MNIST 50-layer Residual Network, trained on ImageNet Inception v3, trained on ImageNet DenseNet-121, trained on ImageNet ...