pre-trained-model

问题 I am starting to work with Intel movidius neural compute stick. To start working, in my case, it is necessary to download pretrained models. In the tutorials they refer to http://download.tensorflow.org/models/. However, there is not a list that shows all the models available for download. If the latest version of a net, lets say inception_v4, is not known, I cannot download the corresponding .tar.gz file. Does anyone know a method to have an updated list of the .tar.gz files of the

来源： https://stackoverflow.com/questions/53251827/pretrained-tensorflow-model-rgb-rgby-channel-extension

问题 Usually pre-trained networks like VGG16 / Inception etc. works with low resolution like < 500px. Is it possible to add a high-resolution convolution layer (or two) before the very first layer of pre-trained VGG16 / Inception to make the network be able to consume high-resolution pictures? As far as I know - the first layers are hardest to train, it took a lot of data and resources to train it. I wonder if it will be possible to freeze pre-trained network and train only the newly attached high

问题 I am using PyTorch. I am going to use the already trained model on multiple GPUs with CPU. how to do this task? I tried on Anaconda 3 and pytorch with cpu only i dont have gpu model = models.get_pose_net(config, is_train=False) gpus = [int(i) for i in config.GPUS.split(',')] model = torch.nn.DataParallel(model, device_ids=gpus).cuda() print('Created model...') print(model) checkpoint = torch.load(config.MODEL.RESUME) model.load_state_dict(checkpoint) model.eval() print('Loaded pretrained

问题 I am using PyTorch. I am going to use the already trained model on multiple GPUs with CPU. how to do this task? I tried on Anaconda 3 and pytorch with cpu only i dont have gpu model = models.get_pose_net(config, is_train=False) gpus = [int(i) for i in config.GPUS.split(',')] model = torch.nn.DataParallel(model, device_ids=gpus).cuda() print('Created model...') print(model) checkpoint = torch.load(config.MODEL.RESUME) model.load_state_dict(checkpoint) model.eval() print('Loaded pretrained

问题 I am trying to re-create the prediction of a trained model but I don't know how to save a model. For example, I want to save the trained Gaussian processing regressor model and recreate the prediction after I trained the model. The package I used to train model is scikit-learn. kernel = DotProduct() + WhiteKernel() gpr = GaussianProcessRegressor(kernel=kernel,random_state=0) gpr.fit(X,y) 回答1: You can use: 1. pickle from sklearn import svm from sklearn import datasets iris = datasets.load_iris

问题 I am referring to the models that can be found here: https://pytorch.org/docs/stable/torchvision/models.html#torchvision-models 回答1: As, @dennlinger mentioned in his answer : torch.utils.model_zoo, is being internally called when you load a pre-trained model. More specifically, the method: torch.utils.model_zoo.load_url() is being called every time a pre-trained model is loaded. The documentation for the same, mentions: The default value of model_dir is $TORCH_HOME/models where $TORCH_HOME

问题 I have image classification problem and i want to use Keras pretrained models for this task. When I use such a model model = tf.keras.Sequential([ hub.KerasLayer("https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4", output_shape=[1280], trainable=False), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(num_classes, activation='softmax') ]) model.build([None, image_size[0], image_size[1], 3]) model.compile( optimizer=tf.keras.optimizers.Adam(), loss='categorical_crossentropy

问题 I have image classification problem and i want to use Keras pretrained models for this task. When I use such a model model = tf.keras.Sequential([ hub.KerasLayer("https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4", output_shape=[1280], trainable=False), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(num_classes, activation='softmax') ]) model.build([None, image_size[0], image_size[1], 3]) model.compile( optimizer=tf.keras.optimizers.Adam(), loss='categorical_crossentropy

问题 I just saved a model, by that code: def train(): with tf.Session() as sess: saver = tf.train.Saver(max_to_keep = 2) Loss = myYoloLoss([Scale1,Scale2,Scale3],[Y1, Y2 ,Y3]) opt = tf.train.AdamOptimizer(2e-4).minimize(Loss) init = tf.global_variables_initializer() sess.run(init) imageNum = 0 Num = 0 while(1): #get batchInput batchImg,batchScale1,batchScale2,batchScale3 = getBatchImage(batchSize = BATCHSIZE) for epoch in range(75): _ , epochloss = sess.run([opt,Loss],feed_dict={X:batchImg,Y1