dropout

问题 I have two trained models ( model_A and model_B ), and both of them have dropout layers. I have freezed model_A and model_B and merged them with a new dense layer to get model_AB (but I have not removed model_A 's and model_B 's dropout layers). model_AB 's weights will be non-trainable, except for the added dense layer. Now my question is: are the dropout layers in model_A and model_B active (i.e. drop neurons) when I am training model_AB ? 回答1: Short answer: The dropout layers will continue

问题 I have two trained models ( model_A and model_B ), and both of them have dropout layers. I have freezed model_A and model_B and merged them with a new dense layer to get model_AB (but I have not removed model_A 's and model_B 's dropout layers). model_AB 's weights will be non-trainable, except for the added dense layer. Now my question is: are the dropout layers in model_A and model_B active (i.e. drop neurons) when I am training model_AB ? 回答1: Short answer: The dropout layers will continue

问题 From the dropout paper: "The idea is to use a single neural net at test time without dropout. The weights of this network are scaled-down versions of the trained weights. If a unit is retained with probability p during training, the outgoing weights of that unit are multiplied by p at test time as shown in Figure 2. This ensures that for any hidden unit the expected output (under the distribution used to drop units at training time) is the same as the actual output at test time." Why do we

问题 The dropout layer from Pytorch changes the values that are not set to zero. Using Pytorch's documentation example: (source): import torch import torch.nn as nn m = nn.Dropout(p=0.5) input = torch.ones(5, 5) print(input) tensor([[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.]]) Then I pass it through a dropout layer: output = m(input) print(output) tensor([[0., 0., 2., 2., 0.], [2., 0., 2., 0., 0.], [0., 0., 0., 0., 2.], [2., 2., 2.,

问题 Can anyone explain the difference between the different dropout styles? From the documentation, I assumed that instead of dropping some units to zero (dropout), GaussianDropout multiplies those units by some distribution. However, when testing in practice, all units are touched. The result looks more like the classic GaussianNoise. tf.random.set_seed(0) layer = tf.keras.layers.GaussianDropout(.05, input_shape=(2,)) data = np.arange(10).reshape(5, 2).astype(np.float32) print(data) outputs =

问题 I am trying to implement Bayesian CNN using Mc Dropout on Pytorch, the main idea is that by applying dropout at test time and running over many forward passes , you get predictions from a variety of different models. I’ve found an application of the Mc Dropout and I really did not get how they applied this method and how exactly they did choose the correct prediction from the list of predictions here is the code def mcdropout_test(model): model.train() test_loss = 0 correct = 0 T = 100 for

问题 This is the model I defined it is a simple lstm with 2 fully connect layers. import copy import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim class mylstm(nn.Module): def __init__(self,input_dim, output_dim, hidden_dim,linear_dim): super(mylstm, self).__init__() self.hidden_dim=hidden_dim self.lstm=nn.LSTMCell(input_dim,self.hidden_dim) self.linear1=nn.Linear(hidden_dim,linear_dim) self.linear2=nn.Linear(linear_dim,output_dim) def forward(self, input)

问题 I have a model composed of 3 LSTM layers followed by a batch norm layer and finally dense layer. Here is the code: def build_uncomplied_model(hparams): inputs = tf.keras.Input(shape=(None, hparams["n_features"])) x = return_RNN(hparams["rnn_type"])(hparams["cell_size_1"], return_sequences=True, recurrent_dropout=hparams['dropout'])(inputs) x = return_RNN(hparams["rnn_type"])(hparams["cell_size_2"], return_sequences=True)(x) x = return_RNN(hparams["rnn_type"])(hparams["cell_size_3"], return

问题 I'm learning regularization in Neural networks from deeplearning.ai course. Here in dropout regularization, the professor says that if dropout is applied, the calculated activation values will be smaller then when the dropout is not applied (while testing). So we need to scale the activations in order to keep the testing phase simpler. I understood this fact, but I don't understand how scaling is done. Here is a code sample which is used to implement inverted dropout. keep_prob = 0.8 # 0 <=

问题 I've trained the following model for some timeseries in Keras: input_layer = Input(batch_shape=(56, 3864)) first_layer = Dense(24, input_dim=28, activation='relu', activity_regularizer=None, kernel_regularizer=None)(input_layer) first_layer = Dropout(0.3)(first_layer) second_layer = Dense(12, activation='relu')(first_layer) second_layer = Dropout(0.3)(second_layer) out = Dense(56)(second_layer) model_1 = Model(input_layer, out) Then I defined a new model with the trained layers of model_1 and