Cannot generate random ints inside TensorFlow2.0 "call" function Question: I am trying to implement a custom dropout layer. In this dropout layer I want to generate a random number and turn on/off that output. The idea is simple and I thought the implementation would be too. I have tried using the regular ‘random’ python function, …
How to add dropout layers automatically to a neural network in pytorch Question: I have a neural network in pytorch and make each layer automatically via the following structure: class FCN(nn.Module): ##Neural Network def __init__(self,layers): super().__init__() #call __init__ from parent class self.activation = nn.Tanh() self.loss_function = nn.MSELoss(reduction =’mean’) ‘Initialise neural network as a list using …
How is Keras Dropout actually perfomed? Question: I was trying to understand how K.layers.Dropout might be implemented, since in the literature it’s always referred as a random independent sampling of 0/1 masks for each element. Given that the literature it’s pretty clear to me, I switched to coding it, and I stumbled upon an issue: …
Adding Dropout Layers to U_Net Segmentation_Models Question: I am using U_Net segmentation model for medical images segmentation with Kersa and Tensorflow 2. I’d like to add a dropout to the model, but I don’t know where to add it? Asked By: Emy Ibrahim || Source Answers: Yes there isn’t dropout layers in the implementation of …
Pytorch: nn.Dropout vs. F.dropout Question: There are two ways to perform dropout: torch.nn.Dropout torch.nn.functional.Dropout I ask: Is there a difference between them? When should I use one over the other? I don’t see any performance difference when I switched them around. Asked By: CutePoison || Source Answers: If you look at the source code of …
Adding Dropout to testing/inference phase Question: 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 …
