dropout

How to deactivate a dropout layer called with training=True in a Keras model?

由 十年热恋 提交于 2019-12-31 04:46:05
问题 I wish to view the final output of training a tf.keras model. In this case it would be an array of predictions from the softmax function, e.g. [0,0,0,1,0,1]. Other threads on here have suggested using model.predict(training_data), but this won't work for my situation since I am using dropout at training and validation, so neurons are randomly dropped and predicting again with the same data will give a different result. def get_model(): inputs = tf.keras.layers.Input(shape=(input_dims,)) x =

Implementing dropout from scratch

由 别来无恙 提交于 2019-12-30 04:16:22
问题 This code attempts to utilize a custom implementation of dropout : %reset -f import torch import torch.nn as nn # import torchvision # import torchvision.transforms as transforms import torch import torch.nn as nn import torch.utils.data as data_utils import numpy as np import matplotlib.pyplot as plt import torch.nn.functional as F num_epochs = 1000 number_samples = 10 from sklearn.datasets import make_moons from matplotlib import pyplot from pandas import DataFrame # generate 2d

Using Dropout in Pytorch: nn.Dropout vs. F.dropout

由 て烟熏妆下的殇ゞ 提交于 2019-12-18 11:47:57
问题 By using pyTorch there is two ways to dropout torch.nn.Dropout and torch.nn.functional.Dropout . I struggle to see the difference between the use of them: When to use what? Does it make a difference? I don't see any performance difference when I switched them around. 回答1: The technical differences have already been shown in the other answer. However the main difference is that nn.Dropout is a torch Module itself which bears some convenience: A short example for illustration of some

Reducing (Versus Delaying) Overfitting in Neural Network

由 别来无恙 提交于 2019-12-14 04:04:15
问题 In neural nets, regularization (e.g. L2, dropout) is commonly used to reduce overfitting. For example, the plot below shows typical loss vs epoch, with and without dropout. Solid lines = Train, dashed = Validation, blue = baseline (no dropout), orange = with dropout. Plot courtesy of Tensorflow tutorials. Weight regularization behaves similarly. Regularization delays the epoch at which validation loss starts to increase, but regularization apparently does not decrease the minimum value of

Input contains NaN, infinity or a value too large for dtype('float64') in Tensorflow

由 元气小坏坏 提交于 2019-12-13 03:46:39
问题 I am trying to train a LSTM and in my model I have an exponential learning rate decay and a dropout layer. In order to deactivate the dropout layer when testing and validating, I have put a placeholder for the dropout rate and given it a default value of 1.0 and when training i am setting it to 0.5. The dropou_rate placeholder value is passed to the tf.layers.dropout(). When I run this during the validation I get the following error. ValueError: Input contains NaN, infinity or a value too

Using noise_shape of the Dropout layer. Batch_size does not fit into provided samples. What to do?

由 非 Y 不嫁゛ 提交于 2019-12-11 07:29:31
问题 I am using a dropout layer in my model. As I use temporal data, I want the noise_shape to be the same per timestep -> (batch_size, 1, features). The problem is if I use a batch size that does not fit into the provided samples, I get an error message. Example: batch_size = 2, samples= 7. In the last iteration, the batch_size (2) is larger than the rest of the samples (1) The other layers (my case: Masking, Dense, and LSTM) apparently don`t have a problem with that and just use a smaller batch

Tensorboard and Dropout Layers

由 不羁的心 提交于 2019-12-06 04:43:13
问题 I have a very basic query. I have made 4 almost identical(Difference being input shapes) CNN and have merged them while connecting to a Feed Forward Network of fully connected layers. Code for the almost identical CNN(s): model3 = Sequential() model3.add(Convolution2D(32, (3, 3), activation='relu', padding='same', input_shape=(batch_size[3], seq_len, channels))) model3.add(MaxPooling2D(pool_size=(2, 2))) model3.add(Dropout(0.1)) model3.add(Convolution2D(64, (3, 3), activation='relu', padding=

Using Dropout with Keras and LSTM/GRU cell

由 点点圈 提交于 2019-12-03 17:19:43
问题 In Keras you can specify a dropout layer like this: model.add(Dropout(0.5)) But with a GRU cell you can specify the dropout as a parameter in the constructor: model.add(GRU(units=512, return_sequences=True, dropout=0.5, input_shape=(None, features_size,))) What's the difference? Is one preferable to the other? In Keras' documentation it adds it as a separate dropout layer (see "Sequence classification with LSTM") 回答1: The recurrent layers perform the same repeated operation over and over. In

Using Dropout with Keras and LSTM/GRU cell

由 冷暖自知 提交于 2019-12-03 07:33:38
In Keras you can specify a dropout layer like this: model.add(Dropout(0.5)) But with a GRU cell you can specify the dropout as a parameter in the constructor: model.add(GRU(units=512, return_sequences=True, dropout=0.5, input_shape=(None, features_size,))) What's the difference? Is one preferable to the other? In Keras' documentation it adds it as a separate dropout layer (see "Sequence classification with LSTM") The recurrent layers perform the same repeated operation over and over. In each timestep, it takes two inputs: Your inputs (a step of your sequence) Internal inputs (can be states and

How to understand SpatialDropout1D and when to use it?

由 妖精的绣舞 提交于 2019-12-03 04:52:44
问题 Occasionally I see some models are using SpatialDropout1D instead of Dropout . For example, in the Part of speech tagging neural network, they use: model = Sequential() model.add(Embedding(s_vocabsize, EMBED_SIZE, input_length=MAX_SEQLEN)) model.add(SpatialDropout1D(0.2)) ##This model.add(GRU(HIDDEN_SIZE, dropout=0.2, recurrent_dropout=0.2)) model.add(RepeatVector(MAX_SEQLEN)) model.add(GRU(HIDDEN_SIZE, return_sequences=True)) model.add(TimeDistributed(Dense(t_vocabsize))) model.add
订阅 dropout