deep-learning

问题 I was trying to use the higher library for meta-learning and I was having issues understanding what the copy_initial_weights mean. The docs say: copy_initial_weights – if true, the weights of the patched module are copied to form the initial weights of the patched module, and thus are not part of the gradient tape when unrolling the patched module. If this is set to False, the actual module weights will be the initial weights of the patched module. This is useful when doing MAML, for example.

问题 I was trying to use the higher library for meta-learning and I was having issues understanding what the copy_initial_weights mean. The docs say: copy_initial_weights – if true, the weights of the patched module are copied to form the initial weights of the patched module, and thus are not part of the gradient tape when unrolling the patched module. If this is set to False, the actual module weights will be the initial weights of the patched module. This is useful when doing MAML, for example.

问题 I would like to modify the ImageNet caffe model as described bellow: As the input channel number for temporal nets is different from that of spatial nets (20 vs. 3), we average the ImageNet model filters of first layer across the channel, and then copy the average results 20 times as the initialization of temporal nets. My question is how can I achive the above results? How can I open the caffe model to be able to do those changes to it? I read the net surgery tutorial but it doesn't cover

问题 I attempting to create a logistic model on CIFAR10 dataset by PyTorch. However I am getting an error: ValueError: Expected input batch_size (900) to match target batch_size (300). What I think is happening is that 3*100 is 300. So may be the 3 axis of the RGB image is doing that but I cant figure how to solve. These are my hyperparameters. batch_size = 100 learning_rate = 0.001 # Other constants input_size = 32*32 num_classes = 10 Here I divide my data into train, validation and test data.

问题 I am trying to build a food classification model with 101 classes. The dataset has 1000 image for each class. The accuracy of the model which I trained is coming less than 6%. I have tried implementing NASNet and VGG16 with imagenet weights but the accuracy did not increase. I have tried using Adam optimizer with or without amsgrad. I have also tried to change the learning rate to both 0.01 and 0.0001 but still, accuracy remains in the single-digit.Please suggest the methods to increase the

问题 I am trying to build a food classification model with 101 classes. The dataset has 1000 image for each class. The accuracy of the model which I trained is coming less than 6%. I have tried implementing NASNet and VGG16 with imagenet weights but the accuracy did not increase. I have tried using Adam optimizer with or without amsgrad. I have also tried to change the learning rate to both 0.01 and 0.0001 but still, accuracy remains in the single-digit.Please suggest the methods to increase the

问题 I have a Keras model which gives me error TypeError: Expected float32, got <tf.Tensor 'recommender_dnn_25/strided_slice_5:0' shape=(None, 1) dtype=float32> of type 'Tensor' instead. To my keras model, I am sending train/validation data of type numpy.ndarray . This is from movielens dataset and the values are movie_id , user_id , zip_code , age , gender . A sample row below: x_train[0] array(['195', '241', 415, 3, 1], dtype=object) The 1st two inputs are trained to an embedding along with the

问题 I have a Keras model which gives me error TypeError: Expected float32, got <tf.Tensor 'recommender_dnn_25/strided_slice_5:0' shape=(None, 1) dtype=float32> of type 'Tensor' instead. To my keras model, I am sending train/validation data of type numpy.ndarray . This is from movielens dataset and the values are movie_id , user_id , zip_code , age , gender . A sample row below: x_train[0] array(['195', '241', 415, 3, 1], dtype=object) The 1st two inputs are trained to an embedding along with the

问题 I am working on a regression problem where I feed a set of spectograms to CNN + LSTM - architecture in keras. My data is shaped as (n_samples, width, height, n_channels) . The question I have how to properly connect the CNN to the LSTM layer. The data needs to be reshaped in some way when the convolution is passed to the LSTM. There are several ideas, such as use of TimeDistributed -wrapper in combination with reshaping but I could not manage to make it work. . height = 256 width = 256 n

问题 In the first example of Learning PyTorch with Examples, the author demonstrates how to create a neural network with numpy. Their code is pasted below for convenience: # from: https://pytorch.org/tutorials/beginner/pytorch_with_examples.html # -*- coding: utf-8 -*- import numpy as np # N is batch size; D_in is input dimension; # H is hidden dimension; D_out is output dimension. N, D_in, H, D_out = 64, 1000, 100, 10 # Create random input and output data x = np.random.randn(N, D_in) y = np