How is the convolution operation carried out when multiple channels are present at the input layer? (e.g. RGB) In such a case you have one 2D kernel per input channel (a.k.a plane). So you perform each convolution (2D Input, 2D kernel) separately and you sum the contributions which gives the final output feature map. Please … Read more
Actually – there is no a good answer to your question. Most of the architectures are usually carefully designed and finetuned during many experiments. I could share with you some of the rules of thumbs one should apply when designing its own architecture: Avoid a dimension collapse in the first layer. Let’s assume that your … Read more
Lets say we have a 3 Channel (RGB) image given by some matrix A A = [[[198 218 227] [196 216 225] [196 214 224] … … [185 201 217] [176 192 208] [162 178 194]] and a blur kernal as K = [[0.1111, 0.1111, 0.1111], [0.1111, 0.1111, 0.1111], [0.1111, 0.1111, 0.1111]] #which is actually … Read more
We used deep convolutional networks on spectrograms for a spoken language identification task. We had around 95% accuracy on a dataset provided in this TopCoder contest. The details are here. Plain convolutional networks do not capture the temporal characteristics, so for example in this work the output of the convolutional network was fed to a … Read more
A decrease in binary cross-entropy loss does not imply an increase in accuracy. Consider label 1, predictions 0.2, 0.4 and 0.6 at timesteps 1, 2, 3 and classification threshold 0.5. timesteps 1 and 2 will produce a decrease in loss but no increase in accuracy. Ensure that your model has enough capacity by overfitting the … Read more
Well, max-pooling and monotonely increasing non-linearities commute. This means that MaxPool(Relu(x)) = Relu(MaxPool(x)) for any input. So the result is the same in that case. So it is technically better to first subsample through max-pooling and then apply the non-linearity (if it is costly, such as the sigmoid). In practice it is often done the … Read more
The Y array in your screenshot is not a 1D array, it’s a 2D array with 300 rows and 1 column, as indicated by its shape being (300, 1). To remove the extra dimension, you can slice the array as Y[:, 0]. To generally convert an n-dimensional array to 1D, you can use np.reshape(a, a.size). … Read more
You’re right to say that kernel_size defines the size of the sliding window. The filters parameters is just how many different windows you will have. (All of them with the same length, which is kernel_size). How many different results or channels you want to produce. When you use filters=100 and kernel_size=4, you are creating 100 … Read more
You could generate the subarrays using as_strided: import numpy as np a = np.array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) sub_shape = (3,3) view_shape = tuple(np.subtract(a.shape, sub_shape) + 1) + sub_shape strides = a.strides + … Read more