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I am building a CNN with Conv1D layers, and it trains pretty well. I'm now looking into how to reduce the number of features before feeding it into a Dense layer at the end of the model, so I've been reducing the size of the Dense layer, but then I came across this article. The article talks about the effect of using a Conv2D filters with a kernel_size=(1,1) to reduce the number of features.
I was wondering what the difference is between using a Conv2D layer with kernel_size=(1,1) tf.keras.layers.Conv2D(filters=n,kernel_size=(1,1)) and using a Dense layer of the same size tf.keras.layers.Dense(units=n)? From my perspective (I'm relatively new to neural nets), a filter with kernel_size=(1,1) is a single number, which is essentially equivalent to weight in a Dense layer, and both layers have biases, so are they equivalent, or am I misunderstanding something? And if my understanding is correct, in my case where I am using Conv1D layers, not Conv2D layers, does that change anything? As in is tf.keras.layers.Conv1D(filters=n, kernel_size=1) equivalent to tf.keras.layers.Dense(units=n)?
Please let me know if you need anything from me to clarify the question. I'm mostly curious about if Conv1D layers with kernel_size=1 and Conv2D layers with kernel_size=(1,1) behave differently than Dense layers.
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1D convolution layer (e.g. temporal convolution). This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs.
Figure-3: PyTorch code to showcase that Conv1d and Linear layer operations are equivalent.
Dense Layer is simple layer of neurons in which each neuron receives input from all the neurons of previous layer, thus called as dense. Dense Layer is used to classify image based on output from convolutional layers. Working of single neuron. A layer contains multiple number of such neurons.
The Conv1D layer learns 12 (c·m = 12) kernels and returns 4 filters.
Yes, since Dense layer is applied on the last dimension of its input (see this answer), Dense(units=N) and Conv1D(filters=N, kernel_size=1) (or Dense(units=N) and Conv2D(filters=N, kernel_size=1)) are basically equivalent to each other both in terms of connections and number of trainable parameters.
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In 1D CNN, the kernel moves in 1 direction. The input and output data of 1D CNN is 2 dimensional. Mostly used on Time-Series Data, Natural Language Processing tasks etc. Definitely gonna see people using it in Kaggle NLP competitions and notebooks.
In 2D CNN, the kernel moves in 2 directions. The input and output data of 2D CNN is 3 dimensional. Mostly used on Image data. Definitely gonna see people using it in Kaggle CNN Image Processing competitions and notebooks
In 3D CNN, the kernel moves in 3 directions. The input and output data of 3D CNN is 4 dimensional. Mostly used on 3D Image data (MRI, CT Scans). Haven't personally seen applied version in competitions
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