Understanding Keras Conv2D layer: 2D Convolution Clearly Explained & Implemented with Python Part B
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In this 3-video series, I explained and implemented the Convolution concept in Image Processing. My main aim is to show how convolution works in Deep Learning from a programmer's perspective. I introduced all the important Concepts, Mechanisms, Parameters, Structure, and Behavior along with practical code examples in Python. This video is Part B of the series.
I hope you would find it helpful!
Have you ever used TensorFlow Keras Conv1d or Conv2d convolution layer?
In this video, I prepared a clear and simple yet comprehensive example of 2D Convolution in 2 dimensions (Conv2D). We will understand its usage and output better.
I hope you will use the Tensorflow Keras Conv2D layer (2D convolution layer e.g. spatial convolution over images) in your solutions effectively.
Keras Conv2D or tf.keras.layers.Conv2D is a 2D convolution layer: This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well.
When using Keras Conv2D layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis).
Args:
filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
padding: one of "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding with zeros evenly to the left/right or up/down of the input. When padding="same" and strides=1, the output has the same size as the input.
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