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Understanding Keras Conv2D layer 2D Convolution Clearly Explained amp Implemented with Python Part C
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Access all tutorials at https://www.muratkarakaya.net • COLAB: https://colab.research.google.com/dri... • Conv1D in Keras playlist: • Conv1D: 1 Dimensional Convolution in ... • Applied Machine Learning with Python Keras playlist: • Applied Machine Learning with Python • tf.data: Understand and Apply TensorFlow Data Pipelines playlist: • TensorFlow Data Pipeline: How to Desi... • • In this 3-video series, I explained and implemented the 2D 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 C 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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