tf.keras.layers.Conv2D in TensorFlow Python
In this tutorial, we are going to see about Keras 2-Dimensional Convolution neural network layer and the important parameters that are needed to be passed.
A convolution neural network is a network in which each layer is connected to the other through the kernel.
Conv2D in Python
tf.keras.layers.Conv2D(no.of.units, kernel_size, activation, input_shape, padding, strides, dilation_rate).
Let us discuss the role of these parameters in detail.
- Kernel size – This represents the size of the kernel.
- Some of the Activation functions are relu, sigmoid, softmax and it takes in string format.
- Input shape – This is included in the first layer of the convolution network. It takes rows, columns, and the number of pixels in tuple form.
- Padding is done to avoid shrinking of the layer during convolving by adding one or more layers around the edges.
padding = ‘valid’ represents no padding.
padding = ‘same’ represents that the size of the output is the same as the input.
- Filter, also called the kernel, is the size of the network that is to be convolved with the input matrix.
- Stride – The default value of stride is 1 (i.e.,) its steps over one position. If stride is 2, it jumps in steps of 2 and so on.
- Dilation rate refers to the distance between subsequent pixels.
Let us see how to implement it in the model.
After instantiating a sequential model, add the 2D convolution layer.
from keras.layers import Conv2D Conv2D(15, kernel_size=2, activation='relu', input_shape=(28, 28, 1), padding='same', strides = 2)
Finally, we can fit the model and predict the results.
I hope this post helps!