Keras pooling layers

Keras implements a pooling operation as a layer that can be added to CNNs between other layers. In this exercise, you will construct a convolutional neural network similar to the one you have constructed before:

Convolution => Convolution => Flatten => Dense

However, you will also add a pooling layer. The architecture will add a single max-pooling layer between the convolutional layer and the dense layer with a pooling of 2x2:

Convolution => Max pooling => Convolution => Flatten => Dense

A Sequential model along with Dense, Conv2D, Flatten, and MaxPool2D objects are available in your workspace.

Bu egzersiz

Image Modeling with Keras

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Add an input convolutional layer (15 units, kernel size of 2, relu activation).
Add a maximum pooling operation (pooling over windows of size 2x2).
Add another convolution layer (5 units, kernel size of 2, relu activation).
Flatten the output of the second convolution and add a Dense layer for output (3 categories, softmax activation).

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Add a convolutional layer
____(____(15, kernel_size=2, activation='relu', 
                 input_shape=(img_rows, img_cols, 1)))

# Add a pooling operation
____

# Add another convolutional layer
____

# Flatten and feed to output layer
model.add(____)
model.add(____(3, activation='softmax'))
model.summary()

Kodu Düzenle ve Çalıştır