This document describes the layers of a convolutional neural network model for classifying images. The model takes input images of size 28x28x1, applies a Conv2D layer to extract 32 features from the input, uses MaxPooling to reduce the spatial dimensions. It then applies Dropout and flattens the output before feeding it to two Dense layers with 128 and 10 units respectively to perform classification into 10 classes.

17. conv2d_1: Conv2D
input:
output:
(None, 28, 28, 1)
(None, 26, 26, 32)
max_pooling2d_1: MaxPooling2D
input:
output:
(None, 26, 26, 32)
(None, 13, 13, 32)
dropout_1: Dropout
input:
output:
(None, 13, 13, 32)
(None, 13, 13, 32)
fatten_1: Flatten
input:
output:
(None, 13, 13, 32)
(None, 5408)
dense_1: Dense
input:
output:
(None, 5408)
(None, 128)
dense_2: Dense
input:
output:
(None, 128)
(None, 10)
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