What is input_shape?

Research & Models
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#1

I was reading a website and saw this

model = models.Sequential()model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))

This a piece of a computer program.

The input_shape receive 3 inputs. What does the first input mean. Is it the width of the image? Is it the height of the image?

#2

Hi @david_lopez1 ,

The first is to input the width of the image, the second is the height of the image and the third is the number of channels (the image has 3 channels (red, green, and blue)).

I hope this helps you!

Thanks.

#4

In the given code snippet, the input_shape (32, 32, 3*)* refers to the dimensions of the input image that the model will accept. The three values in the tuple represent the width, height, and number of color channels of the image, respectively.

1 Like
#6

In the input_shape parameter, the first input (32 in this case) typically represents the height of the image, while the second input (also 32 in this case) represents the width of the image. The third input (3 in this case) indicates the number of color channels in the image, which is typically 3 for RGB images (red, green, blue). So, in this example, the input_shape=(32, 32, 3) suggests that the model expects input images with a height and width of 32 pixels and 3 color channels (RGB).
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