Convolutional Neural Network (CNN) input shape

In your case,Conv2D will be useful. Please refer below description for understanding input shape of Convolution Neural Network (CNN) using Conv2D.

Let’s see how the input shape looks like. The input data to CNN will look like the following picture. We are assuming that our data is a collection of images.

Input shape has (batch_size, height, width, channels). Incase of RGB image would have a channel of 3 and the greyscale image would have a channel of 1.

Let’s look at the following code

import tensorflow as tf
from tensorflow.keras.layers import Conv2D

model=tf.keras.models.Sequential()
model.add(Conv2D(filters=64, kernel_size=1, input_shape=(10,10,3)))
model.summary()

Output:

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 10, 10, 64)        256       
=================================================================

Thought it looks like out input shape is 3D, but you have to pass a 4D array at the time of fitting the data which should be like (batch_size, 10, 10, 3). Since there is no batch size value in the input_shape argument, we could go with any batch size while fitting the data.

The output shape is (None, 10, 10, 64). The first dimension represents the batch size, which is None at the moment. Because the network does not know the batch size in advance.

Note: Once you fit the data, None would be replaced by the batch size you give while fitting the data.

Let’s look at another code with batch Size

    import tensorflow as tf
    from tensorflow.keras.layers import Conv2D

    model=tf.keras.models.Sequential()
    model.add(Conv2D(filters=64, kernel_size=1, batch_input_shape=(16,10,10,3)))
    model.summary()

Output:

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (16, 10, 10, 64)          256       
=================================================================

Here I have replaced input_shape argument with batch_input_shape. As the name suggests, this argument will ask you the batch size in advance, and you can not provide any other batch size at the time of fitting the data.