<jupyter_start><jupyter_text>Layers in CNN  Convolutional Layers<jupyter_code>from keras.models import Sequential
from keras.layers import Conv2D
model = Sequential()
model.add(Conv2D(filters=32,kernel_size=3,strides=2,padding="same",activation="relu",input_shape=(128,128,3)))
model.summary()<jupyter_output>Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 64, 64, 32)        896       
=================================================================
Total params: 896
Trainable params: 896
Non-trainable params: 0
_________________________________________________________________<jupyter_text>Pooling Layers  Max Pooling<jupyter_code>from keras.layers import MaxPooling2D
model = Sequential()
model.add(MaxPooling2D(pool_size=2,strides=2,input_shape=(100,100,15)))
model.summary()<jupyter_output>Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
max_pooling2d (MaxPooling2D) (None, 50, 50, 15)        0         
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
_________________________________________________________________
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