text_plain_output_1.png
import keras

cnn = keras.models.Sequential()
cnn.add(keras.layers.Conv2D(filters=32, kernel_size=3, padding='valid', activation='relu', input_shape=(224, 224, 3)))
cnn.add(keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(keras.layers.Flatten())
cnn.add(keras.layers.Dense(45, activation='relu'))
cnn.add(keras.layers.Dense(15, activation='relu'))
cnn.add(keras.layers.Dropout(rate=0.1, seed=100))
cnn.add(keras.layers.Dense(units=5, activation='sigmoid'))

cnn.summary()