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from tensorflow import keras

import tensorflow as tf

shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))

model.summary()