from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import LearningRateScheduler
from keras.callbacks import ReduceLROnPlateau
model = Sequential()
model.add(Conv2D(32, kernel_size=3, activation='relu', input_shape=(28, 28, 1))
    )
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=5, strides=2, padding='same', activation=
    'relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=5, strides=2, padding='same', activation=
    'relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Conv2D(128, kernel_size=4, activation='relu'))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()

4d1affaf7ef576ee33a6c699003db08b582a169a
<mask token>

<mask token>
model.add(Conv2D(32, kernel_size=3, activation='relu', input_shape=(28, 28, 1))
    )
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=5, strides=2, padding='same', activation=
    'relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=5, strides=2, padding='same', activation=
    'relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Conv2D(128, kernel_size=4, activation='relu'))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()

<mask token>
model = Sequential()
model.add(Conv2D(32, kernel_size=3, activation='relu', input_shape=(28, 28, 1))
    )
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=5, strides=2, padding='same', activation=
    'relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=5, strides=2, padding='same', activation=
    'relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Conv2D(128, kernel_size=4, activation='relu'))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import LearningRateScheduler
from keras.callbacks import ReduceLROnPlateau
model = Sequential()
model.add(Conv2D(32, kernel_size=3, activation='relu', input_shape=(28, 28, 1))
    )
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=5, strides=2, padding='same', activation=
    'relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=5, strides=2, padding='same', activation=
    'relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Conv2D(128, kernel_size=4, activation='relu'))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()
