model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(filters=6,kernel_size=5,activation='sigmoid',input_shape=(224,224,3)),
tf.keras.layers.MaxPool2D(pool_size=2, strides=2),
tf.keras.layers.Conv2D(filters=16,kernel_size=5,activation='sigmoid'),
tf.keras.layers.MaxPool2D(pool_size=2, strides=2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(120,activation='sigmoid'),
tf.keras.layers.Dense(84,activation='sigmoid'),
tf.keras.layers.Dense(10,activation='sigmoid')
])
model = tf.keras.models.Sequential()
model.add(Conv2D(64,(3,3), strides = (1,1), input_shape = (224,224,3), padding = 'same', activation = 'relu'))
model.add(BatchNormalization())
model.add(Conv2D(64,(3,3), strides = (1,1), padding = 'same', activation = 'relu'))
model.add(MaxPooling2D((2,2), strides = (2,2)))
model.add(BatchNormalization())
model.add(Conv2D(128,(3,3), strides = (1,1), padding = 'same', activation = 'relu'))
model.add(BatchNormalization())
model.add(Conv2D(128,(3,3), strides = (1,1), padding = 'same', activation = 'relu'))
model.add(MaxPooling2D((2,2), strides = (2,2)))
model.add(BatchNormalization())
model.add(Conv2D(256,(3,3), strides = (1,1), padding = 'same', activation = 'relu'))
model.add(BatchNormalization())
model.add(Conv2D(256,(3,3), strides = (1,1), padding = 'same', activation = 'relu'))
model.add(BatchNormalization())
model.add(Conv2D(256,(3,3), strides = (1,1), padding = 'same', activation = 'relu'))
model.add(MaxPooling2D((2,2), strides = (2,2)))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(520, activation = 'relu'))
model.add(BatchNormalization())
model.add(Dropout(rate=0.5))
model.add(Dense(520, activation = 'relu'))
model.add(BatchNormalization())
model.add(Dropout(rate=0.5))
model.add(Dense(10, activation = 'softmax'))
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