def small_nn(self):
model = Sequential()
model.add(Conv2D(64, (self.stride, self.stride,), name='conv1',
padding='same',
activation='relu',
input_shape=self.ip_shape[1:]))
model.add(MaxPooling2D(pool_size=(2, 2), name='pool1'))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(32, activation='relu', name='dense1'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax', name='dense2'))
adam = keras.optimizers.Adam(lr=self.learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=["accuracy"])
return model
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