def make_model():
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(Flatten())
model.add(Dense(2500, kernel_initializer='He'))
model.add(ReLU())
model.add(Dense(1500, kernel_initializer='He'))
model.add(ReLU())
model.add(Dense(10, kernel_initializer='He'))
model.add(Softmax())
model.summary()
model.compile(Adam(), 'categorical_crossentropy', 'accuracy')
return model
def make_model():
model = Sequential()
model.add(Input(shape=input_shape))
model.add(
Conv2D(16,
kernel_size=3,
strides=1,
padding='same',
kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization_v2())
model.add(ReLU())
add_residual_block(model, num_filters=16)
add_residual_block(model, num_filters=16)
add_residual_block(model, num_filters=16)
add_residual_block(model, num_filters=32, strides=2, cnn_shortcut=True)
add_residual_block(model, num_filters=32)
add_residual_block(model, num_filters=32)
add_residual_block(model, num_filters=64, strides=2, cnn_shortcut=True)
add_residual_block(model, num_filters=64)
add_residual_block(model, num_filters=64)
model.add(AveragePooling2DAll())
model.add(Flatten())
model.add(Dense(10, kernel_initializer='He'))
model.add(Softmax())
model.summary()
model.compile(Adam(lr=0.001, decay=1e-4), 'categorical_crossentropy',
'accuracy')
return model
def make_model():
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
model.add(BN_LAYER())
model.add(ReLU())
model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
model.add(BN_LAYER())
model.add(ReLU())
model.add(MaxPooling2D(2, 2, stride=2))
model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))
model.add(BN_LAYER())
model.add(ReLU())
model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))
model.add(BN_LAYER())
model.add(ReLU())
model.add(MaxPooling2D(2, 2, stride=2))
model.add(Flatten())
model.add(Dense(512, kernel_initializer='He'))
model.add(BN_LAYER())
model.add(ReLU())
model.add(Dense(10, kernel_initializer='He'))
model.add(Softmax())
model.summary()
model.compile(Adam(), loss='categorical_crossentropy', metric='accuracy')
return model
def make_gan_model(generator, discriminator, optimizer):
discriminator.trainable = False
gan_model = Sequential()
gan_model.add(generator)
gan_model.add(discriminator)
gan_model.compile(loss='binary_crossentropy',
optimizer=optimizer, metric=None)
return gan_model
def make_discriminator(optimizer):
discriminator = Sequential()
discriminator.add(Dense(1024, input_shape=784))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(512))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(256))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(1))
discriminator.add(Sigmoid())
discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer, metric=None)
return discriminator
def make_model():
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Dense(4096))
model.add(LeakyReLU(0.2))
model.add(Dense(2048))
model.add(LeakyReLU(0.2))
model.add(Dense(1024))
model.add(LeakyReLU(0.2))
model.add(Dense(512))
model.add(LeakyReLU(0.2))
model.add(Dense(256))
model.add(LeakyReLU(0.2))
model.add(Dense(10))
model.add(Softmax())
model.summary()
model.compile(Momentum(), 'categorical_crossentropy', 'accuracy')
return model
def make_model():
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(10))
model.add(Softmax())
model.summary()
model.compile(Adam(), 'categorical_crossentropy', 'accuracy')
return model