Python Linear 예제들

예제 #1

파일: model.py 프로젝트: zhixiangxu/adversarial-autoencoder

	def __init__(self, ndim_x=28*28, ndim_y=10, ndim_z=16, ndim_h=1000):
		super(Model, self).__init__()
		self.ndim_x = ndim_x
		self.ndim_y = ndim_y
		self.ndim_z = ndim_z
		self.ndim_h = ndim_h
		with self.init_scope():
			decoder = nn.Module(
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_x),
				nn.Tanh(),
			)
			decoder.merge_y = nn.Linear(ndim_y, ndim_h, nobias=True)
			decoder.merge_z = nn.Linear(ndim_z, ndim_h, nobias=True)
			decoder.merge_bias = nn.Bias(shape=(ndim_h,))
			self.decoder = decoder

			self.encoder = nn.Module(
				nn.Linear(ndim_x, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_z),
			)

			self.discriminator = nn.Module(
				nn.GaussianNoise(std=0.3),
				nn.Linear(ndim_z, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, 2),
			)

		for param in self.params():
			if param.name == "W":
				param.data[...] = np.random.normal(0, 0.01, param.data.shape)

예제 #2

	def __init__(self, ndim_x=28*28, ndim_y=20, ndim_z=2, ndim_h=1000, cluster_head_distance_threshold=2):
		super(Model, self).__init__()
		self.ndim_x = ndim_x
		self.ndim_y = ndim_y
		self.ndim_z = ndim_z
		self.ndim_h = ndim_h
		self.cluster_head_distance_threshold = cluster_head_distance_threshold

		with self.init_scope():
			self.decoder = nn.Module(
				nn.Linear(ndim_z, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_x),
				nn.Tanh(),
			)

			encoder = nn.Module(
				nn.Linear(ndim_x, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_h),
				nn.ReLU(),
			)
			encoder.head_y = nn.Linear(ndim_h, ndim_y)
			encoder.head_z = nn.Linear(ndim_h, ndim_z)
			self.encoder = encoder

			self.discriminator_z = nn.Module(
				nn.GaussianNoise(std=0.3),
				nn.Linear(ndim_z, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, 2),
			)

			self.discriminator_y = nn.Module(
				nn.GaussianNoise(std=0.3),
				# nn.GaussianNoise(0, 0.3),
				nn.Linear(ndim_y, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, ndim_h),
				nn.ReLU(),
				# nn.BatchNormalization(ndim_h),
				nn.Linear(ndim_h, 2),
			)

			self.cluster_head = nn.Linear(ndim_y, ndim_z, nobias=True)

		for param in self.params():
			if param.name == "W":
				param.data[...] = np.random.normal(0, 0.01, param.data.shape)

		for param in self.cluster_head.params():
			if param.name == "W":
				param.data[...] = np.random.normal(0, 1, param.data.shape)