我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用chainer.functions.Linear()。
def __init__(self, n_history, n_action, on_gpu=False): self.n_history = n_history self.n_action = n_action self.on_gpu = on_gpu super(Q, self).__init__( l1=F.Convolution2D(n_history, 32, ksize=8, stride=4, nobias=False, wscale=np.sqrt(2)), l2=F.Convolution2D(32, 64, ksize=3, stride=2, nobias=False, wscale=np.sqrt(2)), l3=F.Convolution2D(64, 64, ksize=3, stride=1, nobias=False, wscale=np.sqrt(2)), l4=F.Linear(3136, 512, wscale=np.sqrt(2)), out=F.Linear(512, self.n_action, initialW=np.zeros((n_action, 512), dtype=np.float32)) ) if on_gpu: self.to_gpu()
def __init__(self, insize, outsize): self.insize = insize self.outsize = outsize self.h = None self.c = None self.peep_dim = insize + 2 * outsize super(DyerLSTM, self).__init__( linear_in=F.Linear(self.peep_dim, outsize, bias=0.25, wscale=_dyer_init(self.peep_dim, outsize)), linear_c=F.Linear(insize + outsize, outsize, wscale=_dyer_init(insize + outsize, outsize)), linear_out=F.Linear(self.peep_dim, outsize, wscale=_dyer_init(self.peep_dim, outsize)) )
def test_forward_invalid(self): f = F.Linear(5, 5) # OK v = chainer.Variable(numpy.random.randn(1, 5).astype(numpy.float32)) result = f(v) assert isinstance(result, chainer.Variable) # Incorrect dtype # in py3, numpy dtypes are represented as class msg = """\ Invalid operation is performed in: LinearFunction \\(Forward\\) Expect: in_types\\[0\\]\\.dtype == <(type|class) 'numpy\\.float32'> Actual: float64 \\!= <(type|class) 'numpy\\.float32'>""" v = chainer.Variable(numpy.random.randn(1, 5)) with six.assertRaisesRegex(self, chainer.utils.type_check.InvalidType, msg): f(v) # Incorrect dim msg = """\ Invalid operation is performed in: LinearFunction \\(Forward\\) Expect: in_types\\[0\\]\\.ndim >= 2 Actual: 1 < 2""" v = chainer.Variable(numpy.random.randn(5).astype(numpy.float32)) with six.assertRaisesRegex(self, chainer.utils.type_check.InvalidType, msg): f(v)
def __init__(self, use_gpu, enable_controller, dim): self.use_gpu = use_gpu self.num_of_actions = len(enable_controller) self.enable_controller = enable_controller self.dim = dim print("Initializing Q-Network...") hidden_dim = 256 self.model = FunctionSet( l4=F.Linear(self.dim*self.hist_size, hidden_dim, wscale=np.sqrt(2)), q_value=F.Linear(hidden_dim, self.num_of_actions, initialW=np.zeros((self.num_of_actions, hidden_dim), dtype=np.float32)) ) if self.use_gpu >= 0: self.model.to_gpu() self.model_target = copy.deepcopy(self.model) self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001) self.optimizer.setup(self.model.collect_parameters()) # History Data : D=[s, a, r, s_dash, end_episode_flag] self.d = [np.zeros((self.data_size, self.hist_size, self.dim), dtype=np.uint8), np.zeros(self.data_size, dtype=np.uint8), np.zeros((self.data_size, 1), dtype=np.int8), np.zeros((self.data_size, self.hist_size, self.dim), dtype=np.uint8), np.zeros((self.data_size, 1), dtype=np.bool)]
def __init__(self, action, other_action, size, epsilon=0.05, hidden=200): self.action = action self.other_action = other_action self.width = size * size self.epsilon = epsilon self.hidden = hidden super(ChainerAgent, self).__init__( l1=F.Linear(self.width, self.hidden, wscale=np.sqrt(2)), l2=F.Linear(self.hidden, 1, wscale=np.sqrt(2)), )
def __init__(self, state_dim = STATE_DIM): super(Q, self).__init__( l1=F.Linear(state_dim, 50), l2=F.Linear(50, 50), q_value=F.Linear(50, 5) )
def __init__(self, state_dim = (STATE_DIM)): super(Q, self).__init__( l1=F.Linear(state_dim, 50), l2=F.Linear(50, 50), q_value=F.Linear(50, NUM_ACTIONS) )
def __init__(self, enable_controller=[0, 1, 3, 4]): self.num_of_actions = len(enable_controller) self.enable_controller = enable_controller # Default setting : "Breakout" print "Initializing DDQN..." # Initialization of Chainer 1.1.0 or older. # print "CUDA init" # cuda.init() print "Model Building" self.model = FunctionSet( l1=F.Convolution2D(4, 32, ksize=8, stride=4, nobias=False, wscale=np.sqrt(2)), l2=F.Convolution2D(32, 64, ksize=4, stride=2, nobias=False, wscale=np.sqrt(2)), l3=F.Convolution2D(64, 64, ksize=3, stride=1, nobias=False, wscale=np.sqrt(2)), l4=F.Linear(3136, 512, wscale=np.sqrt(2)), q_value=F.Linear(512, self.num_of_actions, initialW=np.zeros((self.num_of_actions, 512), dtype=np.float32)) ).to_gpu() if args.resumemodel: # load saved model serializers.load_npz(args.resumemodel, self.model) print "load model from resume.model" self.model_target = copy.deepcopy(self.model) print "Initizlizing Optimizer" self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001) self.optimizer.setup(self.model.collect_parameters()) # History Data : D=[s, a, r, s_dash, end_episode_flag] if args.resumeD1 and args.resumeD2: # load saved D1 and D2 npz_tmp1 = np.load(args.resumeD1) npz_tmp2 = np.load(args.resumeD2) self.D = [npz_tmp1['D0'], npz_tmp1['D1'], npz_tmp1['D2'], npz_tmp2['D3'], npz_tmp2['D4']] npz_tmp1.close() npz_tmp2.close() print "loaded stored D1 and D2" else: self.D = [np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8), np.zeros(self.data_size, dtype=np.uint8), np.zeros((self.data_size, 1), dtype=np.int8), np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8), np.zeros((self.data_size, 1), dtype=np.bool)] print "initialize D data"
