def __init__(self, inplanes, scales=6, mingrid=1): super(TUM, self).__init__() self.scales = scales with self.init_scope(): ecs = [] for s in range(scales-1): if s == 0: conv = Conv2DBNActiv(inplanes, 256, 3, 2, pad=1, nobias=True) elif s == scales-2 and mingrid == 1: conv = Conv2DBNActiv(256, 256, 3, 2, nobias=True) else: conv = Conv2DBNActiv(256, 256, 3, 2, pad=1, nobias=True) ecs.append(conv) self.ecs = ChainList(*ecs) dcs = [] for s in range(scales): if s == scales-1: conv = Conv2DBNActiv(inplanes, 256, 3, pad=1, nobias=True) else: conv = Conv2DBNActiv(256, 256, 3, pad=1, nobias=True) dcs.append(conv) self.dcs = ChainList(*dcs) self.scs = ChainList(*[Conv2DBNActiv(256, 128, 1, nobias=True) for _ in range(scales)])
def __init__(self, levels=8, scales=6, mingrid=1): super(MLFPN, self).__init__() self.levels = levels with self.init_scope(): self.ffmv1 = FFMv1() self.tums = ChainList(*[TUM(768, scales, mingrid) if l == 0 else TUM(256, scales, mingrid) for l in range(levels)]) self.ffmv2s = ChainList(*[FFMv2() for _ in range(levels-1)]) self.sfam = SFAM(levels, scales)
def __init__(self, emb_dim, vocab_size, layer_dims, label_dim, z_dim): super(SequenceEncoder, self).__init__(rnn=Rnn(emb_dim, vocab_size, layer_dims, label_dim, suppress_output=True), ) ls_mu = ChainList() ls_ln_var = ChainList() for d in layer_dims: ls_mu.add_link(L.Linear(d, z_dim)) ls_ln_var.add_link(L.Linear(d, z_dim)) self.add_link('ls_mu', ls_mu) self.add_link('ls_ln_var', ls_ln_var)
def __init__(self, d, f, R): self.d = d self.f = f self.R = R g = ChainList(*[L.Linear(1, f) for i in six.moves.range(AtomIdMax)]) H = ChainList(*[ ChainList(*[L.Linear(f, f) for i in six.moves.range(R)]) for j in six.moves.range(5) ]) W = ChainList(*[L.Linear(f, d) for i in six.moves.range(R)]) self.model = Chain(H=H, W=W, g=g) self.optimizer = optimizers.Adam() self.optimizer.setup(self.model)
def __init__(self, in_dim, hidden_dims, active): super(_Mlp, self).__init__() self.active = active ds = [in_dim] + hidden_dims ls = ChainList() bns = ChainList() for d_in, d_out in zip(ds, ds[1:]): l = L.Linear(d_in, d_out) bn = L.BatchNormalization(d_out) ls.add_link(l) bns.add_link(bn) self.add_link('ls', ls) self.add_link('bns', bns)
def __init__(self, n_input, n_output, n_hidden1=10, n_hidden2=10, n_hidden_layers=1, link=L.LSTM): """ :param n_input: nchannels x height x width :param n_hidden: number of hidden units :param n_output: number of outputs :param n_hidden_layers: number of hidden layers :param link: used recurrent link (LSTM) """ k = 3 # kernel size s = 1 # stride p = 1 # padding n_linear = n_hidden1 * np.prod(1 + (np.array(n_input[1:]) - k + 2*p)/s) links = ChainList() if n_hidden_layers == 0: links.add_link(L.Convolution2D(n_input[0], n_hidden1, k, s, p)) links.add_link(L.Linear(n_linear, n_output)) else: links.add_link(L.Convolution2D(n_input[0], n_hidden1, k, s, p)) links.add_link(link(n_linear, n_hidden2)) for i in range(n_hidden_layers - 1): links.add_link(link(n_hidden2, n_hidden2)) links.add_link(L.Linear(n_hidden2, n_output)) self.n_input = n_input self.n_hidden1 = n_hidden1 self.n_hidden2 = n_hidden2 self.n_output = n_output self.n_hidden_layers = n_hidden_layers self.monitor = [] super(CRNN3, self).__init__(links)
def __init__(self, n_input, n_output, n_hidden=10, n_hidden_layers=1, link=L.LSTM): """ :param n_input: number of inputs :param n_hidden: number of hidden units :param n_output: number of outputs :param n_hidden_layers: number of hidden layers :param link: used recurrent link (LSTM) """ links = ChainList() if n_hidden_layers == 0: links.add_link(L.Linear(n_input, n_output)) else: links.add_link(link(n_input, n_hidden)) for i in range(n_hidden_layers - 1): links.add_link(link(n_hidden, n_hidden)) links.add_link(L.Linear(n_hidden, n_output)) self.n_input = n_input self.n_hidden = n_hidden self.n_output = n_output self.n_hidden_layers = n_hidden_layers self.monitor = [] super(RNN, self).__init__(links)
def __init__(self): super(QNet, self).__init__(_hidden_layers=ChainList( L.Linear(None, 64), L.Linear(None, 64), L.Linear(None, 32), ), _output_layer=L.Linear(None, 2))
def __init__(self, ninput, nhidden, noutput, nlayer=2, link=L.LSTM): """ :param ninput: number of inputs :param nhidden: number of hidden units :param noutput: number of outputs :param nlayer: number of weight matrices (2 = standard RNN with one layer of hidden units) :param link: used recurrent link (LSTM) """ links = ChainList() if nlayer == 1: links.add_link(L.Linear(ninput, noutput)) else: links.add_link(link(ninput, nhidden)) for i in range(nlayer - 2): links.add_link(link(nhidden, nhidden)) links.add_link(L.Linear(nhidden, noutput)) self.ninput = ninput self.nhidden = nhidden self.noutput = noutput self.nlayer = nlayer self.h = {} super(RecurrentNeuralNetwork, self).__init__(links)
def __init__(self, n_input, n_output, n_hidden=10, n_hidden_layers=1, actfun=F.relu): """ :param n_input: number of inputs :param n_output: number of outputs :param n_hidden: number of hidden units :param n_hidden_layers: number of hidden layers (1; standard MLP) :param actfun: used activation function (ReLU) """ links = ChainList() if n_hidden_layers == 0: links.add_link(L.Linear(n_input, n_output)) else: links.add_link(L.Linear(n_input, n_hidden)) for i in range(n_hidden_layers - 1): links.add_link(L.Linear(n_hidden, n_hidden)) links.add_link(L.Linear(n_hidden, n_output)) self.n_input = n_input self.n_hidden = n_hidden self.n_output = n_output self.n_hidden_layers = n_hidden_layers self.actfun = actfun self.monitor = [] super(MLP, self).__init__(links)
def __init__(self, ninput, nhidden, noutput, nlayer=2, actfun=F.relu): """ :param ninput: number of inputs :param nhidden: number of hidden units :param noutput: number of outputs :param nlayer: number of weight matrices (2; standard MLP) :param actfun: used activation function (ReLU) """ links = ChainList() if nlayer == 1: links.add_link(L.Linear(ninput, noutput)) else: links.add_link(L.Linear(ninput, nhidden)) for i in range(nlayer - 2): links.add_link(L.Linear(nhidden, nhidden)) links.add_link(L.Linear(nhidden, noutput)) self.ninput = ninput self.nhidden = nhidden self.noutput = noutput self.nlayer = nlayer self.actfun = actfun self.h = {} super(DeepNeuralNetwork, self).__init__(links)
def __init__(self): super(PolicyNet, self).__init__(hidden_layers=ChainList( L.Linear(None, 32), L.Linear(None, 32), L.Linear(None, 16), ), output_layer=L.Linear(None, 2))
def __init__(self, d, f, R, gpu): self.d = d self.f = f self.R = R self.gpu = gpu g = ChainList(*[L.Linear(1, f) for i in six.moves.range(AtomIdMax)]) H = ChainList(*[L.Linear(f, f) for i in six.moves.range(R)]) W = ChainList(*[L.Linear(f, d) for i in six.moves.range(R + 1)]) self.optimizer = optimizers.Adam() self.model = Chain(H=H, W=W, g=g) if gpu: self.model.to_gpu(0) self.optimizer.setup(self.model) self.to = [[] for i in six.moves.range(2)] self.atom_sid = [[] for i in six.moves.range(2)] self.anum = [[] for i in six.moves.range(2)]
def __init__(self, levels=8, scales=6, planes=1024): super(SFAM, self).__init__() self.levels = levels self.scales = scales with self.init_scope(): self.ses = ChainList(*[SEBlock(planes) for _ in range(scales)])
def __init__(self, num_heads, model_dim, key_dim, value_dim): super().__init__() self.num_heads = num_heads self.model_dim = model_dim self.key_dim = key_dim self.value_dim = value_dim self.multi_head_dim = num_heads * value_dim self.scale = 1. / sqrt(key_dim) with self.init_scope(): self.head_query_links = ChainList() self.head_key_links = ChainList() self.head_value_links = ChainList() for i in range(num_heads): self.head_query_links.append(L.Linear(model_dim, key_dim)) self.head_key_links.append(L.Linear(model_dim, key_dim)) self.head_value_links.append(L.Linear(model_dim, value_dim)) self.output_link = L.Linear(self.multi_head_dim, model_dim)
def __init__(self, depth, num_heads, model_dim, ff_dim, p_drop): super().__init__() with self.init_scope(): self.unit_links = ChainList() for i in range(depth): self.unit_links.append( TransformerDecoderUnit(num_heads, model_dim, ff_dim, p_drop))
def __init__(self, num_molecules, rep_dim, max_degree, num_levels): super(Mol2Vec2, self).__init__() num_degree_type = max_degree + 1 with self.init_scope(): self.mol_embed_layer = L.EmbedID(num_molecules, rep_dim) self.atom_embed_layer = L.EmbedID(MAX_NUMBER_ATOM, rep_dim) self.edge_layer = L.Linear(rep_dim, rep_dim * MAX_EDGE_TYPE) self.out = L.Linear(rep_dim, MAX_ATOM_TYPE) self.H = ChainList(*[ChainList( *[L.Linear(rep_dim, rep_dim) for i in six.moves.range(num_degree_type)]) for j in six.moves.range(num_levels)]) # representation dim of molecules, substructures and atoms self.rep_dim = rep_dim self.max_degree_type = num_degree_type self.num_mol = num_molecules self.n_levels = num_levels
def __init__(self, n_output, resolution, n_stack, n_dilateStack, n_in_channel, n_skip_channel, useGPU): self.n_output = n_output firstConv = L.Convolution2D(None, n_in_channel, ksize=1) wn = [] for s in range(n_stack): for d in range(n_dilateStack): wn.append(WaveBlock(n_in_channel, n_skip_channel, 2**d, useGPU)) lastConv0 = L.Convolution2D(n_skip_channel, n_skip_channel, ksize=1) lastConv1 = L.Convolution2D(n_skip_channel, n_skip_channel, ksize=1) linear = [L.Linear(None, resolution) for i in range(n_output)] super(Wavenet, self).__init__(firstConv=firstConv, waveBlocks=ChainList(*wn), lastConv0=lastConv0, lastConv1=lastConv1, linear=ChainList(*linear))
def __init__(self): super(ValueNet, self).__init__( _hidden_layers = ChainList( L.Linear(None, 32), L.Linear(None, 32), L.Linear(None, 16), ), _output_layer = L.Linear(None, 1) )
def __init__(self, emb_dim, vocab_size, layer_dims, feature_dim, suppress_output, eos_id=0): """ Recurrent Neural Network with multiple layers. in_dim -> layers[0] -> ... -> layers[-1] -> out_dim (optional) :param int emb_dim: dimension of embeddings :param int vocab_size: size of vocabulary :param layer_dims: dimensions of hidden layers :param int feature_dim: dimesion of external feature :type layer_dims: list of int :param bool suppress_output: whether to suppress output :param int eos_id: ID of <BOS> and <EOS> """ super(Rnn, self).__init__(emb=F.EmbedID(vocab_size, emb_dim)) self.emb_dim = emb_dim self.vocab_size = vocab_size self.layer_dims = layer_dims self.feature_dim = feature_dim self.suppress_output = suppress_output self.eos_id = eos_id # add hidden layer_dims ls_xh = ChainList() ls_hh = ChainList() ls_fh = ChainList() layer_dims = [emb_dim] + layer_dims for in_dim, out_dim in zip(layer_dims, layer_dims[1:]): ls_xh.add_link(F.Linear(in_dim, out_dim * 4)) ls_hh.add_link(F.Linear(out_dim, out_dim * 4)) ls_fh.add_link(F.Linear(feature_dim, out_dim * 4)) self.add_link('ls_xh', ls_xh) self.add_link('ls_hh', ls_hh) self.add_link('ls_fh', ls_fh) if not suppress_output: # add output layer self.add_link('l_y', F.Linear(layer_dims[-1], self.vocab_size))
def __init__(self, rnns, dims, pyramidal, dropout_ratio): super(MultiLayerRnn, self).__init__(rnns=rnns, ) self.pyramidal = pyramidal if self.pyramidal: self.add_link('combine_twos', ChainList()) for in_dim, out_dim in zip(dims, dims[1:]): combine_two = CombineTwo(in_dim, out_dim) self.combine_twos.add_link(combine_two) assert len(self.rnns) == len(self.combine_twos) + 1 self.dropout_ratio = dropout_ratio
def __init__(self, emb_dim, vocab_size, layer_dims, label_dim, z_dim): super(SequenceDecoder, self).__init__(rnn=Rnn(emb_dim, vocab_size, layer_dims, label_dim, suppress_output=False), ) ls_zh = ChainList() for d in layer_dims: ls_zh.add_link(L.Linear(z_dim, d)) self.add_link('ls_zh', ls_zh)
def __init__(self, in_dim, hidden_dims, active): super(_Mlp, self).__init__() self.active = active ds = [in_dim] + hidden_dims ls = ChainList() for d_in, d_out in zip(ds, ds[1:]): l = L.Linear(d_in, d_out) ls.add_link(l) self.add_link('ls', ls)
def __init__(self): self.log = { ('test', 'accuracy'): (), ('test', 'loss'): (), ('training', 'accuracy'): (), ('training', 'loss'): () } self.model = ChainList(_WaveNet(), _CRF()) self.optimizer = optimizers.Adam(0.0002, 0.5) self.optimizer.setup(self.model)
def __init__(self, size, levels, first_channels, last_channels): super().__init__() in_channels = [first_channels] * levels out_channels = [last_channels] * levels for i in range(1, levels): channels = min(first_channels, last_channels * 2 ** i) in_channels[-i] = channels out_channels[-i - 1] = channels with self.init_scope(): self.init = InitialSkipArchitecture(size, in_channels[0], out_channels[0]) self.skips = ChainList(*[SkipArchitecture(size, i, o) for i, o in zip(in_channels[1:], out_channels[1:])])
def __init__(self): super(PolicyNet, self).__init__( _hidden_layers = ChainList( L.Linear(None, 32), L.Linear(None, 32), L.Linear(None, 16), ), _output_layer = L.Linear(None, 2) ) self._eps = 1e-5 #sigma=0を避けるための微小数
def __init__(self, probability=0.5): super().__init__() self.probability = probability with self.init_scope(): self.manipulations = ChainList(*[ Mirror(), Rotation(), Shift(), AffineTransformation(), ColorAffineTransformation(), AdditiveNoise(), Cutout()])
def __init__(self, word_emb, hidden_dim, layer_num, out_vocab_size, gru, dropout_ratio): super(AttentionalDecoder, self).__init__( softmax_linear=L.Linear(hidden_dim * 2, out_vocab_size), phi1_linear=L.Linear(hidden_dim, hidden_dim), # TODO: make out dim adjustable phi2_linear=L.Linear(hidden_dim, hidden_dim), # TODO: make out dim adjustable rnns=ChainList(), ) self.hidden_dim = hidden_dim self.layer_num = layer_num self.out_vocab_size = out_vocab_size Rnn = L.GRU if gru else L.StatelessLSTM for i in range(layer_num): rnn = Rnn(hidden_dim, hidden_dim) self.rnns.add_link(rnn) self.word_emb = word_emb self.gru = gru self.dropout_ratio = dropout_ratio
def __init__(self, encoder, decoder, optimizer, epoch=20, batch_size=100, log_path="", export_path="", gpu_flag=-1): self.encoder = encoder self.decoder = decoder self.optimizer = optimizer self.epoch = epoch self.batch_size = batch_size self.log_path = log_path self.export_path = export_path self.autoencoded = ChainList() self.gpu_flag = gpu_flag
def __init__(self, in_vocab_size, hidden_dim, layer_num, out_vocab_size, gru, bidirectional, pyramidal, dropout_ratio, src_vocab_size=None): super(AttentionalEncoderDecoder, self).__init__() if src_vocab_size is None: # use same vocabulary for source/target word_emb = L.EmbedID(in_vocab_size, hidden_dim, ignore_label=IGNORE_ID) self.add_link('word_emb', word_emb) self.word_emb_src = word_emb self.word_emb_trg = word_emb else: word_emb_src = L.EmbedID(src_vocab_size, hidden_dim, ignore_label=IGNORE_ID) word_emb_trg = L.EmbedID(in_vocab_size, hidden_dim, ignore_label=IGNORE_ID) self.add_link('word_emb_src', word_emb_src) self.add_link('word_emb_trg', word_emb_trg) rnns = ChainList() Rnn = GruRnn if gru else LstmRnn for i in range(layer_num): if bidirectional: rnn_f = Rnn(hidden_dim) rnn_b = Rnn(hidden_dim) rnn = BiRnn(rnn_f, rnn_b) else: rnn = Rnn(hidden_dim) rnns.add_link(rnn) multi_rnn = MultiLayerRnn(rnns, [hidden_dim] * layer_num, pyramidal, dropout_ratio) self.add_link('encoder', Encoder(self.word_emb_src, multi_rnn)) self.add_link('decoder', AttentionalDecoder(self.word_emb_trg, hidden_dim, layer_num, out_vocab_size, gru, dropout_ratio)) self.in_vocab_size = in_vocab_size self.hidden_dim = hidden_dim self.layer_num = layer_num self.out_vocab_size = out_vocab_size self.gru = gru self.bidirectional = bidirectional self.pyramidal = pyramidal