MCPcopy Create free account

hub / github.com/MultiPath/CopyNet / functions

Functions547 in github.com/MultiPath/CopyNet

⨍ Functions 547◇ Types & classes 70
Method__init__
(self, input_dim=0, output_dim=128, context_dim=None,
emolga/layers/recurrent.py:374
Method__init__
(self, input_dim, memory_width, shift_width, shift_conv, init='glorot_uniform', inner_init='o
emolga/layers/ntm_minibatch.py:98
Method__init__
(self, input_dim, memory_dim, memory_width,
emolga/layers/ntm_minibatch.py:134
Method__init__
(self, input_dim, memory_width, shift_width, shift_conv, init='glorot_uniform', inner_init='o
emolga/layers/ntm_minibatch.py:374
Method__init__
(self, input_dim, memory_width, shift_width, shift_conv, init='glorot_uniform', inner_init='o
emolga/layers/ntm_minibatch.py:459
Method__init__
(self, input_dim, memory_dim, memory_width,
emolga/layers/ntm_minibatch.py:496
Method__init__
(self, target_dim, source_dim, init='glorot_uniform', use_pipe=True,
emolga/layers/attention.py:81
Method__init__
(self, input_dim, output_dim, init='uniform', name=None)
emolga/layers/embeddings.py:17
Method__init__
(self, # parameters for Grid. output_dims, input_dims, #
emolga/layers/gridlstm.py:248
Method__init__
(self, # parameters for Grid. output_dims, input_dims, #
emolga/layers/gridlstm.py:460
Method__init__
(self, # parameters for Grid. output_dims, input_dims, #
emolga/layers/gridlstm.py:687
Method__init__
(self, # parameters for Grid. output_dims, input_dims, #
emolga/layers/gridlstm.py:880
Method__init__
(self, **kwargs)
emolga/basic/optimizers.py:28
Method__init__
(self, lr=0.001, rho=0.9, epsilon=1e-6, *args, **kwargs)
emolga/basic/optimizers.py:103
Method__init__
(self, lr=0.01, epsilon=1e-6, *args, **kwargs)
emolga/basic/optimizers.py:131
Method__init__
(self, lr=0.1, rho=0.95, epsilon=1e-6, *args, **kwargs)
emolga/basic/optimizers.py:158
Method__init__
(self, lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8, save=False, rng=None, *args, **kwargs)
emolga/basic/optimizers.py:201
Method__init__
(self)
emolga/models/core.py:13
Method__init__
(self, config, n_rng, rng, mode = 'Evaluation', dynamic_pri
emolga/models/variational.py:220
Method__init__
(self, config, n_rng, rng, mode = 'Evaluation', dynamic_pri
emolga/models/variational.py:705
Method__init__
(self, config, n_rng, rng, mode = 'Evaluation', dynamic_pri
emolga/models/variational.py:1033
Method__init__
(self, config, n_rng, rng, mode = 'Evaluation', dynamic_pri
emolga/models/variational.py:1364
Method__init__
(self, config, model='RNN', prefix='enc', use_contxt=True, name=None)
emolga/models/ntm_encdec.py:24
Method__init__
mode = RNN: use a RNN Encoder mode = NTM: use a NTM Encoder
emolga/models/ntm_encdec.py:261
Method__init__
mode = RNN: use a RNN Decoder mode = NTM: use a NTM Decoder (Neural Turing Machine)
emolga/models/ntm_encdec.py:357
Method__init__
(self, config, n_rng, rng, mode='RNN')
emolga/models/ntm_encdec.py:871
Method__init__
(self, config, n_rng, rng, mode='RNN')
emolga/models/ntm_encdec.py:1114
Method__init__
(self, config, n_rng, rng, mode='Evaluation')
emolga/models/ntm_encdec.py:1334
Method__init__
(self, config, rng, prefix='ptrdec')
emolga/models/pointers.py:23
Method__init__
(self, config, rng, prefix='ptrdec')
emolga/models/pointers.py:321
Method__init__
(self, config, n_rng, rng, name='PtrNet', w_mem=True)
emolga/models/pointers.py:679
Method__init__
(self, config, rng, prefix='enc', mode='Evaluation', embed=None, use_context
emolga/models/covc_encdec.py:28
Method__init__
mode = RNN: use a RNN Decoder
emolga/models/covc_encdec.py:213
Method__init__
(self, config, rng, prefix='dec', mode='RNN', embed=None, c
emolga/models/covc_encdec.py:708
Method__init__
mode = RNN: use a RNN Decoder
emolga/models/covc_encdec.py:1243
Method__init__
(self, config, n_rng, rng, mode='Evaluation')
emolga/models/covc_encdec.py:1313
Method__init__
(self, config, n_rng, rng, mode='Evaluation')
emolga/models/covc_encdec.py:1461
Method__init__
(self, config, rng, prefix='enc', mode='Evaluation', embed=None, use_context
emolga/models/encdec.py:178
Method__init__
mode = RNN: use a RNN Decoder
emolga/models/encdec.py:316
Method__init__
(self, config, rng, prefix='dec', mode='RNN', embed=None, c
emolga/models/encdec.py:804
Method__init__
mode = RNN: use a RNN Decoder
emolga/models/encdec.py:1165
Method__init__
(self, config, n_rng, rng, mode='Evaluation')
emolga/models/encdec.py:1235
Method__init__
(self, config, n_rng, rng, mode='Evaluation')
emolga/models/encdec.py:1383
Method__len__
(self)
emolga/utils/io_utils.py:22
Method__repr__
(self)
experiments/bst_dataset.py:128
Method__str__
(self)
experiments/bst_dataset.py:182
Method_build_NTM
Build a simple Neural Turing Machine. We use a feedforward controller here.
emolga/models/ntm_encdec.py:62
Method_build_RNN
()
emolga/models/ntm_encdec.py:41
Method_get_ht
(A, mask=False)
emolga/models/pointers.py:424
Method_grab_prob
(probs, x)
emolga/models/variational.py:921
Method_grab_prob
(probs, x)
emolga/models/variational.py:1255
Method_grab_prob
(probs, x)
emolga/models/variational.py:1616
Method_monitoring
(self)
emolga/layers/core.py:18
Method_recurrence
x_t: (nb_samples, dec_embedd_dim) enc_t: (nb_samples, enc_hidden_dim) dec_t: (nb_samples, dec_hidden_dim)
emolga/models/variational.py:882
Method_recurrence
x_t: (nb_samples, dec_embedd_dim) enc_t: (nb_samples, enc_hidden_dim) dec_t: (nb_samples, dec_hidden_dim)
emolga/models/variational.py:1210
Method_recurrence
x_t: (nb_samples, dec_embedd_dim) enc_t: (nb_samples, enc_hidden_dim) dec_t: (nb_samples, dec_hidden_dim)
emolga/models/variational.py:1571
Method_recurrence
(x, prev_h, c, s, s_mask)
emolga/models/pointers.py:118
Method_recurrence
(x, prev_h, c, s, s_mask)
emolga/models/pointers.py:449
Method_recurrence
(x, x_mask, prev_h, c)
emolga/models/covc_encdec.py:437
Method_recurrence
x: (nb_samples, embed_dims) x_mask: (nb_samples, ) ll: (nb_samples, maxlen_s) xl_mask:(nb_sa
emolga/models/covc_encdec.py:821
Method_recurrence
(x, x_mask, prev_h, c)
emolga/models/encdec.py:532
Method_recurrence
(x, x_mask, prev_h, cc, cm)
emolga/models/encdec.py:888
Method_repeat
(x, dimshuffle=True)
emolga/models/variational.py:854
Method_repeat
(x, dimshuffle=True)
emolga/models/variational.py:1182
Method_repeat
(x, dimshuffle=True)
emolga/models/variational.py:1544
Functionaccuracy
(p, y)
emolga/utils/np_utils.py:43
Methodadd
(self, n, values=[])
emolga/utils/generic_utils.py:152
Methodadd_forget
(self, param)
emolga/basic/optimizers.py:221
Methodadd_noise
(self, param)
emolga/basic/optimizers.py:216
Functionalloc_ones_matrix
(*dims)
emolga/utils/theano_utils.py:34
Methodanalyse_
(self, inputs, outputs, idx2word, inputs_unk=None, return_attend=False, name=None, display=False)
emolga/models/covc_encdec.py:1831
Methodanalyse_cover
(self, inputs, outputs, idx2word, inputs_unk=None, return_attend=False, name=None, display=False)
emolga/models/covc_encdec.py:1888
Methodanalyse_cover
(self, inputs, outputs, idx2word)
emolga/models/encdec.py:1706
Functionbinary_crossentropy
(y_true, y_pred)
emolga/basic/objectives.py:47
Functionbinary_logloss
(p, y)
emolga/utils/np_utils.py:27
Methodbuild_
(self)
emolga/models/variational.py:52
Methodbuild_
(self)
emolga/models/variational.py:235
Methodbuild_
(self)
emolga/models/variational.py:720
Methodbuild_
(self)
emolga/models/variational.py:1048
Methodbuild_
(self)
emolga/models/variational.py:1376
Methodbuild_
(self)
emolga/models/ntm_encdec.py:153
Methodbuild_
(self)
emolga/models/ntm_encdec.py:882
Methodbuild_
(self)
emolga/models/ntm_encdec.py:1125
Methodbuild_
(self)
emolga/models/ntm_encdec.py:1345
Methodbuild_
(self, encoder=None)
emolga/models/pointers.py:689
Methodbuild_
(self)
emolga/models/covc_encdec.py:1324
Methodbuild_
(self)
emolga/models/covc_encdec.py:1472
Methodbuild_
(self, lr=None, iterations=None)
emolga/models/covc_encdec.py:1583
Methodbuild_
(self)
emolga/models/encdec.py:1246
Methodbuild_
(self)
emolga/models/encdec.py:1394
Functionbuild_data
(data)
experiments/lcsts_sample.py:68
Methodbuild_decoder
Build the Pointer Network Decoder Computational Graph
emolga/models/pointers.py:397
Methodbuild_decoder
Build the Computational Graph ::> Context is essential
emolga/models/covc_encdec.py:791
Methodbuild_decoder
Build the Decoder Computational Graph
emolga/models/covc_encdec.py:1281
Methodbuild_decoder
Build the Computational Graph ::> Context is essential
emolga/models/encdec.py:865
Methodbuild_decoder
Build the Decoder Computational Graph
emolga/models/encdec.py:1203
Methodbuild_dynamics
(self, states, action, Y)
emolga/models/variational.py:477
Functionbuild_evaluation
(train_set, segment)
experiments/lcsts_rouge.py:24
Methodbuild_predict_sampler
(self)
emolga/models/pointers.py:925
Functionbuild_ptb
()
emolga/dataset/build_dataset.py:70
← previousnext →301–400 of 547, ranked by callers