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Functions126 in github.com/Newmu/dcgan_code

⨍ Functions 126◇ Types & classes 31
↓ 28 callersFunctionfloatX
(X)
lib/theano_utils.py:7
↓ 27 callersFunctionbatchnorm
batchnorm with support for not using scale and shift parameters as well as inference values (u and s) and partial batchnorm (via a) will
lib/ops.py:52
↓ 17 callersFunctionsharedX
(X, dtype=theano.config.floatX, name=None)
lib/theano_utils.py:10
↓ 12 callersFunctiondeconv
sets up dummy convolutional forward pass and uses its grad as deconv currently only tested/working with same padding
lib/ops.py:85
↓ 7 callersMethod__init__
(self, lr=0.01, *args, **kwargs)
lib/updates.py:59
↓ 7 callersFunctionclip_norms
(gs, c)
lib/updates.py:13
↓ 7 callersMethodgradient_regularize
(self, p, g)
lib/updates.py:35
↓ 7 callersMethodweight_regularize
(self, p)
lib/updates.py:40
↓ 6 callersFunctionnnd_score
(trX, teX, metric='euclidean')
lib/metrics.py:98
↓ 4 callersFunctionOneHot
(X, n=None, negative_class=0.)
lib/data_utils.py:36
↓ 4 callersFunctionconv_cond_concat
concatenate conditioning vector on feature map axis
lib/ops.py:46
↓ 4 callersFunctionfeatures
(X, nbatch=128)
svhn/svhn_semisup_analysis.py:115
↓ 4 callersFunctionl2norm
(x, axis=1, e=1e-8, keepdims=True)
lib/ops.py:17
↓ 4 callersFunctionshuffle
(*arrays, **options)
lib/data_utils.py:30
↓ 3 callersFunctioncolor_grid_vis
(X, (nh, nw), save_path=None)
lib/vis.py:15
↓ 3 callersFunctiongrayscale_grid_vis
(X, (nh, nw), save_path=None)
lib/vis.py:4
↓ 3 callersFunctioniter_data
(*data, **kwargs)
lib/data_utils.py:44
↓ 3 callersFunctionnnc_score
(trX, trY, teX, teY, metric='euclidean')
lib/metrics.py:93
↓ 2 callersFunctiondiscrim
(X, Y, w, w2, w3, wy)
mnist/train_cond_dcgan.py:101
↓ 2 callersFunctiondiscrim
(X, w, w2, g2, b2, w3, g3, b3, w4, g4, b4, wy)
faces/train_uncond_dcgan.py:117
↓ 2 callersFunctioninverse_transform
(X)
mnist/train_cond_dcgan.py:53
↓ 2 callersFunctioninverse_transform
(X)
faces/train_uncond_dcgan.py:32
↓ 2 callersFunctionmean_and_var
(X)
svhn/svhn_semisup_analysis.py:50
↓ 2 callersFunctionsvhn
(extra=False)
svhn/load.py:11
↓ 2 callersFunctiontransform
(X)
mnist/train_cond_dcgan.py:50
↓ 2 callersFunctiontransform
(X)
faces/train_uncond_dcgan.py:28
↓ 1 callersFunctionbnorm_statistics
(X, w, w2, g2, b2, w3, g3, b3, wy)
svhn/svhn_semisup_analysis.py:55
↓ 1 callersFunctioncenter_crop
(x, ph, pw=None)
lib/data_utils.py:6
↓ 1 callersFunctionclip_norm
(g, c, n)
lib/updates.py:8
↓ 1 callersFunctioncosine
(x, y)
lib/ops.py:20
↓ 1 callersFunctiondiscrim
(X, w, w2, g2, b2, w3, g3, b3, w4, g4, b4, w5, g5, b5, w6, g6, b6, wy)
imagenet/load_pretrained.py:55
↓ 1 callersFunctioneuclidean
(x, y, e=1e-8)
lib/ops.py:26
↓ 1 callersFunctionfaces
(ntrain=None, nval=None, ntest=None, batch_size=128)
faces/load.py:11
↓ 1 callersMethodfrob_norm
(self, p, nrows)
lib/updates.py:32
↓ 1 callersFunctiongen
(Z, Y, w, w2, w3, wx)
mnist/train_cond_dcgan.py:88
↓ 1 callersFunctiongen
(Z, w, g, b, w2, g2, b2, w3, g3, b3, w4, g4, b4, w5, g5, b5, w6, g6, b6, wx)
imagenet/load_pretrained.py:44
↓ 1 callersFunctiongen
(Z, w, g, b, w2, g2, b2, w3, g3, b3, w4, g4, b4, wx)
faces/train_uncond_dcgan.py:108
↓ 1 callersFunctiongen_samples
(n, nbatch=128)
mnist/train_cond_dcgan.py:154
↓ 1 callersFunctiongen_samples
(n, nbatch=128)
faces/train_uncond_dcgan.py:163
↓ 1 callersFunctiongpu_nnc_predict
(trX, trY, teX, metric='cosine', batch_size=4096)
lib/metrics.py:33
↓ 1 callersFunctiongpu_nnd_score
(trX, teX, metric='cosine', batch_size=4096)
lib/metrics.py:61
↓ 1 callersFunctioninfer_bnorm_stats
(X, nbatch=128)
svhn/svhn_semisup_analysis.py:70
↓ 1 callersFunctioninverse_transform
(X)
imagenet/load_pretrained.py:66
↓ 1 callersMethodl2_norm
(self, p)
lib/updates.py:29
↓ 1 callersFunctionlist_shuffle
(*data)
lib/data_utils.py:23
↓ 1 callersMethodmax_norm
(self, p, maxnorm)
lib/updates.py:22
↓ 1 callersFunctionmnist
()
mnist/load.py:14
↓ 1 callersFunctionmnist_with_valid_set
()
mnist/load.py:36
↓ 1 callersFunctionmodel
(X, h2_u, h3_u, h2_s, h3_s, w, w2, g2, b2, w3, g3, b3, wy )
svhn/svhn_semisup_analysis.py:87
↓ 1 callersFunctionsvhn_with_valid_set
(extra=False)
svhn/load.py:25
FunctionBinaryCrossEntropy
(y_true, y_pred)
lib/costs.py:7
FunctionCategoricalCrossEntropy
(y_true, y_pred)
lib/costs.py:4
FunctionHinge
(y_true, y_pred)
lib/costs.py:19
FunctionMeanAbsoluteError
(y_true, y_pred)
lib/costs.py:13
FunctionMeanSquaredError
(y_true, y_pred)
lib/costs.py:10
FunctionSquaredHinge
(y_true, y_pred)
lib/costs.py:16
Method__call__
(self, shape, name=None)
lib/inits.py:14
Method__call__
(self, shape, name=None)
lib/inits.py:22
Method__call__
(self, shape, name=None)
lib/inits.py:30
Method__call__
(self, shape, name=None)
lib/inits.py:44
Method__call__
(self, shape, name=None)
lib/inits.py:54
Method__call__
(self, shape, name=None)
lib/inits.py:62
Method__call__
(self, shape, name=None)
lib/inits.py:81
Method__call__
(self, shape, name=None)
lib/inits.py:96
Method__call__
(self, params, grads)
lib/updates.py:54
Method__call__
(self, params, cost)
lib/updates.py:63
Method__call__
(self, params, cost)
lib/updates.py:80
Method__call__
(self, params, cost)
lib/updates.py:102
Method__call__
(self, params, cost)
lib/updates.py:124
Method__call__
(self, params, cost)
lib/updates.py:146
Method__call__
(self, params, cost)
lib/updates.py:177
Method__call__
(self, params, cost)
lib/updates.py:199
Method__call__
(self, params, cost)
lib/updates.py:227
Method__call__
(self, x)
lib/activations.py:9
Method__call__
(self, x)
lib/activations.py:18
Method__call__
(self, x)
lib/activations.py:27
Method__call__
(self, x)
lib/activations.py:41
Method__call__
(self, x)
lib/activations.py:49
Method__call__
(self, x)
lib/activations.py:57
Method__call__
(self, x, leak)
lib/activations.py:67
Method__call__
(self, x)
lib/activations.py:79
Method__call__
(self, x)
lib/activations.py:87
Method__call__
(self, x)
lib/activations.py:95
Method__call__
(self, X)
lib/activations.py:103
Method__call__
(self, X)
lib/activations.py:111
Method__call__
(self, X)
lib/activations.py:119
Method__init__
(self, scale=0.05)
lib/inits.py:11
Method__init__
(self, loc=0., scale=0.05)
lib/inits.py:18
Method__init__
(self, scale=1.1)
lib/inits.py:27
Method__init__
(self)
lib/inits.py:41
Method__init__
(self, c=0.)
lib/inits.py:51
Method__init__
(self, scale=1.)
lib/inits.py:59
Method__init__
(self, scale=0.25)
lib/inits.py:78
Method__init__
(self)
lib/inits.py:93
Method__init__
(self, l1=0., l2=0., maxnorm=0., l2norm=False, frobnorm=False)
lib/updates.py:19
Method__init__
(self, regularizer=Regularizer(), clipnorm=0.)
lib/updates.py:51
Method__init__
(self, lr=0.01, momentum=0.9, *args, **kwargs)
lib/updates.py:76
Method__init__
(self, lr=0.01, momentum=0.9, *args, **kwargs)
lib/updates.py:98
Method__init__
(self, lr=0.001, rho=0.9, epsilon=1e-6, *args, **kwargs)
lib/updates.py:120
Method__init__
(self, lr=0.001, b1=0.9, b2=0.999, e=1e-8, l=1-1e-8, *args, **kwargs)
lib/updates.py:142
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