log_softmax_var * self.input_ph, axis=1))# apply regularization to weightsreg = l2_regularizer(self.lam) reg_var =apply_regularization(reg, self.weights)# tensorflow l2 regularization multiply 0.5 to the l2 norm# multiply 2 so that it is back in the same scaleloss = neg_ll +2* reg...
batch_norm =Falseimage_size = (128,128) output_size =2if(len(feature_maps) != len(conv_sizes)orlen(feature_maps) != len(pool_sizes)orlen(feature_maps) != len(repeat_times)):raiseValueError("OMG, inconsistent arguments")# Use ReLUs everywhere and softmax for the final predictionconv_ac...
class VAEEncodeArgMax(VAEEncode): def encode(self, vae, pixels): assert isinstance( vae.first_stage_model, AutoencoderKL ), "ArgMax only supported for AutoencoderKL" original_sample_mode = vae.first_stage_model.regularization.sample vae.first_stage_model.regularization.sample = False ret = ...
in_channels, 3, 1, 1, groups=in_channels) self.bn_frelu = nn.BatchNorm2d(in_channels) def forward(self, x): x1 = self.conv_frelu(x) x1 = self.bn_frelu(x1) x2 = torch.stack([x, x1], dim=0) out, _ = torch.max(x2, dim=0) return out ...
baseline_max_t =0, snr =3.0, inverse_method='dSPM'):sub_corticals = read_sub_corticals_code_file(sub_corticals_codes_file)iflen(sub_corticals) ==0:returnlambda2 =1.0/ snr **2lut = read_freesurfer_lookup_table(FREE_SURFER_HOME)forcondinevents_id.keys(): ...
cost.name ='cost_with_regularization'fromblocks.bricksimportMLP mlp = MLP(activations=[Rectifier(), Softmax()], dims=[784,100,10]).apply(x)fromblocks.initializationimportIsotropicGaussian, Constant input_to_hidden.weights_init = hidden_to_output.weights_init = IsotropicGaussian(0.01) ...