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checked in a bunch of stuff I hadn't been tracking
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Ian Goodfellow
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Oct 18, 2012
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,24 @@ | ||
| from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix | ||
| import numpy as np | ||
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| class HackDataset(DenseDesignMatrix): | ||
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| def __init__(self, labels_from, X, start, stop): | ||
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| super(HackDataset, self).__init__(X = X, y = labels_from.y) | ||
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| convert_to_one_hot = True | ||
| if convert_to_one_hot: | ||
| if not ( self.y.min() == 0): | ||
| raise AssertionError("Expected y.min == 0 but y.min == "+str(self.y.min())) | ||
| nclass = self.y.max() + 1 | ||
| y = np.zeros((self.y.shape[0], nclass), dtype='float32') | ||
| for i in xrange(self.y.shape[0]): | ||
| y[i,self.y[i]] = 1. | ||
| self.y = y | ||
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| self.X = self.X[start:stop,:] | ||
| assert self.X.shape[0] == stop - start | ||
| self.y = self.y[start:stop,:] | ||
| assert self.y.shape[0] == stop - start | ||
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|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| # like cifar10_N3 but with both dense and convolutional units | ||
| !obj:pylearn2.scripts.train.Train { | ||
| dataset: &data !obj:galatea.datasets.zca_dataset.ZCA_Dataset { | ||
| preprocessed_dataset: !pkl: "/data/lisa/data/cifar10/pylearn2_gcn_whitened/train.pkl", | ||
| preprocessor: !pkl: "/data/lisa/data/cifar10/pylearn2_gcn_whitened/preprocessor.pkl" | ||
| }, | ||
| model: !obj:galatea.dbm.inpaint.super_dbm.SuperDBM { | ||
| batch_size : 2, # 50 failed | ||
| niter: 6, #note: since we have to backprop through the whole thing, this does | ||
| #increase the memory usage | ||
| visible_layer: !obj:galatea.dbm.inpaint.super_dbm.GaussianConvolutionalVisLayer { | ||
| rows: 32, | ||
| cols: 32, | ||
| channels: 3, | ||
| init_beta: 3.7, | ||
| init_mu: 0. | ||
| }, | ||
| hidden_layers: [ | ||
| !obj:galatea.dbm.inpaint.super_dbm.CompositeLayer { | ||
| layer_name: "h0", | ||
| components: [ | ||
| !obj:galatea.dbm.inpaint.super_dbm.DenseMaxPool { | ||
| pool_size : 1, | ||
| detector_layer_dim: 400, | ||
| irange: 0.02, | ||
| init_bias: -1, | ||
| layer_name: 'h0_dense' | ||
| }, | ||
| !obj:galatea.dbm.inpaint.super_dbm.ConvMaxPool { | ||
| border_mode : 'full', | ||
| output_channels: 64, | ||
| kernel_rows: 9, | ||
| kernel_cols: 9, | ||
| pool_rows: 2, | ||
| pool_cols: 2, | ||
| irange: 0.05, | ||
| layer_name: 'h0_conv', | ||
| init_bias: -5. | ||
| } | ||
| ], | ||
| }, | ||
| !obj:galatea.dbm.inpaint.super_dbm.CompositeLayer { | ||
| layer_name: "h1", | ||
| components: [ | ||
| !obj:galatea.dbm.inpaint.super_dbm.DenseMaxPool { | ||
| pool_size : 1, | ||
| detector_layer_dim: 400, | ||
| irange: 0.02, | ||
| init_bias: -1, | ||
| layer_name: 'h1_dense' | ||
| }, | ||
| !obj:galatea.dbm.inpaint.super_dbm.ConvMaxPool { | ||
| border_mode : 'full', | ||
| output_channels: 96, | ||
| kernel_rows: 5, | ||
| kernel_cols: 5, | ||
| pool_rows: 3, | ||
| pool_cols: 3, | ||
| irange: 0.3, | ||
| layer_name: 'h1_conv', | ||
| init_bias: -4.5 | ||
| } | ||
| ], | ||
| inputs_to_components: { 0: [0], 1: [0, 1] } | ||
| }, | ||
| !obj:galatea.dbm.inpaint.super_dbm.CompositeLayer { | ||
| layer_name: "h2", | ||
| components: [ | ||
| !obj:galatea.dbm.inpaint.super_dbm.DenseMaxPool { | ||
| pool_size : 1, | ||
| detector_layer_dim: 400, | ||
| irange: 0.02, | ||
| init_bias: -1, | ||
| layer_name: 'h2_dense' | ||
| }, | ||
| !obj:galatea.dbm.inpaint.super_dbm.ConvMaxPool { | ||
| border_mode : 'full', | ||
| output_channels: 128, | ||
| kernel_rows: 3, | ||
| kernel_cols: 3, | ||
| pool_rows: 2, | ||
| pool_cols: 2, | ||
| irange: 0.3, | ||
| layer_name: 'h2_conv', | ||
| init_bias: -4. | ||
| } | ||
| ], | ||
| inputs_to_components: { 0: [0], 1: [0, 1] } | ||
| } | ||
| ] | ||
| }, | ||
| algorithm: !obj:galatea.dbm.inpaint.inpaint_alg.InpaintAlgorithm { | ||
| batches_per_iter : 10, | ||
| monitoring_batches : 1, | ||
| monitoring_dataset : *data, | ||
| init_alpha : [0.256, 1.28, 2.56, 12.8, 25.6], | ||
| max_iter: 2, | ||
| cost : !obj:galatea.dbm.inpaint.super_inpaint.SuperInpaint { | ||
| both_directions : 1, | ||
| l1_act_targets: [ | ||
| [ [.0, .0], [.06, .0]], | ||
| [ [.0, .0], [.12, .0]], | ||
| [ [.0, .0], [ .16, .0]] | ||
| ], | ||
| l1_act_coeffs: [ | ||
| [ [.0, .0], [1., 0.]], | ||
| [ [.0, .0], [1., 0.]], | ||
| [ [.0, .0], [ .1, 0.]] | ||
| ], | ||
| noise : 1 | ||
| }, | ||
| mask_gen : !obj:galatea.dbm.inpaint.super_inpaint.MaskGen { | ||
| drop_prob: 0.5, | ||
| balance: 0, | ||
| sync_channels: 1 | ||
| } | ||
| }, | ||
| save_path: "${PYLEARN2_TRAIN_FILE_FULL_STEM}.pkl", | ||
| save_freq : 1 | ||
| } | ||
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