Python DenseDesignMatrix.get_topological_view方法代码示例

# 需要导入模块: from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix [as 别名]
# 或者: from pylearn2.datasets.dense_design_matrix.DenseDesignMatrix import get_topological_view [as 别名]
def test_init_with_X_or_topo():
    # tests that constructing with topo_view works
    # tests that construction with design matrix works
    # tests that conversion from topo_view to design matrix and back works
    # tests that conversion the other way works too
    rng = np.random.RandomState([1, 2, 3])
    topo_view = rng.randn(5, 2, 2, 3)
    d1 = DenseDesignMatrix(topo_view=topo_view)
    X = d1.get_design_matrix()
    d2 = DenseDesignMatrix(X=X, view_converter=d1.view_converter)
    topo_view_2 = d2.get_topological_view()
    assert np.allclose(topo_view, topo_view_2)
    X = rng.randn(*X.shape)
    topo_view_3 = d2.get_topological_view(X)
    X2 = d2.get_design_matrix(topo_view_3)
    assert np.allclose(X, X2)

# 需要导入模块: from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix [as 别名]
# 或者: from pylearn2.datasets.dense_design_matrix.DenseDesignMatrix import get_topological_view [as 别名]
def get_feats_from_cnn(rows, model=None):
    """
    fprop rows using best trained model and returns activations of the
    penultimate layer
    """
    conf = utils.get_config()
    patch_size = conf['patch_size']
    region_size = conf['region_size']
    batch_size = None
    preds = utils.get_predictor(model=model, return_all=True)
    y = np.zeros(len(rows))
    samples = np.zeros(
        (len(rows), region_size, region_size, 1), dtype=np.float32)
    for i, row in enumerate(rows):
        print 'processing %i-th image: %s' % (i, row['image_filename'])
        try:
            samples[i] = utils.get_samples_from_image(row, False)[0]
        except ValueError as e:
            print '{1} Value error: {0}'.format(str(e), row['image_filename'])
        y[i] = utils.is_positive(row)
    ds = DenseDesignMatrix(topo_view=samples)
    pipeline = utils.get_pipeline(
        ds.X_topo_space.shape, patch_size, batch_size)
    pipeline.apply(ds)
    return preds[-2](ds.get_topological_view()), y

# 需要导入模块: from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix [as 别名]
# 或者: from pylearn2.datasets.dense_design_matrix.DenseDesignMatrix import get_topological_view [as 别名]
    print 'compiling theano function'
    f = function([V],feat)

    print 'running theano function'
    feat = f(X2)

    feat_dataset = DenseDesignMatrix(X = feat, view_converter = DefaultViewConverter([1, 1, feat.shape[1]] ) )

    print 'reassembling features'
    ns = 32 - size + 1
    depatchifier = ReassembleGridPatches( orig_shape  = (ns, ns), patch_shape=(1,1) )
    feat_dataset.apply_preprocessor(depatchifier)

    print 'making topological view'
    topo_feat = feat_dataset.get_topological_view()
    assert topo_feat.shape[0] == X.shape[0]

    print 'assembling visualizer'

    n = np.ceil(np.sqrt(model.nhid))

    pv3 = PatchViewer(grid_shape = (X.shape[0], num_filters), patch_shape=(ns,ns), is_color= False)
    pv4 = PatchViewer(grid_shape = (n,n), patch_shape = (size,size), is_color = True, pad = (7,7))
    pv5 = PatchViewer(grid_shape = (1,num_filters), patch_shape = (size,size), is_color = True, pad = (7,7))

    idx = sorted(range(model.nhid), key = lambda l : -topo_feat[:,:,:,l].std() )

    W = model.W.get_value()

    weights_view = dataset.get_weights_view( W.T )

# 需要导入模块: from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix [as 别名]
# 或者: from pylearn2.datasets.dense_design_matrix.DenseDesignMatrix import get_topological_view [as 别名]
from pylearn2.utils import serial

stl10 = serial.load('/data/lisa/data/stl10/stl10_32x32/train.pkl')

batch = stl10.X[24:25,:]

img = stl10.view_converter.design_mat_to_topo_view(batch)[0,...] / 127.5

from pylearn2.gui.patch_viewer import PatchViewer

pv = PatchViewer((27,27),(6,6),pad=(1,1),is_color=True)


pipeline = serial.load('/data/lisa/data/stl10/stl10_patches/preprocessor.pkl')
del pipeline.items[0]
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix, DefaultViewConverter

for row in xrange(27):
    for col in xrange(27):
        patch = img[row:row+6,col:col+6]

        d = DenseDesignMatrix( topo_view = patch.reshape(1,6,6,3), view_converter = DefaultViewConverter((6,6,3)) )

        d.apply_preprocessor(pipeline)

        pv.add_patch(d.get_topological_view()[0,...], rescale = True)
pv.show()

# 需要导入模块: from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix [as 别名]
# 或者: from pylearn2.datasets.dense_design_matrix.DenseDesignMatrix import get_topological_view [as 别名]
def make_majority_vote():

    model_paths = ['convnet_' + str(i+1) + '.pkl' for i in range(10)]
    out_path = 'submission.csv'

    models = []

    for model_path in model_paths:
        print('Loading ' + model_path + '...')
        try:
            with open(model_path, 'rb') as f:
                models.append(pkl.load(f))
        except Exception as e:
            try:
                with gzip.open(model_path, 'rb') as f:
                    models.append(pkl.load(f))
            except Exception as e:
                usage()
                print(model_path + "doesn't seem to be a valid model path, I got this error when trying to load it: ")
                print(e)

    # load the test set
    with open('test_data_for_pylearn2.pkl', 'rb') as f:
        dataset = pkl.load(f)

    dataset = DenseDesignMatrix(X=dataset, view_converter=DefaultViewConverter(shape=[32, 32, 1], axes=['b', 0, 1, 'c']))
    preprocessor = GlobalContrastNormalization(subtract_mean=True, sqrt_bias=0.0, use_std=True)
    preprocessor.apply(dataset)

    predictions = []
    print('Model description:')
    print('')
    print(models[1])
    print('')

    for model in models:

        model.set_batch_size(dataset.X.shape[0])

        X = model.get_input_space().make_batch_theano()
        Y = model.fprop(X) # forward prop the test data

        y = T.argmax(Y, axis=1)

        f = function([X], y)

        x_arg = dataset.get_topological_view()
        y = f(x_arg.astype(X.dtype))

        assert y.ndim == 1
        assert y.shape[0] == dataset.X.shape[0]

        # add one to the results!
        y += 1

        predictions.append(y)

    predictions = np.array(predictions, dtype='int32')

    y = mode(predictions.T, axis=1)[0]
    y = np.array(y, dtype='int32')

    import itertools
    y = list(itertools.chain(*y))

    assert len(y) == dataset.X.shape[0]

    util.write_results(y, out_path)

    print('Wrote predictions to submission.csv.')
    return np.reshape(y, (1, -1))

# 需要导入模块: from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix [as 别名]
# 或者: from pylearn2.datasets.dense_design_matrix.DenseDesignMatrix import get_topological_view [as 别名]
print(len(models))

for model in models:

    print(model)

    model.set_batch_size(dataset.X.shape[0])

    X = model.get_input_space().make_batch_theano()
    Y = model.fprop(X) # forward prop the test data

    y = T.argmax(Y, axis=1)

    f = function([X], y)

    x_arg = dataset.get_topological_view()
    y = f(x_arg.astype(X.dtype))

    assert y.ndim == 1
    assert y.shape[0] == dataset.X.shape[0]

    # add one to the results!
    y += 1

    predictions.append(y)
    print(y)

predictions = np.array(predictions, dtype='int32')

y = mode(predictions.T, axis=1)[0]
y = np.array(y, dtype='int32')