コード例 #1
0
def test_softmax_mf_sample_consistent():

    # A test of the Softmax class
    # Verifies that the mean field update is consistent with
    # the sampling function

    # Since a Softmax layer contains only one random variable
    # (with n_classes possible values) the mean field assumption
    # does not impose any restriction so mf_update simply gives
    # the true expected value of h given v.
    # We can thus use mf_update to compute the expected value
    # of a sample of y conditioned on v, and check that samples
    # drawn using the layer's sample method convert to that
    # value.

    rng = np.random.RandomState([2012, 11, 1, 1154])
    theano_rng = MRG_RandomStreams(2012 + 11 + 1 + 1154)
    num_samples = 1000
    tol = .042

    # Make DBM
    num_vis = rng.randint(1, 11)
    n_classes = rng.randint(1, 11)

    v = BinaryVector(num_vis)
    v.set_biases(rng.uniform(-1., 1., (num_vis, )).astype(config.floatX))

    y = Softmax(n_classes=n_classes, layer_name='y', irange=1.)
    y.set_biases(rng.uniform(-1., 1., (n_classes, )).astype(config.floatX))

    dbm = DBM(visible_layer=v, hidden_layers=[y], batch_size=1, niter=50)

    # Randomly pick a v to condition on
    # (Random numbers are generated via dbm.rng)
    layer_to_state = dbm.make_layer_to_state(1)
    v_state = layer_to_state[v]
    y_state = layer_to_state[y]

    # Infer P(y | v) using mean field
    expected_y = y.mf_update(state_below=v.upward_state(v_state))

    expected_y = expected_y[0, :]

    expected_y = expected_y.eval()

    # copy all the states out into a batch size of num_samples
    cause_copy = sharedX(np.zeros((num_samples, ))).dimshuffle(0, 'x')
    v_state = v_state[0, :] + cause_copy
    y_state = y_state[0, :] + cause_copy

    y_samples = y.sample(state_below=v.upward_state(v_state),
                         theano_rng=theano_rng)

    y_samples = function([], y_samples)()

    check_multinomial_samples(y_samples, (num_samples, n_classes), expected_y,
                              tol)
コード例 #2
0
ファイル: test_dbm.py プロジェクト: BloodNg/pylearn2
def test_softmax_mf_sample_consistent():

    # A test of the Softmax class
    # Verifies that the mean field update is consistent with
    # the sampling function

    # Since a Softmax layer contains only one random variable
    # (with n_classes possible values) the mean field assumption
    # does not impose any restriction so mf_update simply gives
    # the true expected value of h given v.
    # We can thus use mf_update to compute the expected value
    # of a sample of y conditioned on v, and check that samples
    # drawn using the layer's sample method convert to that
    # value.

    rng = np.random.RandomState([2012,11,1,1154])
    theano_rng = MRG_RandomStreams(2012+11+1+1154)
    num_samples = 1000
    tol = .042

    # Make DBM
    num_vis = rng.randint(1,11)
    n_classes = rng.randint(1, 11)

    v = BinaryVector(num_vis)
    v.set_biases(rng.uniform(-1., 1., (num_vis,)).astype(config.floatX))

    y = Softmax(
            n_classes = n_classes,
            layer_name = 'y',
            irange = 1.)
    y.set_biases(rng.uniform(-1., 1., (n_classes,)).astype(config.floatX))

    dbm = DBM(visible_layer = v,
            hidden_layers = [y],
            batch_size = 1,
            niter = 50)

    # Randomly pick a v to condition on
    # (Random numbers are generated via dbm.rng)
    layer_to_state = dbm.make_layer_to_state(1)
    v_state = layer_to_state[v]
    y_state = layer_to_state[y]

    # Infer P(y | v) using mean field
    expected_y = y.mf_update(
            state_below = v.upward_state(v_state))

    expected_y = expected_y[0, :]

    expected_y = expected_y.eval()

    # copy all the states out into a batch size of num_samples
    cause_copy = sharedX(np.zeros((num_samples,))).dimshuffle(0,'x')
    v_state = v_state[0,:] + cause_copy
    y_state = y_state[0,:] + cause_copy

    y_samples = y.sample(state_below = v.upward_state(v_state), theano_rng=theano_rng)

    y_samples = function([], y_samples)()

    check_multinomial_samples(y_samples, (num_samples, n_classes), expected_y, tol)