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How to use do_forward method in localstack

Best Python code snippet using localstack_python

tests.py

Source:tests.py

...74        """75        Test the backprop using finite differences76        :return:77        """78        finite_differences(lambda x : vg.Sum.do_forward(x))79    def test_fd1(self):80        """81        Test the backprop using finite differences82        :return:83        """84        finite_differences(lambda x : vg.Sum.do_forward(vg.Sigmoid.do_forward(x)), input='rand')85    def test_fd2(self):86        """87        Test the backprop using finite differences88        :return:89        """90        finite_differences(input='rand', function=lambda x:91            vg.Sum.do_forward(92                vg.Sigmoid.do_forward(93                    vg.MatrixMultiply.do_forward(x, x)94                )))95    def test_fd3(self):96        """97        Test the backprop using finite differences98        :return:99        """100        def fn(x):101            x = vg.Exp.do_forward(x)102            x = vg.Normalize.do_forward(x)103            return vg.Sum.do_forward(x)104        finite_differences(105            # input=np.asarray([[10.2, 20.4]]),106            input=np.asarray([[0.6931471805599453, 0.0]]),107            # input=np.random.randn(10, 2),108            function=fn)109    def test_mlp(self):110        fd_mlp()111    def testmax(self):112        x = np.asarray([[0., 1.],[4., 5.],[9, 0.]])113        ctx = {}114        vg.RowMax.forward(ctx, x)115        grad = vg.RowMax.backward(ctx, np.asarray([.1, .2, .3]))116        self.assertTrue( (np.asarray([[0.,  .1], [0., .2 ], [.3, 0. ]]) == grad ).all() )117    def testsum(self):118        x = np.asarray([[0., 1.],[4., 0.],[9, 0.]])119        ctx = {}120        vg.RowMax.forward(ctx, x)121        grad = vg.RowSum.backward(ctx, np.arange(3.0) + 0.1)122        self.assertTrue( (np.asarray([[0.1,  0.1], [1.1, 1.1], [2.1, 2.1]]) == grad ).all() )123    def testlogsoftmax(self):124        x = np.asarray([[0., 0.],[2., 0.],[3., 0.]])125        x = vg.TensorNode(x)126        s = np.exp(vg.logsoftmax(x).value).sum(axis=1)127        self.assertTrue( ( (s - 1.0) ** 2. < 1e-10).all() )128    def testlogsoftmax2(self):129        x = np.random.randn(4, 5)130        x = vg.TensorNode(x)131        els = np.exp(vg.logsoftmax(x).value)132        s = vg.softmax(x).value133        self.assertTrue( ((els - s) ** 2. < 1e-7).all() )134    def testdiamond(self):135        a = vg.TensorNode(np.asarray([1.0]))136        b = vg.Id.do_forward(a)137        c1, c2 = vg.Id.do_forward(b), vg.Id.do_forward(b)138        d = c1 + c2139        a.name  = 'a'140        b.name  = 'b'141        c1.name = 'c1'142        c2.name = 'c2'143        d.name  = 'd'144        # a.debug = True145        d.backward()146        self.assertEqual(2.0, float(a.grad))147    def testdoublediamond(self):148        a0 = vg.TensorNode(np.asarray([1.0]))149        a = vg.Id.do_forward(a0)150        b1, b2 = vg.Id.do_forward(a), vg.Id.do_forward(a)151        c1, c2, c3, c4 = vg.Id.do_forward(b1), vg.Id.do_forward(b1), vg.Id.do_forward(b2), vg.Id.do_forward(b2)152        d1 = c1 + c2153        d2 = c3 + c4154        e = d1 + d2155        e.backward()156        self.assertEqual(4.0, float(a.grad))157    def testseqdiamond(self):158        a = vg.TensorNode(np.asarray([1.0]))159        b = vg.Id.do_forward(a)160        c1, c2 = vg.Id.do_forward(b), vg.Id.do_forward(b)161        d = c1 + c2162        e = vg.Id.do_forward(d)163        f = e + a164        g1, g2 = vg.Id.do_forward(f), vg.Id.do_forward(f)165        h = g1 + g2166        h.backward()167        self.assertEqual(2.0, float(f.grad))168        self.assertEqual(2.0, float(e.grad))169        self.assertEqual(2.0, float(c1.grad))170        self.assertEqual(4.0, float(b.grad))...

functions.py

Source:functions.py

...52    :param outputs: Predictions from the model, a distribution over the classes53    :param targets: True class values, given as integers54    :return: A single loss value: the lower the value, the better the outputs match the targets.55    """56    logprobs = Log.do_forward(outputs)57    return logceloss(logprobs, targets)58def logceloss(logprobs, targets):59    """60    Implementation of the cross-entropy loss from logprobabilities61    We separate this from the celoss, because computing the probabilities explicitly (as done there) is numerically62    unstable. It's much more stable to compute the log-probabilities directly, using the log-softmax function.63    :param outputs:64    :param targets:65    :return:66    """67    # The log probability of the correct class, per instance68    per_instance = Select.do_forward(logprobs, indices=targets)69    # The loss sums all these. The higher the better, so we return the negative of this.70    return Sum.do_forward(per_instance) * - 1.071def sigmoid(x):72    """73    Wrap the sigmoid op in a funciton (just for symmetry with the softmax).74    :param x:75    :return:76    """77    return Sigmoid.do_forward(x)78def softmax(x):79    """80    Applies a row-wise softmax to a matrix81    NB: Softmax is almost never computed like this in serious settings. It's much better82        to start from logits and use the logsumexp trick, returning83        `log(softmax(x))`. See the logsoftmax function below.84    :param x: A matrix.85    :return: A matrix of the same size as x, with normalized rows.86    """87    return Normalize.do_forward(Exp.do_forward(x))88def logsoftmax(x):89    """90    Computes the logarithm of the softmax.91    This function uses the "log sum exp trick" to compute the logarithm of the softmax92    in a numerically stable fashion.93    Here is a good explanation: https://gregorygundersen.com/blog/2020/02/09/log-sum-exp/94    :param x: A matrix.95    :return: A matrix of the same size as x, with normalized rows.96    """97    # -- Max over the rows and expand back to the size of x98    xcols = x.value.shape[1]99    xmax = RowMax.do_forward(x)100    xmax = Unsqueeze.do_forward(xmax, dim=1)101    xmax = Expand.do_forward(xmax, repeats=xcols, dim=1)102    assert(xmax.value.shape == x.value.shape), f'{xmax.value.shape}    {x.value.shape}'103    diff = x - xmax104    denominator = RowSum.do_forward( Exp.do_forward(diff) )105    denominator = Log.do_forward(denominator)106    denominator = Unsqueeze.do_forward(denominator, dim=1)107    denominator = Expand.do_forward(denominator, repeats=xcols, dim=1)108    assert(denominator.value.shape == x.value.shape), f'{denominator.value.shape}    {x.value.shape}'109    res = diff - denominator...

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