def testKLDivergenceLossGrad(self): np.random.seed(1701) layer = core_layers.KLDivergenceLossLayer(name='loss') checker = gradcheck.GradChecker(1e-6) shape = (4, 5) # For the input, we make sure it is not too close to 0 (which would # create numerical issues). input_blob = base.Blob(shape, filler=fillers.RandFiller(min=0.1, max=0.9)) # normalize input blob input_data = input_blob.data() input_data /= input_data.sum(1)[:, np.newaxis] # check index input target_blob = base.Blob(shape[:1], dtype=np.int, filler=fillers.RandIntFiller(high=shape[1])) result = checker.check(layer, [input_blob, target_blob], [], check_indices=[0]) print(result) self.assertTrue(result[0]) # also, check if weight works. self._testWeight(layer, [input_blob, target_blob]) # check full input target_blob = base.Blob(shape, filler=fillers.RandFiller()) # normalize target target_data = target_blob.data() target_data /= target_data.sum(1)[:, np.newaxis] result = checker.check(layer, [input_blob, target_blob], [], check_indices=[0]) print(result) self.assertTrue(result[0]) # also, check if weight works. self._testWeight(layer, [input_blob, target_blob])
def testMultinomialLogisticLossGrad(self): np.random.seed(1701) layer = core_layers.MultinomialLogisticLossLayer(name='loss') checker = gradcheck.GradChecker(1e-6) shape = (10, 5) # check index input input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) target_blob = base.Blob(shape[:1], dtype=np.int, filler=fillers.RandIntFiller(high=shape[1])) result = checker.check(layer, [input_blob, target_blob], [], check_indices=[0]) print(result) self.assertTrue(result[0]) # also, check if weight works. self._testWeight(layer, [input_blob, target_blob]) # check full input target_blob = base.Blob(shape, filler=fillers.RandFiller()) # normalize target target_data = target_blob.data() target_data /= target_data.sum(1)[:, np.newaxis] result = checker.check(layer, [input_blob, target_blob], [], check_indices=[0]) print(result) self.assertTrue(result[0]) # also, check if weight works. self._testWeight(layer, [input_blob, target_blob])
def testGroupConvolutionGrad(self): np.random.seed(1701) output_blob = base.Blob() checker = gradcheck.GradChecker(1e-3) shapes = [(1, 5, 5, 4)] num_kernels = 1 group = 2 params = [(3, 1, 'valid'), (3, 1, 'same'), (3, 1, 'full'), (2, 1, 'valid'), (2, 1, 'full'), (3, 2, 'valid'), (3, 2, 'same'), (3, 2, 'full')] for shape in shapes: for ksize, stride, mode in params: print(ksize, stride, mode, shape) input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) layer = core_layers.GroupConvolutionLayer( name='gconv', ksize=ksize, stride=stride, mode=mode, num_kernels=num_kernels, group=group, filler=fillers.GaussianRandFiller()) result = checker.check(layer, [input_blob], [output_blob]) self.assertEqual(output_blob.data().shape[-1], num_kernels * group) print(result) self.assertTrue(result[0]) # check if we will be able to produce an exception input_blob = base.Blob((1, 5, 5, 3), filler=fillers.GaussianRandFiller()) self.assertRaises(RuntimeError, checker.check, layer, [input_blob], [output_blob])
def testLocalResponseNormalizeLayer(self): np.random.seed(1701) output_blob = base.Blob() checker = gradcheck.GradChecker(1e-6) shapes = [(1,10), (5,10)] alphas = [1.0, 2.0] betas = [0.75, 1.0] for shape in shapes: for alpha in alphas: for beta in betas: input_blob = base.Blob(shape, filler=fillers.RandFiller()) # odd size layer = core_layers.LocalResponseNormalizeLayer( name='normalize', k = 1., alpha=alpha, beta=beta, size=5) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0]) layer = core_layers.LocalResponseNormalizeLayer( name='normalize', k = 2., alpha=alpha, beta=beta, size=5) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0]) # even size layer = core_layers.LocalResponseNormalizeLayer( name='normalize', k = 1., alpha=alpha, beta=beta, size=6) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0])
def testReLUGrad(self): np.random.seed(1701) shapes = [(4, 3), (1, 10), (2, 5, 5, 1), (2, 5, 5, 3)] output_blob = base.Blob() layer = core_layers.ReLULayer(name='relu') checker = gradcheck.GradChecker(1e-5) for shape in shapes: input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0])
def testDropoutGrad(self): np.random.seed(1701) input_blob = base.Blob((4, 3), filler=fillers.GaussianRandFiller()) output_blob = base.Blob() checker = gradcheck.GradChecker(1e-5) layer = core_layers.DropoutLayer(name='dropout', ratio=0.5, debug_freeze=True) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0])
def testSplitGrad(self): np.random.seed(1701) output_blobs = [base.Blob(), base.Blob()] checker = gradcheck.GradChecker(1e-5) shapes = [(5, 4), (5, 1), (1, 5), (1, 5, 5), (1, 5, 5, 3), (1, 5, 5, 1)] for shape in shapes: input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) layer = base.SplitLayer(name='split') result = checker.check(layer, [input_blob], output_blobs) print(result) self.assertTrue(result[0])
def testMeanNormalizeLayer(self): np.random.seed(1701) output_blob = base.Blob() checker = gradcheck.GradChecker(1e-5) shapes = [(1,5,5,1), (1,5,5,3), (5,5), (1,5)] for shape in shapes: input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) layer = core_layers.MeanNormalizeLayer( name='normalize') result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0])
def testPaddingGrad(self): np.random.seed(1701) output_blob = base.Blob() checker = gradcheck.GradChecker(1e-5) shapes = [(1, 5, 5, 1), (1, 5, 5, 3), (1, 4, 3, 1), (1, 4, 3, 3)] pads = [1, 2, 3] for pad in pads: for shape in shapes: input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) layer = core_layers.PaddingLayer(name='padding', pad=pad) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0])
def testSoftmaxGrad(self): np.random.seed(1701) input_blob = base.Blob((10,5), filler=fillers.GaussianRandFiller()) output_blob = base.Blob() layer = core_layers.SoftmaxLayer(name='softmax') checker = gradcheck.GradChecker(1e-5) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0]) # Also, let's check the result pred = input_blob.data() prob = np.exp(pred) / np.exp(pred).sum(1)[:, np.newaxis] np.testing.assert_array_almost_equal( output_blob.data(), prob)
def testAutoencoderLossGrad(self): np.random.seed(1701) shapes = [(4, 3), (1, 10), (4, 3, 2)] layer = core_layers.AutoencoderLossLayer(name='loss', ratio=0.5) checker = gradcheck.GradChecker(1e-5) for shape in shapes: input_blob = base.Blob(shape, filler=fillers.RandFiller(min=0.05, max=0.95)) result = checker.check(layer, [input_blob], []) print(result) self.assertTrue(result[0]) # also, check if weight works. self._testWeight(layer, [input_blob])
def testLogisticLossGrad(self): np.random.seed(1701) layer = core_layers.LogisticLossLayer(name='logistic') checker = gradcheck.GradChecker(1e-6) input_blob = base.Blob((10, 1), filler=fillers.GaussianRandFiller()) target_blob = base.Blob((10, ), dtype=np.int, filler=fillers.RandIntFiller(high=2)) result = checker.check(layer, [input_blob, target_blob], [], check_indices=[0]) print(result) self.assertTrue(result[0]) # also, check if weight works. self._testWeight(layer, [input_blob, target_blob])
def testSquaredLossGrad(self): np.random.seed(1701) shapes = [(4, 3), (1, 10), (4, 3, 2)] layer = core_layers.SquaredLossLayer(name='squared') checker = gradcheck.GradChecker(1e-6) for shape in shapes: input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) target_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) result = checker.check(layer, [input_blob, target_blob], [], check_indices=[0]) print(result) self.assertTrue(result[0]) # also, check if weight works. self._testWeight(layer, [input_blob, target_blob])
def testIm2colGrad(self): np.random.seed(1701) output_blob = base.Blob() checker = gradcheck.GradChecker(1e-4) shapes = [(1, 5, 5, 1), (1, 5, 5, 3), (1, 4, 3, 1), (1, 4, 3, 3)] params = [(2, 1), (2, 2), (3, 1), (3, 2)] for psize, stride in params: for shape in shapes: input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) layer = core_layers.Im2colLayer(name='im2col', psize=psize, stride=stride) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0])
def testConvolutionGrad(self): np.random.seed(1701) output_blob = base.Blob() checker = gradcheck.GradChecker(1e-4) shapes = [(1,5,5,1), (1,5,5,3)] num_kernels = 2 params = [(3,1,'valid'), (3,1,'same'), (3,1,'full'), (2,1,'valid'), (2,1,'full'), (3,2,'valid'), (3,2,'same'), (3,2,'full')] for shape in shapes: for ksize, stride, mode in params: print(ksize, stride, mode, shape) input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) layer = core_layers.ConvolutionLayer( name='conv', ksize=ksize, stride=stride, mode=mode, num_kernels=num_kernels, filler=fillers.GaussianRandFiller()) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0])
def testPoolingGrad(self): np.random.seed(1701) output_blob = base.Blob() checker = gradcheck.GradChecker(1e-4) shapes = [(1, 7, 7, 1), (2, 7, 7, 1), (1, 7, 7, 3), (1, 8, 8, 3), (1, 13, 13, 1), (1, 13, 13, 2)] params = [(3, 2, 'max'), (3, 2, 'ave'), (3, 3, 'max'), (3, 3, 'ave'), (5, 3, 'max'), (5, 3, 'ave'), (5, 5, 'max'), (5, 5, 'ave')] for shape in shapes: for psize, stride, mode in params: print(psize, stride, mode, shape) input_blob = base.Blob(shape, filler=fillers.GaussianRandFiller()) layer = core_layers.PoolingLayer(name='pool', psize=psize, stride=stride, mode=mode) result = checker.check(layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0])
def testInnerproductGrad(self): np.random.seed(1701) input_blob = base.Blob((4, 3), filler=fillers.GaussianRandFiller()) output_blob = base.Blob() checker = gradcheck.GradChecker(1e-5) ip_layer = core_layers.InnerProductLayer( name='ip', num_output=5, bias=True, filler=fillers.GaussianRandFiller(), bias_filler=fillers.GaussianRandFiller(), reg=None) result = checker.check(ip_layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0]) ip_layer = core_layers.InnerProductLayer( name='ip', num_output=5, bias=False, filler=fillers.GaussianRandFiller(), reg=None) result = checker.check(ip_layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0]) ip_layer = core_layers.InnerProductLayer( name='ip', num_output=5, bias=True, filler=fillers.GaussianRandFiller(), bias_filler=fillers.GaussianRandFiller(), reg=regularization.L2Regularizer(weight=0.1)) result = checker.check(ip_layer, [input_blob], [output_blob]) print(result) self.assertTrue(result[0])