def _get_net(W_std, b_std, filter_shape, is_conv, use_pooling, is_res, padding, phi, strides, width, is_ntk, proj_into_2d, layer_norm, parameterization, use_dropout): fc = partial(stax.Dense, W_std=W_std, b_std=b_std, parameterization=parameterization) conv = partial(stax.Conv, filter_shape=filter_shape, strides=strides, padding=padding, W_std=W_std, b_std=b_std, parameterization=parameterization) affine = conv(width) if is_conv else fc(width) rate = np.onp.random.uniform(0.5, 0.9) dropout = stax.Dropout(rate, mode='train') ave_pool = stax.AvgPool((2, 3), None, 'SAME' if padding == 'SAME' else 'CIRCULAR') ave_pool_or_identity = ave_pool if use_pooling else stax.Identity() dropout_or_identity = dropout if use_dropout else stax.Identity() layer_norm_or_identity = (stax.Identity() if layer_norm is None else stax.LayerNorm(axis=layer_norm)) res_unit = stax.serial(ave_pool_or_identity, phi, dropout_or_identity, affine) if is_res: block = stax.serial(affine, stax.FanOut(2), stax.parallel(stax.Identity(), res_unit), stax.FanInSum(), layer_norm_or_identity) else: block = stax.serial(affine, res_unit, layer_norm_or_identity) if proj_into_2d == 'FLAT': proj_layer = stax.Flatten() elif proj_into_2d == 'POOL': proj_layer = stax.GlobalAvgPool() elif proj_into_2d.startswith('ATTN'): n_heads = int(np.sqrt(width)) n_chan_val = int(np.round(float(width) / n_heads)) fixed = proj_into_2d == 'ATTN_FIXED' proj_layer = stax.serial( stax.GlobalSelfAttention(width, n_chan_key=width, n_chan_val=n_chan_val, n_heads=n_heads, fixed=fixed, W_key_std=W_std, W_value_std=W_std, W_query_std=W_std, W_out_std=1.0, b_std=b_std), stax.Flatten()) else: raise ValueError(proj_into_2d) readout = stax.serial(proj_layer, fc(1 if is_ntk else width)) return stax.serial(block, readout)
def WideResnetnt( block_size, k, num_classes, batchnorm='std'): #, batch_norm=None,layer_norm=None,freezelast=None): """Based off of WideResnet from paper, with or without BatchNorm. (Set config.wrn_block_size=3, config.wrn_widening_f=10 in that case). Uses default weight and bias init.""" parameterization = 'standard' layers_lst = [ stax_nt.Conv(16, (3, 3), padding='SAME', parameterization=parameterization), WideResnetGroupnt(block_size, 16 * k, parameterization=parameterization, batchnorm=batchnorm), WideResnetGroupnt(block_size, 32 * k, (2, 2), parameterization=parameterization, batchnorm=batchnorm), WideResnetGroupnt(block_size, 64 * k, (2, 2), parameterization=parameterization, batchnorm=batchnorm) ] layers_lst += [_batch_norm_internal(batchnorm), stax_nt.Relu()] layers_lst += [ stax_nt.AvgPool((8, 8)), stax_nt.Flatten(), stax_nt.Dense(num_classes, parameterization=parameterization) ] return stax_nt.serial(*layers_lst)
def testPredictOnCPU(self): x_train = random.normal(random.PRNGKey(1), (10, 4, 5, 3)) x_test = random.normal(random.PRNGKey(1), (8, 4, 5, 3)) y_train = random.uniform(random.PRNGKey(1), (10, 7)) _, _, kernel_fn = stax.serial(stax.Conv(1, (3, 3)), stax.Relu(), stax.Flatten(), stax.Dense(1)) for store_on_device in [False, True]: for device_count in [0, 1]: for get in ['ntk', 'nngp', ('nngp', 'ntk'), ('ntk', 'nngp')]: with self.subTest(store_on_device=store_on_device, device_count=device_count, get=get): kernel_fn_batched = batch.batch( kernel_fn, 2, device_count, store_on_device) predictor = predict.gradient_descent_mse_gp( kernel_fn_batched, x_train, y_train, x_test, get, 0., True) gp_inference = predict.gp_inference( kernel_fn_batched, x_train, y_train, x_test, get, 0., True) self.assertAllClose(predictor(None), predictor(np.inf), True) self.assertAllClose(predictor(None), gp_inference, True)
def wide_resnet(block_size, k, num_classes): return stax.serial(stax.Conv(16, (3, 3), padding='SAME'), wide_resnet_group(block_size, int(16 * k)), wide_resnet_group(block_size, int(32 * k), (2, 2)), wide_resnet_group(block_size, int(64 * k), (2, 2)), stax.AvgPool((8, 8)), stax.Flatten(), stax.Dense(num_classes, 1., 0.))
def test_composition_conv(self, avg_pool, same_inputs): rng = random.PRNGKey(0) x1 = random.normal(rng, (3, 5, 5, 3)) x2 = None if same_inputs else random.normal(rng, (4, 5, 5, 3)) Block = stax.serial(stax.Conv(256, (3, 3)), stax.Relu()) if avg_pool: Readout = stax.serial(stax.Conv(256, (3, 3)), stax.GlobalAvgPool(), stax.Dense(10)) else: Readout = stax.serial(stax.Flatten(), stax.Dense(10)) block_ker_fn, readout_ker_fn = Block[2], Readout[2] _, _, composed_ker_fn = stax.serial(Block, Readout) composed_ker_out = composed_ker_fn(x1, x2) ker_out_no_marg = readout_ker_fn(block_ker_fn(x1, x2, diagonal_spatial=False)) ker_out_default = readout_ker_fn(block_ker_fn(x1, x2)) self.assertAllClose(composed_ker_out, ker_out_no_marg) self.assertAllClose(composed_ker_out, ker_out_default) if avg_pool: with self.assertRaises(ValueError): ker_out = readout_ker_fn(block_ker_fn(x1, x2, diagonal_spatial=True)) else: ker_out_marg = readout_ker_fn(block_ker_fn(x1, x2, diagonal_spatial=True)) self.assertAllClose(composed_ker_out, ker_out_marg)
def main(unused_argv): key1, key2, key3 = random.split(random.PRNGKey(1), 3) x1 = random.normal(key1, (2, 8, 8, 3)) x2 = random.normal(key2, (3, 8, 8, 3)) # A vanilla CNN. init_fn, f, _ = stax.serial( stax.Conv(8, (3, 3)), stax.Relu(), stax.Conv(8, (3, 3)), stax.Relu(), stax.Conv(8, (3, 3)), stax.Flatten(), stax.Dense(10) ) _, params = init_fn(key3, x1.shape) kwargs = dict( f=f, trace_axes=(), vmap_axes=0, ) # Default, baseline Jacobian contraction. jacobian_contraction = nt.empirical_ntk_fn( **kwargs, implementation=nt.NtkImplementation.JACOBIAN_CONTRACTION) # (6, 3, 10, 10) full `np.ndarray` test-train NTK ntk_jc = jacobian_contraction(x2, x1, params) # NTK-vector products-based implementation. ntk_vector_products = nt.empirical_ntk_fn( **kwargs, implementation=nt.NtkImplementation.NTK_VECTOR_PRODUCTS) ntk_vp = ntk_vector_products(x2, x1, params) # Structured derivatives-based implementation. structured_derivatives = nt.empirical_ntk_fn( **kwargs, implementation=nt.NtkImplementation.STRUCTURED_DERIVATIVES) ntk_sd = structured_derivatives(x2, x1, params) # Auto-FLOPs-selecting implementation. Doesn't work correctly on CPU/GPU. auto = nt.empirical_ntk_fn( **kwargs, implementation=nt.NtkImplementation.AUTO) ntk_auto = auto(x2, x1, params) # Check that implementations match for ntk1 in [ntk_jc, ntk_vp, ntk_sd, ntk_auto]: for ntk2 in [ntk_jc, ntk_vp, ntk_sd, ntk_auto]: diff = np.max(np.abs(ntk1 - ntk2)) print(f'NTK implementation diff {diff}.') assert diff < (1e-4 if jax.default_backend() != 'tpu' else 0.1), diff print('All NTK implementations match.')
def test_composition_conv(self, avg_pool): rng = random.PRNGKey(0) x1 = random.normal(rng, (5, 10, 10, 3)) x2 = random.normal(rng, (5, 10, 10, 3)) Block = stax.serial(stax.Conv(256, (3, 3)), stax.Relu()) if avg_pool: Readout = stax.serial(stax.GlobalAvgPool(), stax.Dense(10)) marginalization = 'none' else: Readout = stax.serial(stax.Flatten(), stax.Dense(10)) marginalization = 'auto' block_ker_fn, readout_ker_fn = Block[2], Readout[2] _, _, composed_ker_fn = stax.serial(Block, Readout) ker_out = readout_ker_fn( block_ker_fn(x1, marginalization=marginalization)) composed_ker_out = composed_ker_fn(x1) self.assertAllClose(ker_out, composed_ker_out, True) if avg_pool: with self.assertRaises(ValueError): ker_out = readout_ker_fn(block_ker_fn(x1)) ker_out = readout_ker_fn( block_ker_fn(x1, x2, marginalization=marginalization)) composed_ker_out = composed_ker_fn(x1, x2) self.assertAllClose(ker_out, composed_ker_out, True)
def WideResnet(block_size, k, num_classes): return stax.serial( stax.Conv(16, (3, 3), padding='SAME'), ntk_generator.ResnetGroup(block_size, int(16 * k)), ntk_generator.ResnetGroup(block_size, int(32 * k), (2, 2)), ntk_generator.ResnetGroup(block_size, int(64 * k), (2, 2)), stax.Flatten(), stax.Dense(num_classes, 1., 0.))
def _get_net(W_std, b_std, filter_shape, is_conv, use_pooling, is_res, padding, phi, strides, width, is_ntk): fc = partial(stax.Dense, W_std=W_std, b_std=b_std) conv = partial( stax.Conv, filter_shape=filter_shape, strides=strides, padding=padding, W_std=W_std, b_std=b_std) affine = conv(width) if is_conv else fc(width) res_unit = stax.serial((stax.AvgPool( (2, 3), None, 'SAME' if padding == 'SAME' else 'CIRCULAR') if use_pooling else stax.Identity()), phi, affine) if is_res: block = stax.serial(affine, stax.FanOut(2), stax.parallel(stax.Identity(), res_unit), stax.FanInSum()) else: block = stax.serial(affine, res_unit) readout = stax.serial(stax.GlobalAvgPool() if use_pooling else stax.Flatten(), fc(1 if is_ntk else width)) net = stax.serial(block, readout) return net
def test_nested_parallel(self, same_inputs, kernel_type): platform = default_backend() rtol = RTOL if platform != 'tpu' else 0.05 rng = random.PRNGKey(0) (input_key1, input_key2, input_key3, input_key4, mask_key, mc_key) = random.split(rng, 6) x1_1, x2_1 = _get_inputs(input_key1, same_inputs, (BATCH_SIZE, 5)) x1_2, x2_2 = _get_inputs(input_key2, same_inputs, (BATCH_SIZE, 2, 2, 2)) x1_3, x2_3 = _get_inputs(input_key3, same_inputs, (BATCH_SIZE, 2, 2, 3)) x1_4, x2_4 = _get_inputs(input_key4, same_inputs, (BATCH_SIZE, 3, 4)) m1_key, m2_key, m3_key, m4_key = random.split(mask_key, 4) x1_1 = test_utils.mask( x1_1, mask_constant=-1, mask_axis=(1,), key=m1_key, p=0.5) x1_2 = test_utils.mask( x1_2, mask_constant=-1, mask_axis=(2, 3,), key=m2_key, p=0.5) if not same_inputs: x2_3 = test_utils.mask( x2_3, mask_constant=-1, mask_axis=(1, 3,), key=m3_key, p=0.5) x2_4 = test_utils.mask( x2_4, mask_constant=-1, mask_axis=(2,), key=m4_key, p=0.5) x1 = (((x1_1, x1_2), x1_3), x1_4) x2 = (((x2_1, x2_2), x2_3), x2_4) if not same_inputs else None N_in = 2 ** 7 # We only include dropout on non-TPU backends, because it takes large N to # converge on TPU. dropout_or_id = stax.Dropout(0.9) if platform != 'tpu' else stax.Identity() init_fn, apply_fn, kernel_fn = stax.parallel( stax.parallel( stax.parallel(stax.Dense(N_in), stax.serial(stax.Conv(N_in + 1, (2, 2)), stax.Flatten())), stax.serial(stax.Conv(N_in + 2, (2, 2)), dropout_or_id, stax.GlobalAvgPool())), stax.Conv(N_in + 3, (2,))) kernel_fn_empirical = nt.monte_carlo_kernel_fn( init_fn, apply_fn, mc_key, N_SAMPLES, implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION, vmap_axes=(((((0, 0), 0), 0), (((0, 0), 0), 0), {}) if platform == 'tpu' else None) ) test_utils.assert_close_matrices( self, kernel_fn(x1, x2, get=kernel_type, mask_constant=-1), kernel_fn_empirical(x1, x2, get=kernel_type, mask_constant=-1), rtol)
def _build_network(input_shape, network, out_logits, use_dropout): dropout = stax.Dropout(0.9, mode='train') if use_dropout else stax.Identity() if len(input_shape) == 1: assert network == 'FLAT' return stax.serial(stax.Dense(WIDTH, W_std=2.0, b_std=0.5), dropout, stax.Dense(out_logits, W_std=2.0, b_std=0.5)) elif len(input_shape) == 3: if network == POOLING: return stax.serial( stax.Conv(CONVOLUTION_CHANNELS, (2, 2), W_std=2.0, b_std=0.05), stax.GlobalAvgPool(), dropout, stax.Dense(out_logits, W_std=2.0, b_std=0.5)) elif network == FLAT: return stax.serial( stax.Conv(CONVOLUTION_CHANNELS, (2, 2), W_std=2.0, b_std=0.05), stax.Flatten(), dropout, stax.Dense(out_logits, W_std=2.0, b_std=0.5)) elif network == INTERMEDIATE_CONV: return stax.Conv(CONVOLUTION_CHANNELS, (2, 2), W_std=2.0, b_std=0.05) else: raise ValueError( 'Unexpected network type found: {}'.format(network)) else: raise ValueError('Expected flat or image test input.')
def test_hermite(self, same_inputs, degree, get, readout): key = random.PRNGKey(1) key1, key2, key = random.split(key, 3) if degree > 2: width = 10000 n_samples = 5000 test_utils.skip_test(self) else: width = 10000 n_samples = 100 x1 = np.cos(random.normal(key1, [2, 6, 6, 3])) x2 = x1 if same_inputs else np.cos(random.normal(key2, [3, 6, 6, 3])) conv_layers = [ stax.Conv(width, (3, 3), W_std=2., b_std=0.5), stax.LayerNorm(), stax.Hermite(degree), stax.GlobalAvgPool() if readout == 'pool' else stax.Flatten(), stax.Dense(1) if get == 'ntk' else stax.Identity()] init_fn, apply_fn, kernel_fn = stax.serial(*conv_layers) analytic_kernel = kernel_fn(x1, x2, get) mc_kernel_fn = nt.monte_carlo_kernel_fn(init_fn, apply_fn, key, n_samples) mc_kernel = mc_kernel_fn(x1, x2, get) rot = degree / 2. * 1e-2 test_utils.assert_close_matrices(self, mc_kernel, analytic_kernel, rot)
def _build_network(input_shape, network, out_logits): if len(input_shape) == 1: assert network == FLAT return stax.Dense(out_logits, W_std=2.0, b_std=0.5) elif len(input_shape) == 3: if network == POOLING: return stax.serial( stax.Conv(CONVOLUTION_CHANNELS, (3, 3), W_std=2.0, b_std=0.05), stax.GlobalAvgPool(), stax.Dense(out_logits, W_std=2.0, b_std=0.5)) elif network == CONV: return stax.serial( stax.Conv(CONVOLUTION_CHANNELS, (1, 2), W_std=1.5, b_std=0.1), stax.Relu(), stax.Conv(CONVOLUTION_CHANNELS, (3, 2), W_std=2.0, b_std=0.05), ) elif network == FLAT: return stax.serial( stax.Conv(CONVOLUTION_CHANNELS, (3, 3), W_std=2.0, b_std=0.05), stax.Flatten(), stax.Dense(out_logits, W_std=2.0, b_std=0.5)) else: raise ValueError( 'Unexpected network type found: {}'.format(network)) else: raise ValueError('Expected flat or image test input.')
def testPredictOnCPU(self): x_train = random.normal(random.PRNGKey(1), (4, 4, 4, 2)) x_test = random.normal(random.PRNGKey(1), (8, 4, 4, 2)) y_train = random.uniform(random.PRNGKey(1), (4, 2)) _, _, kernel_fn = stax.serial( stax.Conv(1, (3, 3)), stax.Relu(), stax.Flatten(), stax.Dense(1)) for store_on_device in [False, True]: for device_count in [0, 1]: for get in ['ntk', 'nngp', ('nngp', 'ntk'), ('ntk', 'nngp')]: for x in [None, 'x_test']: with self.subTest( store_on_device=store_on_device, device_count=device_count, get=get, x=x): kernel_fn_batched = batch.batch(kernel_fn, 2, device_count, store_on_device) predictor = predict.gradient_descent_mse_ensemble( kernel_fn_batched, x_train, y_train) x = x if x is None else x_test predict_none = predictor(None, x, get, compute_cov=True) predict_inf = predictor(np.inf, x, get, compute_cov=True) self.assertAllClose(predict_none, predict_inf) if x is not None: on_cpu = (not store_on_device or xla_bridge.get_backend().platform == 'cpu') self.assertEqual(on_cpu, utils.is_on_cpu(predict_inf)) self.assertEqual(on_cpu, utils.is_on_cpu(predict_none))
def Resnet(block_size, num_classes): return stax.serial(stax.Conv(64, (3, 3), padding='SAME'), ResnetGroup(block_size, 64), ResnetGroup(block_size, 128, (2, 2)), ResnetGroup(block_size, 256, (2, 2)), ResnetGroup(block_size, 512, (2, 2)), stax.Flatten(), stax.Dense(num_classes, 1., 0.05))
def _test_activation(self, activation_fn, same_inputs, model, get, rbf_gamma=None): if 'conv' in model: test_utils.skip_test(self) key = random.PRNGKey(1) key, split = random.split(key) output_dim = 1024 if get == 'nngp' else 1 b_std = 0.5 W_std = 2.0 if activation_fn[2].__name__ == 'Sin': W_std = 0.9 if activation_fn[2].__name__ == 'Rbf': W_std = 1.0 b_std = 0.0 if model == 'fc': rtol = 0.04 X0_1 = random.normal(key, (4, 2)) X0_2 = None if same_inputs else random.normal(split, (2, 2)) affine = stax.Dense(1024, W_std, b_std) readout = stax.Dense(output_dim) depth = 1 else: rtol = 0.05 X0_1 = random.normal(key, (2, 4, 4, 3)) X0_2 = None if same_inputs else random.normal(split, (4, 4, 4, 3)) affine = stax.Conv(512, (3, 2), W_std=W_std, b_std=b_std, padding='SAME') readout = stax.serial(stax.GlobalAvgPool() if 'pool' in model else stax.Flatten(), stax.Dense(output_dim)) depth = 2 if default_backend() == 'cpu': num_samplings = 200 rtol *= 2 else: num_samplings = (500 if activation_fn[2].__name__ in ('Sin', 'Rbf') else 300) init_fn, apply_fn, kernel_fn = stax.serial( *[affine, activation_fn]*depth, readout) analytic_kernel = kernel_fn(X0_1, X0_2, get) mc_kernel_fn = nt.monte_carlo_kernel_fn( init_fn, apply_fn, split, num_samplings, implementation=2, vmap_axes=0 ) empirical_kernel = mc_kernel_fn(X0_1, X0_2, get) test_utils.assert_close_matrices(self, analytic_kernel, empirical_kernel, rtol) # Check match with explicit RBF if rbf_gamma is not None and get == 'nngp' and model == 'fc': input_dim = X0_1.shape[1] _, _, kernel_fn = self._RBF(rbf_gamma / input_dim) direct_rbf_kernel = kernel_fn(X0_1, X0_2, get) test_utils.assert_close_matrices(self, analytic_kernel, direct_rbf_kernel, rtol)
def _get_inputs_and_model(width=1, n_classes=2): key = random.PRNGKey(1) key, split = random.split(key) x1 = random.normal(key, (8, 4, 3, 2)) x2 = random.normal(split, (4, 4, 3, 2)) init_fun, apply_fun, ker_fun = stax.serial(stax.Conv(width, (3, 3)), stax.Relu(), stax.Flatten(), stax.Dense(n_classes, 2., 0.5)) return x1, x2, init_fun, apply_fun, ker_fun, key
def testPredictOnCPU(self): key1 = stateless_uniform(shape=[2], seed=[1, 1], minval=None, maxval=None, dtype=tf.int32) key2 = stateless_uniform(shape=[2], seed=[1, 1], minval=None, maxval=None, dtype=tf.int32) key3 = stateless_uniform(shape=[2], seed=[1, 1], minval=None, maxval=None, dtype=tf.int32) x_train = np.asarray(normal((4, 4, 4, 2), seed=key1)) x_test = np.asarray(normal((8, 4, 4, 2), seed=key2)) y_train = np.asarray(stateless_uniform(shape=(4, 2), seed=key3)) _, _, kernel_fn = stax.serial(stax.Conv(1, (3, 3)), stax.Relu(), stax.Flatten(), stax.Dense(1)) for store_on_device in [False, True]: for device_count in [0, 1]: for get in ['ntk', 'nngp', ('nngp', 'ntk'), ('ntk', 'nngp')]: for x in [None, 'x_test']: with self.subTest(store_on_device=store_on_device, device_count=device_count, get=get, x=x): kernel_fn_batched = batch.batch( kernel_fn, 2, device_count, store_on_device) predictor = predict.gradient_descent_mse_ensemble( kernel_fn_batched, x_train, y_train) x = x if x is None else x_test predict_none = predictor(None, x, get, compute_cov=True) predict_inf = predictor(np.inf, x, get, compute_cov=True) self.assertAllClose(predict_none, predict_inf) if x is not None: on_cpu = (not store_on_device or xla_bridge.get_backend().platform == 'cpu') self.assertEqual(on_cpu, utils.is_on_cpu(predict_inf)) self.assertEqual(on_cpu, utils.is_on_cpu(predict_none))
def WideResnet(block_size, k, num_classes): return stax.serial( stax.Conv(16, (3, 3), padding='SAME', parameterization='standard'), WideResnetGroup(block_size, int(16 * k)), WideResnetGroup(block_size, int(32 * k), (2, 2)), WideResnetGroup(block_size, int(64 * k), (2, 2)), stax.AvgPool((8, 8), padding='SAME'), stax.Flatten(), stax.Dense(num_classes, 1.0, 0.0, parameterization='standard'), )
def _get_net(W_std, b_std, filter_shape, is_conv, use_pooling, is_res, padding, phi, strides, width, is_ntk, proj_into_2d): fc = partial(stax.Dense, W_std=W_std, b_std=b_std) conv = partial( stax.Conv, filter_shape=filter_shape, strides=strides, padding=padding, W_std=W_std, b_std=b_std) affine = conv(width) if is_conv else fc(width) res_unit = stax.serial((stax.AvgPool( (2, 3), None, 'SAME' if padding == 'SAME' else 'CIRCULAR') if use_pooling else stax.Identity()), phi, affine) if is_res: block = stax.serial(affine, stax.FanOut(2), stax.parallel(stax.Identity(), res_unit), stax.FanInSum()) else: block = stax.serial(affine, res_unit) if proj_into_2d == 'FLAT': proj_layer = stax.Flatten() elif proj_into_2d == 'POOL': proj_layer = stax.GlobalAvgPool() elif proj_into_2d.startswith('ATTN'): n_heads = int(np.sqrt(width)) n_chan_val = int(np.round(float(width) / n_heads)) fixed = proj_into_2d == 'ATTN_FIXED' proj_layer = stax.serial( stax.GlobalSelfAttention( width, n_chan_key=width, n_chan_val=n_chan_val, n_heads=n_heads, fixed=fixed, W_key_std=W_std, W_value_std=W_std, W_query_std=W_std, W_out_std=1.0, b_std=b_std), stax.Flatten()) else: raise ValueError(proj_into_2d) readout = stax.serial(proj_layer, fc(1 if is_ntk else width)) return stax.serial(block, readout)
def WideResnet(block_size, k, num_classes, W_std=1., b_std=0.): return stax.serial( stax.Conv(16, (3, 3), W_std=W_std, b_std=b_std, padding='SAME'), WideResnetGroup(block_size, int(16 * k), W_std=W_std, b_std=b_std), WideResnetGroup(block_size, int(32 * k), (2, 2), W_std=W_std, b_std=b_std), WideResnetGroup(block_size, int(64 * k), (2, 2), W_std=W_std, b_std=b_std), stax.AvgPool((7, 7)), stax.Flatten(), stax.Dense(num_classes, W_std=W_std, b_std=b_std))
def _get_inputs_and_model(width=1, n_classes=2, use_conv=True): key = random.PRNGKey(1) key, split = random.split(key) x1 = random.normal(key, (8, 4, 3, 2)) x2 = random.normal(split, (4, 4, 3, 2)) if not use_conv: x1 = np.reshape(x1, (x1.shape[0], -1)) x2 = np.reshape(x2, (x2.shape[0], -1)) init_fn, apply_fn, kernel_fn = stax.serial( stax.Conv(width, (3, 3)) if use_conv else stax.Dense(width), stax.Relu(), stax.Flatten(), stax.Dense(n_classes, 2., 0.5)) return x1, x2, init_fn, apply_fn, kernel_fn, key
def build_le_net(network_width): """ Construct the LeNet of width network_width with average pooling using neural tangent's stax.""" return stax.serial( stax.Conv(out_chan=6 * network_width, filter_shape=(3, 3), strides=(1, 1), padding='VALID'), stax.Relu(), stax.AvgPool(window_shape=(2, 2), strides=(2, 2)), stax.Conv(out_chan=16 * network_width, filter_shape=(3, 3), strides=(1, 1), padding='VALID'), stax.Relu(), stax.AvgPool(window_shape=(2, 2), strides=(2, 2)), stax.Flatten(), stax.Dense(120 * network_width), stax.Relu(), stax.Dense(84 * network_width), stax.Relu(), stax.Dense(10))
def test_flatten_first(self, same_inputs): key = random.PRNGKey(1) X0_1 = random.normal(key, (5, 4, 3, 2)) X0_2 = None if same_inputs else random.normal(key, (3, 4, 3, 2)) X0_1_flat = np.reshape(X0_1, (X0_1.shape[0], -1)) X0_2_flat = None if same_inputs else np.reshape( X0_2, (X0_2.shape[0], -1)) _, _, fc_flat = stax.serial(stax.Dense(10, 2., 0.5), stax.Erf()) _, _, fc = stax.serial(stax.Flatten(), stax.Dense(10, 2., 0.5), stax.Erf()) K_flat = fc_flat(X0_1_flat, X0_2_flat) K = fc(X0_1, X0_2) self.assertAllClose(K_flat, K, True)
def _MyrtleNetwork(width, depth, W_std=jnp.sqrt(2.0), b_std=0.0): layer_factor = {5: [2, 1, 1], 7: [2, 2, 2], 10: [3, 3, 3]} activation_fn = stax.Relu() layers = [] conv = functools.partial(stax.Conv, W_std=W_std, b_std=b_std, padding="SAME") layers += [conv(width, (3, 3)), activation_fn] * layer_factor[depth][0] layers += [stax.AvgPool((2, 2), strides=(2, 2))] layers += [conv(width, (3, 3)), activation_fn] * layer_factor[depth][1] layers += [stax.AvgPool((2, 2), strides=(2, 2))] layers += [conv(width, (3, 3)), activation_fn] * layer_factor[depth][2] layers += [stax.AvgPool((2, 2), strides=(2, 2))] * 3 layers += [stax.Flatten(), stax.Dense(10, W_std, b_std)] return stax.serial(*layers)
def _get_inputs_and_model(width=1, n_classes=2, use_conv=True): key = stateless_uniform(shape=[2], seed=[1, 1], minval=None, maxval=None, dtype=tf.int32) keys = tf_random_split(key) key = keys[0] split = keys[1] x1 = np.asarray(normal((8, 4, 3, 2), seed=key)) x2 = np.asarray(normal((4, 4, 3, 2), seed=split)) if not use_conv: x1 = np.reshape(x1, (x1.shape[0], -1)) x2 = np.reshape(x2, (x2.shape[0], -1)) init_fn, apply_fn, kernel_fn = stax.serial( stax.Conv(width, (3, 3)) if use_conv else stax.Dense(width), stax.Relu(), stax.Flatten(), stax.Dense(n_classes, 2., 0.5)) return x1, x2, init_fn, apply_fn, kernel_fn, key
def test_elementwise_numerical(self, same_inputs, model, phi, get): if 'conv' in model: test_utils.skip_test(self) key, split = random.split(random.PRNGKey(1)) output_dim = 1 b_std = 0.01 W_std = 1.0 rtol = 2e-3 deg = 25 if get == 'ntk': rtol *= 2 if default_backend() == 'tpu': rtol *= 2 if model == 'fc': X0_1 = random.normal(key, (3, 7)) X0_2 = None if same_inputs else random.normal(split, (5, 7)) affine = stax.Dense(1024, W_std, b_std) readout = stax.Dense(output_dim) depth = 1 else: X0_1 = random.normal(key, (2, 8, 8, 3)) X0_2 = None if same_inputs else random.normal(split, (3, 8, 8, 3)) affine = stax.Conv(1024, (3, 2), W_std=W_std, b_std=b_std, padding='SAME') readout = stax.serial(stax.GlobalAvgPool() if 'pool' in model else stax.Flatten(), stax.Dense(output_dim)) depth = 2 _, _, kernel_fn = stax.serial(*[affine, phi] * depth, readout) analytic_kernel = kernel_fn(X0_1, X0_2, get) fn = lambda x: phi[1]((), x) _, _, kernel_fn = stax.serial( *[affine, stax.ElementwiseNumerical(fn, deg=deg)] * depth, readout) numerical_activation_kernel = kernel_fn(X0_1, X0_2, get) test_utils.assert_close_matrices(self, analytic_kernel, numerical_activation_kernel, rtol)
def CNNStandard(n_channels, L, filter=(3, 3), data='cifar10', gap=True, nonlinearity='relu', parameterization='standard', order=None): if data == 'cifar10': num_classes = 10 if data == 'cifar100': num_classes = 100 if nonlinearity == 'relu': nonlin = Relu elif nonlinearity == 'swish': nonlin = Swish init_fn, f = jax_stax.serial(*[ jax_stax.serial( MyConv(n_channels, filter, parameterization=parameterization, order=order), nonlin, ) for _ in range(L) ]) if gap: init_fn, f = jax_stax.serial((init_fn, f), stax.GlobalAvgPool()[:2], MyDense(num_classes, parameterization=parameterization, order=order)) else: init_fn, f = jax_stax.serial((init_fn, f), stax.Flatten()[:2], MyDense(num_classes, parameterization=parameterization, order=order)) return init_fn, f
def cnn1d(hidden_widths, nonlin, args): layers = [] layers_ker = [] window = (3, ) stride = (1, ) if args != None: window = args for i, ch in enumerate(hidden_widths): layers += [ stax.Conv(ch, window, stride, 'CIRCULAR', b_std=0, parameterization='ntk') ] if nonlin != 'relu': layers_ker += [i] else: layers += [stax.Relu()] layers_ker += [2 * i + 1] layers += [stax.Flatten()] if nonlin != 'relu': layers_ker += [i + 1] else: layers += [stax.Relu()] layers_ker += [2 * i + 3] layers += [stax.Dense(10, parameterization='ntk')] if nonlin != 'relu': layers_ker += [i + 2] else: layers_ker += [2 * i + 4] return layers, layers_ker
def test_fan_in_conv(self, same_inputs, axis, n_branches, get, branch_in, readout, fan_in_mode): test_utils.skip_test(self) if fan_in_mode in ['FanInSum', 'FanInProd']: if axis != 0: raise absltest.SkipTest( '`FanInSum` and `FanInProd()` are skipped when ' 'axis != 0.') axis = None if (fan_in_mode == 'FanInSum' or axis in [0, 1, 2]) and branch_in == 'dense_after_branch_in': raise absltest.SkipTest('`FanInSum` and `FanInConcat(0/1/2)` ' 'require `is_gaussian`.') if ((axis == 3 or fan_in_mode == 'FanInProd') and branch_in == 'dense_before_branch_in'): raise absltest.SkipTest( '`FanInConcat` or `FanInProd` on feature axis ' 'requires a dense layer after concatenation ' 'or Hadamard product.') if fan_in_mode == 'FanInSum': fan_in_layer = stax.FanInSum() elif fan_in_mode == 'FanInProd': fan_in_layer = stax.FanInProd() else: fan_in_layer = stax.FanInConcat(axis) key = random.PRNGKey(1) X0_1 = random.normal(key, (2, 5, 6, 3)) X0_2 = None if same_inputs else random.normal(key, (3, 5, 6, 3)) if default_backend() == 'tpu': width = 2048 n_samples = 1024 tol = 0.02 else: width = 1024 n_samples = 512 tol = 0.01 conv = stax.Conv(out_chan=width, filter_shape=(3, 3), padding='SAME', W_std=1.25, b_std=0.1) input_layers = [conv, stax.FanOut(n_branches)] branches = [] for b in range(n_branches): branch_layers = [FanInTest._get_phi(b)] for i in range(b): multiplier = 1 if axis not in (3, -1) else (1 + 0.25 * i) branch_layers += [ stax.Conv(out_chan=int(width * multiplier), filter_shape=(i + 1, 4 - i), padding='SAME', W_std=1.25 + i, b_std=0.1 + i), FanInTest._get_phi(i) ] if branch_in == 'dense_before_branch_in': branch_layers += [conv] branches += [stax.serial(*branch_layers)] output_layers = [ fan_in_layer, stax.Relu(), stax.GlobalAvgPool() if readout == 'pool' else stax.Flatten() ] if branch_in == 'dense_after_branch_in': output_layers.insert(1, conv) nn = stax.serial(*(input_layers + [stax.parallel(*branches)] + output_layers)) init_fn, apply_fn, kernel_fn = stax.serial( nn, stax.Dense(1 if get == 'ntk' else width, 1.25, 0.5)) kernel_fn_mc = nt.monte_carlo_kernel_fn( init_fn, apply_fn, key, n_samples, device_count=0 if axis in (0, -4) else -1, implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION, vmap_axes=None if axis in (0, -4) else 0, ) exact = kernel_fn(X0_1, X0_2, get=get) empirical = kernel_fn_mc(X0_1, X0_2, get=get) test_utils.assert_close_matrices(self, empirical, exact, tol)