Python tensorflow.sparse_add() Examples
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Example #1
Source File: sparse_add_op_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testAddSparseDense(self): np.random.seed(1618) # Make it reproducible. n, m = np.random.randint(30, size=2) for dtype in [np.float32, np.float64, np.int64, np.complex64]: for index_dtype in [np.int32, np.int64]: rand_vals_np = np.random.randn(n, m).astype(dtype) dense_np = np.random.randn(n, m).astype(dtype) with self.test_session(use_gpu=False): sparse, unused_nnz = _sparsify(rand_vals_np, index_dtype=index_dtype) s = tf.sparse_add(sparse, tf.constant(dense_np)).eval() self.assertAllEqual(dense_np + rand_vals_np, s) self.assertTrue(s.dtype == dtype) # check commutativity s = tf.sparse_add(tf.constant(dense_np), sparse).eval() self.assertAllEqual(dense_np + rand_vals_np, s) self.assertTrue(s.dtype == dtype)
Example #2
Source File: sparse_add_op_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def _s2d_add_vs_sparse_add(sparsity, n, m, num_iters=50): np.random.seed(1618) with tf.Session(graph=tf.Graph()) as sess: sp_vals = np.random.rand(n, m).astype(np.float32) sp_t, unused_nnz = _sparsify(sp_vals, thresh=sparsity, index_dtype=np.int32) vals = np.random.rand(n, m).astype(np.float32) s2d = tf.add(tf.sparse_tensor_to_dense(sp_t), tf.constant(vals)) sa = tf.sparse_add(sp_t, tf.constant(vals)) timeit.timeit(lambda: sess.run(s2d), number=3) timeit.timeit(lambda: sess.run(sa), number=3) s2d_total = timeit.timeit(lambda: sess.run(s2d), number=num_iters) sa_total = timeit.timeit(lambda: sess.run(sa), number=num_iters) # per-iter latency; secs to millis return s2d_total * 1e3 / num_iters, sa_total * 1e3 / num_iters
Example #3
Source File: model_utils.py From gcnn-survey-paper with Apache License 2.0 | 6 votes |
def sp_compute_adj_att(node_features, adj_matrix_sp): """Self-attention for edges as in GAT with sparse adjacency.""" out_dim = node_features.shape[-1] # Self-attention mechanism a_row = tf.get_variable( initializer=WEIGHT_INIT, dtype=tf.float32, name='selfatt-row', shape=(out_dim, 1)) a_col = tf.get_variable( initializer=WEIGHT_INIT, dtype=tf.float32, name='selfatt-col', shape=(out_dim, 1)) alpha_row = tf.matmul(node_features, a_row) alpha_col = tf.matmul(node_features, a_col) # Compute matrix with self-attention scores using broadcasting alpha = tf.sparse_add(adj_matrix_sp * alpha_row, adj_matrix_sp * tf.transpose(alpha_col, perm=[1, 0])) return alpha
Example #4
Source File: sparse_add_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAddSelfAndNegation(self): with self.test_session(use_gpu=False) as sess: sp_a = self._SparseTensor_3x3() sp_b = self._SparseTensor_3x3(negate=True) sp_sum = tf.sparse_add(sp_a, sp_b, 0.1) sum_out = sess.run(sp_sum) self.assertEqual(sp_sum.shape.get_shape(), [2]) self.assertAllEqual(sum_out.indices, np.empty([0, 2])) self.assertAllEqual(sum_out.values, []) self.assertAllEqual(sum_out.shape, [3, 3])
Example #5
Source File: sparse_add_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testSmallValuesShouldVanish(self): with self.test_session(use_gpu=False) as sess: sp_a = self._SparseTensor_3x3() sp_b = self._SparseTensor_3x3_v2() # sum: # [ 2] # [.1 ] # [ 6 -.2] # two values should vanish: |.1| < .21, and |-.2| < .21 sp_sum = tf.sparse_add(sp_a, sp_b, thresh=0.21) sum_out = sess.run(sp_sum) self.assertEqual(sp_sum.shape.get_shape(), [2]) self.assertAllEqual(sum_out.indices, [[0, 1], [2, 0]]) self.assertAllEqual(sum_out.values, [2, 6]) self.assertAllEqual(sum_out.shape, [3, 3]) # only .1 vanishes sp_sum = tf.sparse_add(sp_a, sp_b, thresh=0.11) sum_out = sess.run(sp_sum) self.assertEqual(sp_sum.shape.get_shape(), [2]) self.assertAllEqual(sum_out.indices, [[0, 1], [2, 0], [2, 1]]) self.assertAllClose(sum_out.values, [2, 6, -.2]) self.assertAllEqual(sum_out.shape, [3, 3])
Example #6
Source File: sparse_add_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testGradients(self): np.random.seed(1618) # Make it reproducible. with self.test_session(use_gpu=False): for n in [10, 31]: for m in [4, 17]: sp_a, nnz_a = self._randomTensor([n, m], np.float32) sp_b, nnz_b = self._randomTensor([n, m], np.float32) sp_sum = tf.sparse_add(sp_a, sp_b) nnz_sum = len(sp_sum.values.eval()) err = tf.test.compute_gradient_error([sp_a.values, sp_b.values], [(nnz_a,), (nnz_b,)], sp_sum.values, (nnz_sum,)) self.assertLess(err, 1e-3)
Example #7
Source File: sparse_add_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def benchmarkSparseAddDense(self): print("SparseAddDense: add with sparse_to_dense vs. sparse_add") print("%nnz \t n \t m \t millis(s2d) \t millis(sparse_add) \t speedup") for sparsity in [0.99, 0.5, 0.01]: for n in [1, 256, 50000]: for m in [100, 1000]: s2d_dt, sa_dt = _s2d_add_vs_sparse_add(sparsity, n, m) print("%.2f \t %d \t %d \t %.4f \t %.4f \t %.2f" % (sparsity, n, m, s2d_dt, sa_dt, s2d_dt / sa_dt))
Example #8
Source File: loss_graphs.py From tensorrec with Apache License 2.0 | 5 votes |
def connect_loss_graph(self, tf_interactions, tf_prediction, **kwargs): error = tf.sparse_add(tf_interactions, -1.0 * tf_prediction) return tf.sqrt(tf.reduce_mean(tf.square(error)))
Example #9
Source File: sparse_add_op_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAddSelf(self): with self.test_session(use_gpu=False) as sess: for sp_a in (self._SparseTensorValue_3x3(), self._SparseTensor_3x3()): for sp_b in (self._SparseTensorValue_3x3(), self._SparseTensor_3x3()): sp_sum = tf.sparse_add(sp_a, sp_b) sum_out = sess.run(sp_sum) self.assertEqual(sp_sum.shape.get_shape(), [2]) self.assertAllEqual( sum_out.indices, [[0, 1], [1, 0], [2, 0], [2, 1]]) self.assertAllEqual(sum_out.values, [2, 4, 6, 8]) self.assertAllEqual(sum_out.shape, [3, 3])
Example #10
Source File: rotation_matrix_builder.py From MedicalDataAugmentationTool with GNU General Public License v3.0 | 5 votes |
def create(self): rotation = tf.eye(self.dim) #rotation = tf.SparseTensor(indices=[[i, i] for i in range(self.dim)], values=[1.0 for _ in range(self.dim)], dense_shape=[self.dim, self.dim]) for plane in self.planes: old_rotation = rotation new_rotation = self.rotation_matrix(plane) #rotation = tf.matmul(old_rotation, tf.sparse_add(tf.zeros([self.dim, self.dim]), new_rotation)) rotation = tf.sparse_tensor_dense_matmul(new_rotation, old_rotation) return rotation
Example #11
Source File: layers.py From GAT with MIT License | 4 votes |
def sp_attn_head(seq, out_sz, adj_mat, activation, nb_nodes, in_drop=0.0, coef_drop=0.0, residual=False): with tf.name_scope('sp_attn'): if in_drop != 0.0: seq = tf.nn.dropout(seq, 1.0 - in_drop) seq_fts = tf.layers.conv1d(seq, out_sz, 1, use_bias=False) # simplest self-attention possible f_1 = tf.layers.conv1d(seq_fts, 1, 1) f_2 = tf.layers.conv1d(seq_fts, 1, 1) f_1 = tf.reshape(f_1, (nb_nodes, 1)) f_2 = tf.reshape(f_2, (nb_nodes, 1)) f_1 = adj_mat*f_1 f_2 = adj_mat * tf.transpose(f_2, [1,0]) logits = tf.sparse_add(f_1, f_2) lrelu = tf.SparseTensor(indices=logits.indices, values=tf.nn.leaky_relu(logits.values), dense_shape=logits.dense_shape) coefs = tf.sparse_softmax(lrelu) if coef_drop != 0.0: coefs = tf.SparseTensor(indices=coefs.indices, values=tf.nn.dropout(coefs.values, 1.0 - coef_drop), dense_shape=coefs.dense_shape) if in_drop != 0.0: seq_fts = tf.nn.dropout(seq_fts, 1.0 - in_drop) # As tf.sparse_tensor_dense_matmul expects its arguments to have rank-2, # here we make an assumption that our input is of batch size 1, and reshape appropriately. # The method will fail in all other cases! coefs = tf.sparse_reshape(coefs, [nb_nodes, nb_nodes]) seq_fts = tf.squeeze(seq_fts) vals = tf.sparse_tensor_dense_matmul(coefs, seq_fts) vals = tf.expand_dims(vals, axis=0) vals.set_shape([1, nb_nodes, out_sz]) ret = tf.contrib.layers.bias_add(vals) # residual connection if residual: if seq.shape[-1] != ret.shape[-1]: ret = ret + conv1d(seq, ret.shape[-1], 1) # activation else: ret = ret + seq return activation(ret) # activation
Example #12
Source File: GAT.py From OpenHINE with MIT License | 4 votes |
def sp_attn_head(seq, out_sz, adj_mat, activation, nb_nodes, in_drop=0.0, coef_drop=0.0, residual=False): with tf.name_scope('sp_attn'): if in_drop != 0.0: seq = tf.nn.dropout(seq, 1.0 - in_drop) seq_fts = tf.layers.conv1d(seq, out_sz, 1, use_bias=False) # simplest self-attention possible f_1 = tf.layers.conv1d(seq_fts, 1, 1) f_2 = tf.layers.conv1d(seq_fts, 1, 1) logits = tf.sparse_add(adj_mat * f_1, adj_mat * tf.transpose(f_2, [0, 2, 1])) lrelu = tf.SparseTensor(indices=logits.indices, values=tf.nn.leaky_relu(logits.values), dense_shape=logits.dense_shape) coefs = tf.sparse_softmax(lrelu) if coef_drop != 0.0: coefs = tf.SparseTensor(indices=coefs.indices, values=tf.nn.dropout( coefs.values, 1.0 - coef_drop), dense_shape=coefs.dense_shape) if in_drop != 0.0: seq_fts = tf.nn.dropout(seq_fts, 1.0 - in_drop) # As tf.sparse_tensor_dense_matmul expects its arguments to have rank-2, # here we make an assumption that our input is of batch size 1, and reshape appropriately. # The method will fail in all other cases! coefs = tf.sparse_reshape(coefs, [nb_nodes, nb_nodes]) seq_fts = tf.squeeze(seq_fts) vals = tf.sparse_tensor_dense_matmul(coefs, seq_fts) vals = tf.expand_dims(vals, axis=0) vals.set_shape([1, nb_nodes, out_sz]) ret = tf.contrib.layers.bias_add(vals) # residual connection if residual: if seq.shape[-1] != ret.shape[-1]: ret = ret + conv1d(seq, ret.shape[-1], 1) # activation else: seq_fts = ret + seq return activation(ret) # activation # final_embed, att_val = layers.SimpleAttLayer(multi_embed, mp_att_size, # time_major=False, # return_alphas=True)
Example #13
Source File: models.py From GP-VAE with MIT License | 4 votes |
def __call__(self, x): mapped = self.net(x) batch_size = mapped.shape.as_list()[0] time_length = mapped.shape.as_list()[1] # Obtain mean and precision matrix components num_dim = len(mapped.shape.as_list()) perm = list(range(num_dim - 2)) + [num_dim - 1, num_dim - 2] mapped_transposed = tf.transpose(mapped, perm=perm) mapped_mean = mapped_transposed[:, :self.z_size] mapped_covar = mapped_transposed[:, self.z_size:] # tf.nn.sigmoid provides more stable performance on Physionet dataset if self.data_type == 'physionet': mapped_covar = tf.nn.sigmoid(mapped_covar) else: mapped_covar = tf.nn.softplus(mapped_covar) mapped_reshaped = tf.reshape(mapped_covar, [batch_size, self.z_size, 2*time_length]) dense_shape = [batch_size, self.z_size, time_length, time_length] idxs_1 = np.repeat(np.arange(batch_size), self.z_size*(2*time_length-1)) idxs_2 = np.tile(np.repeat(np.arange(self.z_size), (2*time_length-1)), batch_size) idxs_3 = np.tile(np.concatenate([np.arange(time_length), np.arange(time_length-1)]), batch_size*self.z_size) idxs_4 = np.tile(np.concatenate([np.arange(time_length), np.arange(1,time_length)]), batch_size*self.z_size) idxs_all = np.stack([idxs_1, idxs_2, idxs_3, idxs_4], axis=1) # ~10x times faster on CPU then on GPU with tf.device('/cpu:0'): # Obtain covariance matrix from precision one mapped_values = tf.reshape(mapped_reshaped[:, :, :-1], [-1]) prec_sparse = tf.sparse.SparseTensor(indices=idxs_all, values=mapped_values, dense_shape=dense_shape) prec_sparse = tf.sparse.reorder(prec_sparse) prec_tril = tf.sparse_add(tf.zeros(prec_sparse.dense_shape, dtype=tf.float32), prec_sparse) eye = tf.eye(num_rows=prec_tril.shape.as_list()[-1], batch_shape=prec_tril.shape.as_list()[:-2]) prec_tril = prec_tril + eye cov_tril = tf.linalg.triangular_solve(matrix=prec_tril, rhs=eye, lower=False) cov_tril = tf.where(tf.math.is_finite(cov_tril), cov_tril, tf.zeros_like(cov_tril)) num_dim = len(cov_tril.shape) perm = list(range(num_dim - 2)) + [num_dim - 1, num_dim - 2] cov_tril_lower = tf.transpose(cov_tril, perm=perm) z_dist = tfd.MultivariateNormalTriL(loc=mapped_mean, scale_tril=cov_tril_lower) return z_dist # Decoders
Example #14
Source File: layers.py From hetsann with Apache License 2.0 | 4 votes |
def sp_attn_head(seq, out_sz, adj_mat, activation, nb_nodes, in_drop=0.0, coef_drop=0.0, residual=False): with tf.name_scope('sp_attn'): if in_drop != 0.0: seq = tf.nn.dropout(seq, 1.0 - in_drop) seq_fts = tf.layers.conv1d(seq, out_sz, 1, use_bias=False) # simplest self-attention possible f_1 = tf.layers.conv1d(seq_fts, 1, 1) f_2 = tf.layers.conv1d(seq_fts, 1, 1) f_1 = tf.reshape(f_1, (nb_nodes, 1)) f_2 = tf.reshape(f_2, (nb_nodes, 1)) f_1 = adj_mat*f_1 f_2 = adj_mat * tf.transpose(f_2, [1,0]) logits = tf.sparse_add(f_1, f_2) lrelu = tf.SparseTensor(indices=logits.indices, values=tf.nn.leaky_relu(logits.values), dense_shape=logits.dense_shape) coefs = tf.sparse_softmax(lrelu) if coef_drop != 0.0: coefs = tf.SparseTensor(indices=coefs.indices, values=tf.nn.dropout(coefs.values, 1.0 - coef_drop), dense_shape=coefs.dense_shape) if in_drop != 0.0: seq_fts = tf.nn.dropout(seq_fts, 1.0 - in_drop) # As tf.sparse_tensor_dense_matmul expects its arguments to have rank-2, # here we make an assumption that our input is of batch size 1, and reshape appropriately. # The method will fail in all other cases! coefs = tf.sparse_reshape(coefs, [nb_nodes, nb_nodes]) seq_fts = tf.squeeze(seq_fts) vals = tf.sparse_tensor_dense_matmul(coefs, seq_fts) vals = tf.expand_dims(vals, axis=0) vals.set_shape([1, nb_nodes, out_sz]) ret = tf.contrib.layers.bias_add(vals) # residual connection if residual: if seq.shape[-1] != ret.shape[-1]: ret = ret + conv1d(seq, ret.shape[-1], 1) # activation else: ret = ret + seq return activation(ret) # activation
Example #15
Source File: layers.py From hetsann with Apache License 2.0 | 4 votes |
def sp_attn_head(seq, out_sz, adj_mat, activation, nb_nodes, in_drop=0.0, coef_drop=0.0, residual=False): with tf.name_scope('sp_attn'): if in_drop != 0.0: seq = tf.nn.dropout(seq, 1.0 - in_drop) seq_fts = tf.layers.conv1d(seq, out_sz, 1, use_bias=False) # simplest self-attention possible f_1 = tf.layers.conv1d(seq_fts, 1, 1) f_2 = tf.layers.conv1d(seq_fts, 1, 1) f_1 = tf.reshape(f_1, (nb_nodes, 1)) f_2 = tf.reshape(f_2, (nb_nodes, 1)) f_1 = adj_mat*f_1 f_2 = adj_mat * tf.transpose(f_2, [1,0]) logits = tf.sparse_add(f_1, f_2) lrelu = tf.SparseTensor(indices=logits.indices, values=tf.nn.leaky_relu(logits.values), dense_shape=logits.dense_shape) coefs = tf.sparse_softmax(lrelu) if coef_drop != 0.0: coefs = tf.SparseTensor(indices=coefs.indices, values=tf.nn.dropout(coefs.values, 1.0 - coef_drop), dense_shape=coefs.dense_shape) if in_drop != 0.0: seq_fts = tf.nn.dropout(seq_fts, 1.0 - in_drop) # As tf.sparse_tensor_dense_matmul expects its arguments to have rank-2, # here we make an assumption that our input is of batch size 1, and reshape appropriately. # The method will fail in all other cases! coefs = tf.sparse_reshape(coefs, [nb_nodes, nb_nodes]) seq_fts = tf.squeeze(seq_fts) vals = tf.sparse_tensor_dense_matmul(coefs, seq_fts) vals = tf.expand_dims(vals, axis=0) vals.set_shape([1, nb_nodes, out_sz]) ret = tf.contrib.layers.bias_add(vals) # residual connection if residual: if seq.shape[-1] != ret.shape[-1]: ret = ret + conv1d(seq, ret.shape[-1], 1) # activation else: ret = ret + seq return activation(ret) # activation
Example #16
Source File: layers.py From hetsann with Apache License 2.0 | 4 votes |
def sp_attn_head(seq, out_sz, adj_mat, activation, nb_nodes, in_drop=0.0, coef_drop=0.0, residual=False): with tf.name_scope('sp_attn'): if in_drop != 0.0: seq = tf.nn.dropout(seq, 1.0 - in_drop) seq_fts = tf.layers.conv1d(seq, out_sz, 1, use_bias=False) # simplest self-attention possible f_1 = tf.layers.conv1d(seq_fts, 1, 1) f_2 = tf.layers.conv1d(seq_fts, 1, 1) f_1 = tf.reshape(f_1, (nb_nodes, 1)) f_2 = tf.reshape(f_2, (nb_nodes, 1)) f_1 = adj_mat*f_1 f_2 = adj_mat * tf.transpose(f_2, [1,0]) logits = tf.sparse_add(f_1, f_2) lrelu = tf.SparseTensor(indices=logits.indices, values=tf.nn.leaky_relu(logits.values), dense_shape=logits.dense_shape) coefs = tf.sparse_softmax(lrelu) if coef_drop != 0.0: coefs = tf.SparseTensor(indices=coefs.indices, values=tf.nn.dropout(coefs.values, 1.0 - coef_drop), dense_shape=coefs.dense_shape) if in_drop != 0.0: seq_fts = tf.nn.dropout(seq_fts, 1.0 - in_drop) # As tf.sparse_tensor_dense_matmul expects its arguments to have rank-2, # here we make an assumption that our input is of batch size 1, and reshape appropriately. # The method will fail in all other cases! coefs = tf.sparse_reshape(coefs, [nb_nodes, nb_nodes]) seq_fts = tf.squeeze(seq_fts) vals = tf.sparse_tensor_dense_matmul(coefs, seq_fts) vals = tf.expand_dims(vals, axis=0) vals.set_shape([1, nb_nodes, out_sz]) ret = tf.contrib.layers.bias_add(vals) # residual connection if residual: if seq.shape[-1] != ret.shape[-1]: ret = ret + conv1d(seq, ret.shape[-1], 1) # activation else: ret = ret + seq return activation(ret) # activation
Example #17
Source File: adversarial_sparse.py From neural-structured-learning with Apache License 2.0 | 4 votes |
def get_loss_vat(inputs, predictions, mask, is_train, model, placeholders, predictions_var_scope): """Computes the virtual adversarial loss for the provided inputs. Args: inputs: A batch of input features, where the batch is the first dimension. predictions: The logits predicted by a model on the provided inputs. mask: A tensor of booleans specifying which samples to apply the virtual adversarial loss to. is_train: A boolean placeholder specifying if this is a training or testing setting. model: The model that generated the logits. placeholders: Placeholders for model encodings. predictions_var_scope: Variable scope for obtaining the predictions. Returns: A float value representing the virtual adversarial loss. """ mask = tf.cast(mask, dtype=tf.float32) r_vadv = generate_virtual_adversarial_perturbation( inputs, predictions, model, placeholders, mask, predictions_var_scope, is_train=is_train) predictions = tf.stop_gradient(predictions) logit_p = predictions new_inputs = tf.sparse_add(inputs, r_vadv) with tf.variable_scope( predictions_var_scope, auxiliary_name_scope=False, reuse=True): encoding_m, _, _ = model.get_encoding_and_params( inputs=new_inputs, is_train=is_train, update_batch_stats=False, **placeholders) logit_m, _, _ = model.get_predictions_and_params( encoding=encoding_m, is_train=is_train, **placeholders) num_non_zero = tf.reduce_sum(mask) loss = kl_divergence_with_logit(logit_p, logit_m, mask) return tf.reduce_sum(loss) / num_non_zero