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Paddle
未验证 提交 dc7ec7de 编写于 作者: A Aurelius84 提交者： GitHub
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cherry-pick fix en doc (#20214) test=develop, test=document_fix (#20335) 

* refine fc/seq_conv/seq_concat en doc test=develop, test=document_fix

* refine seq_pool/reshape/reverse test=develop, test=document_fix (#20233)

* fix en_doc api of one-hot and embedding (#20187)
上级 b9badff4
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Showing with 649 addition and 264 deletion
paddle/fluid/API.spec
浏览文件 @ dc7ec7de
......@@ -123,11 +123,11 @@ paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywo
paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'eaa04fd68661a3af59abd0e19b3b6eda'))
paddle.fluid.initializer.NumpyArrayInitializer ('paddle.fluid.initializer.NumpyArrayInitializer', ('document', '064f134a27c16372967d450f499762ab'))
paddle.fluid.initializer.NumpyArrayInitializer.__init__ (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', 'd4ac047e0d5e6b7b1c5ff6ef7d7cfff5'))
paddle.fluid.one_hot (ArgSpec(args=['input', 'depth', 'allow_out_of_range'], varargs=None, keywords=None, defaults=(False,)), ('document', 'eef66730acc806088f9e8ba90252bda1'))
paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, None)), ('document', '0dc8181f14a33f91fbae9385a9b3d9fd'))
paddle.fluid.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', 'c830c324bdc58e8e023d85eb616c3940'))
paddle.fluid.one_hot (ArgSpec(args=['input', 'depth', 'allow_out_of_range'], varargs=None, keywords=None, defaults=(False,)), ('document', 'e822420dcdc743526ab5caebd89a4b4f'))
paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, None)), ('document', 'e28421f1253a3545d9bfe81a8028ea68'))
paddle.fluid.layers.center_loss (ArgSpec(args=['input', 'label', 'num_classes', 'alpha', 'param_attr', 'update_center'], varargs=None, keywords=None, defaults=(True,)), ('document', '18112442f55b5862bbec8feee841c905'))
paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', 'd8e405486a1e4e189b51d6ee28d67b1e'))
paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', 'c51fcac7a4f5786ca41f27fa60bd22c5'))
paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', '6d3ee14da70adfa36d85c40b18716ef2'))
paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None)), ('document', 'c37d51aad655c8a9f9b045c64717320a'))
paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '83617c165827e030636c80486d5de6f3'))
......@@ -139,10 +139,10 @@ paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label',
paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6263dfdeb6c670fa0922c9cbc8fb1bf4'))
paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', 'bbb9e708bab250359864fefbdf48e9d9'))
paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types', 'seq_length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b02844e0ad4bd713c5fe6802aa13219c'))
paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'padding_start', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, True, None, None, None, None, None)), ('document', '2bf23e7884c380c3b27f2709aa322cb9'))
paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'padding_start', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, True, None, None, None, None, None)), ('document', 'ebddcc5a1073ef065d22b4673e36b1d2'))
paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCHW')), ('document', 'e91c63b8ac8c35982c0ac518537e44bf'))
paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCDHW')), ('document', 'feff9c8ebb4d4d0be5345f9042f57c8e'))
paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', 'e90a93251c52dc4e6fb34fb3991b3f82'))
paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', '5a709f7ef3fdb8fc819d09dc4fbada9a'))
paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'eaa9d0bbd3d4e017c8bc4ecdac483711'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', '7ccaea1b93fe4f7387a6036692986c6b'))
paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCHW')), ('document', '630cae697d46b4b575b15d56cf8be25a'))
......@@ -167,8 +167,8 @@ paddle.fluid.layers.reduce_min (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'
paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'b386471f0476c80c61d8c8672278063d'))
paddle.fluid.layers.reduce_all (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '8ab17ab51f68a6e76302b27f928cedf3'))
paddle.fluid.layers.reduce_any (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '0483ac3b7a99e879ccde583ae8d7a60d'))
paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'f2dfd65b859de9844e7261e7a4503f63'))
paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '1af2e3a887e4f914f9d6650406186ab6'))
paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '227a75392ae194de0504f5c6812dade9'))
paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '34372f58331247749e8b0a1663cf233b'))
paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '39fbc5437be389f6c0c769f82fc1fba2'))
paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', '392dd4bad607fd853f71fec71801044f'))
paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '78cf3a7323d1a7697658242e13f63759'))
......@@ -178,7 +178,7 @@ paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'],
paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', '3720b4a386585094435993deb028b592'))
paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e50940f3ce5a08cc477b72f517491bf3'))
paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'input_length', 'label_length'], varargs=None, keywords=None, defaults=(0, False, None, None)), ('document', 'a5be881ada816e47ea7a6ee4396da357'))
paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'f568714a876425004aca4ea2d4a27701'))
paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'eeb1591cfc854c6ffdac77b376313c44'))
paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8e72db173d4c082e27cb11f31d8c9bfa'))
paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '33134416fc27dd65a767e5f15116ee16'))
paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', '83d4ca6dfb957912807f535756e76992'))
......@@ -191,8 +191,8 @@ paddle.fluid.layers.layer_norm (ArgSpec(args=['input', 'scale', 'shift', 'begin_
paddle.fluid.layers.group_norm (ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None)), ('document', '87dd4b818f102bc1a780e1804c28bd38'))
paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '9461e67095a6fc5d568fb2ce8fef66ff'))
paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax', 'axis'], varargs=None, keywords=None, defaults=(False, -100, True, False, -1)), ('document', '54e1675aa0364f4a78fa72804ec0f413'))
paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'cbe8940643ac80ef75e1abdfbdb09e88'))
paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth', 'allow_out_of_range'], varargs=None, keywords=None, defaults=(False,)), ('document', 'ec4115591be842868c86b2e5334245c6'))
paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'ecb75c1b00c4c76c98b482f633b7a10c'))
paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth', 'allow_out_of_range'], varargs=None, keywords=None, defaults=(False,)), ('document', 'cdf5dc2078f1e20dc61dd0bec7e28a29'))
paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', '98e7927f09ee2270535b29f048e481ec'))
paddle.fluid.layers.reshape (ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'ca73fdc4551c5765c92eb00f24874289'))
paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbac07662a6e22e8e299ced880c7775'))
......@@ -245,7 +245,7 @@ paddle.fluid.layers.sequence_enumerate (ArgSpec(args=['input', 'win_size', 'pad_
paddle.fluid.layers.unique (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=('int32',)), ('document', 'cab0b06e5683875f12f0efc62fa230a9'))
paddle.fluid.layers.unique_with_counts (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=('int32',)), ('document', '1cb59c65b41766116944b8ed1e6ad345'))
paddle.fluid.layers.expand (ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7b97042c3ba55fb5fec6a06308523b73'))
paddle.fluid.layers.sequence_concat (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b992616c1afbd6b0c2a897ac23036381'))
paddle.fluid.layers.sequence_concat (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f47f9d207ac60b6f294087bcb1b64ae8'))
paddle.fluid.layers.scale (ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None)), ('document', '463e4713806e5adaa4d20a41e2218453'))
paddle.fluid.layers.elementwise_add (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '0c9c260e7738165a099f6a76da0b7814'))
paddle.fluid.layers.elementwise_div (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '4701ffd4eb4b7ee19756d3b90532c5f2'))
......@@ -278,7 +278,7 @@ paddle.fluid.layers.sigmoid_cross_entropy_with_logits (ArgSpec(args=['x', 'label
paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '406eee439e41988c8a0304186626a0dd'))
paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '26decdea9376b6b9a0d3432d82ca207b'))
paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f85b263b7b6698d000977529a28f202b'))
paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65c8362e48810b8226e311c5d046db51'))
paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5b32ed21ab89140a8e758002923a0da3'))
paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', '9f303c67538e468a36c5904a0a3aa110'))
paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '18ec2e3afeb90e70c8b73d2b71c40fdb'))
paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'a0b73c21be618cec0281e7903039e5e3'))
......@@ -287,7 +287,7 @@ paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name']
paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e399f9436fed5f7ff480d8532e42c937'))
paddle.fluid.layers.bilinear_tensor_product (ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '45fc3652a8e1aeffbe4eba371c54f756'))
paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2b0e5d5c155ce24bafc38b78cd0b164'))
paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3e60aec040a6f740a130353323580bff'))
paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2c568321feb4d16c41a83df43f95089d'))
paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', 'baa7327ed89df6b7bdd32f9ffdb62f63'))
paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '276a1213dd431228cefa33c3146df34a'))
paddle.fluid.layers.temporal_shift (ArgSpec(args=['x', 'seg_num', 'shift_ratio', 'name'], varargs=None, keywords=None, defaults=(0.25, None)), ('document', '13b1cdcb01f5ffdc26591ff9a2ec4669'))
......@@ -891,8 +891,8 @@ paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs (ArgSpec(args=
paddle.fluid.transpiler.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', '90a40b80e0106f69262cc08b861c3e39'))
paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '0e47f020304e2b824e87ff03475c17cd'))
paddle.fluid.transpiler.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '418c7e8b268e9be4104f2809e654c2f7'))
paddle.fluid.transpiler.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, True)), ('document', '2348247f684bfd5bb9466470f35be064'))
paddle.fluid.transpiler.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd38c5b8b2b2e0bb19bcf1b581a80a7e4'))
paddle.fluid.transpiler.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, True)), ('document', '2be29dc8ecdec9baa7728fb0c7f80e24'))
paddle.fluid.transpiler.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', '2be29dc8ecdec9baa7728fb0c7f80e24'))
paddle.fluid.transpiler.HashName ('paddle.fluid.transpiler.ps_dispatcher.HashName', ('document', '8190ddc66ee412441f5d97fd3f702bdd'))
paddle.fluid.transpiler.HashName.__init__ (ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.transpiler.HashName.dispatch (ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......
python/paddle/fluid/input.py
浏览文件 @ dc7ec7de
......@@ -21,26 +21,80 @@ __all__ = ['one_hot', 'embedding']
def one_hot(input, depth, allow_out_of_range=False):
"""
This layer creates the one-hot representations for input indices.
The operator converts each id in the input to an one-hot vector with a
depth length. The value in the vector dimension corresponding to the id
is 1, and the value in the remaining dimension is 0.
The shape of output Tensor or LoDTensor is generated by appending depth dimension
behind the last dimension of the input shape.
.. code-block:: text
Example 1 (allow_out_of_range=False):
input:
X.shape = [4]
X.data = [1, 1, 3, 0]
depth = 4
output:
Out.shape = [4, 4]
Out.data = [[0., 1., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 1.],
[1., 0., 0., 0.]]
Example 2 (allow_out_of_range=True):
input:
X.shape = [4]
X.data = [1, 1, 5, 0]
depth = 4
allow_out_of_range = True
output:
Out.shape = [4, 4]
Out.data = [[0., 1., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 0.], # This id is 5, which goes beyond depth, so set it all-zeros data.
[1., 0., 0., 0.]]
Example 3 (allow_out_of_range=False):
input:
X.shape = [4]
X.data = [1, 1, 5, 0]
depth = 4
allow_out_of_range = False
output: Throw an exception for Illegal value
The second dimension in X is 5, which is greater than depth.
Allow_out_of_range =False means that does not allow the word id to exceed depth,
so it throws an exception.
Args:
input(Variable): Input indices represent locations, which takes value 1.0
in indices, while all other locations take value 0.
depth(scalar): An interger defining the depth of the one-hot dimension.
input(Variable): Tensor or LoDTensor with shape :math:`[N_1, N_2, ..., N_k]` ,
which contains at least one dimension. The data type is int32 or int64.
depth(int): An integer defining the depth of the one hot dimension. If input
is word id, depth is generally the dictionary size.
allow_out_of_range(bool): A bool value indicating whether the input
indices could be out of range [0, depth). When input indices are
out of range, exceptions is raised if allow_out_of_range is False,
or zero-filling representations is created if it is set True
indices could be out of range :math:`[0, depth)` . When input indices are
out of range, exceptions :code:`Illegal value` is raised if :attr:`allow_out_of_range`
is False, or zero-filling representations is created if it is set True.
Default: False.
Returns:
Variable: The one-hot representations of input.
Variable: The one-hot representations of input. A Tensor or LoDTensor with type float32.
Examples:
.. code-block:: python
import paddle.fluid as fluid
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
one_hot_label = fluid.one_hot(input=label, depth=10)
# Correspond to the first example above, where label.shape is 4 and one_hot_label.shape is [4, 4].
label = fluid.data(name="label", shape=[4], dtype="int64")
one_hot_label = fluid.one_hot(input=label, depth=4)
"""
helper = LayerHelper("one_hot_v2", **locals())
......@@ -75,43 +129,105 @@ def embedding(input,
param_attr=None,
dtype='float32'):
"""
**Embedding Layer**
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
a lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
The operator is used to lookup embeddings vector of ids provided by :attr:`input` .
It automatically constructs a 2D embedding matrix based on the
input :attr:`size` (vocab_size, emb_size) and :attr:`dtype` .
The shape of output Tensor is generated by appending an emb_size dimension to the
last dimension of the input Tensor shape.
**Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` ,
otherwise the program will throw an exception and exit.
.. code-block:: text
Case 1:
input is a Tensor. padding_idx = -1
input.data = [[1, 3], [2, 4], [4, 127]]
input.shape = [3, 2]
Given size = [128, 16]
output is a Tensor:
out.shape = [3, 2, 16]
out.data = [[[0.129435295, 0.244512452, ..., 0.436322452],
[0.345421456, 0.524563927, ..., 0.144534654]],
[[0.345249859, 0.124939536, ..., 0.194353745],
[0.945345345, 0.435394634, ..., 0.435345365]],
[[0.945345345, 0.435394634, ..., 0.435345365],
[0.0, 0.0, ..., 0.0 ]]] # padding data
The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127
It will pad all-zero data when ids is 127.
Case 2:
input is a LoDTensor with 1-level LoD. padding_idx = 0
input.lod = [[2, 3]]
input.data = [[1], [3], [2], [4], [0]]
input.shape = [5, 1]
Given size = [128, 16]
output is a LoDTensor:
out.lod = [[2, 3]]
out.shape = [5, 1, 16]
out.data = [[[0.129435295, 0.244512452, ..., 0.436322452]],
[[0.345421456, 0.524563927, ..., 0.144534654]],
[[0.345249859, 0.124939536, ..., 0.194353745]],
[[0.945345345, 0.435394634, ..., 0.435345365]],
[[0.0, 0.0, ..., 0.0 ]]] # padding data
It will pad all-zero data when ids is 0.
All the input variables are passed in as local variables to the LayerHelper
constructor.
Args:
input(Variable): Input is a Tensor<int64> Variable, which contains the IDs information.
The value of the input IDs should satisfy :math:`0<= id < size[0]`.
size(tuple|list): The shape of the look up table parameter. It should
have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update.
is_distributed(bool): Whether to run lookup table from remote parameter server.
padding_idx(int|long|None): It will output all-zero padding data whenever
lookup encounters :math:`padding\_idx` in Ids. If set :attr:`None`, it makes
no effect to output. If :math:`padding\_idx < 0`, the :math:`padding\_idx`
will automatically be converted to :math:`size[0] + padding\_idx` to use.
Default: None.
param_attr(ParamAttr): Parameters for this layer.
dtype(np.dtype|core.VarDesc.VarType|str): The dtype refers to the data type of output
tensor. It can be float32, float_16, int etc.
input(Variable): A Tensor or LoDTensor with type int64, which contains the id information.
The value of the input id should satisfy :math:`0<= id < size[0]` .
size(tuple|list): The shape of lookup table parameter. It should have two elements which
indicates the size of the dictionary of embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update. This parameter only
affects the performance of the backwards gradient update. It is recommended to set
True because sparse update is faster. But some optimizer does not support sparse update,
such as :ref:`api_fluid_optimizer_AdadeltaOptimizer` , :ref:`api_fluid_optimizer_AdamaxOptimizer` ,
:ref:`api_fluid_optimizer_DecayedAdagradOptimizer` , :ref:`api_fluid_optimizer_FtrlOptimizer` ,
:ref:`api_fluid_optimizer_LambOptimizer` and :ref:`api_fluid_optimizer_LarsMomentumOptimizer` .
In these case, is_sparse must be False. Default: False.
is_distributed(bool): Whether to store the embedding matrix in a distributed manner. Only used
in multi-machine distributed CPU training. Default: False.
padding_idx(int|long|None): padding_idx needs to be in the interval [-vocab_size, vocab_size).
If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted
to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever lookup
encounters :math:`padding\_idx` in id. And the padding data will not be updated while training.
If set None, it makes no effect to output. Default: None.
param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . In addition,
user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter.
The local word vector needs to be transformed into numpy format, and the shape of local word
vector shoud be consistent with :attr:`size` . Then :ref:`api_fluid_initializer_NumpyArrayInitializer`
is used to load custom or pre-trained word vectors. See code example 2 for details.
dtype(str|core.VarDesc.VarType): It refers to the data type of output Tensor.
It must be float32 or float64. Default: float32.
Returns:
Variable: The tensor variable storing the embeddings of the \
supplied inputs.
Variable: Embedding Tensor or LoDTensor mapped by input. The data type is the same as :attr:`dtype` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
# [batch_size, 20] -> [batch_size, 20, 64]
data = fluid.layers.data(name='sequence', shape=[20], dtype='int64')
emb = fluid.embedding(input=data, size=[128, 64])
import numpy as np
data = fluid.data(name='x', shape=[None, 10], dtype='int64')
# exampel 1
emb_1 = fluid.embedding(input=data, size=[128, 64])
# example 2: load custom or pre-trained word vectors
weight_data = np.random.random(size=(128, 100)) # word vectors with numpy format
w_param_attrs = fluid.ParamAttr(
name="emb_weight",
learning_rate=0.5,
initializer=fluid.initializer.NumpyArrayInitializer(weight_data),
trainable=True)
emb_2 = fluid.embedding(input=data, size=(128, 100), param_attr=w_param_attrs, dtype='float32')
"""
helper = LayerHelper('embedding', **locals())
......
python/paddle/fluid/layers/nn.py
浏览文件 @ dc7ec7de
......@@ -238,24 +238,24 @@ def fc(input,
"""
**Fully Connected Layer**
This function creates a fully connected layer in the network. It can take
one or multiple tensors as its inputs(input can be a list of Variable, see
Args in detail). It creates a variable called weights for each input tensor,
This operator creates a fully connected layer in the network. It can take
a Tensor(or LoDTensor) or a list of Tensor(or LoDTensor) as its inputs(see
Args in detail). It creates a variable called weight for each input Tensor,
which represents a fully connected weight matrix from each input unit to
each output unit. The fully connected layer multiplies each input tensor
with its corresponding weight to produce an output Tensor with shape [M, `size`],
where M is batch size. If multiple input tensors are given, the results of
multiple output tensors with shape [M, `size`] will be summed up. If bias_attr
each output unit. The fully connected layer multiplies each input Tensor
with its corresponding weight to produce an output Tensor with shape :math:`[M, size]` ,
where M is batch size. If a list of Tensor is given, the results of
multiple output Tensors with shape :math:`[M, size]` will be summed up. If :attr:`bias_attr`
is not None, a bias variable will be created and added to the output.
Finally, if activation is not None, it will be applied to the output as well.
Finally, if :attr:`act` is not None, it will be applied to the output as well.
When the input is single tensor:
When the input is a single Tensor(or LoDTensor):
.. math::
Out = Act({XW + b})
When the input are multiple tensors:
When the input is a list of Tensor(or LoDTensor):
.. math::
......@@ -268,13 +268,24 @@ def fc(input,
* :math:`W_i`: The i-th weights matrix corresponding i-th input tensor.
* :math:`b`: The bias parameter created by this layer (if needed).
* :math:`Act`: The activation function.
* :math:`Out`: The output tensor.
See below for an example.
* :math:`Out`: The output Tensor.
.. code-block:: text
Given:
Case 1:
Given a single Tensor data_1, and num_flatten_dims = 2:
data_1.data = [[[0.1, 0.2],
[0.3, 0.4]]]
data_1.shape = (1, 2, 2) # 1 is batch_size
out = fluid.layers.fc(input=data_1, size=1, num_flatten_dims=2)
Then output is:
out.data = [[0.83234344], [0.34936576]]
out.shape = (1, 2, 1)
Case 2:
Given a list of Tensor:
data_1.data = [[[0.1, 0.2],
[0.3, 0.4]]]
data_1.shape = (1, 2, 2) # 1 is batch_size
......@@ -289,43 +300,46 @@ def fc(input,
out.shape = (1, 2)
Args:
input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
the input tensor(s) is at least 2.
size(int): The number of output units in this layer.
num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
input (Variable|list of Variable): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` or
a list of Tensor(or LoDTensor). The dimensions of the input Tensor is at least 2 and the data
type should be float32 or float64.
size(int): The number of output units in this layer, which also means the feature size of ouput
Tensor(or LoDTensor).
num_flatten_dims (int): The fc layer can accept an input Tensor with more than
two dimensions. If this happens, the multidimensional tensor will first be flattened
into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
into a 2-D matrix. The parameter :attr:`num_flatten_dims` determines how the input
Tensor is flattened: the first :attr:`num_flatten_dims` (inclusive, index starts from 1)
dimensions will be flatten to form the first dimension of the final matrix (height of
the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
form the second dimension of the final matrix (width of the matrix). For example, suppose
`X` is a 5-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
parameters/weights of this layer.
bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
of this layer. If it is set to False, no bias will be added to the output units.
If it is set to None, the bias is initialized zero. Default: None.
act (str, default None): Activation to be applied to the output of this layer.
name (str, default None): The name of this layer.
Returns:
Variable: The transformation result.
the matrix), and the rest :math:`rank(X) - num\_flatten\_dims` dimensions are flattened to
form the second dimension of the final matrix (width of the matrix). For example, assuming that
X is a 5-dimensional Tensor with a shape [2, 3, 4, 5, 6], and :attr:`num_flatten_dims` = 3.
Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default: 1.
param_attr (ParamAttr): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` .
bias_attr (ParamAttr): To specify the bias parameter property. Default: None, which means the
default bias parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` .
act (str): Activation to be applied to the output of this layer, such as tanh, softmax,
sigmoid, relu. For more information, please refer to :ref:`api_guide_activations_en` . Default: None.
name (str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Returns:
Variable: Tensor or LoDTensor calculated by fc layer. The data type is same with input.
Raises:
ValueError: If rank of the input tensor is less than 2.
ValueError: If dimensions of the input Tensor is less than 2.
Examples:
.. code-block:: python
import paddle.fluid as fluid
# when input is single tensor
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
data = fluid.data(name="data", shape=[-1, 32], dtype="float32")
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
# when input are multiple tensors
data_1 = fluid.layers.data(name="data_1", shape=[32, 32], dtype="float32")
data_2 = fluid.layers.data(name="data_2", shape=[24, 36], dtype="float32")
data_1 = fluid.data(name="data_1", shape=[-1, 32], dtype="float32")
data_2 = fluid.data(name="data_2", shape=[-1, 36], dtype="float32")
fc = fluid.layers.fc(input=[data_1, data_2], size=1000, act="tanh")
"""
helper = LayerHelper("fc", **locals())
......@@ -462,42 +476,110 @@ def embedding(input,
param_attr=None,
dtype='float32'):
"""
**Embedding Layer**
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
a lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
**WARING:** This OP will be deprecated in a future release. This OP requires the
last dimension of Tensor shape must be equal to 1. It is recommended to use
fluid. :ref:`api_fluid_embedding` .
All the input variables are passed in as local variables to the LayerHelper
constructor.
Args:
input(Variable): Input is a Tensor<int64> Variable, which contains the IDs information.
The value of the input IDs should satisfy :math:`0<= id < size[0]`.
size(tuple|list): The shape of the look up table parameter. It should
have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update.
is_distributed(bool): Whether to run lookup table from remote parameter server.
padding_idx(int|long|None): It will output all-zero padding data whenever
lookup encounters :math:`padding\_idx` in Ids. If set :attr:`None`, it makes
no effect to output. If :math:`padding\_idx < 0`, the :math:`padding\_idx`
will automatically be converted to :math:`size[0] + padding\_idx` to use.
Default: None.
param_attr(ParamAttr): Parameters for this layer.
dtype(np.dtype|core.VarDesc.VarType|str): The dtype refers to the data type of output
tensor. It can be float32, float_16, int etc.
The operator is used to lookup embeddings vector of ids provided by :attr:`input` .
It automatically constructs a 2D embedding matrix based on the
input :attr:`size` (vocab_size, emb_size) and :attr:`dtype` .
Returns:
Variable: The tensor variable storing the embeddings of the \
supplied inputs.
This OP requires the last dimension of Tensor shape must be equal to 1. The shape
of output Tensor is generated by replacing the last dimension of the input Tensor shape
with emb_size.
**Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` ,
otherwise the program will throw an exception and exit.
.. code-block:: text
Case 1:
input is a Tensor. padding_idx = -1
input.data = [[[1], [3]], [[2], [4]], [[4], [127]]]
input.shape = [3, 2, 1]
Given size = [128, 16]
output is a Tensor:
out.shape = [3, 2, 16]
out.data = [[[0.129435295, 0.244512452, ..., 0.436322452],
[0.345421456, 0.524563927, ..., 0.144534654]],
[[0.345249859, 0.124939536, ..., 0.194353745],
[0.945345345, 0.435394634, ..., 0.435345365]],
[[0.945345345, 0.435394634, ..., 0.435345365],
[0.0, 0.0, ..., 0.0 ]]] # padding data
The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127
It will pad all-zero data when ids is 127.
Case 2:
input is a LoDTensor with 1-level LoD. padding_idx = 0
input.lod = [[2, 3]]
input.data = [[1], [3], [2], [4], [0]]
input.shape = [5, 1]
Given size = [128, 16]
output is a LoDTensor:
out.lod = [[2, 3]]
out.shape = [5, 16]
out.data = [[0.129435295, 0.244512452, ..., 0.436322452],
[0.345421456, 0.524563927, ..., 0.144534654],
[0.345249859, 0.124939536, ..., 0.194353745],
[0.945345345, 0.435394634, ..., 0.435345365],
[0.0, 0.0, ..., 0.0 ]] # padding data
It will pad all-zero data when ids is 0.
Args:
input(Variable): A Tensor or LoDTensor with type int64, which contains the id information.
The last dimension of Tensor shape must be equal to 1. The value of the input id should
satisfy :math:`0<= id < size[0]` .
size(tuple|list): The shape of lookup table parameter. It should have two elements which
indicates the size of the dictionary of embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update. This parameter only
affects the performance of the backwards gradient update. It is recommended to set
True because sparse update is faster. But some optimizer does not support sparse update,
such as :ref:`api_fluid_optimizer_AdadeltaOptimizer` , :ref:`api_fluid_optimizer_AdamaxOptimizer` ,
:ref:`api_fluid_optimizer_DecayedAdagradOptimizer` , :ref:`api_fluid_optimizer_FtrlOptimizer` ,
:ref:`api_fluid_optimizer_LambOptimizer` and :ref:`api_fluid_optimizer_LarsMomentumOptimizer` .
In these case, is_sparse must be False. Default: False.
is_distributed(bool): Whether to store the embedding matrix in a distributed manner. Only used
in multi-machine distributed CPU training. Default: False.
padding_idx(int|long|None): padding_idx needs to be in the interval [-vocab_size, vocab_size).
If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted
to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever lookup
encounters :math:`padding\_idx` in id. And the padding data will not be updated while training.
If set None, it makes no effect to output. Default: None.
param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . In addition,
user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter.
The local word vector needs to be transformed into numpy format, and the shape of local word
vector shoud be consistent with :attr:`size` . Then :ref:`api_fluid_initializer_NumpyArrayInitializer`
is used to load custom or pre-trained word vectors. See code example 2 for details.
dtype(str|core.VarDesc.VarType): It refers to the data type of output Tensor.
It must be float32 or float64. Default: float32.
Returns:
Variable: Embedding Tensor or LoDTensor mapped by input. The data type is the same as :attr:`dtype` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1)
emb = fluid.layers.embedding(input=data, size=[128, 64])
import numpy as np
data = fluid.data(name='x', shape=[None, 1], dtype='int64')
# exampel 1
emb_1 = fluid.embedding(input=data, size=[128, 64])
# example 2: load custom or pre-trained word vectors
weight_data = np.random.random(size=(128, 100)) # word vectors with numpy format
w_param_attrs = fluid.ParamAttr(
name="emb_weight",
learning_rate=0.5,
initializer=fluid.initializer.NumpyArrayInitializer(weight_data),
trainable=True)
emb_2 = fluid.layers.embedding(input=data, size=(128, 100), param_attr=w_param_attrs, dtype='float32')
"""
helper = LayerHelper('embedding', **locals())
......@@ -2066,24 +2148,25 @@ def sequence_conv(input,
act=None,
name=None):
"""
The sequence_conv receives input sequences with variable length and other convolutional
configuration parameters for the filter and stride to apply the convolution operation.
**Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use conv2d Op.(fluid.layers.** :ref:`api_fluid_layers_conv2d` ).
This operator receives input sequences with variable length and other convolutional
configuration parameters(num_filters, filter_size) to apply the convolution operation.
It fills all-zero padding data on both sides of the sequence by default to ensure that
the output is the same length as the input. You can customize the padding behavior by
configuring the parameter :attr:`padding\_start`.
configuring the parameter :attr:`padding\_start` .
**Warning:** the parameter :attr:`padding` take no effect and will be deprecated in the future.
.. code-block:: text
Here we'll illustrate the details of the padding operation:
Here we will illustrate the details of the padding operation:
For a mini-batch of 2 variable lengths sentences, containing 3, and 1 time-steps:
Assumed input (X) is a [4, M, N] float LoDTensor, and X->lod()[0] = [0, 3, 4].
Besides, for the sake of simplicity, we assume M=1 and N=2.
X = [[a1, a2;
b1, b2;
c1, c2]
[d1, d2]]
Assumed input (X) is a [4, N] float LoDTensor, and for the sake of simplicity, we assume N=2.
input.data = [[1, 1],
[2, 2],
[3, 3],
[4, 4]]
This is to say that input (X) has 4 words and the dimension of each word
representation is 2.
......@@ -2096,25 +2179,36 @@ def sequence_conv(input,
down_pad_len = max(0, filter_size + padding_start - 1) = 1
The output of the input sequence after padding is:
data_aftet_padding = [[0, 0, a1, a2, b1, b2;
a1, a2, b1, b2, c1, c2;
b1, b2, c1, c2, 0, 0 ]
[0, 0, d1, d2, 0, 0 ]]
data_aftet_padding = [[0, 0, 1, 1, 2, 2],
[1, 1, 2, 2, 3, 3],
[2, 2, 3, 3, 0, 0],
[0, 0, 4, 4, 0, 0]]
It will be multiplied by the filter weight to get the final output.
Assume num_filters = 3
output.data = [[ 0.3234, -0.2334, 0.7433],
[ 0.5646, 0.9464, -0.1223],
[-0.1343, 0.5653, 0.4555],
[ 0.9954, -0.1234, -0.1234]]
output.shape = [4, 3] # 3 = num_filters
output.lod = [[0, 3, 4]] # Remain the same
Args:
input (Variable): ${x_comment}
input (Variable): LoDTensor with shape :math:`(M, K)`, where M is the total time-step of mini-batch
and K is hidden_size of input. Only lod_level of 1 is supported. The data type should be float32 or
float64.
num_filters (int): the number of filters.
filter_size (int): the height of filter, the width is hidden size by default.
filter_size (int): the height of filter. Specified filter width is not supported, the width is
hidden_size by default. Default: 3.
filter_stride (int): stride of the filter. Currently only supports :attr:`stride` = 1.
padding (bool): the parameter :attr:`padding` take no effect and will be discarded in the
future. Currently, it will always pad input to make sure the length of the output is
the same as input whether :attr:`padding` is set true or false. Because the length of
input sequence may be shorter than :attr:`filter\_size`, which will cause the convolution
result to not be computed correctly. These padding data will not be trainable or updated
while trainnig.
padding_start (int|None): It is used to indicate the start index for padding the input
while trainnig. Default: True.
padding_start (int): It is used to indicate the start index for padding the input
sequence, which can be negative. The negative number means to pad
:attr:`|padding_start|` time-steps of all-zero data at the beginning of each instance.
The positive number means to skip :attr:`padding_start` time-steps of each instance,
......@@ -2122,23 +2216,18 @@ def sequence_conv(input,
at the end of the sequence to ensure that the output is the same length as the input.
If set None, the same length :math:`\\frac{filter\_size}{2}` of data will be filled
on both sides of the sequence. If set 0, the length of :math:`filter\_size - 1` data
is padded at the end of each input sequence.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of sequence_conv.
If it is set to False, no bias will be added to the output units.
If it is set to None or one attribute of ParamAttr, sequence_conv
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
of sequence_conv. If it is set to None or one attribute of ParamAttr, sequence_conv
will create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
act (str): Activation type, if it is set to None, activation is not appended.
Default: None.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
is padded at the end of each input sequence. Default: None.
bias_attr (ParamAttr): To specify the bias parameter property. Default: None, which means the
default bias parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` .
param_attr (ParamAttr): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` .
act (str): Activation to be applied to the output of this layer, such as tanh, softmax,
sigmoid, relu. For more information, please refer to :ref:`api_guide_activations_en` . Default: None.
name (str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Returns:
Variable: output of sequence_conv
Variable: LoDTensor with the same length as input. The data type is float32 or float64, which is same as input.
Examples:
......@@ -2146,7 +2235,7 @@ def sequence_conv(input,
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10,10], append_batch_size=False, dtype='float32')
x = fluid.data(name='x', shape=[-1, 10], dtype='float32', lod_level=1)
x_conved = fluid.layers.sequence_conv(input=x, num_filters=2, filter_size=3, padding_start=-1)
"""
......@@ -2823,48 +2912,73 @@ def conv3d(input,
def sequence_pool(input, pool_type, is_test=False, pad_value=0.0):
"""
This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied
on top of the input using pool_type mentioned in the parameters.
**Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use pool2d Op.(fluid.layers.** :ref:`api_fluid_layers_pool2d` ).
This operator only supports LoDTensor as input. It will apply specified pooling
operation on the input LoDTensor. It pools features of all time-steps of each
sequence at the last lod_level using :attr:`pool_type` mentioned in the parameters,
such as sum, average, sqrt, etc.
It supports four pool_type:
It supports six pool_type:
- average: :math:`Out[i] = \\frac{\sum_i X_i}{N}`
- sum: :math:`Out[i] = \sum_jX_{ij}`
- sqrt: :math:`Out[i] = \\frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}`
- max: :math:`Out[i] = max(X_i)`
- last: :math:`Out[i] = X_{N_i}`
- first: :math:`Out[i]` = X_0
where :math:`N_i` is the length of i-th input sequence.
.. code-block:: text
x is a 1-level LoDTensor and **pad_value** = 0.0:
x.lod = [[2, 3, 2, 0]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
Case 1:
input is a 1-level LoDTensor and pad_value = 0.0:
input.lod = [[0, 2, 5, 7, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
then output is a Tensor:
out.dim = [4, 1]
with condition len(x.lod[-1]) == out.dims[0]
output is LoDTensor:
out.shape = [4, 1]
with condition out.shape[0] == len(x.lod[-1]) == 4
for different pool_type:
average: out.data = [2, 4, 3, 0.0], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
sum : out.data = [4, 12, 6, 0.0], where 4=1+3, 12=2+4+6, 6=5+1
sqrt : out.data = [2.82, 6.93, 4.24, 0.0], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
max : out.data = [3, 6, 5, 0.0], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
last : out.data = [3, 6, 1, 0.0], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
first : out.data = [1, 2, 5, 0.0], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
average: out.data = [[2.], [4.], [3.], [0.0]], where 2.=(1. + 3.)/2, 4.=(2. + 4. + 6.)/3, 3.=(5. + 1.)/2
sum : out.data = [[4.], [12.], [6.], [0.0]], where 4.=1. + 3., 12.=2. + 4. + 6., 6.=5. + 1.
sqrt : out.data = [[2.82], [6.93], [4.24], [0.0]], where 2.82=(1. + 3.)/sqrt(2), 6.93=(2. + 4. + 6.)/sqrt(3), 4.24=(5. + 1.)/sqrt(2)
max : out.data = [[3.], [6.], [5.], [0.0]], where 3.=max(1., 3.), 6.=max(2., 4., 6.), 5.=max(5., 1.)
last : out.data = [[3.], [6.], [1.], [0.0]], where 3.=last(1., 3.), 6.=last(2., 4., 6.), 1.=last(5., 1.)
first : out.data = [[1.], [2.], [5.], [0.0]], where 1.=first(1., 3.), 2.=first(2., 4., 6.), 5.=first(5., 1.)
and all above 0.0 = **pad_value**.
and all above [0.0] at last of out.data is padding data.
Case 2:
input is a 2-level LoDTensor containing 3 sequences with length info [2, 0, 3],
where 0 means empty sequence.
The first sequence contains 2 subsequence with length info [1, 2];
The last sequence contains 3 subsequence with length info [1, 0, 3].
input.lod = [[0, 2, 2, 5], [0, 1, 3, 4, 4, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
If pool_typ = sum, it will apply pooling on last lod_level [0, 1, 3, 4, 4, 7]. pad_value = 0.0
output is LoDTensor:
out.shape= [5, 1]
out.lod = [[0, 2, 2, 5]]
where out.shape[0] == len(x.lod[-1]) == 5
sum: out.data = [[1.], [5.], [4.], [0.0], [12.]]
where 1.=1., 5.=3. + 2., 4.=4., 0.0=pad_value, 12.=6. + 5. + 1.
Args:
input (variable): The input variable which is a LoDTensor.
pool_type (string): The pooling type of sequence_pool.
It supports average, sum, sqrt and max.
is_test (bool): Used to distinguish training from scoring mode. Default False.
pad_value (float): Used to pad the pooling result for empty input sequence.
input (variable): LoDTensor with lod_level no more than 2. The data type should be float32.
pool_type (str): The pooling type that supports average, sum, sqrt, max, last or first.
is_test (bool): Only works when :attr:`pool_type` is max. If set False, a temporary Tenosr maxIndex is
created to record the index information corresponding to the maximum value, which is used for backward
gradient calculation in the training phase. Default: False.
pad_value (float): Used to pad the pooling result for empty input sequence. Default: 0.0
Returns:
The sequence pooling variable which is a Tensor.
Variable: LoDTensor after pooling with data type float32.
Examples:
......@@ -2872,8 +2986,7 @@ def sequence_pool(input, pool_type, is_test=False, pad_value=0.0):
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x = fluid.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
......@@ -2910,22 +3023,45 @@ def sequence_pool(input, pool_type, is_test=False, pad_value=0.0):
@templatedoc()
def sequence_concat(input, name=None):
"""
${comment}
**Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use concat Op.(fluid.layers.** :ref:`api_fluid_layers_concat` ).
This operator only supports LoDTensor as input. It concatenates the multiple LoDTensor from input by the LoD information,
and outputs the concatenated LoDTensor.
.. code-block:: text
input is a list of LoDTensor:
input = [x1, x2]
where:
x1.lod = [[0, 3, 5]]
x1.data = [[1], [2], [3], [4], [5]]
x1.shape = [5, 1]
x2.lod = [[0, 2, 4]]
x2.data = [[6], [7], [8], [9]]
x2.shape = [4, 1]
and should satisfy: len(x1.lod[0]) == len(x2.lod[0])
output is LoDTensor:
out.lod = [[0, 3+2, 5+4]]
out.data = [[1], [2], [3], [6], [7], [4], [5], [8], [9]]
out.shape = [9, 1]
Args:
input(list): List of Variables to be concatenated.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
input(list of Variable): List of LoDTensor to be concatenated. The length of each LoDTensor should be same.
The data type can be float32, float64 or int64.
name(str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Returns:
Variable: Output variable of the concatenation.
Variable: Output the concatenated LoDTensor. The data type is same as input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10], dtype='float32')
y = fluid.layers.data(name='y', shape=[10], dtype='float32')
x = fluid.data(name='x', shape=[-1, 10], dtype='float32', lod_level=1)
y = fluid.data(name='y', shape=[-1, 10], dtype='float32', lod_level=1)
out = fluid.layers.sequence_concat(input=[x, y])
"""
assert not in_dygraph_mode(), (
......@@ -2939,33 +3075,51 @@ def sequence_concat(input, name=None):
def sequence_first_step(input):
"""
This function gets the first step of sequence.
This operator only supports LoDTensor as input. Given the input LoDTensor, it will
select first time-step feature of each sequence as output.
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[2, 3, 2]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
Case 1:
input is 1-level LoDTensor:
input.lod = [[0, 2, 5, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
output is a LoDTensor:
out.shape = [3, 1]
out.shape[0] == len(x.lod[-1]) == 3
out.data = [[1.], [2.], [5.]], where 1.=first(1., 3.), 2.=first(2., 4., 6.), 5.=first(5., 1.)
then output is a Tensor:
out.dim = [3, 1]
with condition len(x.lod[-1]) == out.dims[0]
out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
Case 2:
input is a 2-level LoDTensor containing 3 sequences with length info [2, 0, 3],
where 0 means empty sequence.
The first sequence contains 2 subsequence with length info [1, 2];
The last sequence contains 3 subsequence with length info [1, 0, 3].
input.lod = [[0, 2, 2, 5], [0, 1, 3, 4, 4, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
It will apply pooling on last lod_level [0, 1, 3, 4, 4, 7]. pad_value = 0.0
output is a LoDTensor:
out.shape= [5, 1]
out.lod = [[0, 2, 2, 5]]
out.shape[0] == len(x.lod[-1]) == 5
out.data = [[1.], [3.], [4.], [0.0], [6.]]
where 1.=first(1.), 3.=first(3., 2.), 4.=first(4.), 0.0 = pad_value, 6.=first(6., 5., 1.)
Args:
input(variable): The input variable which is a LoDTensor.
input(Variable): LoDTensor with lod_level no more than 2. The data type should be float32.
Returns:
The sequence's first step variable which is a Tensor.
Variable: LoDTensor consist of the sequence's first step vector. The data type is float32.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x = fluid.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x)
"""
return sequence_pool(input=input, pool_type="first")
......@@ -2973,33 +3127,52 @@ def sequence_first_step(input):
def sequence_last_step(input):
"""
This function gets the last step of sequence.
This operator only supports LoDTensor as input. Given the input LoDTensor, it will
select last time-step feature of each sequence as output.
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[2, 3, 2]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
Case 1:
input is 1-level LoDTensor:
input.lod = [[0, 2, 5, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
output is a LoDTensor:
out.shape = [3, 1]
out.shape[0] == len(x.lod[-1]) == 3
out.data = [[3.], [6.], [1.]], where 3.=last(1., 3.), 6.=last(2., 4., 6.), 1.=last(5., 1.)
Case 2:
input is a 2-level LoDTensor containing 3 sequences with length info [2, 0, 3],
where 0 means empty sequence.
The first sequence contains 2 subsequence with length info [1, 2];
The last sequence contains 3 subsequence with length info [1, 0, 3].
input.lod = [[0, 2, 2, 5], [0, 1, 3, 4, 4, 7]]
input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
input.shape = [7, 1]
It will apply pooling on last lod_level [0, 1, 3, 4, 4, 7]. pad_value = 0.0
output is a LoDTensor:
out.shape= [5, 1]
out.lod = [[0, 2, 2, 5]]
out.shape[0] == len(x.lod[-1]) == 5
out.data = [[1.], [2.], [4.], [0.0], [1.]]
where 1.=last(1.), 2.=last(3., 2.), 4.=last(4.), 0.0 = pad_value, 1=last(6., 5., 1.)
then output is a Tensor:
out.dim = [3, 1]
with condition len(x.lod[-1]) == out.dims[0]
out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
input(Variable): LoDTensor with lod_level no more than 2. The data type should be float32.
Returns:
The sequence's last step variable which is a Tensor.
Variable: LoDTensor consist of the sequence's last step vector. The data type is float32.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x = fluid.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
x_last_step = fluid.layers.sequence_last_step(input=x)
"""
return sequence_pool(input=input, pool_type="last")
......@@ -6677,51 +6850,47 @@ def warpctc(input,
def sequence_reshape(input, new_dim):
"""
**Sequence Reshape Layer**
**Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use reshape Op.(fluid.layers.** :ref:`api_fluid_layers_reshape` ).
This layer will rearrange the input sequences. The new dimension is set by
user. Length of each sequence is computed according to original length,
original dimension and new dimension. The following example will help to
illustrate the function of this layer:
This operator only supports LoDTensor as input. Given :attr:`new_dim` ,
it will compute new shape according to original length of each sequence,
original dimensions and :attr:`new_dim` . Then it will output a new LoDTensor
containing :attr:`new_dim` . Currently it only supports 1-level LoDTensor.
Please make sure that (original length * original dimensions) can be divided
by the :attr:`new_dim` with no remainder for each sequence.
.. code-block:: text
x is a LoDTensor:
x.lod = [[0, 2, 6]]
x.data = [[1, 2], [3, 4],
input is a LoDTensor:
input.lod = [[0, 2, 6]]
input.data = [[1, 2], [3, 4],
[5, 6], [7, 8],
[9, 10], [11, 12]]
x.dims = [6, 2]
input.shape = [6, 2]
set new_dim = 4
then out is a LoDTensor:
out is a LoDTensor:
out.lod = [[0, 1, 3]]
out.data = [[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]]
out.dims = [3, 4]
out.shape = [3, 4]
Currently, only 1-level LoDTensor is supported and please make sure
(original length * original dimension) can be divided by new dimension with
no remainder for each sequence.
Args:
input (Variable): A 2-D LoDTensor with shape being [N, M] where M for dimension.
input (Variable): 1-level LoDTensor with shape :math:`[M, K]` . The data type should
be int32, int64, float32 or float64.
new_dim (int): New dimension that the input LoDTensor is reshaped to.
Returns:
Variable: Reshaped LoDTensor according to new dimension.
Variable: Reshaped LoDTensor according to new dimension. The data type is same as input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[2, 6], append_batch_size=False, dtype='float32', lod_level=1)
x = fluid.data(name='x', shape=[None, 16], dtype='float32', lod_level=1)
x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=4)
"""
assert not in_dygraph_mode(), (
......@@ -7753,25 +7922,83 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
def one_hot(input, depth, allow_out_of_range=False):
"""
This layer creates the one-hot representations for input indices.
**WARING:** This OP requires the last dimension of Tensor shape must be equal to 1.
This OP will be deprecated in a future release. It is recommended to use fluid. :ref:`api_fluid_one_hot` .
The operator converts each id in the input to an one-hot vector with a
:attr:`depth` length. The value in the vector dimension corresponding to the id
is 1, and the value in the remaining dimension is 0.
The shape of output Tensor or LoDTensor is generated by adding :attr:`depth` dimension
behind the last dimension of the input shape.
.. code-block:: text
Example 1 (allow_out_of_range=False):
input:
X.shape = [4, 1]
X.data = [[1], [1], [3], [0]]
depth = 4
output:
Out.shape = [4, 4]
Out.data = [[0., 1., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 1.],
[1., 0., 0., 0.]]
Example 2 (allow_out_of_range=True):
input:
X.shape = [4, 1]
X.data = [[1], [1], [5], [0]]
depth = 4
allow_out_of_range = True
output:
Out.shape = [4, 4]
Out.data = [[0., 1., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 0.], # This id is 5, which goes beyond depth, so set it all-zeros data.
[1., 0., 0., 0.]]
Example 3 (allow_out_of_range=False):
input:
X.shape = [4, 1]
X.data = [[1], [1], [5], [0]]
depth = 4
allow_out_of_range = False
output: Throw an exception for Illegal value
The second dimension in X is 5, which is greater than depth.
Allow_out_of_range =False means that does not allow the word id to exceed depth,
so it throws an exception.
Args:
input(Variable): Input indices, last dimension must be 1.
depth(scalar): An interger defining the depth of the one-hot dimension.
input(Variable): Tensor or LoDTensor with shape :math:`[N_1, N_2, ..., N_k, 1]` ,
which contains at least one dimension and the last dimension must be 1.
The data type is int32 or int64.
depth(scalar): An integer defining the :attr:`depth` of the one hot dimension. If input
is word id, depth is generally the dictionary size.
allow_out_of_range(bool): A bool value indicating whether the input
indices could be out of range [0, depth). When input indices are
out of range, exceptions is raised if allow_out_of_range is False,
or zero-filling representations is created if it is set True
indices could be out of range :math:`[0, depth)` . When input indices are
out of range, exceptions :code:`Illegal value` is raised if :attr:`allow_out_of_range`
is False, or zero-filling representations is created if it is set True.
Default: False.
Returns:
Variable: The one-hot representations of input.
Variable: The one-hot representations of input. A Tensor or LoDTensor with type float32.
Examples:
.. code-block:: python
import paddle.fluid as fluid
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
one_hot_label = fluid.layers.one_hot(input=label, depth=10)
# Correspond to the first example above, where label.shape is [4, 1] and one_hot_label.shape is [4, 4].
label = fluid.data(name="label", shape=[4, 1], dtype="int64")
one_hot_label = fluid.layers.one_hot(input=label, depth=4)
"""
helper = LayerHelper("one_hot", **locals())
......@@ -13373,20 +13600,46 @@ def space_to_depth(x, blocksize, name=None):
@templatedoc()
def sequence_reverse(x, name=None):
"""
${comment}
**Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use reverse Op.(fluid.layers.** :ref:`api_fluid_layers_reverse` ).
This operator only supports LoDTensor as input. It will reverse each sequence for input LoDTensor.
Currently it only supports 1-level LoDTensor. This operator is very useful when building a
reverse :ref:`api_fluid_layers_DynamicRNN` network.
.. code-block:: text
input(x) is a LoDTensor:
x.lod = [[0, 2, 5]]
x.data = [[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13,14, 15, 16],
[17,18, 19, 20]]
x.shape = [5, 4]
output LoDTensor with same shape and LoD info:
out.lod = [[0, 2, 5]]
out.data = [[5, 6, 7, 8],
[1, 2, 3, 4],
[17,18, 19, 20],
[13,14, 15, 16],
[9, 10, 11, 12]]
out.shape = [5, 4]
Args:
x(${x_type}): ${x_comment}
name(basestring|None): Name of the output.
x(Variable): LoDTensor with 1-level LoD info. Currently it only supports 1-level LoDTensor.
The data type should be float32, float64, int8, int32 or int64.
name(str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Returns:
out(${y_type}): ${y_comment}
Variable: LoDTensor reversed from input. The data type is same with input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[2, 6], dtype='float32')
x = fluid.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
x_reversed = fluid.layers.sequence_reverse(x)
"""
assert not in_dygraph_mode(), (
......@@ -13974,14 +14227,30 @@ def bilinear_tensor_product(x,
@templatedoc()
def get_tensor_from_selected_rows(x, name=None):
"""
${comment}
This operator gets tensor data from input with SelectedRows type, and outputs a LoDTensor.
.. code-block:: text
input x is SelectedRows:
x.rows = [0, 5, 5, 4, 19]
x.height = 20
x.value = [[1, 1] [2, 2] [2, 2] [3, 3] [6, 6]]
Ouput is LoDTensor:
out.shape = [5, 2]
out.data = [[1, 1],
[2, 2],
[2, 2],
[3, 3],
[6, 6]]
Args:
x(${x_type}): ${x_comment}
name(basestring|None): Name of the output.
x(SelectedRows): Input with SelectedRows type. The data type is float32, float64, int32 or int64.
name(str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Returns:
out(${out_type}): ${out_comment}
Variable: LoDTensor transformed from SelectedRows. The data type is same with input.
Examples:
.. code-block:: python
......
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