diff --git a/doc/fluid/api_cn/fluid_cn.rst b/doc/fluid/api_cn/fluid_cn.rst index 57789869c6c..0d5b49745ed 100644 --- a/doc/fluid/api_cn/fluid_cn.rst +++ b/doc/fluid/api_cn/fluid_cn.rst @@ -1015,10 +1015,10 @@ feed map为该program提供输入数据。fetch_list提供program训练结束后 .. code-block:: python - data = layers.data(name='X', shape=[1], dtype='float32') - hidden = layers.fc(input=data, size=10) + data = fluid.layers.data(name='X', shape=[1], dtype='float32') + hidden = fluid.layers.fc(input=data, size=10) layers.assign(hidden, out) - loss = layers.mean(out) + loss = fluid.layers.mean(out) adam = fluid.optimizer.Adam() adam.minimize(loss) diff --git a/doc/fluid/api_cn/initializer_cn.rst b/doc/fluid/api_cn/initializer_cn.rst index 9815d43653d..dae42179d9a 100644 --- a/doc/fluid/api_cn/initializer_cn.rst +++ b/doc/fluid/api_cn/initializer_cn.rst @@ -125,7 +125,7 @@ init_on_cpu .. code-block:: python with init_on_cpu(): - step = layers.create_global_var() + step = fluid.layers.create_global_var() diff --git a/doc/fluid/api_cn/layers_cn.rst b/doc/fluid/api_cn/layers_cn.rst index 3591c06cd7e..2290024f773 100644 --- a/doc/fluid/api_cn/layers_cn.rst +++ b/doc/fluid/api_cn/layers_cn.rst @@ -117,7 +117,7 @@ array_write tmp = fluid.layers.zeros(shape=[10], dtype='int32') i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) - arr = layers.array_write(tmp, i=i) + arr = fluid.layers.array_write(tmp, i=i) @@ -704,15 +704,15 @@ While .. code-block:: python - d0 = layers.data("d0", shape=[10], dtype='float32') - data_array = layers.array_write(x=d0, i=i) - array_len = layers.fill_constant(shape=[1],dtype='int64', value=3) + d0 = fluid.layers.data("d0", shape=[10], dtype='float32') + data_array = fluid.layers.array_write(x=d0, i=i) + array_len = fluid.layers.fill_constant(shape=[1],dtype='int64', value=3) - cond = layers.less_than(x=i, y=array_len) - while_op = layers.While(cond=cond) + cond = fluid.layers.less_than(x=i, y=array_len) + while_op = fluid.layers.While(cond=cond) with while_op.block(): - d = layers.array_read(array=data_array, i=i) - i = layers.increment(x=i, in_place=True) + d = fluid.layers.array_read(array=data_array, i=i) + i = fluid.layers.increment(x=i, in_place=True) layers.array_write(result, i=i, array=d) layers.less_than(x=i, y=array_len, cond=cond) @@ -1761,13 +1761,13 @@ beam_search # 假设 `probs` 包含计算神经元所得的预测结果 # `pre_ids` 和 `pre_scores` 为beam_search之前时间步的输出 - topk_scores, topk_indices = layers.topk(probs, k=beam_size) - accu_scores = layers.elementwise_add( + topk_scores, topk_indices = fluid.layers.topk(probs, k=beam_size) + accu_scores = fluid.layers.elementwise_add( x=layers.log(x=topk_scores)), y=layers.reshape( pre_scores, shape=[-1]), axis=0) - selected_ids, selected_scores = layers.beam_search( + selected_ids, selected_scores = fluid.layers.beam_search( pre_ids=pre_ids, pre_scores=pre_scores, ids=topk_indices, @@ -1816,7 +1816,7 @@ beam_search_decode # 假设 `ids` 和 `scores` 为 LodTensorArray变量，它们保留了 # 选择出的所有时间步的id和score - finished_ids, finished_scores = layers.beam_search_decode( + finished_ids, finished_scores = fluid.layers.beam_search_decode( ids, scores, beam_size=5, end_id=0) @@ -2536,7 +2536,7 @@ crf_decoding .. code-block:: python - crf_decode = layers.crf_decoding( + crf_decode = fluid.layers.crf_decoding( input=hidden, param_attr=ParamAttr(name="crfw")) @@ -3982,7 +3982,7 @@ gaussian_random算子。 .. code-block:: python - out = layers.gaussian_random(shape=[20, 30]) + out = fluid.layers.gaussian_random(shape=[20, 30]) @@ -4020,9 +4020,9 @@ gaussian_random_batch_size_like .. code-block:: python - input = layers.data(name="input", shape=[13, 11], dtype='float32') + input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32') - out = layers.gaussian_random_batch_size_like( + out = fluid.layers.gaussian_random_batch_size_like( input, shape=[-1, 11], mean=1.0, std=2.0) @@ -4786,9 +4786,9 @@ label_smooth .. code-block:: python - label = layers.data(name="label", shape=[1], dtype="float32") - one_hot_label = layers.one_hot(input=label, depth=10) - smooth_label = layers.label_smooth( + label = fluid.layers.data(name="label", shape=[1], dtype="float32") + one_hot_label = fluid.layers.one_hot(input=label, depth=10) + smooth_label = fluid.layers.label_smooth( label=one_hot_label, epsilon=0.1, dtype="float32") @@ -5033,9 +5033,9 @@ lod_reset .. code-block:: python - x = layers.data(name='x', shape=[10]) - y = layers.data(name='y', shape=[10, 20], lod_level=2) - out = layers.lod_reset(x=x, y=y) + x = fluid.layers.data(name='x', shape=[10]) + y = fluid.layers.data(name='y', shape=[10, 20], lod_level=2) + out = fluid.layers.lod_reset(x=x, y=y) @@ -5413,10 +5413,10 @@ sigmoid的计算公式为： :math:`sigmoid(x) = 1 / (1 + e^{-x})` 。 input_size = 100 hidden_size = 150 num_layers = 1 - init_hidden1 = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0, stop_grad=False) - init_cell1 = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0, stop_grad=False) + init_hidden1 = fluid.layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0, stop_grad=False) + init_cell1 = fluid.layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0, stop_grad=False) - rnn_out, last_h, last_c = layers.lstm( input, init_h, init_c, max_len, dropout_prob, input_size, hidden_size, num_layers) + rnn_out, last_h, last_c = fluid.layers.lstm( input, init_h, init_c, max_len, dropout_prob, input_size, hidden_size, num_layers) @@ -5912,18 +5912,18 @@ nce if i == label_word: continue - emb = layers.embedding(input=words[i], size=[dict_size, 32], + emb = fluid.layers.embedding(input=words[i], size=[dict_size, 32], param_attr='emb.w', is_sparse=True) embs.append(emb) - embs = layers.concat(input=embs, axis=1) - loss = layers.nce(input=embs, label=words[label_word], + embs = fluid.layers.concat(input=embs, axis=1) + loss = fluid.layers.nce(input=embs, label=words[label_word], num_total_classes=dict_size, param_attr='nce.w', bias_attr='nce.b') #使用custom distribution dist = fluid.layers.assign(input=np.array([0.05,0.5,0.1,0.3,0.05]).astype("float32")) - loss = layers.nce(input=embs, label=words[label_word], + loss = fluid.layers.nce(input=embs, label=words[label_word], num_total_classes=5, param_attr='nce.w', bias_attr='nce.b', num_neg_samples=3, @@ -5960,8 +5960,8 @@ one_hot .. code-block:: python - label = layers.data(name="label", shape=[1], dtype="float32") - one_hot_label = layers.one_hot(input=label, depth=10) + label = fluid.layers.data(name="label", shape=[1], dtype="float32") + one_hot_label = fluid.layers.one_hot(input=label, depth=10) @@ -7316,13 +7316,13 @@ sampling_id算子。用于从输入的多项分布中对id进行采样的图层 .. code-block:: python - x = layers.data( + x = fluid.layers.data( name="X", shape=[13, 11], dtype='float32', append_batch_size=False) - out = layers.sampling_id(x) + out = fluid.layers.sampling_id(x) @@ -7631,7 +7631,7 @@ sequence_expand x = fluid.layers.data(name='x', shape=[10], dtype='float32') y = fluid.layers.data(name='y', shape=[10, 20], dtype='float32', lod_level=1) - out = layers.sequence_expand(x=x, y=y, ref_level=0) + out = fluid.layers.sequence_expand(x=x, y=y, ref_level=0) @@ -7701,7 +7701,7 @@ Sequence Expand As Layer x = fluid.layers.data(name='x', shape=[10], dtype='float32') y = fluid.layers.data(name='y', shape=[10, 20], dtype='float32', lod_level=1) - out = layers.sequence_expand_as(x=x, y=y) + out = fluid.layers.sequence_expand_as(x=x, y=y) @@ -8354,9 +8354,9 @@ shape算子 .. code-block:: python - input = layers.data( + input = fluid.layers.data( name="input", shape=[3, 100, 100], dtype="float32") - out = layers.shape(input) + out = fluid.layers.shape(input) @@ -8645,10 +8645,10 @@ slice算子。 ends = [3, 3, 4] axes = [0, 1, 2] - input = layers.data( + input = fluid.layers.data( name="input", shape=[3, 4, 5, 6], dtype='float32') - out = layers.slice(input, axes=axes, starts=starts, ends=ends) + out = fluid.layers.slice(input, axes=axes, starts=starts, ends=ends) @@ -8966,9 +8966,9 @@ square_error_cost .. code-block:: python - y = layers.data(name='y', shape=[1], dtype='float32') - y_predict = layers.data(name='y_predict', shape=[1], dtype='float32') - cost = layers.square_error_cost(input=y_predict, label=y) + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + y_predict = fluid.layers.data(name='y_predict', shape=[1], dtype='float32') + cost = fluid.layers.square_error_cost(input=y_predict, label=y) @@ -9018,8 +9018,8 @@ squeeze .. code-block:: python - x = layers.data(name='x', shape=[5, 1, 10]) - y = layers.sequeeze(input=x, axes=[1]) + x = fluid.layers.data(name='x', shape=[5, 1, 10]) + y = fluid.layers.sequeeze(input=x, axes=[1]) @@ -9118,8 +9118,8 @@ sum算子。 .. code-block:: python - input = layers.data(name="input", shape=[13, 11], dtype='float32') - out = layers.sum(input) + input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32') + out = fluid.layers.sum(input) @@ -9242,7 +9242,7 @@ topk .. code-block:: python - top5_values, top5_indices = layers.topk(input, k=5) + top5_values, top5_indices = fluid.layers.topk(input, k=5) @@ -9280,7 +9280,7 @@ transpose # 在数据张量中添加多余的batch大小维度 x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32', append_batch_size=False) - x_transposed = layers.transpose(x, perm=[1, 0, 2]) + x_transposed = fluid.layers.transpose(x, perm=[1, 0, 2]) @@ -9316,21 +9316,21 @@ tree_conv .. code-block:: python - nodes_vector = layers.data(name='vectors', shape=[None, 10, 5], dtype='float32) + nodes_vector = fluid.layers.data(name='vectors', shape=[None, 10, 5], dtype='float32) # batch size为None, 10代表数据集最大节点大小max_node_size,5表示向量宽度 - edge_set = layers.data(name='edge_set', shape=[None, 10, 2], dtype='float32') + edge_set = fluid.layers.data(name='edge_set', shape=[None, 10, 2], dtype='float32') # None 代表batch size, 10 代表数据集的最大节点大小max_node_size, 2 代表每条边连接两个节点 # 边必须为有向边 - out_vector = layers.tree_conv(nodes_vector, edge_set, 6, 1, 2, 'tanh', + out_vector = fluid.layers.tree_conv(nodes_vector, edge_set, 6, 1, 2, 'tanh', ParamAttr(initializer=Constant(1.0), ParamAttr(initializer=Constant(1.0)) # 输出的形会是[None, 10, 6, 1], # None 代表batch size, 10数据集的最大节点大小max_node_size, 6 代表输出大小output size, 1 代表 1 个filter - out_vector = layers.reshape(out_vector, shape=[None, 10, 6]) + out_vector = fluid.layers.reshape(out_vector, shape=[None, 10, 6]) # reshape之后, 输出张量output tensor为下一个树卷积的nodes_vector - out_vector_2 = layers.tree_conv(out_vector, edge_set, 3, 4, 2, 'tanh', + out_vector_2 = fluid.layers.tree_conv(out_vector, edge_set, 3, 4, 2, 'tanh', ParamAttr(initializer=Constant(1.0), ParamAttr(initializer=Constant(1.0)) # 输出tensor也可以用来池化(论文中称为global pooling) - pooled = layers.reduce_max(out_vector, dims=2) # global 池化 + pooled = fluid.layers.reduce_max(out_vector, dims=2) # global 池化 @@ -9376,8 +9376,8 @@ uniform_random_batch_size_like算子。 .. code-block:: python - input = layers.data(name="input", shape=[13, 11], dtype='float32') - out = layers.uniform_random_batch_size_like(input, [-1, 11]) + input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32') + out = fluid.layers.uniform_random_batch_size_like(input, [-1, 11]) @@ -9408,8 +9408,8 @@ unsqueeze .. code-block:: python - x = layers.data(name='x', shape=[5, 10]) - y = layers.unsequeeze(input=x, axes=[1]) + x = fluid.layers.data(name='x', shape=[5, 10]) + y = fluid.layers.unsequeeze(input=x, axes=[1]) @@ -10686,12 +10686,12 @@ sums tmp = fluid.layers.zeros(shape=[10], dtype='int32') i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) - a0 = layers.array_read(array=tmp, i=i) - i = layers.increment(x=i) - a1 = layers.array_read(array=tmp, i=i) - mean_a0 = layers.mean(a0) - mean_a1 = layers.mean(a1) - a_sum = layers.sums(input=[mean_a0, mean_a1]) + a0 = fluid.layers.array_read(array=tmp, i=i) + i = fluid.layers.increment(x=i) + a1 = fluid.layers.array_read(array=tmp, i=i) + mean_a0 = fluid.layers.mean(a0) + mean_a1 = fluid.layers.mean(a1) + a_sum = fluid.layers.sums(input=[mean_a0, mean_a1]) @@ -11482,13 +11482,13 @@ Detection Output Layer for Single Shot Multibox Detector(SSD) .. code-block:: python - pb = layers.data(name='prior_box', shape=[10, 4], + pb = fluid.layers.data(name='prior_box', shape=[10, 4], append_batch_size=False, dtype='float32') - pbv = layers.data(name='prior_box_var', shape=[10, 4], + pbv = fluid.layers.data(name='prior_box_var', shape=[10, 4], append_batch_size=False, dtype='float32') - loc = layers.data(name='target_box', shape=[2, 21, 4], + loc = fluid.layers.data(name='target_box', shape=[2, 21, 4], append_batch_size=False, dtype='float32') - scores = layers.data(name='scores', shape=[2, 21, 10], + scores = fluid.layers.data(name='scores', shape=[2, 21, 10], append_batch_size=False, dtype='float32') nmsed_outs = fluid.layers.detection_output(scores=scores, loc=loc, @@ -11997,13 +11997,13 @@ rpn_target_assign .. code-block:: python - bbox_pred = layers.data(name=’bbox_pred’, shape=[100, 4], + bbox_pred = fluid.layers.data(name=’bbox_pred’, shape=[100, 4], append_batch_size=False, dtype=’float32’) - cls_logits = layers.data(name=’cls_logits’, shape=[100, 1], + cls_logits = fluid.layers.data(name=’cls_logits’, shape=[100, 1], append_batch_size=False, dtype=’float32’) - anchor_box = layers.data(name=’anchor_box’, shape=[20, 4], + anchor_box = fluid.layers.data(name=’anchor_box’, shape=[20, 4], append_batch_size=False, dtype=’float32’) - gt_boxes = layers.data(name=’gt_boxes’, shape=[10, 4], + gt_boxes = fluid.layers.data(name=’gt_boxes’, shape=[10, 4], append_batch_size=False, dtype=’float32’) loc_pred, score_pred, loc_target, score_target, bbox_inside_weight= fluid.layers.rpn_target_assign(bbox_pred=bbox_pred, @@ -12162,9 +12162,9 @@ target_assign .. code-block:: python matched_indices, matched_dist = fluid.layers.bipartite_match(iou) - gt = layers.data( + gt = fluid.layers.data( name='gt', shape=[1, 1], dtype='int32', lod_level=1) - trg, trg_weight = layers.target_assign( + trg, trg_weight = fluid.layers.target_assign( gt, matched_indices, mismatch_value=0) diff --git a/doc/fluid/api_cn/metrics_cn.rst b/doc/fluid/api_cn/metrics_cn.rst index 21670356c2d..66b4aa84c80 100644 --- a/doc/fluid/api_cn/metrics_cn.rst +++ b/doc/fluid/api_cn/metrics_cn.rst @@ -105,7 +105,7 @@ ChunkEvaluator labels = fluid.layers.data(name="data", shape=[1], dtype="int32") data = fluid.layers.data(name="data", shape=[32, 32], dtype="int32") pred = fluid.layers.fc(input=data, size=1000, act="tanh") - precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks = layers.chunk_eval( + precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks = fluid.layers.chunk_eval( input=pred, label=label) metric = fluid.metrics.ChunkEvaluator()