forked from mindspore-Ecosystem/mindspore
236 lines
9.4 KiB
Python
236 lines
9.4 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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from collections import namedtuple
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import numpy as np
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import pytest
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from mindspore import ops, nn, Tensor, Parameter, ParameterTuple, context, set_seed
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from mindspore.common.initializer import initializer, XavierUniform
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import mindspore.dataset as ds
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from mindspore.train import Callback, Model
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from mindspore.common import dtype as mstype
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import mindspore as ms
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class CrossNet(nn.Cell):
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def __init__(self, hidden_size, num_layer, l2_reg=0):
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super(CrossNet, self).__init__()
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self.l2_reg = l2_reg
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self.num_layers = num_layer
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kernels = []
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bias_list = []
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for i in range(self.num_layers):
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kernel = Parameter(initializer(XavierUniform(0.02), (hidden_size, 1), mstype.float32),
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requires_grad=True, name="kernerl" + str(i))
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kernels.append(kernel)
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bias = Parameter(Tensor(np.zeros((hidden_size, 1)), mstype.float32),
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requires_grad=True, name="bias" + str(i))
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bias_list.append(bias)
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self.kernels = ParameterTuple(kernels)
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self.bias = ParameterTuple(bias_list)
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self.expand_dim = ops.ExpandDims()
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self.squeeze = ops.Squeeze(2)
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self.matmul = ops.MatMul()
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def construct(self, x):
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x_0 = self.expand_dim(x, 2)
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x_l = x_0
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for i in range(self.num_layers):
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xl_w = ops.tensor_dot(x_l, self.kernels[i], axes=(1, 0))
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dot = ops.matmul(x_0, xl_w)
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x_l = dot + self.bias[i] + x_l
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x_l = self.squeeze(x_l)
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return x_l
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class DNN(nn.Cell):
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def __init__(self, input_size, hidden_units, activation='relu', l2_reg=0, dropout_rate=0):
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super(DNN, self).__init__()
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self.input_size = input_size
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self.num_layers = len(hidden_units)
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self.hidden_units = [self.input_size] + list(hidden_units)
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self.activation = activation
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self.l2_reg = l2_reg
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self.dropout_rate = dropout_rate
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dense_layers = []
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drop_layers = []
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for i in range(self.num_layers):
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dense_layer = nn.Dense(in_channels=self.hidden_units[i], out_channels=self.hidden_units[i + 1],
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activation=self.activation, weight_init="heUniform")
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dense_layers.append(dense_layer)
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drop_layer = nn.Dropout(p=self.dropout_rate)
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drop_layers.append(drop_layer)
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self.dense_layers = nn.CellList(dense_layers)
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self.drop_layers = nn.CellList(drop_layers)
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def construct(self, x):
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for i in range(self.num_layers):
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x = self.dense_layers[i](x)
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x = self.drop_layers[i](x)
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return x
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class DCN(nn.Cell):
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def __init__(self, numeric_size, sparse_list, hidden_units, cross_layer, output_num=1):
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super(DCN, self).__init__()
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self.embed_list = nn.CellList()
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for sparse_feature in sparse_list:
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embed = nn.Embedding(
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sparse_feature.voc_size, sparse_feature.embed_size, embedding_table='xavierUniform')
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self.embed_list.append(embed)
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self.input_size = sum(
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sparse_feature.embed_size for sparse_feature in sparse_list) + numeric_size
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self.hidden_units = hidden_units
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self.cross_layer = cross_layer
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self.cross_net = CrossNet(self.input_size, self.cross_layer)
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self.dense_net = DNN(self.input_size, self.hidden_units)
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self.output_num = output_num
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self.in_channels = self.input_size + self.hidden_units[-1]
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self.out_dense = nn.Dense(in_channels=self.in_channels, out_channels=self.output_num,
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has_bias=False, weight_init="xavierUniform")
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self.split = ops.Split(1, len(sparse_list))
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self.squeeze = ops.Squeeze(1)
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self.transpose = ops.Transpose()
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self.reduce_sum = ops.ReduceSum(keep_dims=False)
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self.cast = ops.Cast()
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self.shape = ops.Shape()
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self.concat = ops.Concat(1)
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def construct(self, x, cellist):
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inputs = []
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cellist = self.transpose(cellist, (1, 0))
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for i, ele in enumerate(self.split(cellist)):
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embed = self.embed_list[i]
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inputs.append(embed(self.squeeze(ele)))
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inputs.append(x)
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concat_x = self.concat(inputs)
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cross_x = self.cross_net(concat_x)
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dense_x = self.dense_net(concat_x)
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concat_x = self.concat((cross_x, dense_x))
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x = self.out_dense(concat_x)
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return x
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class PairWiseLoss(nn.Cell):
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def __init__(self):
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super(PairWiseLoss, self).__init__()
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self.sub = ops.Sub()
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self.mul = ops.Mul()
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self.relu = ops.ReLU()
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self.expandim = ops.ExpandDims()
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self.cast = ops.Cast()
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self.greater = ops.Greater()
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self.ones = Tensor(np.ones(1), mstype.float32)
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self.reduce_sum = ops.ReduceSum()
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def construct(self, y_pred, y_true):
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pairwise_label_diff = self.sub(self.expandim(y_true, 1), y_true)
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pairwise_logits = self.sub(self.expandim(y_pred, 1), y_pred)
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pairwise_labels = self.cast(self.greater(
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pairwise_label_diff, 0), mstype.float32)
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losses = self.mul(pairwise_labels, self.relu(
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self.ones - pairwise_logits))
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loss = self.reduce_sum(losses)
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return loss
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class MyWithLossCell(nn.Cell):
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def __init__(self, backbone, loss_fn):
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super(MyWithLossCell, self).__init__(auto_prefix=False)
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self._backbone = backbone
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self._loss_fn = loss_fn
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self._squeeze = ops.Squeeze(1)
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@property
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def backbone_network(self):
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return self._backbone
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def construct(self, x, y, label):
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out = self._backbone(x, y)
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out = self._squeeze(out)
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return self._loss_fn(out, label)
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class LossCallback(Callback):
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def __init__(self):
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super(LossCallback, self).__init__()
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self.loss_list = []
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def epoch_end(self, run_context):
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cb_params = run_context.original_args()
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result = cb_params.net_outputs
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self.loss_list.append(result.asnumpy().mean())
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def gen_data(numeric_columns, sparse_columns, batch_size_list):
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np.random.seed(0)
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data_list = []
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for batch in batch_size_list:
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numeric_values = np.random.randn(
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batch, numeric_columns[0].size).astype(np.float32)
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sparse_values = []
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for sparse_column in sparse_columns:
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voc_size = sparse_column.voc_size
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sparse_value = np.random.randint(
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0, voc_size, (1, batch), dtype=np.int32)
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sparse_values.append(sparse_value)
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sparse_values = np.concatenate(sparse_values)
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label_values = np.random.randint(0, 10, batch).astype(np.float32)
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data_list.append((numeric_values, sparse_values, label_values))
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return data_list
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def get_train_loss(numeric_columns, sparse_columns, data_list, mode):
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context.set_context(mode=mode, device_target="GPU")
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dataset = ds.GeneratorDataset(
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data_list, ["dense", "category", "label"], shuffle=False)
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numeric_size = numeric_columns[0].size
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net = DCN(numeric_size, sparse_columns, hidden_units=(32, 32), cross_layer=2, output_num=1)
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loss_fn = PairWiseLoss()
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loss_net = MyWithLossCell(net, loss_fn)
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train_net = nn.TrainOneStepCell(loss_net, nn.Adam(net.trainable_params(), learning_rate=1e-3, weight_decay=1e-5))
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train_net.set_train()
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train_net.set_inputs(Tensor(shape=[None, numeric_size], dtype=ms.float32),
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Tensor(shape=[len(sparse_columns), None], dtype=ms.int32),
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Tensor(shape=[None], dtype=ms.float32))
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loss_callback = LossCallback()
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model = Model(train_net)
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sink_step = dataset.get_dataset_size()
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model.train(sink_step, dataset, callbacks=loss_callback, sink_size=1, dataset_sink_mode=True)
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loss_list = loss_callback.loss_list
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return loss_list
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_train():
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"""
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Feature: Test the dcn_dynamic network with small shape.
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Description: The batch of inputs is dynamic.
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Expectation: Assert that results of GRAPH_MODE(static graph) are consistent with expected result.
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"""
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batch_size_list = [6, 70, 123]
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DenseFeature = namedtuple("DenseFeature", ['name', 'size'])
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numeric_columns = [DenseFeature("dense", 32)]
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SparseFeature = namedtuple("SparseFeature", ['name', 'voc_size', 'embed_size'])
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sparse_columns = [SparseFeature('a', 7, 6), SparseFeature('b', 136, 18), SparseFeature('c', 3, 6)]
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data_list = gen_data(numeric_columns, sparse_columns, batch_size_list)
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# GRAPH_MODE is temporarily not supported due to some new features that are not completely complete
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set_seed(0)
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graph_loss = get_train_loss(numeric_columns, sparse_columns, data_list, context.GRAPH_MODE)
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expect_loss = [6.687461, 2928.5852, 8715.267]
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assert np.allclose(graph_loss, expect_loss, 1e-3, 1e-3)
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