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- import hetu as ht
- from hetu import init
-
-
- def wdl_adult(X_deep, X_wide, y_, dense_param_ctx):
- lr = 5 / 128
- dim_wide = 809
- dim_deep = 68
-
- with ht.context(dense_param_ctx):
- W = init.random_normal([dim_wide+20, 2], stddev=0.1, name="W")
- W1 = init.random_normal([dim_deep, 50], stddev=0.1, name="W1")
- b1 = init.random_normal([50], stddev=0.1, name="b1")
- W2 = init.random_normal([50, 20], stddev=0.1, name="W2")
- b2 = init.random_normal([20], stddev=0.1, name="b2")
-
- # deep
- Embedding = []
- X_deep_input = None
-
- for i in range(8):
- Embedding_name = "Embedding_deep_" + str(i)
- Embedding.append(init.random_normal(
- [50, 8], stddev=0.1, name=Embedding_name))
- now = ht.embedding_lookup_op(Embedding[i], X_deep[i])
- now = ht.array_reshape_op(now, (-1, 8))
- if X_deep_input is None:
- X_deep_input = now
- else:
- X_deep_input = ht.concat_op(X_deep_input, now, 1)
-
- for i in range(4):
- now = ht.array_reshape_op(X_deep[i + 8], (-1, 1))
- X_deep_input = ht.concat_op(X_deep_input, now, 1)
-
- mat1 = ht.matmul_op(X_deep_input, W1)
- add1 = mat1 + ht.broadcastto_op(b1, mat1)
- relu1 = ht.relu_op(add1)
- dropout1 = relu1
- mat2 = ht.matmul_op(dropout1, W2)
- add2 = mat2 + ht.broadcastto_op(b2, mat2)
- relu2 = ht.relu_op(add2)
- dropout2 = relu2
- dmodel = dropout2
-
- # wide
- wmodel = ht.concat_op(X_wide, dmodel, 1)
- wmodel = ht.matmul_op(wmodel, W)
-
- prediction = wmodel
- loss = ht.softmaxcrossentropy_op(prediction, y_)
- loss = ht.reduce_mean_op(loss, [0])
-
- opt = ht.optim.SGDOptimizer(learning_rate=lr)
- train_op = opt.minimize(loss)
-
- return loss, prediction, y_, train_op
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