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- import hetu as ht
- from hetu import init
-
- import numpy as np
- import time
-
-
- def cross_layer(x0, x1):
- # x0: input embedding feature (batch_size, 26 * embedding_size + 13)
- # x1: the output of last layer (batch_size, 26 * embedding_size + 13)
-
- embedding_len = 26 * 128 + 13
- weight = init.random_normal(
- shape=(embedding_len, 1), stddev=0.01, name='weight')
- bias = init.random_normal(shape=(embedding_len,), stddev=0.01, name='bias')
- x1w = ht.matmul_op(x1, weight) # (batch_size, 1)
- y = ht.mul_op(x0, ht.broadcastto_op(x1w, x0))
- y = y + x1 + ht.broadcastto_op(bias, y)
- return y
-
-
- def build_cross_layer(x0, num_layers=3):
- x1 = x0
- for i in range(num_layers):
- x1 = cross_layer(x0, x1)
- return x1
-
-
- def dcn_criteo(dense_input, sparse_input, y_):
- feature_dimension = 33762577
- embedding_size = 128
- learning_rate = 0.003
-
- Embedding = init.random_normal(
- [feature_dimension, embedding_size], stddev=0.01, name="snd_order_embedding", ctx=ht.cpu(0))
- sparse_input = ht.embedding_lookup_op(
- Embedding, sparse_input, ctx=ht.cpu(0))
- sparse_input = ht.array_reshape_op(sparse_input, (-1, 26*embedding_size))
- x = ht.concat_op(sparse_input, dense_input, axis=1)
- # Cross Network
- cross_output = build_cross_layer(x, num_layers=3)
-
- # DNN
- flatten = x
- W1 = init.random_normal(
- [26*embedding_size + 13, 256], stddev=0.01, name="W1")
- W2 = init.random_normal([256, 256], stddev=0.01, name="W2")
- W3 = init.random_normal([256, 256], stddev=0.01, name="W3")
-
- W4 = init.random_normal(
- [256 + 26*embedding_size + 13, 1], stddev=0.01, name="W4")
-
- fc1 = ht.matmul_op(flatten, W1)
- relu1 = ht.relu_op(fc1)
- fc2 = ht.matmul_op(relu1, W2)
- relu2 = ht.relu_op(fc2)
- y3 = ht.matmul_op(relu2, W3)
-
- y4 = ht.concat_op(cross_output, y3, axis=1)
- y = ht.matmul_op(y4, W4)
- y = ht.sigmoid_op(y)
-
- loss = ht.binarycrossentropy_op(y, y_)
- loss = ht.reduce_mean_op(loss, [0])
- opt = ht.optim.SGDOptimizer(learning_rate=learning_rate)
- train_op = opt.minimize(loss)
-
- return loss, y, y_, train_op
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