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- import hetu as ht
- from hetu import init
-
- import numpy as np
- import time
-
-
- def residual_layer(x0, input_dim, hidden_dim):
-
- embedding_len = input_dim
- weight_1 = init.random_normal(
- shape=(input_dim, hidden_dim), stddev=0.1, name='weight_1')
- bias_1 = init.random_normal(shape=(hidden_dim,), stddev=0.1, name='bias_1')
- weight_2 = init.random_normal(
- shape=(hidden_dim, input_dim), stddev=0.1, name='weight_2')
- bias_2 = init.random_normal(shape=(input_dim,), stddev=0.1, name='bias_2')
-
- x0w = ht.matmul_op(x0, weight_1) # (batch, hidden_dim)
- x0w_b = x0w + ht.broadcastto_op(bias_1, x0w)
-
- relu1 = ht.relu_op(x0w_b)
- x1w = ht.matmul_op(relu1, weight_2) # (batch, input_dim)
- x1w_b = x1w + ht.broadcastto_op(bias_2, x1w)
- residual = x1w_b + x0
- y = ht.relu_op(residual)
- return y
-
-
- def build_residual_layers(x0, input_dim, hidden_dim, num_layers=3):
- for i in range(num_layers):
- x0 = residual_layer(x0, input_dim, hidden_dim)
- return x0
-
-
- def dc_criteo(dense_input, sparse_input, y_):
-
- feature_dimension = 33762577
- embedding_size = 8
- learning_rate = 0.001
-
- Embedding = init.random_normal(
- [feature_dimension, embedding_size], stddev=0.01, name="snd_order_embedding")
- sparse_input = ht.embedding_lookup_op(Embedding, sparse_input)
- sparse_input = ht.array_reshape_op(sparse_input, (-1, 26*embedding_size))
-
- # dc_model
- x = ht.concat_op(sparse_input, dense_input, axis=1)
-
- input_dim = 26 * 8 + 13
- hidden_dim = input_dim
- residual_out = build_residual_layers(
- x, input_dim, hidden_dim, num_layers=5)
-
- W4 = init.random_normal([26*embedding_size + 13, 1], stddev=0.1, name="W4")
- y = ht.matmul_op(residual_out, 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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