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- import hetu as ht
- from hetu import init
-
- import numpy as np
-
-
- def neural_mf(user_input, item_input, y_, num_users, num_items):
- embed_dim = 8
- layers = [64, 32, 16, 8]
- learning_rate = 0.01
-
- User_Embedding = init.random_normal(
- (num_users, embed_dim + layers[0] // 2), stddev=0.01, name="user_embed", ctx=ht.cpu(0))
- Item_Embedding = init.random_normal(
- (num_items, embed_dim + layers[0] // 2), stddev=0.01, name="item_embed", ctx=ht.cpu(0))
-
- user_latent = ht.embedding_lookup_op(
- User_Embedding, user_input, ctx=ht.cpu(0))
- item_latent = ht.embedding_lookup_op(
- Item_Embedding, item_input, ctx=ht.cpu(0))
-
- mf_user_latent = ht.slice_op(user_latent, (0, 0), (-1, embed_dim))
- mlp_user_latent = ht.slice_op(user_latent, (0, embed_dim), (-1, -1))
- mf_item_latent = ht.slice_op(item_latent, (0, 0), (-1, embed_dim))
- mlp_item_latent = ht.slice_op(item_latent, (0, embed_dim), (-1, -1))
-
- W1 = init.random_normal((layers[0], layers[1]), stddev=0.1, name='W1')
- W2 = init.random_normal((layers[1], layers[2]), stddev=0.1, name='W2')
- W3 = init.random_normal((layers[2], layers[3]), stddev=0.1, name='W3')
- W4 = init.random_normal((embed_dim + layers[3], 1), stddev=0.1, name='W4')
-
- mf_vector = ht.mul_op(mf_user_latent, mf_item_latent)
- mlp_vector = ht.concat_op(mlp_user_latent, mlp_item_latent, axis=1)
- fc1 = ht.matmul_op(mlp_vector, W1)
- relu1 = ht.relu_op(fc1)
- fc2 = ht.matmul_op(relu1, W2)
- relu2 = ht.relu_op(fc2)
- fc3 = ht.matmul_op(relu2, W3)
- relu3 = ht.relu_op(fc3)
- concat_vector = ht.concat_op(mf_vector, relu3, axis=1)
- y = ht.matmul_op(concat_vector, 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, train_op
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