cjolivier01 commented on a change in pull request #7903: Refactor AdaGrad
optimizer to support sparse tensors
URL: https://github.com/apache/incubator-mxnet/pull/7903#discussion_r139765735
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File path: tests/python/unittest/test_module.py
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@@ -462,74 +462,105 @@ def test_shared_exec_group(exec_grp_shared,
exec_grp_created, shared_arg_names=N
def test_factorization_machine_module():
""" Test factorization machine model with sparse operators """
- mx.random.seed(11)
- rnd.seed(11)
-
- def fm(factor_size, feature_dim, init):
- x = mx.symbol.Variable("data", stype='csr')
- v = mx.symbol.Variable("v", shape=(feature_dim, factor_size),
- init=init, stype='row_sparse')
-
- w1_weight = mx.symbol.var('w1_weight', shape=(feature_dim, 1),
- init=init, stype='row_sparse')
- w1_bias = mx.symbol.var('w1_bias', shape=(1))
- w1 = mx.symbol.broadcast_add(mx.symbol.dot(x, w1_weight), w1_bias)
-
- v_s = mx.symbol._internal._square_sum(data=v, axis=1, keepdims=True)
- x_s = mx.symbol.square(data=x)
- bd_sum = mx.sym.dot(x_s, v_s)
-
- w2 = mx.symbol.dot(x, v)
- w2_squared = 0.5 * mx.symbol.square(data=w2)
-
- w_all = mx.symbol.Concat(w1, w2_squared, dim=1)
- sum1 = mx.symbol.sum(data=w_all, axis=1, keepdims=True)
- sum2 = 0.5 * mx.symbol.negative(bd_sum)
- model = mx.sym.elemwise_add(sum1, sum2)
-
- y = mx.symbol.Variable("label")
- model = mx.symbol.LinearRegressionOutput(data=model, label=y)
- return model
-
- # model
- ctx = default_context()
- init = mx.initializer.Normal(sigma=0.01)
- factor_size = 4
- feature_dim = 10000
- model = fm(factor_size, feature_dim, init)
-
- # data iter
- num_batches = 5
- batch_size = 64
- num_samples = batch_size * num_batches
- # generate some random csr data
- csr_nd = rand_ndarray((num_samples, feature_dim), 'csr', 0.1)
- label = mx.nd.ones((num_samples,1))
- # the alternative is to use LibSVMIter
- train_iter = mx.io.NDArrayIter(data=csr_nd, label={'label':label},
- batch_size=batch_size,
last_batch_handle='discard')
- # create module
- mod = mx.mod.Module(symbol=model, data_names=['data'],
label_names=['label'])
- # allocate memory by given the input data and lable shapes
- mod.bind(data_shapes=train_iter.provide_data,
label_shapes=train_iter.provide_label)
- # initialize parameters by uniform random numbers
- mod.init_params(initializer=init)
- # use Sparse SGD with learning rate 0.1 to train
- adam = mx.optimizer.Adam(clip_gradient=5.0, learning_rate=0.001,
rescale_grad=1.0/batch_size)
- mod.init_optimizer(optimizer=adam)
- # use accuracy as the metric
- metric = mx.metric.create('MSE')
- # train 10 epoch
- for epoch in range(10):
- train_iter.reset()
- metric.reset()
- for batch in train_iter:
- mod.forward(batch, is_train=True) # compute predictions
- mod.update_metric(metric, batch.label) # accumulate prediction
accuracy
- mod.backward() # compute gradients
- mod.update() # update parameters
- # print('Epoch %d, Training %s' % (epoch, metric.get()))
- assert(metric.get()[1] < 0.05), metric.get()[1]
+ def check_factorization_machine_module(optimizer=None, num_epochs=None):
Review comment:
test_optimizer appears to test C++ version against python version. There is
only a python version for AdaGrad, therefore it's not clear what it tests
against. I am using the test_module() test with an expected accuracy rate to
test.
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