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93 lines
2.3 KiB
93 lines
2.3 KiB
import paddle.v2.framework.core as core
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import unittest
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import numpy as np
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import paddle.v2.framework.create_op_creation_methods as creation
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ops = creation.op_creations
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def create_tensor(scope, name, shape):
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tensor = scope.create_var(name).get_tensor()
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tensor.set_dims(shape)
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tensor.alloc_float()
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tensor.set(np.random.random(shape))
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return tensor
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class TestRNN(unittest.TestCase):
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'''
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Test RNNOp
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equation:
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h_t = \sigma (W x_t + U h_{t-1})
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weights:
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- W
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- U
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vars:
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- x
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memories:
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- h
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outputs:
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- h
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'''
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def init(self):
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input_dim = 30
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batch_size = 50
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weight_dim = 15
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self.scope = core.Scope(None)
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# create vars
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create_tensor(self.scope, "x", [batch_size, input_dim])
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create_tensor(self.scope, "W", [input_dim, weight_dim])
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create_tensor(self.scope, "U", [weight_dim, weight_dim])
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create_tensor(self.scope, "h_boot", [batch_size, weight_dim])
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x_alias = "x@alias"
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y_alias = "y@alias"
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memory = "h@alias"
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prememory = "h@pre"
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output = "rnn_out"
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output_alias = "rnn_out@alias"
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# create step net
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stepnet_var = self.scope.create_var("stepnet")
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stepnet = stepnet_var.get_net()
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# stepnet = core.Net.create()
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x_fc_op = ops.fc(X=x_alias, W="W", Y="Wx")
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h_fc_op = ops.fc(X=prememory, W="U", Y="Uh")
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sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum")
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sig_op = ops.sigmoid(X="sum", Y=memory)
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stepnet.add_op(x_fc_op)
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stepnet.add_op(h_fc_op)
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stepnet.add_op(sum_op)
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stepnet.add_op(sig_op)
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stepnet.complete_add_op(True)
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# create RNNOp
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rnnop = ops.recurrent_op(
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# inputs
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inlinks=["x"],
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boot_memories=["h_boot"],
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step_net="stepnet",
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# outputs
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outlinks=[output],
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step_scopes="step_scopes",
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# attributes
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inlink_alias=["x@alias"],
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outlink_alias=[output_alias],
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pre_memories=[prememory],
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memories=[memory])
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ctx = core.DeviceContext.cpu_context()
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rnnop.infer_shape(self.scope)
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rnnop.run(self.scope, ctx)
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def test_recurrent(self):
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self.init()
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if __name__ == '__main__':
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unittest.main()
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