本文整理汇总了Python中Device.Device.make_givens方法的典型用法代码示例。如果您正苦于以下问题：Python Device.make_givens方法的具体用法？Python Device.make_givens怎么用？Python Device.make_givens使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Device.Device的用法示例。
在下文中一共展示了Device.make_givens方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_multi_target_init
# 需要导入模块: from Device import Device [as 别名]
# 或者: from Device.Device import make_givens [as 别名]
#.........这里部分代码省略.........
assert_equal(dataset.is_data_sparse("t1"), True)
assert_equal(dataset.is_data_sparse("t2"), True)
# Copy to device allocation.
success = assign_dev_data_single_seq(device, dataset, 0)
assert_true(success, "failed to allocate & assign data")
# Check allocated data.
assert_equal(device.targets["data"].shape, (1, 1, 3)) # input shape. (time,batch,dim)
assert_in("t1", device.targets)
assert_in("t2", device.targets)
assert_equal(device.targets["t1"].shape, (1, 1))
assert_equal(device.targets["t2"].shape, (1, 1))
assert_equal(device.output_index["data"].shape, (1, 1))
numpy.testing.assert_equal(device.output_index["data"], numpy.array([[1]]))
assert_equal(device.output_index["t1"].shape, (1, 1))
numpy.testing.assert_equal(device.output_index["t1"], numpy.array([[1]]))
# Forward test.
device.update_data()
device.testnet.costs["out1"].name = "out1_cost" # nice in the func graph
out_i1 = device.testnet.output["out1"].index
out_i1_nonzero = device.testnet.output["out1"].i
nll1, pcx1 = T.nnet.crossentropy_softmax_1hot(x=device.testnet.output["out1"].y_m[out_i1_nonzero],
y_idx=device.testnet.output["out1"].y_data_flat[out_i1_nonzero])
forward_func = theano.function(
inputs=[device.block_start, device.block_end],
outputs=[
device.testnet.j["t1"], out_i1, out_i1_nonzero[0], nll1, pcx1,
device.testnet.costs["out1"],
device.testnet.output["out1"].p_y_given_x,
device.testnet.costs["out2"],
device.testnet.output["out2"].p_y_given_x],
givens=device.make_givens(device.testnet),
no_default_updates=True,
on_unused_input='warn',
name="forward")
#print "forward func:"
#theano.printing.debugprint(forward_func)
net_j1, out_i1_val, out_i1_nz_val, nll1_val, pcx1_val, t1_cost, t1_y, t2_cost, t2_y = forward_func(0, 1)
print "forward results:"
pprint(net_j1)
pprint(out_i1_val)
pprint(out_i1_nz_val)
pprint(nll1_val)
pprint(pcx1_val)
pprint(t1_cost)
pprint(t1_y)
pprint(t2_cost)
pprint(t2_y)
assert_equal(net_j1, numpy.array([[1]]))
assert_equal(out_i1_val, numpy.array([[1]]))
assert_equal(out_i1_nz_val, numpy.array([0]))
assert_almost_equal(nll1_val, numpy.array([t1_cost]))
numpy.testing.assert_almost_equal(t1_y, pcx1_val)
assert_almost_equal(t1_cost, 1.440189698561195, places=6)
assert_almost_equal(t2_cost, 0.45191439593759336, places=6)
numpy.testing.assert_almost_equal(t1_y, numpy.array([[ 0.0320586 , 0.08714432, 0.23688282, 0.64391426]]), decimal=6)
numpy.testing.assert_almost_equal(t2_y, numpy.array([[ 0.01165623, 0.03168492, 0.08612854, 0.23412166, 0.63640865]]), decimal=6)
# One train step.
device.set_learning_rate(config.typed_value("learning_rate"))
device.run("train")
output_list, outputs_format = device.result()
assert_is_instance(output_list, list)
assert_true(outputs_format, "for train, we should always get the format")