def test_multi_target_init():
config = Config()
config.update({
"multiprocessing": False,
"blocking": True,
"device": "cpu",
"num_epochs": 1,
"num_inputs": 3,
"num_outputs": {
"t1": 4,
"t2": 5
},
"learning_rate": 1.0,
})
config.network_topology_json = """
{
"fw0": {"class": "hidden", "activation": "identity", "n_out": 3},
"out1": {"class": "softmax", "loss": "ce", "target": "t1", "from": ["fw0"]},
"out2": {"class": "softmax", "loss": "ce", "target": "t2", "from": ["fw0"]}
}
"""
device = Device("cpu", config=config, blocking=True)
assert_true(device.trainnet, "train network initialized")
assert_true(device.testnet, "test network initialized")
param_vars = device.trainnet.get_all_params_vars()
print "params:", param_vars
assert_equal(len(param_vars), 6, "W, b vars for each out, and fw")
num_params = get_num_params(param_vars)
assert_equal(num_params, (3 * 3 + 3) + (3 * 4 + 4) + (3 * 5 + 5),
"W, b for each out, and fw")
assert_in("fw0", device.testnet.hidden)
assert_in("out1", device.testnet.output)
assert_in("out2", device.testnet.output)
assert_is(device.testnet.j["t1"], device.testnet.output["out1"].index)
assert_true(device.updater)
update_list = device.updater.getUpdateList()
print "update list:"
pprint(update_list)
update_dict = dict(update_list)
assert_equal(len(update_dict), len(update_list),
"all params in update list only once")
assert_in("fw0", device.trainnet.hidden)
assert_equal(len(device.trainnet.hidden), 1)
assert_in("W_in_data_fw0", device.trainnet.hidden["fw0"].params)
assert_in("b_fw0", device.trainnet.hidden["fw0"].params)
assert_equal(len(device.trainnet.hidden["fw0"].params), 2)
assert_in("out1", device.trainnet.output)
assert_equal(len(device.trainnet.output), 2)
assert_in("W_in_fw0_out1", device.trainnet.output["out1"].params)
assert_in("b_out1", device.trainnet.output["out1"].params)
assert_equal(len(device.trainnet.output["out1"].params), 2)
assert_in(device.trainnet.hidden["fw0"].params["W_in_data_fw0"],
update_dict)
assert_in(device.trainnet.hidden["fw0"].params["b_fw0"], update_dict)
assert_in(device.trainnet.output["out1"].params["W_in_fw0_out1"],
update_dict)
assert_in(device.trainnet.output["out1"].params["b_out1"], update_dict)
assert_in(device.trainnet.output["out2"].params["W_in_fw0_out2"],
update_dict)
assert_in(device.trainnet.output["out2"].params["b_out2"], update_dict)
assert_equal(len(update_dict), 6)
# Set net params.
net_params = {
"fw0": {
"W_in_data_fw0": numpy.identity(3, dtype="float32"),
"b_fw0": numpy.zeros((3, ), dtype="float32")
},
"out1": {
"W_in_fw0_out1":
numpy.arange(0.0, 1.2, 0.1, dtype="float32").reshape((3, 4)),
"b_out1":
numpy.arange(0.0, 4, dtype="float32")
},
"out2": {
"W_in_fw0_out2":
numpy.arange(0.0, 1.5, 0.1, dtype="float32").reshape((3, 5)),
"b_out2":
numpy.arange(0.0, 5, dtype="float32")
}
}
device.trainnet.set_params_by_dict(net_params)
device.testnet.set_params_by_dict(net_params)
# Show params.
for p in param_vars:
print "init %s:" % p
pprint(p.get_value())
# Init dataset.
dataset = StaticDataset(data=[{
"data":
numpy.array([[0.1, 0.2, -0.3]], dtype="float32"),
"t1":
numpy.array([2]),
"t2":
numpy.array([4])
}],
output_dim=config.typed_value("num_outputs"))
dataset.init_seq_order()
assert_equal(dataset.is_data_sparse("data"), False)
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")
outputs = Device.make_result_dict(output_list, outputs_format)
pprint(outputs)
assert_in("cost:out1", outputs)
assert_greater(outputs["cost:out1"], 0)
assert_almost_equal(outputs["cost:out1"], t1_cost)
# Get net params.
params = device.get_net_train_params(device.trainnet)
references_params = {
"W_in_data_fw0":
numpy.array([[1.00055406e+00, 5.54056978e-04, 5.54056978e-04],
[1.10811396e-03, 1.00110811e+00, 1.10811396e-03],
[-1.66217093e-03, -1.66217093e-03, 9.98337829e-01]]),
"b_fw0":
numpy.array([0.00554057, 0.00554057, 0.00554057]),
"W_in_fw0_out1":
numpy.array([[-0.00320586, 0.09128557, 0.27631172, 0.23560857],
[0.39358828, 0.48257114, 0.75262344, 0.57121715],
[0.80961758, 0.9261433, 0.77106485, 1.29317428]]),
"b_out1":
numpy.array([-0.0320586, 0.91285568, 2.76311718, 2.35608574]),
"W_in_fw0_out2":
numpy.array([[
-1.16562310e-03, 9.68315079e-02, 1.91387146e-01, 2.76587834e-01,
4.36359135e-01
],
[
4.97668754e-01, 5.93663016e-01, 6.82774291e-01,
7.53175669e-01, 9.72718271e-01
],
[
1.00349687e+00, 1.10950548e+00, 1.22583856e+00,
1.37023650e+00, 1.29092259e+00
]]),
"b_out2":
numpy.array(
[-0.01165623, 0.96831508, 1.91387146, 2.76587834, 4.36359135])
}
assert_equal(len(param_vars), len(params))
for p, v in zip(param_vars, params):
print "%s:" % p
pprint(v)
assert_true(p.name)
numpy.testing.assert_almost_equal(references_params[p.name],
v,
decimal=6)
def test_multi_target_init():
config = Config()
config.update({
"multiprocessing": False,
"blocking": True,
"device": "cpu",
"num_epochs": 1,
"num_inputs": 3,
"num_outputs": {"t1": 4, "t2": 5},
"learning_rate": 1.0,
})
config.network_topology_json = """
{
"fw0": {"class": "hidden", "activation": "identity", "n_out": 3},
"out1": {"class": "softmax", "loss": "ce", "target": "t1", "from": ["fw0"]},
"out2": {"class": "softmax", "loss": "ce", "target": "t2", "from": ["fw0"]}
}
"""
device = Device("cpu", config=config, blocking=True)
assert_true(device.trainnet, "train network initialized")
assert_true(device.testnet, "test network initialized")
param_vars = device.trainnet.get_all_params_vars()
print "params:", param_vars
assert_equal(len(param_vars), 6, "W, b vars for each out, and fw")
num_params = get_num_params(param_vars)
assert_equal(num_params, (3 * 3 + 3) + (3 * 4 + 4) + (3 * 5 + 5), "W, b for each out, and fw")
assert_in("fw0", device.testnet.hidden)
assert_in("out1", device.testnet.output)
assert_in("out2", device.testnet.output)
assert_is(device.testnet.j["t1"], device.testnet.output["out1"].index)
assert_true(device.updater)
update_list = device.updater.getUpdateList()
print "update list:"
pprint(update_list)
update_dict = dict(update_list)
assert_equal(len(update_dict), len(update_list), "all params in update list only once")
assert_in("fw0", device.trainnet.hidden)
assert_equal(len(device.trainnet.hidden), 1)
assert_in("W_in_data_fw0", device.trainnet.hidden["fw0"].params)
assert_in("b_fw0", device.trainnet.hidden["fw0"].params)
assert_equal(len(device.trainnet.hidden["fw0"].params), 2)
assert_in("out1", device.trainnet.output)
assert_equal(len(device.trainnet.output), 2)
assert_in("W_in_fw0_out1", device.trainnet.output["out1"].params)
assert_in("b_out1", device.trainnet.output["out1"].params)
assert_equal(len(device.trainnet.output["out1"].params), 2)
assert_in(device.trainnet.hidden["fw0"].params["W_in_data_fw0"], update_dict)
assert_in(device.trainnet.hidden["fw0"].params["b_fw0"], update_dict)
assert_in(device.trainnet.output["out1"].params["W_in_fw0_out1"], update_dict)
assert_in(device.trainnet.output["out1"].params["b_out1"], update_dict)
assert_in(device.trainnet.output["out2"].params["W_in_fw0_out2"], update_dict)
assert_in(device.trainnet.output["out2"].params["b_out2"], update_dict)
assert_equal(len(update_dict), 6)
# Set net params.
net_params = {
"fw0": {"W_in_data_fw0": numpy.identity(3, dtype="float32"),
"b_fw0": numpy.zeros((3,), dtype="float32")},
"out1": {"W_in_fw0_out1": numpy.arange(0.0, 1.2, 0.1, dtype="float32").reshape((3, 4)),
"b_out1": numpy.arange(0.0, 4, dtype="float32")},
"out2": {"W_in_fw0_out2": numpy.arange(0.0, 1.5, 0.1, dtype="float32").reshape((3, 5)),
"b_out2": numpy.arange(0.0, 5, dtype="float32")}
}
device.trainnet.set_params_by_dict(net_params)
device.testnet.set_params_by_dict(net_params)
# Show params.
for p in param_vars:
print "init %s:" % p
pprint(p.get_value())
# Init dataset.
dataset = StaticDataset(data=[{
"data": numpy.array([[0.1, 0.2, -0.3]], dtype="float32"),
"t1": numpy.array([2]),
"t2": numpy.array([4])
}], output_dim=config.typed_value("num_outputs"))
dataset.init_seq_order()
assert_equal(dataset.is_data_sparse("data"), False)
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")
outputs = Device.make_result_dict(output_list, outputs_format)
pprint(outputs)
assert_in("cost:out1", outputs)
assert_greater(outputs["cost:out1"], 0)
assert_almost_equal(outputs["cost:out1"], t1_cost)
# Get net params.
params = device.get_net_train_params(device.trainnet)
references_params = {
"W_in_data_fw0":
numpy.array([[ 1.00055406e+00, 5.54056978e-04, 5.54056978e-04],
[ 1.10811396e-03, 1.00110811e+00, 1.10811396e-03],
[ -1.66217093e-03, -1.66217093e-03, 9.98337829e-01]]),
"b_fw0":
numpy.array([ 0.00554057, 0.00554057, 0.00554057]),
"W_in_fw0_out1":
numpy.array([[-0.00320586, 0.09128557, 0.27631172, 0.23560857],
[ 0.39358828, 0.48257114, 0.75262344, 0.57121715],
[ 0.80961758, 0.9261433 , 0.77106485, 1.29317428]]),
"b_out1":
numpy.array([-0.0320586 , 0.91285568, 2.76311718, 2.35608574]),
"W_in_fw0_out2":
numpy.array([[ -1.16562310e-03, 9.68315079e-02, 1.91387146e-01,
2.76587834e-01, 4.36359135e-01],
[ 4.97668754e-01, 5.93663016e-01, 6.82774291e-01,
7.53175669e-01, 9.72718271e-01],
[ 1.00349687e+00, 1.10950548e+00, 1.22583856e+00,
1.37023650e+00, 1.29092259e+00]]),
"b_out2":
numpy.array([-0.01165623, 0.96831508, 1.91387146, 2.76587834, 4.36359135])
}
assert_equal(len(param_vars), len(params))
for p, v in zip(param_vars, params):
print "%s:" % p
pprint(v)
assert_true(p.name)
numpy.testing.assert_almost_equal(references_params[p.name], v, decimal=6)
