def __init__(self):
self.X = C.input_variable(shape=(1, ))
self.h = C.layers.Dense(1,
activation=None,
init=C.uniform(1),
bias=False)(self.X)
self.pred = C.layers.Dense(1,
activation=None,
init=C.uniform(1),
bias=False)(self.h)
self.y = C.input_variable(shape=(1, ))
self.loss = C.squared_error(self.pred, self.y)
def test_large_model_serialization_double(tmpdir):
import os;
two_gb = 2**31
type_size = np.dtype(np.float64).itemsize
size = two_gb / type_size + 10
assert size * type_size > two_gb
device = C.device.cpu()
i = C.sequence.input(size, dtype=np.float64)
w = C.Parameter((size,), dtype=np.float64,
init=C.uniform(3.0, seed=12345), device=device)
z = C.times(i, w)
filename = str(tmpdir / 'test_large_model_serialization_double.out')
z.save(filename)
assert os.path.getsize(filename) > two_gb
y = C.Function.load(filename, device=device)
assert (len(z.parameters) == len(y.parameters))
for param_pair in zip(z.parameters, y.parameters):
assert param_pair[0].shape == param_pair[1].shape
assert np.allclose(param_pair[0].value, param_pair[1].value)
def test_large_model_serialization_double(tmpdir):
import os
two_gb = 2**31
type_size = np.dtype(np.float64).itemsize
size = two_gb / type_size + 10
assert size * type_size > two_gb
device = C.device.cpu()
i = C.sequence.input(size, dtype=np.float64)
w = C.Parameter((size, ),
dtype=np.float64,
init=C.uniform(3.0, seed=12345),
device=device)
z = C.times(i, w)
filename = str(tmpdir / 'test_large_model_serialization_double.out')
z.save(filename)
assert os.path.getsize(filename) > two_gb
y = C.Function.load(filename, device=device)
assert (len(z.parameters) == len(y.parameters))
for param_pair in zip(z.parameters, y.parameters):
assert param_pair[0].shape == param_pair[1].shape
assert np.allclose(param_pair[0].value, param_pair[1].value)
def test_large_model_serialization_float(tmpdir):
import os
from cntk.layers import Recurrence, LSTM, Dense
type_size = np.dtype(np.float32).itemsize
two_gb = 2**31
size = (2097152 + 4, 256, 512, 4096)
assert size[0] * size[1] * type_size > two_gb
device = C.device.cpu()
i = C.sequence.input(size[0])
w = C.Parameter((size[0], size[1]),
init=C.uniform(3.0, seed=12345),
device=device)
e = C.times(i, w)
h_fwd = Recurrence(LSTM(size[2]))(e)
h_bwd = Recurrence(LSTM(size[2]), go_backwards=True)(e)
h_last_fwd = C.sequence.last(h_fwd)
h_first_bwd = C.sequence.first(h_bwd)
t = C.splice(h_last_fwd, h_first_bwd)
z1 = Dense(size[2], activation=C.relu)(t)
z = Dense(2, activation=None)(z1)
filename = str(tmpdir / 'test_large_model_serialization_float.out')
z.save(filename)
assert os.path.getsize(filename) > two_gb
y = C.Function.load(filename, device=device)
assert (len(z.parameters) == len(y.parameters))
for param_pair in zip(z.parameters, y.parameters):
assert param_pair[0].shape == param_pair[1].shape
assert np.allclose(param_pair[0].value, param_pair[1].value)
def test_large_model_serialization_float(tmpdir):
import os;
from cntk.layers import Recurrence, LSTM, Dense
type_size = np.dtype(np.float32).itemsize
two_gb = 2**31
size = (2097152 + 4, 256, 512, 4096)
assert size[0] * size[1] * type_size > two_gb
device = C.device.cpu()
i = C.sequence.input(size[0])
w = C.Parameter((size[0], size[1]), init=C.uniform(3.0, seed=12345),
device=device)
e = C.times(i, w)
h_fwd = Recurrence(LSTM(size[2]))(e)
h_bwd = Recurrence(LSTM(size[2]), go_backwards=True)(e)
h_last_fwd = C.sequence.last(h_fwd)
h_first_bwd = C.sequence.first(h_bwd)
t = C.splice(h_last_fwd, h_first_bwd)
z1 = Dense(size[2], activation=C.relu)(t)
z = Dense(2, activation=None)(z1)
filename = str(tmpdir / 'test_large_model_serialization_float.out')
z.save(filename)
assert os.path.getsize(filename) > two_gb
y = C.Function.load(filename, device=device)
assert (len(z.parameters) == len(y.parameters))
for param_pair in zip(z.parameters, y.parameters):
assert param_pair[0].shape == param_pair[1].shape
assert np.allclose(param_pair[0].value, param_pair[1].value)
