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import numpy
from hypothesis.extra.numpy import arrays
from hypothesis.strategies import floats, integers, just, tuples
from thinc.api import Linear, NumpyOps
def get_ops():
return NumpyOps()
def get_model(W_values, b_values):
model = Linear(W_values.shape[0], W_values.shape[1], ops=NumpyOps())
model.initialize()
model.set_param("W", W_values)
model.set_param("b", b_values)
return model
def get_output(input_, W_values, b_values):
return numpy.einsum("oi,bi->bo", W_values, input_) + b_values
def get_input(nr_batch, nr_in):
ops = NumpyOps()
return ops.alloc2f(nr_batch, nr_in)
def lengths(lo=1, hi=10):
return integers(min_value=lo, max_value=hi)
def shapes(min_rows=1, max_rows=100, min_cols=1, max_cols=100):
return tuples(lengths(lo=min_rows, hi=max_rows), lengths(lo=min_cols, hi=max_cols))
def ndarrays_of_shape(shape, lo=-10.0, hi=10.0, dtype="float32", width=32):
if dtype.startswith("float"):
return arrays(
dtype, shape=shape, elements=floats(min_value=lo, max_value=hi, width=width)
)
else:
return arrays(dtype, shape=shape, elements=integers(min_value=lo, max_value=hi))
def ndarrays(min_len=0, max_len=10, min_val=-10.0, max_val=10.0):
return lengths(lo=1, hi=2).flatmap(
lambda n: ndarrays_of_shape(n, lo=min_val, hi=max_val)
)
def arrays_BI(min_B=1, max_B=10, min_I=1, max_I=100):
shapes = tuples(lengths(lo=min_B, hi=max_B), lengths(lo=min_I, hi=max_I))
return shapes.flatmap(ndarrays_of_shape)
def arrays_BOP(min_B=1, max_B=10, min_O=1, max_O=100, min_P=1, max_P=5):
shapes = tuples(
lengths(lo=min_B, hi=max_B),
lengths(lo=min_O, hi=max_O),
lengths(lo=min_P, hi=max_P),
)
return shapes.flatmap(ndarrays_of_shape)
def arrays_BOP_BO(min_B=1, max_B=10, min_O=1, max_O=100, min_P=1, max_P=5):
shapes = tuples(
lengths(lo=min_B, hi=max_B),
lengths(lo=min_O, hi=max_O),
lengths(lo=min_P, hi=max_P),
)
return shapes.flatmap(
lambda BOP: tuples(ndarrays_of_shape(BOP), ndarrays_of_shape(BOP[:-1]))
)
def arrays_BI_BO(min_B=1, max_B=10, min_I=1, max_I=100, min_O=1, max_O=100):
shapes = tuples(
lengths(lo=min_B, hi=max_B),
lengths(lo=min_I, hi=max_I),
lengths(lo=min_O, hi=max_O),
)
return shapes.flatmap(
lambda BIO: tuples(
ndarrays_of_shape((BIO[0], BIO[1])), ndarrays_of_shape((BIO[0], BIO[2]))
)
)
def arrays_OI_O_BI(
min_batch=1, max_batch=16, min_out=1, max_out=16, min_in=1, max_in=16
):
shapes = tuples(
lengths(lo=min_batch, hi=max_batch),
lengths(lo=min_in, hi=max_out),
lengths(lo=min_in, hi=max_in),
)
def W_b_inputs(shape):
batch_size, nr_out, nr_in = shape
W = ndarrays_of_shape((nr_out, nr_in))
b = ndarrays_of_shape((nr_out,))
input_ = ndarrays_of_shape((batch_size, nr_in))
return tuples(W, b, input_)
return shapes.flatmap(W_b_inputs)
def arrays_OPFI_BI_lengths(max_B=5, max_P=3, max_F=5, max_I=8):
shapes = tuples(
lengths(hi=max_B),
lengths(hi=max_P),
lengths(hi=max_F),
lengths(hi=max_I),
arrays("int32", shape=(5,), elements=integers(min_value=1, max_value=10)),
)
strat = shapes.flatmap(
lambda opfi_lengths: tuples(
ndarrays_of_shape(opfi_lengths[:-1]),
ndarrays_of_shape((sum(opfi_lengths[-1]), opfi_lengths[-2])),
just(opfi_lengths[-1]),
)
)
return strat
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