def test_shapes(self):
# test shape tuple
shape1 = dc.lvec([2, 3, 4, 5])
shape2 = dc.lvec([5, 4, 3, 2])
a = dc.random(2, 3, 4, 5).astype('int')
assert a.rank() == 4
assert a.shape() == (2, 3, 4, 5)
# reshape to new dimensions
a.reshape(shape2)
assert a.shape() == (5, 4, 3, 2)
# return a new tensor with flattened dimensions.
b = a.flatten()
assert a.shape() == (5, 4, 3, 2)
assert b.shape() == (120, )
# flatten the same tensor
a.flatteninplace()
assert a.shape() == (120, )
shape3 = dc.lvec([8, 15, 1, 1])
# new shape
a.reshape(shape3)
# confirm new shape
assert a.shape() == (8, 15, 1, 1)
# dnnc method to reshape.
a = dc.random(2, 3, 4, 5)
dc.reshape(a, (8, 15, 1, 1))
assert a.shape() == (8, 15, 1, 1)
dc.reshape(a, (120, ))
assert a.shape() == (120, )
def test_data(self):
# confirm type as class tuple.
a = dc.zeros(2, 3).astype('int')
adata = a.data()
assert type(adata) == type((1, ))
# load new data
new_data_list = [10, 11, 12, 13, 14, 15]
a.load(dc.ivec(new_data_list))
assert a[0] == 10
# load one element with flat index
a[0] = 777
assert a[0] == 777
# reshape, fetch and load with multi indices
a = dc.arange(12).astype('int')
a.reshape(dc.lvec([2, 2, 3]))
assert a[0, 1, 1] == 4
a[1, 1, 1] = 200
assert a[1, 1, 1] == 200
# negative test
try:
# This throws ValueError
print(a[0, 0, 9, 9, 9])
except ValueError as e:
assert e
import os, sys
sys.path.append(os.path.abspath('.'))
import dnnc as dc
t1 = dc.array(2, 3)
t2 = dc.array(3, 2)
mul = dc.matmul(t1, t2)
#print ("multiplication : " , mul.to_string())
add = dc.add(t1, t1)
#print ("addition : " , add.to_string())
t3 = dc.array(2, 3, 4)
#print("old shape", t1.shape())
new_shape = dc.lvec([2, 12])
t3.reshape(new_shape)
#print("new shape", t1.shape())
#t4 = dc.thresholded_relu(t1);
#print("relu", t4.to_string())
#replace first few values in tensor with new values.
data = dc.fvec([1.0, 2.0, 3.0, 4.0])
t3.load(data)
#print(t3.to_string())
arr = dc.array([1, 2])
#print(arr)
arr2D = dc.array([[1, 2], [10, 20]]).astype('int')
#print(arr2D)
arrRand = dc.random(2, 3)