| /external/tensorflow/tensorflow/compiler/tests/ |
| D | nary_ops_test.py | 23 import numpy as np namespace
58 [np.array([[1, 2, 3]], dtype=np.float32)],
59 expected=np.array([[1, 2, 3]], dtype=np.float32))
62 [np.array([1, 2], dtype=np.float32),
63 np.array([10, 20], dtype=np.float32)],
64 expected=np.array([11, 22], dtype=np.float32))
66 [np.array([-4], dtype=np.float32),
67 np.array([10], dtype=np.float32),
68 np.array([42], dtype=np.float32)],
69 expected=np.array([48], dtype=np.float32))
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| D | binary_ops_test.py | 23 import numpy as np namespace
60 rtol = 1e-15 if a.dtype == np.float64 else 1e-3
62 atol = 1e-15 if a.dtype == np.float64 else 1e-6
87 np.array([[[[-1, 2.00009999], [-3, b]]]], dtype=dtype),
88 np.array([[[[a, 2], [-3.00009, 4]]]], dtype=dtype),
89 expected=np.array([[[[False, True], [True, False]]]], dtype=dtype))
93 np.array([3, 3, -1.5, -8, 44], dtype=dtype),
94 np.array([2, -2, 7, -4, 0], dtype=dtype),
95 expected=np.array(
102 np.array([0, np.sqrt(2), 1, np.sqrt(2), 0], dtype),
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| D | unary_ops_test.py | 23 import numpy as np namespace
42 return np.transpose(x, [0, rank - 1] + list(range(1, rank - 1)))
92 self.assertAllEqual(np.sort(result), result)
101 for dtype in self.numeric_types - {np.int8, np.uint8}:
103 array_ops.diag, np.array([1, 2, 3, 4], dtype=dtype),
104 np.array(
109 np.arange(36).reshape([2, 3, 2, 3]).astype(dtype),
110 np.array([[0, 7, 14], [21, 28, 35]], dtype=dtype))
112 array_ops.diag, np.array([[1, 2], [3, 4]], dtype=dtype),
113 np.array(
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| D | complex_div_test.py | 23 import numpy as np namespace
54 np.array([
56 complex(1, np.inf),
57 complex(1, np.nan),
58 complex(np.inf, 1),
59 complex(np.inf, np.inf),
60 complex(np.inf, np.nan),
61 complex(np.nan, 1),
62 complex(np.nan, np.inf),
63 complex(np.nan, np.nan),
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| D | ternary_ops_test.py | 22 import numpy as np namespace
54 expected = np.linspace(start, end, num, dtype=np.float32)
57 np.float32(start),
58 np.float32(end),
59 np.int32(num),
69 np.int32(1),
70 np.int32(2),
71 np.int32(1),
72 expected=np.array([1], dtype=np.int32))
75 np.int32(1),
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| D | gather_nd_op_test.py | 21 import numpy as np namespace
44 np.array([7, 7, 8], dtype=dtype),
46 np.array([8, 1, 2, 3, 7, 5], dtype=dtype),
47 np.array([[4], [4], [0]], np.int32)))
52 params = np.ones((3, 3), dtype=np.float32)
54 indices_empty = np.empty((0, 2), dtype=np.int32)
56 self.assertAllClose(np.empty((0,), dtype=np.float32), gather_nd_ok_val)
58 indices_empty = np.empty((0, 1), dtype=np.int32)
60 self.assertAllClose(np.empty((0, 3), dtype=np.float32), gather_nd_ok_val)
62 params_empty = np.empty((0, 3), dtype=np.float32)
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| D | dynamic_stitch_test.py | 21 import numpy as np namespace
53 idx1 = np.array([0, 2], dtype=np.int32)
54 idx2 = np.array([[1], [3]], dtype=np.int32)
55 val1 = np.array([[], []], dtype=np.int32)
56 val2 = np.array([[[]], [[]]], dtype=np.int32)
59 expected=np.array([[], [], [], []], np.int32))
62 idx1 = np.array([], dtype=np.int32)
63 idx2 = np.array([[], []], dtype=np.int32)
64 val1 = np.ndarray(shape=(0, 9), dtype=np.int32)
65 val2 = np.ndarray(shape=(2, 0, 9), dtype=np.int32)
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| D | reduce_ops_test.py | 25 import numpy as np namespace
79 np.zeros(shape=(2, 0)),
80 np.zeros(shape=(0, 30)),
81 np.arange(1, 7).reshape(2, 3),
82 np.arange(-10, -4).reshape(2, 3),
83 np.arange(-4, 2).reshape(2, 3),
86 np.zeros(shape=(2, 0)).astype(np.complex64),
87 np.zeros(shape=(0, 30)).astype(np.complex64),
88 np.arange(1, 13, dtype=np.float32).view(np.complex64).reshape(2, 3),
89 np.arange(-14, -2, dtype=np.float32).view(np.complex64).reshape(2, 3),
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| D | scatter_nd_op_test.py | 23 import numpy as np namespace
33 return v.astype(vtype) if isinstance(v, np.ndarray) else vtype(v)
68 ref = np.zeros(shape, dtype=updates.dtype)
80 np.random.seed(8)
92 for coord, _ in np.ndenumerate(np.empty(indexable_area_shape, vtype))
94 np.random.shuffle(all_indices)
95 indices = np.array(all_indices[:num_updates])
100 indices = np.append(
101 indices, [indices[np.random.randint(num_updates // 2)]], axis=0)
102 np.random.shuffle(indices)
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| D | xla_ops_test.py | 24 import numpy as np namespace
61 args=(np.array([1, 2, 3], dtype=dtype),
62 np.array([4, 5, 6], dtype=dtype)),
63 expected=np.array([5, 7, 9], dtype=dtype))
67 args=(np.array([[1, 2], [3, 4]], dtype=dtype),
68 np.array([7, 11], dtype=dtype)),
69 expected=np.array([[8, 9], [14, 15]], dtype=dtype))
73 args=(np.array([[1, 2], [3, 4]], dtype=dtype),
74 np.array([7, 11], dtype=dtype)),
75 expected=np.array([[8, 13], [10, 15]], dtype=dtype))
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| /external/tensorflow/tensorflow/python/lib/core/ |
| D | bfloat16_test.py | 29 import numpy as np namespace
40 a = a.astype(np.float32) if a.dtype == bfloat16 else a
41 b = b.astype(np.float32) if b.dtype == bfloat16 else b
42 return np.testing.assert_allclose(a, b, **kwargs)
62 np.testing.assert_equal(v, float(bfloat16(v)))
65 for dtype in [np.float16, np.float32, np.float64]:
66 np.testing.assert_equal(-3.75, dtype(bfloat16(dtype(-3.75))))
67 np.testing.assert_equal(1.5, float(bfloat16(dtype(1.5))))
68 np.testing.assert_equal(4.5, dtype(bfloat16(np.array(4.5, dtype))))
69 np.testing.assert_equal(
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| /external/tensorflow/tensorflow/python/kernel_tests/boosted_trees/ |
| D | training_ops_test.py | 20 import numpy as np namespace
50 feature1_nodes = np.array([0], dtype=np.int32)
51 feature1_gains = np.array([7.62], dtype=np.float32)
52 feature1_thresholds = np.array([52], dtype=np.int32)
53 feature1_left_node_contribs = np.array([[-4.375]], dtype=np.float32)
54 feature1_right_node_contribs = np.array([[7.143]], dtype=np.float32)
56 feature2_nodes = np.array([0], dtype=np.int32)
57 feature2_gains = np.array([0.63], dtype=np.float32)
58 feature2_thresholds = np.array([23], dtype=np.int32)
59 feature2_left_node_contribs = np.array([[-0.6]], dtype=np.float32)
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | dense_attention_test.py | 22 import numpy as np namespace
42 scores = np.array([[[1.1]]], dtype=np.float32)
44 v = np.array([[[1.6]]], dtype=np.float32)
46 scores_mask = np.array([[[True]]], dtype=np.bool_)
51 expected_scores = np.array([[[1.]]], dtype=np.float32)
55 expected = np.array([[[1.6]]], dtype=np.float32)
60 scores = np.array([[[1.1]]], dtype=np.float32)
62 v = np.array([[[1.6]]], dtype=np.float32)
67 expected_scores = np.array([[[1.]]], dtype=np.float32)
71 expected = np.array([[[1.6]]], dtype=np.float32)
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | cwise_ops_unary_test.py | 23 import numpy as np namespace
46 def _sparsify(x, thresh=0.5, index_dtype=np.int64):
49 non_zero = np.where(x)
50 x_indices = np.vstack(non_zero).astype(index_dtype).T
66 if dtype == np.float16:
68 elif dtype in (np.float32, np.complex64):
70 elif dtype in (np.float64, np.complex128):
89 if x.dtype == np.float16:
96 if x.dtype in (np.complex64, np.complex128) and tf_func == math_ops.sign:
99 if x.dtype in (np.float16, dtypes_lib.bfloat16.as_numpy_dtype):
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| D | cast_op_test.py | 21 import numpy as np namespace
38 if dtype == np.float32:
40 elif dtype == np.float64:
42 elif dtype == np.int32:
44 elif dtype == np.int64:
46 elif dtype == np.bool:
48 elif dtype == np.complex64:
50 elif dtype == np.complex128:
57 val = constant_op.constant(x, self._toDataType(np.array([x]).dtype))
71 np.float32, np.float64, np.int64, np.complex64, np.complex128
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| D | cwise_ops_binary_test.py | 21 import numpy as np namespace
49 def _sparsify(x, thresh=0.5, index_dtype=np.int64):
52 non_zero = np.where(x)
53 x_indices = np.vstack(non_zero).astype(index_dtype).T
67 if dtype == np.float16:
69 elif dtype in (np.float32, np.complex64):
71 elif dtype in (np.float64, np.complex128):
99 if np_ans.dtype != np.object:
127 if x.dtype in (np.float32, np.float64):
157 if x.dtype in (np.float32, np.float64):
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| D | relu_op_test.py | 21 import numpy as np namespace
40 return np.exp(activation)
47 return np.maximum(np_features, np.zeros(np_features.shape))
51 np.array([[0.0, 0.7, 0.0, 0.3, 0.0], [0.1, 0.0, 0.5, 0.0, 0.9]]),
53 np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7,
63 for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
67 np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
72 for t in [np.float16, np.float32, np.float64]:
74 np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
79 inputs = np.array([[-50, 7, 23, 0], [-1, -5, 6, 11]])
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| D | parse_single_example_op_test.py | 23 import numpy as np namespace
53 return (np.empty(shape=(0, len(shape)), dtype=np.int64),
54 np.array([], dtype=dtype), np.array(shape, dtype=np.int64))
132 b_default = np.random.rand(3, 3).astype(bytes)
133 c_default = np.random.rand(2).astype(np.float32)
136 np.empty((0, 1), dtype=np.int64), # indices
137 np.empty((0,), dtype=np.int64), # sp_a is DT_INT64
138 np.array([0], dtype=np.int64)) # max_elems = 0
142 a_name: np.array([a_default]),
143 b_name: np.array(b_default),
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| D | cwise_ops_test.py | 21 import numpy as np namespace
56 def _sparsify(x, thresh=0.5, index_dtype=np.int64):
59 non_zero = np.where(x)
60 x_indices = np.vstack(non_zero).astype(index_dtype).T
74 if dtype == np.float16:
76 elif dtype in (np.float32, np.complex64):
78 elif dtype in (np.float64, np.complex128):
89 ops.convert_to_tensor(np.array([x]).astype(dtype)),
90 ops.convert_to_tensor(np.array([y]).astype(dtype)))
95 dtypes = [np.float16, np.float32, np.float64, np.int32, np.int64]
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| D | transpose_op_test.py | 23 import numpy as np namespace
38 ret = np.copy(x)
45 perm = (rank - 1) - np.arange(rank)
50 np_ans = np.conj(np_ans)
60 xs = list(np.shape(x))
61 ys = list(np.shape(tf_ans))
62 if x.dtype in [np.float32, np.complex64]:
66 elif x.dtype in [np.float64, np.complex128]:
76 perm = (rank - 1) - np.arange(rank)
81 np_ans = np.conj(np_ans)
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| D | bincount_op_test.py | 21 import numpy as np namespace
49 bincount_ops.bincount([], minlength=0, dtype=np.float32)).dtype,
50 np.float32)
53 bincount_ops.bincount([], minlength=3, dtype=np.float64)).dtype,
54 np.float64)
73 self.evaluate(bincount_ops.bincount(np.arange(10000))),
74 np.ones(10000))
88 np.random.seed(42)
90 arr = np.random.randint(0, 1000, num_samples)
92 weights = np.random.randint(-100, 100, num_samples)
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| /external/tensorflow/tensorflow/python/ops/ |
| D | bincount_ops_test.py | 22 import numpy as np namespace
42 "x": np.array([[3, 2, 1], [5, 4, 4]], dtype=np.int32),
48 "x": np.array([[3, 2, 1, 7], [7, 0, 4, 4]], dtype=np.int32),
55 "x": np.array([[3, 2, 1, 7], [7, 0, 4, 4]], dtype=np.int32),
63 "x": np.array([[3, 2, 1, 7], [7, 0, 4, 4]], dtype=np.int32),
71 "x": np.array([[3, 2, 1], [5, 4, 4]], dtype=np.int32),
78 "x": np.array([[3, 2, 1, 7], [7, 0, 4, 4]], dtype=np.int32),
86 "x": np.array([[3, 2, 1, 7], [7, 0, 4, 4]], dtype=np.int32),
95 "x": np.array([[3, 2, 1, 7], [7, 0, 4, 4]], dtype=np.int32),
104 "x": np.array([[3, 2, 1], [5, 4, 4]], dtype=np.int32),
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| /external/python/pybind11/tests/ |
| D | test_numpy_vectorize.py | 5 np = pytest.importorskip("numpy") variable
9 assert np.isclose(m.vectorized_func3(np.array(3 + 7j)), [6 + 14j])
13 assert np.isclose(f(1, 2, 3), 6)
16 assert np.isclose(f(np.array(1), np.array(2), 3), 6)
19 assert np.allclose(f(np.array([1, 3]), np.array([2, 4]), 3), [6, 36])
28 a = np.array([[1, 2], [3, 4]], order="F")
29 b = np.array([[10, 20], [30, 40]], order="F")
32 assert np.allclose(result, a * b * c)
46 np.array([[1, 3, 5], [7, 9, 11]]),
47 np.array([[2, 4, 6], [8, 10, 12]]),
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| /external/tensorflow/tensorflow/python/data/kernel_tests/ |
| D | from_tensor_slices_test.py | 23 import numpy as np namespace
42 np.tile(np.array([[1], [2], [3], [4]]), 20), np.tile(
43 np.array([[12], [13], [14], [15]]), 22),
44 np.array([37.0, 38.0, 39.0, 40.0])
91 indices=np.array([[0, 0], [1, 0], [2, 0]]),
92 values=np.array([0, 0, 0]),
93 dense_shape=np.array([3, 1])),
95 indices=np.array([[0, 0], [1, 1], [2, 2]]),
96 values=np.array([1, 2, 3]),
97 dense_shape=np.array([3, 3])))
[all …]
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| /external/tensorflow/tensorflow/python/ops/numpy_ops/ |
| D | np_arrays_test.py | 21 import numpy as np namespace
38 self.assertIs(a.dtype.as_numpy_dtype, np.int64)
43 a = ops.convert_to_tensor(value=1.1, dtype=dtypes.float32).astype(np.int32)
44 self.assertIs(a.dtype.as_numpy_dtype, np.int32)
47 np.bool_)
48 self.assertIs(a.dtype.as_numpy_dtype, np.bool_)
56 a = ops.convert_to_tensor(value=a, dtype=np.int32)
57 b = ops.convert_to_tensor(value=b, dtype=np.int32)
59 out = ops.convert_to_tensor(value=out, dtype=np.int32)
61 types = [[np.int32, np.int32, np.int32], [np.int64, np.int32, np.int64],
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