| /external/tensorflow/tensorflow/compiler/tests/ |
| binary_ops_test.py | 41 pa = array_ops.placeholder(dtypes.as_dtype(a.dtype), a.shape, name="a")
42 pb = array_ops.placeholder(dtypes.as_dtype(b.dtype), b.shape, name="b")
60 for dtype in self.float_types:
61 if dtype == dtypes.bfloat16.as_numpy_dtype:
69 np.array([[[[-1, 2.00009999], [-3, b]]]], dtype=dtype),
70 np.array([[[[a, 2], [-3.00009, 4]]]], dtype=dtype),
71 expected=np.array([[[[False, True], [True, False]]]], dtype=dtype))
[all...] |
| unary_ops_test.py | 66 dtypes.as_dtype(inp.dtype), inp.shape, name="a")
82 for dtype in self.numeric_types:
85 np.array([1, 2, 3, 4], dtype=dtype),
87 dtype=dtype))
90 np.arange(36).reshape([2, 3, 2, 3]).astype(dtype),
91 np.array([[0, 7, 14], [21, 28, 35]], dtype=dtype))
93 array_ops.diag, np.array([[1, 2], [3, 4]], dtype=dtype)
[all...] |
| segment_reduction_ops_test.py | 35 d = array_ops.placeholder(data.dtype, shape=data.shape)
39 i = array_ops.placeholder(indices.dtype, shape=indices.shape)
46 for dtype in self.numeric_types:
51 dtype=dtype),
53 np.array([0, 1, 2, 3, 4, 5], dtype=dtype), 2, 4))
56 for dtype in self.numeric_types:
58 np.array([1, 3, 2, 9], dtype=dtype),
[all...] |
| nary_ops_test.py | 38 array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape)
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)
[all...] |
| adagrad_test.py | 34 for dtype in self.float_types:
36 var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
37 var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
38 grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
39 grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
59 for dtype in self.float_types
[all...] |
| argminmax_test.py | 43 dtypes.as_dtype(inp.dtype), inp.shape, name="a")
51 for dtype in minmax_types:
54 np.array([1, 10, 27, 3, 3, 4], dtype=dtype),
58 np.array([[4, 1, 7], [3, 2, 4]], dtype=dtype),
59 expected=np.array([0, 1, 0], dtype=np.int32))
62 np.array([[4, 1], [3, 2]], dtype=dtype),
63 expected=np.array([0, 0], dtype=np.int32)
[all...] |
| random_ops_test.py | 35 def _testRngIsNotConstant(self, rng, dtype):
39 x = rng(dtype)
54 def rng(dtype):
55 return random_ops.random_uniform(shape=[2], dtype=dtype,
58 for dtype in self._random_types():
59 self._testRngIsNotConstant(rng, dtype)
62 def rng(dtype):
63 return random_ops.random_normal(shape=[2], dtype=dtype)
[all...] |
| ternary_ops_test.py | 35 pa = array_ops.placeholder(dtypes.as_dtype(a.dtype), a.shape, name="a")
36 pb = array_ops.placeholder(dtypes.as_dtype(b.dtype), b.shape, name="b")
37 pc = array_ops.placeholder(dtypes.as_dtype(c.dtype), c.shape, name="c")
48 expected=np.array([1], dtype=np.float32))
54 expected=np.array([1, 2.5, 4], dtype=np.float32))
62 expected=np.array([1], dtype=np.int32))
68 expected=np.array([1, 3, 5], dtype=np.int32))
73 np.array(0, dtype=np.bool),
74 np.array(2, dtype=np.float32),
75 np.array(7, dtype=np.float32)
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| warn_about_ints.cc | 22 DataType dtype; local
23 OP_REQUIRES_OK(context, context->GetAttr("T", &dtype));
24 if (DataTypeIsInteger(dtype)) {
26 << context->def().op() << " used with integer dtype "
27 << DataTypeString(dtype)
|
| /external/tensorflow/tensorflow/python/training/ |
| adagrad_da_test.py | 36 for dtype in [dtypes.float64, dtypes.float32]:
38 global_step = variables.Variable(0, dtype=dtypes.int64)
40 var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
41 var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
43 var0 = variables.Variable([0.0, 0.0], dtype=dtype)
44 var1 = variables.Variable([0.0, 0.0], dtype=dtype)
[all...] |
| gradient_descent_test.py | 36 for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
38 var0 = variables.Variable([1.0, 2.0], dtype=dtype)
39 var1 = variables.Variable([3.0, 4.0], dtype=dtype)
40 grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
41 grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
59 for dtype in [dtypes.half, dtypes.float32, dtypes.float64]
[all...] |
| ftrl_test.py | 39 for dtype in [dtypes.half, dtypes.float32]:
42 var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
43 var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
45 var0 = variables.Variable([0.0, 0.0], dtype=dtype)
46 var1 = variables.Variable([0.0, 0.0], dtype=dtype)
47 grads0 = constant_op.constant([0.1, 0.2], dtype=dtype
[all...] |
| /external/tensorflow/tensorflow/contrib/bayesflow/python/kernel_tests/ |
| variational_sgd_optimizer_test.py | 32 for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
34 var0 = variables.Variable([1.1, 2.1], dtype=dtype)
35 var1 = variables.Variable([3.0, 4.0], dtype=dtype)
36 grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
37 grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
59 for dtype in [dtypes.half, dtypes.float32, dtypes.float64]
[all...] |
| sgld_optimizer_test.py | 32 for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
34 var0 = variables.Variable([1.1, 2.1], dtype=dtype)
35 var1 = variables.Variable([3.0, 4.0], dtype=dtype)
36 grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
37 grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
60 for dtype in [dtypes.half, dtypes.float32, dtypes.float64]
[all...] |
| /external/tensorflow/tensorflow/contrib/signal/python/ops/ |
| window_ops.py | 32 def hann_window(window_length, periodic=True, dtype=dtypes.float32, name=None):
41 dtype: The data type to produce. Must be a floating point type.
45 A `Tensor` of shape `[window_length]` of type `dtype`.
48 ValueError: If `dtype` is not a floating point type.
53 dtype, 0.5, 0.5)
56 def hamming_window(window_length, periodic=True, dtype=dtypes.float32,
66 dtype: The data type to produce. Must be a floating point type.
70 A `Tensor` of shape `[window_length]` of type `dtype`.
73 ValueError: If `dtype` is not a floating point type.
78 dtype, 0.54, 0.46
[all...] |
| /bionic/libm/upstream-freebsd/lib/msun/src/ |
| s_llrint.c | 6 #define dtype long long macro
|
| s_llrintf.c | 6 #define dtype long long macro
|
| s_llrintl.c | 6 #define dtype long long macro
|
| s_lrint.c | 35 #define dtype long macro
42 * that overflows depends on the rounding mode when 'dtype' has more
46 dtype
50 dtype d;
53 d = (dtype)roundit(x);
|
| s_lrintf.c | 6 #define dtype long macro
|
| s_lrintl.c | 6 #define dtype long macro
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| sets_test.py | 40 def _values(values, dtype):
43 dtype=(np.unicode if (dtype == dtypes.string) else dtype.as_numpy_dtype))
46 def _constant(values, dtype):
47 return constant_op.constant(_values(values, dtype), dtype=dtype)
50 def _dense_to_sparse(dense, dtype):
62 values.append(str(cell) if dtype == dtypes.string else cell
[all...] |
| /external/tensorflow/tensorflow/python/ops/ |
| random_ops.py | 40 dtype = dtypes.int32
42 dtype = None
43 return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
51 dtype=dtypes.float32,
58 mean: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
60 stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
62 dtype: The type of the output.
74 mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean"
[all...] |
| /external/tensorflow/tensorflow/contrib/reduce_slice_ops/python/kernel_tests/ |
| reduce_slice_ops_test.py | 31 x = np.array([1, 40, 700], dtype=np.int32)
32 indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
33 result = np.array([1, 741, 40, 740, 41], dtype=np.int32)
39 x = np.array([[1, 2, 3], [40, 50, 60], [700, 800, 900]], dtype=np.int32)
40 indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
42 [740, 850, 960], [41, 52, 63]], dtype=np.int32)
49 [[600, 700], [800, 900]]], dtype=np.int32)
50 indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
55 [[51, 62], [73, 84]]], dtype=np.int32)
62 [700, 800, 900]], dtype=np.int32)
[all...] |
| /external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/ |
| attention_wrapper_test.py | 53 dtype = np.dtype variable
57 collections.namedtuple('ResultSummary', ('shape', 'dtype', 'mean'))):
63 return ResultSummary(x.shape, x.dtype, x.mean())
187 dtype=dtypes.float32, batch_size=batch_size))
265 shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0052250605),
267 shape=(5, 3), dtype=dtype('int32'), mean=1.4))
271 shape=(5, 9), dtype=dtype('float32'), mean=-0.0040092287)
[all...] |
