| /external/tensorflow/tensorflow/compiler/tests/ |
| D | nary_ops_test.py | 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))
94 [np.array([[1, 2, 3]], dtype=np.float32)],
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| D | tensor_list_ops_test.py | 39 element_dtype=dtypes.float32,
50 element_dtype=dtypes.float32,
56 l, e2 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
57 _, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
63 val = array_ops.placeholder(dtype=dtypes.float32)
66 element_dtype=dtypes.float32,
75 l, e2 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
76 _, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
84 element_dtype=dtypes.float32,
89 _, e11 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
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| D | stack_ops_test.py | 36 v = array_ops.placeholder(dtypes.float32)
37 h = gen_data_flow_ops.stack_v2(size, dtypes.float32, stack_name="foo")
40 c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
46 x = array_ops.placeholder(dtypes.float32)
47 h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
50 c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
55 v = array_ops.placeholder(dtypes.float32)
56 h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
59 c1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32)
60 h2 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="bar")
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| D | image_ops_test.py | 112 flt_x = image_ops.convert_image_dtype(x, dtypes.float32)
122 x_np = np.array(x_data, dtype=np.float32).reshape(x_shape) / 255.
128 y_np = np.array(y_data, dtype=np.float32).reshape(x_shape) / 255.
149 x = array_ops.placeholder(np.float32)
184 flt_x = image_ops.convert_image_dtype(x, dtypes.float32)
202 flt_x = image_ops.convert_image_dtype(x, dtypes.float32)
220 flt_x = image_ops.convert_image_dtype(x, dtypes.float32)
247 x = array_ops.placeholder(dtypes.float32)
311 flt_image = image_ops.convert_image_dtype(image, dtypes.float32)
399 x = array_ops.placeholder(dtypes.float32, shape=x_shape)
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| D | function_test.py | 39 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
40 bval = np.array([5, 6, 7, 8]).reshape([2, 2]).astype(np.float32)
45 @function.Defun(dtypes.float32, dtypes.float32)
65 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
66 bval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
71 @function.Defun(dtypes.float32, dtypes.float32)
89 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
90 bval = np.array([5, 6, 7, 8]).reshape([2, 2]).astype(np.float32)
95 @function.Defun(dtypes.float32, dtypes.float32)
110 @function.Defun(dtypes.float32, dtypes.int32, dtypes.int32)
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| D | fused_batchnorm_test.py | 78 x_val = np.random.random_sample(x_shape).astype(np.float32)
79 scale_val = np.random.random_sample(scale_shape).astype(np.float32)
80 offset_val = np.random.random_sample(scale_shape).astype(np.float32)
94 np.float32, shape=x_val_converted.shape, name="x")
95 scale = array_ops.placeholder(np.float32, shape=scale_shape, name="scale")
97 np.float32, shape=scale_shape, name="offset")
119 x_val = np.random.random_sample(x_shape).astype(np.float32)
120 scale_val = np.random.random_sample(scale_shape).astype(np.float32)
121 offset_val = np.random.random_sample(scale_shape).astype(np.float32)
122 mean_val = np.random.random_sample(scale_shape).astype(np.float32)
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| /external/tensorflow/tensorflow/lite/testing/nnapi_tflite_zip_tests/ |
| D | not_supported.txt | 9 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf…
10 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf…
13 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf…
14 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf…
17 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.i…
18 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.i…
21 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[5,5],is_arg_max=True,output_type=tf.int…
22 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[5,5],is_arg_max=True,output_type=tf.int…
25 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[10],is_arg_max=True,output_type=tf.int32
26 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[10],is_arg_max=True,output_type=tf.int64
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| D | test_manifest.txt | 1 add/add_activation=True,dtype=tf.float32,input_shape_1=[1,3,4,3],input_shape_2=[1,3,4,3]
3 add/add_activation=False,dtype=tf.float32,input_shape_1=[5],input_shape_2=[5]
4 add/add_activation=True,dtype=tf.float32,input_shape_1=[5],input_shape_2=[5]
5 add/add_activation=True,dtype=tf.float32,input_shape_1=[1,3,4,3],input_shape_2=[3]
6 add/add_activation=False,dtype=tf.float32,input_shape_1=[1,3,4,3],input_shape_2=[3]
11 add/add_activation=True,dtype=tf.float32,input_shape_1=[3],input_shape_2=[1,3,4,3]
12 add/add_activation=False,dtype=tf.float32,input_shape_1=[3],input_shape_2=[1,3,4,3]
15 DISABLED_add/add_activation=False,dtype=tf.float32,input_shape_1=[],input_shape_2=[]
16 DISABLED_add/add_activation=False,dtype=tf.float32,input_shape_1=[0],input_shape_2=[1]
65 concat/concat_axis=0,base_shape=[1,3,4,3],num_tensors=1,type=tf.float32
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | dense_attention_test.py | 34 scores = np.array([[[1.1]]], dtype=np.float32)
36 v = np.array([[[1.6]]], dtype=np.float32)
44 expected = np.array([[[1.6]]], dtype=np.float32)
49 scores = np.array([[[1.1]]], dtype=np.float32)
51 v = np.array([[[1.6]]], dtype=np.float32)
57 expected = np.array([[[1.6]]], dtype=np.float32)
62 scores = np.array([[[1., 0., 1.]]], dtype=np.float32)
64 v = np.array([[[1.6], [0.7], [-0.8]]], dtype=np.float32)
79 expected = np.array([[[1.35795272077]]], dtype=np.float32)
84 scores = np.array([[[1., 0., 1.]]], dtype=np.float32)
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | list_ops_test.py | 50 element_dtype=dtypes.float32,
54 l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
73 element_dtype=dtypes.float32, element_shape=[], max_num_elements=1)
85 element_dtype=dtypes.float32,
90 l = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
95 element_dtype=dtypes.float32, element_shape=[2, 3], num_elements=3)
96 _, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
101 element_dtype=dtypes.float32, element_shape=[None, 3], num_elements=3)
103 l, element_dtype=dtypes.float32, element_shape=[4, 3])
108 element_dtype=dtypes.float32, element_shape=None, num_elements=3)
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| D | sparse_conditional_accumulator_test.py | 63 dtypes_lib.float32, name="Q")
79 dtypes_lib.float32, name="Q", reduction_type="Invalid")
84 dtypes_lib.float32,
106 dtypes_lib.float32, name="Q")
113 dtypes_lib.float32, name="Q", shape=tensor_shape.TensorShape([1]))
121 dtypes_lib.float32, name="Q", shape=tensor_shape.TensorShape([3, 3]))
125 values=np.array([[0, 0, 1], [3, 0, 4]]).astype(np.float32)))
132 dtypes = [dtypes_lib.float16, dtypes_lib.float32, dtypes_lib.float64]
156 dtypes_lib.float32, name="Q", shape=tensor_shape.TensorShape([2, 2]))
158 dtypes_lib.float32, name="Q", shape=tensor_shape.TensorShape([2, 2]))
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| D | sparse_matmul_op_test.py | 38 64) / 128.0).reshape([rows, cols]).astype(np.float32)
50 x_dtype=dtypes.float32,
51 y_dtype=dtypes.float32):
63 np_x = math_ops.cast(tf_x, dtypes.float32).eval()
64 np_y = math_ops.cast(tf_y, dtypes.float32).eval()
77 x = np.arange(0., 4.).reshape([4, 1]).astype(np.float32)
78 y = np.arange(-1., 1.).reshape([1, 2]).astype(np.float32)
79 for x_dtype in (dtypes.float32, dtypes.bfloat16):
80 for y_dtype in (dtypes.float32, dtypes.bfloat16):
85 x = np.ones((4, 0)).astype(np.float32)
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| D | stack_ops_test.py | 39 -1, elem_type=dtypes.float32, stack_name="foo")
42 c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
53 x = constant_op.constant(a, dtype=dtypes.float32)
55 -1, elem_type=dtypes.float32, stack_name="foo")
58 c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
70 -1, elem_type=dtypes.float32, stack_name="foo")
77 a = constant_op.constant(np.ones(2000), dtype=dtypes.float32)
84 v = constant_op.constant(np.zeros(2000), dtype=dtypes.float32)
92 ny = y + gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
107 -1, elem_type=dtypes.float32, stack_name="foo")
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| D | variable_ops_test.py | 36 np.float32: dtypes.float32,
53 for dtype in [np.float32, np.float64, np.int32, np.int64]:
69 p = state_ops.variable_op([1, 2], dtypes.float32)
71 p = state_ops.variable_op([1, 2], dtypes.float32, set_shape=False)
77 var = state_ops.variable_op(value.shape, dtypes.float32)
85 var = state_ops.variable_op(value.shape, dtypes.float32)
93 var = state_ops.variable_op(value.shape, dtypes.float32, set_shape=False)
101 var = state_ops.variable_op(value.shape, dtypes.float32, set_shape=False)
107 tensor = array_ops.placeholder(dtypes.float32)
115 var = state_ops.variable_op(shape, dtypes.float32)
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| D | parse_single_example_op_test.py | 133 c_default = np.random.rand(2).astype(np.float32)
160 (2,), dtypes.float32, default_value=c_default),
179 (2,), dtype=dtypes.float32),
212 "a": parsing_ops.FixedLenFeature((1, 3), dtypes.float32)
229 "a": parsing_ops.FixedLenFeature(None, dtypes.float32)
254 np.array([3.0, 4.0], dtype=np.float32),
259 "st_c": empty_sparse(np.float32),
262 "st_c": empty_sparse(np.float32),
266 np.array([1.0, 2.0, -1.0], dtype=np.float32),
276 "st_c": parsing_ops.VarLenFeature(dtypes.float32),
[all …]
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| /external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
| D | conditioning_utils_test.py | 34 array_ops.placeholder(dtypes.float32, tensor_shape),
35 array_ops.placeholder(dtypes.float32, conditioning_shape))
40 array_ops.placeholder(dtypes.float32, (4, 1)),
41 array_ops.placeholder(dtypes.float32, (5, 1)))
45 array_ops.placeholder(dtypes.float32, (5, None)),
46 array_ops.placeholder(dtypes.float32, (5, 1)))
50 array_ops.placeholder(dtypes.float32, (5, 2)),
51 array_ops.placeholder(dtypes.float32, (5)))
55 array_ops.placeholder(dtypes.float32, (5, 4, 1)),
56 array_ops.placeholder(dtypes.float32, (5, 10)))
[all …]
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| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| D | vector_diffeomixture_test.py | 46 np.float32([2.]*dims),
51 multiplier=np.float32(1.1),
54 diag=np.linspace(2.5, 3.5, dims, dtype=np.float32),
75 np.float32([2.]*dims),
80 multiplier=np.float32(1.1),
83 diag=np.linspace(2.5, 3.5, dims, dtype=np.float32),
104 np.float32([2.]*dims),
109 multiplier=[np.float32(1.1)],
113 np.linspace(2.5, 3.5, dims, dtype=np.float32),
114 np.linspace(2.75, 3.25, dims, dtype=np.float32),
[all …]
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| D | vector_student_t_test.py | 80 df = np.asarray(3., dtype=np.float32)
82 loc = np.asarray([1], dtype=np.float32)
83 scale_diag = np.asarray([2.], dtype=np.float32)
91 x = 2. * self._rng.rand(4, 1).astype(np.float32) - 1.
102 df = np.asarray([1., 2, 3], dtype=np.float32)
107 dtype=np.float32)
111 dtype=np.float32)
114 x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
131 df = np.asarray([1., 2, 3], dtype=np.float32)
136 dtype=np.float32)
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| D | independent_test.py | 53 loc = np.float32([-1., 1])
54 scale = np.float32([0.1, 0.5])
74 loc = np.float32([[-1., 1], [1, -1]])
75 scale = np.float32([1., 0.5])
98 loc = np.float32([[-1., 1], [1, -1]])
99 scale = np.float32([1., 0.5])
133 loc=np.float32([-1., 1]),
134 scale=np.float32([0.1, 0.5])),
138 loc=np.float32(-1),
139 scale=np.float32(0.5)),
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| /external/tensorflow/tensorflow/python/kernel_tests/boosted_trees/ |
| D | training_ops_test.py | 47 feature1_gains = np.array([7.62], dtype=np.float32)
49 feature1_left_node_contribs = np.array([[-4.375]], dtype=np.float32)
50 feature1_right_node_contribs = np.array([[7.143]], dtype=np.float32)
53 feature2_gains = np.array([0.63], dtype=np.float32)
55 feature2_left_node_contribs = np.array([[-0.6]], dtype=np.float32)
56 feature2_right_node_contribs = np.array([[0.24]], dtype=np.float32)
60 feature3_gains = np.array([7.65], dtype=np.float32)
62 feature3_left_node_contribs = np.array([[-4.89]], dtype=np.float32)
63 feature3_right_node_contribs = np.array([[5.3]], dtype=np.float32)
152 gradients = np.array([[5.]], dtype=np.float32)
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| /external/tensorflow/tensorflow/python/ops/ |
| D | nn_fused_batchnorm_test.py | 167 x32_init_val = x_init_val.astype(np.float32)
219 x32 = constant_op.constant(x_val, name='x32', dtype=dtypes.float32)
305 x32 = constant_op.constant(x_val, dtype=dtypes.float32, name='x32')
307 grad_y_val, dtype=dtypes.float32, name='grad_y32')
344 for dtype in [np.float16, np.float32]:
347 x_shape, dtype, [1], np.float32, use_gpu=True, data_format='NHWC')
349 x_shape, dtype, [1], np.float32, use_gpu=True, data_format='NCHW')
351 x_shape, dtype, [1], np.float32, use_gpu=False, data_format='NHWC')
356 for dtype in [np.float16, np.float32]:
358 x_shape, dtype, [2], np.float32, use_gpu=True, data_format='NHWC')
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| /external/tensorflow/tensorflow/python/eager/ |
| D | function_argument_naming_test.py | 49 tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32),
50 tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32))
73 tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32),
92 x=tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32),
93 y=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32))
105 z=(tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32),
106 tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32)),
107 y=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32,
111 z=(tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32,
113 tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32,
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_infeed_test.py | 39 tuple_types=[dtypes.float32, dtypes.int32, dtypes.int32])
42 [dtypes.float32, dtypes.int32, dtypes.int32])
60 number_of_tuple_elements=2, tuple_types=[dtypes.float32])
71 i.set_tuple_types([dtypes.float32, dtypes.int32])
72 self.assertEqual(i.tuple_types, [dtypes.float32, dtypes.int32])
73 i.set_tuple_types([dtypes.float32, dtypes.float32])
74 self.assertEqual(i.tuple_types, [dtypes.float32, dtypes.float32])
76 i.set_tuple_types([dtypes.float32])
88 t2 = constant_op.constant(2.0, dtypes.float32, shape=[3, 18])
91 self.assertEqual(i.tuple_types, [dtypes.int32, dtypes.float32])
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| /external/tensorflow/tensorflow/python/keras/mixed_precision/experimental/ |
| D | autocast_variable_test.py | 76 x = get_var(1., dtypes.float32)
81 self.assertEqual(x.dtype, dtypes.float32)
82 self.assertEqual(x.value().dtype, dtypes.float32)
83 self.assertEqual(x.read_value().dtype, dtypes.float32)
84 self.assertEqual(array_ops.identity(x).dtype, dtypes.float32)
96 dtypes.float32):
97 self.assertEqual(x.dtype, dtypes.float32)
98 self.assertEqual(x.value().dtype, dtypes.float32)
99 self.assertEqual(x.read_value().dtype, dtypes.float32)
100 self.assertEqual(array_ops.identity(x).dtype, dtypes.float32)
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| /external/tensorflow/tensorflow/python/tools/ |
| D | optimize_for_inference_test.py | 81 unused_constant_name, value=0, dtype=dtypes.float32, shape=[])
86 self.set_attr_dtype(unconnected_add_node, "T", dtypes.float32)
89 a_constant_name, value=1, dtype=dtypes.float32, shape=[])
98 b_constant_name, value=1, dtype=dtypes.float32, shape=[])
108 self.set_attr_dtype(add_node, "T", dtypes.float32)
112 self.set_attr_dtype(unused_output_add_node, "T", dtypes.float32)
117 a_constant_name, value=1, dtype=dtypes.float32, shape=[])
120 b_constant_name, value=1, dtype=dtypes.float32, shape=[])
124 self.set_attr_dtype(add_node, "T", dtypes.float32)
128 graph_def, [], [add_name], dtypes.float32.as_datatype_enum)
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