| /external/tensorflow/tensorflow/contrib/image/python/kernel_tests/ |
| distort_image_ops_test.py | 44 def _adjust_hue_in_yiq_np(self, x_np, delta_h):
51 x_np: input x with last dimension = 3.
55 Adjusted y with the same shape as x_np.
57 self.assertEqual(x_np.shape[-1], 3)
58 x_v = x_np.reshape([-1, 3])
72 return y_v.reshape(x_np.shape)
74 def _adjust_hue_in_yiq_tf(self, x_np, delta_h):
76 x = constant_op.constant(x_np)
98 x_np = np.random.rand(*x_shape) * 255.
103 x_np[..., 1] = x_np[..., 0
[all...] |
| single_image_random_dot_stereograms_ops_test.py | 36 x_np = [[1, 2, 3, 3, 2, 1],
41 x_tf = constant_op.constant(x_np)
|
| /external/tensorflow/tensorflow/compiler/tests/ |
| image_ops_test.py | 103 def _testContrast(self, x_np, y_np, contrast_factor):
105 x = array_ops.placeholder(x_np.dtype, shape=x_np.shape)
110 y_tf = y.eval({x: x_np})
116 x_np = np.array(x_data, dtype=np.float32).reshape(x_shape) / 255.
124 self._testContrast(x_np, y_np, contrast_factor=2.0)
129 x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
134 self._testContrast(x_np, y_np, contrast_factor=2.0)
136 def _adjustContrastNp(self, x_np, contrast_factor):
137 mean = np.mean(x_np, (1, 2), keepdims=True
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| spacetobatch_op_test.py | 92 x_np = [[[[1], [2]], [[3], [4]]]]
95 self._testOne(x_np, block_size, x_out)
99 x_np = [[[[1], [2]], [[3], [4]]]]
104 self._testPad(x_np, paddings, block_size, x_out)
109 x_np = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]]
112 self._testOne(x_np, block_size, x_out)
117 x_np = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]],
122 self._testOne(x_np, block_size, x_out)
127 x_np = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]],
132 self._testOne(x_np, block_size, x_out
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| variable_ops_test.py | 197 self.x_np = np.array(x).astype(dtype)
206 x = constant_op.constant(self.x_np, dtype=self.dtype)
215 valnp = np.copy(self.x_np)
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| spacetodepth_op_test.py | 56 x_np = [[[[1], [2]], [[3], [4]]]]
59 self._testOne(x_np, block_size, x_out)
64 x_np = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]],
69 self._testOne(x_np, block_size, x_out)
74 x_np = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]],
78 self._testOne(x_np, block_size, x_out)
83 x_np = [[[[1, 10], [2, 20]], [[3, 30], [4, 40]]]]
86 self._testOne(x_np, block_size, x_out)
91 x_np = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]]
94 self._testOne(x_np, block_size, x_out
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| depthtospace_op_test.py | 57 x_np = [[[[1, 2, 3, 4]]]]
60 self._testOne(x_np, block_size, x_out)
65 x_np = [[[[1, 2, 3, 4],
74 self._testOne(x_np, block_size, x_out)
89 x_np = [batch_input_elt(i) for i in range(batch_size)]
91 self._testOne(x_np, block_size, x_out)
95 x_np = [[[[1, 10, 2, 20, 3, 30, 4, 40]],
105 self._testOne(x_np, block_size, x_out)
110 x_np = [[[[1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 13, 14, 11, 12, 15, 16]]]]
116 self._testOne(x_np, block_size, x_out
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| spacetobatch_op_test.py | 119 x_np = [[[[1], [2]], [[3], [4]]]]
122 self._testOne(x_np, block_size, x_out)
126 x_np = [[[[1], [2]], [[3], [4]]]]
131 self._testPad(x_np, paddings, block_size, x_out)
136 x_np = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]]
139 self._testOne(x_np, block_size, x_out)
144 x_np = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]],
149 self._testOne(x_np, block_size, x_out)
154 x_np = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]],
159 self._testOne(x_np, block_size, x_out
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| qr_op_test.py | 117 x_np = np.random.uniform(
120 x_np += 1j * np.random.uniform(
126 x_tf = constant_op.constant(x_np)
134 q_tf_val, r_tf_val = sess.run([q_tf, r_tf], feed_dict={x_tf: x_np})
141 x_reshape = np.reshape(x_np, (-1, x_np.shape[-2], x_np.shape[-1]))
151 CheckApproximation(self, x_np, q_tf_val, r_tf_val)
|
| array_ops_test.py | 277 x_np = 4
280 x_tf = array_ops.reverse_v2(x_np, []).eval()
281 self.assertAllEqual(x_tf, x_np)
284 x_np = np.array([1, 200, 3, 40, 5], dtype=np_dtype)
289 x_tf = array_ops.reverse_v2(x_np,
292 self.assertAllEqual(x_tf, np.asarray(x_np)[::-1])
295 x_np = np.array([[1, 200, 3], [4, 5, 60]], dtype=np_dtype)
301 x_tf_1 = reverse_f(x_np, constant_op.constant(
303 x_tf_2 = reverse_f(x_np, constant_op.constant(
305 x_tf_3 = reverse_f(x_np, constant_op.constant
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| batchtospace_op_test.py | 75 x_np = [[[1], [2]], [[3], [4]]]
79 _ = self.batch_to_space(x_np, crops, block_size)
83 x_np = [[[[1], [2]], [[3], [4]]]]
87 out_tf = self.batch_to_space(x_np, crops, block_size)
92 x_np = [[[[1], [2]], [[3], [4]]]]
96 out_tf = self.batch_to_space(x_np, crops, block_size)
101 x_np = [[[[1], [2]], [[3], [4]]]]
105 out_tf = self.batch_to_space(x_np, crops, block_size)
110 x_np = [[[[1], [2], [3]], [[3], [4], [7]]]]
114 _ = self.batch_to_space(x_np, crops, block_size
[all...] |
| svd_op_test.py | 136 x_np = np.random.uniform(
139 x_np += 1j * np.random.uniform(
145 x_tf = constant_op.constant(x_np)
156 [s_tf, u_tf, v_tf], feed_dict={x_tf: x_np})
163 s_tf_val = sess.run(s_tf, feed_dict={x_tf: x_np})
167 x_np, compute_uv=compute_uv_, full_matrices=full_matrices_)
170 x_np, compute_uv=compute_uv_, full_matrices=full_matrices_)
181 CheckApproximation(self, x_np, u_tf_val, s_tf_val, v_tf_val,
|
| shape_ops_test.py | 62 def _compareShapeSparse(self, x_np, use_gpu=False):
63 np_ans = np.array(np.shape(x_np))
64 x_tf, unused_nnz = _sparsify(x_np)
91 def _compareRankSparse(self, x_np, use_gpu=False):
92 np_ans = np.asarray(np.ndim(x_np))
93 x_tf, unused_nnz = _sparsify(x_np)
111 def _compareSizeSparse(self, x_np, use_gpu=False):
112 np_ans = np.asarray(np.size(x_np))
113 x_tf, unused_nnz = _sparsify(x_np)
|
| fft_ops_test.py | 46 x_np = self._npFFT(x, rank, fft_length)
53 self.assertAllClose(x_np, x_tf, rtol=1e-4, atol=1e-4)
56 x_np = self._npIFFT(x, rank, fft_length)
63 self.assertAllClose(x_np, x_tf, rtol=1e-4, atol=1e-4)
|
| transpose_op_test.py | 400 x_np = [[1, 2, 3], [4, 5, 6]]
403 x_tf = array_ops.transpose(x_np).eval()
|
| /external/tensorflow/tensorflow/contrib/solvers/python/kernel_tests/ |
| util_test.py | 34 x_np = np.array([[2.], [-3.]], dtype=dtype)
39 x = constant_op.constant(x_np, dtype=dtype)
54 x: x_np,
57 self.assertAllClose(ax_val, np.dot(a_np, x_np))
69 x_np = np.array([[2.], [-3.]], dtype=dtype)
74 x = constant_op.constant(x_np, dtype=dtype)
90 x: x_np,
94 self.assertAllClose(ax_val, x_np)
105 x_np = np.array([[2], [-3.], [5.]])
106 x_norm_np = np.linalg.norm(x_np)
[all...] |
| linear_equations_test.py | 54 x_np = np.zeros_like(rhs_np)
61 x = constant_op.constant(x_np)
90 x: x_np,
|
| /external/tensorflow/tensorflow/python/ops/ |
| image_ops_test.py | 167 def _TestRGBToGrayscale(self, x_np):
168 y_np = self._RGBToGrayscale(x_np)
171 x_tf = constant_op.constant(x_np, shape=x_np.shape)
178 x_np = np.array(
180 self._TestRGBToGrayscale(x_np)
183 x_np = np.array([[1, 2, 3], [4, 10, 1]], dtype=np.uint8).reshape([1, 2, 3])
184 self._TestRGBToGrayscale(x_np)
188 x_np = np.array([[1, 2]], dtype=np.uint8).reshape([1, 1, 2, 1])
193 x_tf = constant_op.constant(x_np, shape=x_np.shape
[all...] |
| math_ops_test.py | 78 x_np = np.random.rand(5, 5).astype(dtype)
80 y_tf_np = math_ops.reduce_logsumexp(x_np).eval()
81 y_np = log(np.sum(exp(x_np)))
86 x_np = np.random.rand(5, 5).astype(dtype)
88 y_tf = math_ops.reduce_logsumexp(x_np, reduction_indices=[0])
89 y_np = log(np.sum(exp(x_np), axis=0))
96 x_np = np.random.rand(5, 5).astype(dtype)
98 y_tf = math_ops.reduce_logsumexp(x_np, reduction_indices=0)
99 y_np = log(np.sum(exp(x_np), axis=0))
106 x_np = np.random.rand(5, 5).astype(dtype
[all...] |
| nn_test.py | 53 x_np = np.random.randint(0, 2, size=x_shape).astype(np.float32)
54 y_np = self._ZeroFraction(x_np)
56 x_tf = constant_op.constant(x_np)
82 x_np = np.random.randn(*x_shape).astype(np.float32)
83 y_np = self._softmax(x_np)
84 x_tf = constant_op.constant(x_np)
107 x_np = np.random.randn(*x_shape).astype(np.float64)
109 x_tf = constant_op.constant(x_np)
129 x_np = np.random.randn(*x_shape).astype(np.float32)
131 y_np = self._log_poisson_loss(x_np, z_np, compute_full_loss=False
[all...] |
| check_ops.py | 357 x_np = x.numpy().reshape((-1,))
359 x_sum = min(x_np.size, summarize)
363 (x_sum, x_np[:x_sum],
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| /external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
| recurrent_test.py | 147 x_np = np.random.random((6, 5, 5)) variable in class:RNNTest.test_minimal_rnn_cell_layer.MinimalRNNCell
148 y_np = model.predict(x_np)
156 y_np_2 = model.predict(x_np)
170 x_np = np.random.random((6, 5, 5)) variable in class:RNNTest.test_minimal_rnn_cell_layer.MinimalRNNCell
171 y_np = model.predict(x_np)
179 y_np_2 = model.predict(x_np)
241 x_np = np.random.random((6, 5, 5)) variable in class:RNNTest.test_rnn_cell_with_constants_layer.RNNCellWithConstants
243 y_np = model.predict([x_np, c_np])
252 y_np_2 = model.predict([x_np, c_np])
262 y_np_3 = model.predict([x_np, c_np]
281 x_np = np.random.random((6, 5, 5)) variable in class:RNNTest.test_rnn_cell_with_constants_layer.RNNCellWithConstants
354 x_np = np.random.random((6, 5, 5)) variable in class:RNNTest.test_rnn_cell_with_constants_layer_passing_initial_state.RNNCellWithConstants
[all...] |
| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| scaled_softplus_test.py | 50 x_np = np.random.randn(*x_shape).astype(np.float32)
54 x_tf = constant_op.constant(x_np)
62 [x_np, alpha_np],
68 [x_np, alpha_np, clip_np],
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/engine/ |
| training_eager_test.py | 521 x_np = np.random.random((10, 3))
525 model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': w_np})
528 model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=[w_np])
530 model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=w_np)
533 model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
536 model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
539 model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
|
| training_test.py | 663 x_np = np.random.random((10, 3))
667 model.fit(x_np, [y_np, y_np], epochs=1,
671 model.fit(x_np, [y_np, y_np], epochs=1,
674 model.fit(x_np, [y_np, y_np], epochs=1,
678 model.fit(x_np, [y_np, y_np], epochs=1,
682 model.fit(x_np, [y_np, y_np], epochs=1,
686 model.fit(x_np, [y_np, y_np], epochs=1,
[all...] |
