| /external/tensorflow/tensorflow/core/kernels/ |
| bias_op.h | 38 output.reshape(one_d).device(d) =
39 input.reshape(one_d) + bias.broadcast(bcast).reshape(one_d);
45 To32Bit(output).reshape(one_d).device(d) =
46 To32Bit(input).reshape(one_d) +
47 To32Bit(bias).broadcast(bcast).reshape(one_d);
|
| batch_norm_op.h | 52 output.reshape(rest_by_depth).device(d) =
53 (input.reshape(rest_by_depth) -
54 mean.reshape(one_by_depth).broadcast(rest_by_one)) *
57 .reshape(one_by_depth)
59 beta.reshape(one_by_depth).broadcast(rest_by_one);
61 output.reshape(rest_by_depth).device(d) =
62 (input.reshape(rest_by_depth) -
63 mean.reshape(one_by_depth).broadcast(rest_by_one)) *
66 .reshape(one_by_depth)
68 beta.reshape(one_by_depth).broadcast(rest_by_one)
[all...] |
| training_ops_gpu.cu.cc | 36 var.device(d) -= lr.reshape(single).broadcast(bcast) * grad;
50 var.device(d) -= lr.reshape(single).broadcast(bcast) * grad * accum.rsqrt();
67 accum.device(d) = accum * rho.reshape(single).broadcast(bcast) +
69 rho.reshape(single).broadcast(bcast));
71 (accum_update + epsilon.reshape(single).broadcast(bcast)).sqrt() *
72 (accum + epsilon.reshape(single).broadcast(bcast)).rsqrt() * grad;
73 var.device(d) -= update * lr.reshape(single).broadcast(bcast);
75 accum_update * rho.reshape(single).broadcast(bcast) +
77 (grad.constant(T(1)) - rho.reshape(single).broadcast(bcast));
91 accum.device(d) = accum * momentum.reshape(single).broadcast(bcast) + grad
[all...] |
| extract_image_patches_op.h | 42 .reshape(output_32bit.dimensions());
48 .reshape(output.dimensions());
|
| adjust_contrast_op.h | 74 .reshape(reshape_dims)
78 contrast_factor.reshape(scalar).broadcast(scalar_broadcast);
82 auto min_bcast = min_value.reshape(scalar).broadcast(scalar_broadcast);
83 auto max_bcast = max_value.reshape(scalar).broadcast(scalar_broadcast);
144 .reshape(reshape_dims)
147 contrast_factor.reshape(scalar).broadcast(scalar_broadcast);
|
| unravel_index_op.cc | 100 Eigen::array<int64, 2> reshape{{dims_tensor.NumElements(), 1}};
106 .reshape(indices_reshape)
108 output = output.binaryExpr(strides.reshape(reshape).broadcast(bcast),
110 strides_shifted.reshape(reshape).broadcast(bcast);
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| softmax_op_functor.h | 69 .reshape(batch_by_one)
80 .reshape(batch_by_one)
92 .reshape(batch_by_one)
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| reshape_op_test.py | 34 np_ans = x.reshape(y)
35 tf_ans = array_ops.reshape(x, y)
42 tf_ans = array_ops.reshape(x, y64)
52 x = np.arange(1., 7.).reshape([1, 6]) > 3
56 x = np.arange(1., 7.).reshape([1, 6]).astype(np.float32)
60 x = np.arange(1., 7.).reshape([1, 6]).astype(np.float64)
64 x = np.arange(1., 7.).reshape([1, 6]).astype(np.int32)
68 x = np.arange(1., 7.).reshape([1, 6]).astype(np.complex64)
72 x = np.arange(1., 7.).reshape([1, 6]).astype(np.complex128)
76 x = np.arange(1., 28.).reshape([1, 27]).astype(np.float32
[all...] |
| weights_broadcast_test.py | 32 return np.reshape(np.cumsum(np.ones(shape), dtype=np.int32), newshape=shape)
59 weights=np.asarray((5,)).reshape((1, 1, 1)),
64 weights=np.asarray((5, 7, 11, 3)).reshape((1, 1, 4)),
69 weights=np.asarray((5, 11)).reshape((1, 2, 1)),
74 weights=np.asarray((5, 7, 11, 3, 2, 13, 7, 5)).reshape((1, 2, 4)),
79 weights=np.asarray((5, 7, 11)).reshape((3, 1, 1)),
85 5, 7, 11, 3, 2, 12, 7, 5, 2, 17, 11, 3)).reshape((3, 1, 4)),
92 2, 17, 11, 3, 5, 7, 11, 3, 2, 12, 7, 5)).reshape((3, 2, 4)),
115 weights=np.asarray((5,)).reshape((1, 1)),
120 weights=np.asarray((5, 7, 11, 3, 2, 12)).reshape((3, 2))
[all...] |
| batch_matmul_op_test.py | 40 xr = x.reshape([num, x.shape[-2], x.shape[-1]])
41 yr = y.reshape([num, y.shape[-2], y.shape[-1]])
42 zr = z.reshape([num, z.shape[-2], z.shape[-1]])
55 x = np.array([0., 1., 2., 3.]).reshape([1, 2, 2])
56 y = np.array([1., 2., 3., 4.]).reshape([1, 2, 2])
58 z1 = np.array([3., 4., 11., 16.]).reshape([1, 2, 2])
61 x = np.array([1., (1j), (-1.), (-1j)]).reshape([1, 2, 2])
64 z1 = np.array([2., (2.j), -2., (-2.j)]).reshape([1, 2, 2])
68 z1 = np.array([(2. - 2.j), (-2. + 2.j), (-2. + 2.j), (2. - 2.j)]).reshape(
73 z1 = np.array([(2. + 2.j), (-2. + 2.j), (2. - 2.j), (2. + 2.j)]).reshape(
[all...] |
| transpose_op_test.py | 132 vector = np.arange(0, 2).reshape((1, 1, 1, 2, 1))
151 inp = np.arange(1, total_size + 1, dtype=datatype).reshape(input_shape)
172 inp = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_shape)
207 inp = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_shape)
229 inp = np.arange(1, total_size + 1, dtype=datatype).reshape(input_shape)
250 inp = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_shape)
313 self._compareCpu(np.arange(0, 6).reshape([3, 2]).astype(np.float32), [0, 1])
317 np.arange(0, 8).reshape([2, 4]).astype(np.float32),
322 x = np.arange(0, 8).reshape([2, 4]).astype(np.float32)
334 self._compare(np.arange(0, 21).reshape([3, 7]).astype(np.float16)
[all...] |
| extract_image_patches_op_test.py | 59 image = np.reshape(range(120), [2, 3, 4, 5])
61 patches = np.reshape(range(120), [2, 3, 4, 5])
74 image = np.reshape(range(120), [2, 4, 5, 3])
117 image = np.arange(16).reshape(1, 4, 4, 1).astype(np.float32)
|
| /external/tensorflow/tensorflow/python/training/ |
| checkpoint_ops_test.py | 45 np.reshape(np.linspace(0.0, 79, 5 * 16), (5, 16)))
109 np.reshape([18, 34, 50, self.init_val, self.init_val], [5, 1]),
110 np.reshape([16, 32, 48, self.init_val, self.init_val], [5, 1]),
111 np.reshape([self.init_val] * 5, [5, 1]),
112 np.reshape([17, 33, 49, self.init_val, self.init_val], [5, 1]),
113 np.reshape([self.init_val] * 5, [5, 1])
140 np.reshape([2, 18, 34, 50, self.init_val, self.init_val], [6, 1]),
141 np.reshape([0, 16, 32, 48, self.init_val, self.init_val], [6, 1]),
142 np.reshape([self.init_val] * 6, [6, 1]),
143 np.reshape([1, 17, 33, 49, self.init_val, self.init_val], [6, 1])
[all...] |
| /cts/apps/CameraITS/pymodules/its/ |
| image.py | 102 analysis_image = img.reshape(2,h,w,4)
103 mean_image = analysis_image[0,:,:,:].reshape(h,w,4)
104 var_image = analysis_image[1,:,:,:].reshape(h,w,4)
124 cap["data"] = unpack_raw10_image(cap["data"].reshape(h,w*5/4))
149 msbs = msbs.reshape(h,w)
151 lsbs = img[::, 4::5].reshape(h,w/4)
153 numpy.packbits(numpy.unpackbits(lsbs).reshape(h,w/4,4,2),3), 6)
155 lsbs = lsbs.reshape(h,w/4,4)[:,:,::-1]
156 lsbs = lsbs.reshape(h,w)
158 img16 = numpy.bitwise_or(msbs, lsbs).reshape(h,w
[all...] |
| /external/tensorflow/tensorflow/contrib/periodic_resample/python/kernel_tests/ |
| periodic_resample_op_test.py | 34 input_tensor = numpy.arange(12).reshape((3, 4))
36 output_tensor = input_tensor.reshape((6, 2))
45 input_tensor = numpy.arange(12).reshape((3, 4))
47 output_tensor = input_tensor.reshape((6, 2))[:-1]
56 input_tensor = numpy.arange(2 * 2 * 4).reshape((2, 2, 4))
62 # NOTE: output_tensor != input_tensor.reshape((4, 4, -1))
74 input_tensor = numpy.arange(2 * 2 * 2 * 8).reshape((2, 2, 2, 8))
87 # NOTE: output_tensor != input_tensor.reshape((4, 4, 4, -1))
|
| /external/tensorflow/tensorflow/contrib/linear_optimizer/python/ |
| sdca_optimizer.py | 118 array_ops.reshape(
121 array_ops.reshape(
124 array_ops.reshape(math_ops.to_float(sparse_values), [-1]))
133 # TODO(sibyl-vie3Poto): Reshape variables stored as values in column_to_variables
148 # Reshape to [batch_size, dense_column_dimension].
150 transformed_tensor = array_ops.reshape(transformed_tensor, [
182 array_ops.reshape(
187 array_ops.reshape(transformed_tensor.values, [-1]), None))
194 array_ops.reshape(
198 array_ops.reshape(id_tensor.values, [-1])
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/learn_io/ |
| generator_io_test.py | 54 self.assertAllEqual(res[0]['a'], np.asarray([0, 1]).reshape(-1, 1))
55 self.assertAllEqual(res[0]['b'], np.asarray([32, 33]).reshape(-1, 1))
56 self.assertAllEqual(res[1], np.asarray([-32, -31]).reshape(-1, 1))
80 self.assertAllEqual(res[0]['a'], np.asarray([0, 1]).reshape(-1, 1))
113 self.assertAllEqual(res[0]['a'], np.asarray([0, 1]).reshape(-1, 1))
114 self.assertAllEqual(res[0]['b'], np.asarray([32, 33]).reshape(-1, 1))
115 self.assertAllEqual(res[1]['label'], np.asarray([-32, -31]).reshape(
118 np.asarray([-64, -63]).reshape(-1, 1))
152 (10, 10)))).reshape(2, 10, 10))
155 (5, 5)))).reshape(2, 5, 5) + 32
[all...] |
| /external/webrtc/webrtc/modules/video_processing/test/ |
| readYUV420file.m | 32 Y(:,:,k)=uint8(reshape(X(1:nPx), width, height).');
35 U(:,:,k)=uint8(reshape(X(nPx + (1:nPx/4)), width/2, height/2).');
38 V(:,:,k)=uint8(reshape(X(nPx + nPx/4 + (1:nPx/4)), width/2, height/2).');
|
| /cts/apps/CameraITS/tests/scene0/ |
| test_gyro_bias.py | 60 xs = xs.reshape(nevents/N, N).mean(1)
61 ys = ys.reshape(nevents/N, N).mean(1)
62 zs = zs.reshape(nevents/N, N).mean(1)
|
| /external/clang/test/Analysis/ |
| malloc-interprocedural.c | 77 static char *reshape(char *in) { function
83 v = reshape(v);
84 v = reshape(v);// expected-warning {{Potential leak of memory pointed to by 'v'}}
|
| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| reshape.cc | 25 namespace reshape { namespace in namespace:tflite::ops::builtin
41 // Tensorflow's Reshape allows one of the shape components to have the
81 } // namespace reshape
84 static TfLiteRegistration r = {nullptr, nullptr, reshape::Prepare,
85 reshape::Eval};
|
| /cts/apps/CameraITS/tests/inprog/scene2/ |
| test_dng_tags.py | 52 print "HAL reported ccm:\n", numpy.array(ccm).reshape(3,3)
53 print "HAL reported cal:\n", numpy.array(cal).reshape(3,3)
70 props[cm_str[i]])).reshape(3,3)
72 props[fm_str[i]])).reshape(3,3)
|
| /external/tensorflow/tensorflow/compiler/tests/ |
| extract_image_patches_op_test.py | 63 image = np.reshape(range(120), [2, 3, 4, 5])
65 patches = np.reshape(range(120), [2, 3, 4, 5])
78 image = np.reshape(range(120), [2, 4, 5, 3])
121 image = np.arange(16).reshape(1, 4, 4, 1).astype(np.float32)
|
| /external/tensorflow/tensorflow/contrib/seq2seq/python/ops/ |
| loss.py | 88 logits_flat = array_ops.reshape(logits, [-1, num_classes])
89 targets = array_ops.reshape(targets, [-1])
95 crossent *= array_ops.reshape(weights, [-1])
104 crossent = array_ops.reshape(crossent, [batch_size, sequence_length])
|
| /cts/apps/CameraITS/tests/inprog/ |
| test_rawstats.py | 41 m = mean_image[:,:,ch].reshape(h,w,1)/1023.0
42 v = var_image[:,:,ch].reshape(h,w,1)
|
