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  /external/tensorflow/tensorflow/contrib/gan/python/eval/python/
sliced_wasserstein_test.py 39 def np_pyr_down(minibatch): # matches cv2.pyrDown()
40 assert minibatch.ndim == 4
42 minibatch,
46 def np_pyr_up(minibatch): # matches cv2.pyrUp()
47 assert minibatch.ndim == 4
48 s = minibatch.shape
49 res = np.zeros((s[0], s[1], s[2] * 2, s[3] * 2), minibatch.dtype)
50 res[:, :, ::2, ::2] = minibatch
56 def np_laplacian_pyramid(minibatch, num_levels):
58 pyramid = [minibatch.astype('f').copy()
    [all...]
  /external/tensorflow/tensorflow/contrib/training/python/training/
sampling_ops.py 121 minibatch = input_ops.maybe_batch(
129 if isinstance(minibatch, ops.Tensor):
130 minibatch = [minibatch]
132 return minibatch
  /external/tensorflow/tensorflow/contrib/gan/python/features/python/
virtual_batchnorm_test.py 48 minibatch = array_ops.zeros([5, 3, 16, 3, 15])
50 vbn(minibatch)
155 other examples in the minibatch. In this test, we verify this property.
180 minibatch = array_ops.stack([fixed_example] + examples)
181 vbn_minibatch = vbn(minibatch)
  /external/tensorflow/tensorflow/core/kernels/
serialize_sparse_op.cc 226 // Get groups by minibatch dimension
227 sparse::GroupIterable minibatch = input_st.group({0}); variable
228 for (const auto& subset : minibatch) {
sparse_tensors_map_ops.cc 273 // minibatch entries.
279 // Get groups by minibatch dimension
281 sparse::GroupIterable minibatch = input_st.group({0}); variable
282 for (const auto& subset : minibatch) {
  /external/tensorflow/tensorflow/core/util/
example_proto_fast_parsing.cc     [all...]
  /external/tensorflow/tensorflow/go/op/
wrappers.go     [all...]

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