Lines Matching refs:reshape
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))
335 self._compare(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.float16))
337 np.arange(0, 16).reshape([1, 2, 1, 2, 1, 2, 1, 2]).astype(np.float16))
340 self._compare_cpu_gpu(np.arange(0, 21).reshape([3, 7]).astype(np.float32))
342 np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.float32))
344 np.arange(0, 16).reshape([1, 2, 1, 2, 1, 2, 1, 2]).astype(np.float32))
347 self._compare_cpu_gpu(np.arange(0, 21).reshape([3, 7]).astype(np.float64))
349 np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.float64))
351 np.arange(0, 16).reshape([1, 2, 1, 2, 1, 2, 1, 2]).astype(np.float64))
356 np.arange(0, 21).reshape([3, 7]).astype(np.complex64))
359 np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.complex64))
362 np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.complex64))
367 np.arange(0, 21).reshape([3, 7]).astype(np.complex128))
370 np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.complex128))
373 np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.complex128))
376 self._testBoth(np.arange(0, 21).reshape([3, 7]).astype(np.int8))
377 self._testBoth(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int8))
379 np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.int8))
382 self._testBoth(np.arange(0, 21).reshape([3, 7]).astype(np.int16))
383 self._testBoth(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int16))
385 np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.int16))
388 self._testBoth(np.arange(0, 21).reshape([3, 7]).astype(np.int32))
389 self._testBoth(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int32))
391 np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.int32))
394 self._testBoth(np.arange(0, 21).reshape([3, 7]).astype(np.int64))
395 self._testBoth(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int64))
397 np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.int64))
408 # can be collapsed and expanded using reshape without changing the
410 # ascending order, the shuffled singletons will be transposed by a reshape,
418 np.arange(np.prod(shape)).reshape(shape).astype(np.float32))
467 np.arange(0., 30).reshape([2, 3, 5]), [[0, 1], [2, 3]])
469 array_ops.transpose(np.arange(0., 30).reshape([2, 3, 5]), [0, 1, 3])
471 np.arange(0., 30).reshape([2, 3, 5]), [0, 1, 1], "2 is missing")
