220 lines
7.8 KiB
Python
220 lines
7.8 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for tensorflow.ops.reshape_op."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gradient_checker_v2
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from tensorflow.python.platform import test
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class ReshapeTest(test.TestCase):
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def _testReshape(self, x, y, use_gpu=False):
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with self.cached_session(use_gpu=use_gpu):
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np_ans = x.reshape(y)
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tf_ans = array_ops.reshape(x, y)
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out = self.evaluate(tf_ans)
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self.assertEqual(tf_ans.get_shape(), out.shape)
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self.assertShapeEqual(np_ans, tf_ans)
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# Repeat with an int64 shape tensor.
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y64 = constant_op.constant(y, dtype=dtypes.int64)
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tf_ans = array_ops.reshape(x, y64)
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out = self.evaluate(tf_ans)
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self.assertEqual(tf_ans.get_shape(), out.shape)
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self.assertShapeEqual(np_ans, tf_ans)
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def _testZeroDimReshape(self, x, shape, expected, use_gpu=False):
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with self.cached_session(use_gpu=use_gpu):
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y = array_ops.reshape(x, shape)
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out = self.evaluate(y)
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self.assertEqual(expected, out.shape)
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# Repeat with an int64 shape tensor.
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shape64 = constant_op.constant(shape, dtype=dtypes.int64)
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y = array_ops.reshape(x, shape64)
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out = self.evaluate(y)
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self.assertEqual(expected, out.shape)
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def _testBothReshape(self, x, y):
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self._testReshape(x, y, False)
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self._testReshape(x, y, True)
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def testBoolBasic(self):
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x = np.arange(1., 7.).reshape([1, 6]) > 3
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self._testBothReshape(x, [2, 3])
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def testFloatBasic(self):
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x = np.arange(1., 7.).reshape([1, 6]).astype(np.float32)
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self._testBothReshape(x, [2, 3])
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def testDoubleBasic(self):
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x = np.arange(1., 7.).reshape([1, 6]).astype(np.float64)
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self._testBothReshape(x, [2, 3])
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def testInt32Basic(self):
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x = np.arange(1., 7.).reshape([1, 6]).astype(np.int32)
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self._testBothReshape(x, [2, 3])
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def testComplex64Basic(self):
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x = np.arange(1., 7.).reshape([1, 6]).astype(np.complex64)
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self._testBothReshape(x, [2, 3])
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def testComplex128Basic(self):
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x = np.arange(1., 7.).reshape([1, 6]).astype(np.complex128)
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self._testBothReshape(x, [2, 3])
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def testFloatReshapeThreeDimensions(self):
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x = np.arange(1., 28.).reshape([1, 27]).astype(np.float32)
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self._testBothReshape(x, [3, 3, 3])
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def testFloatUnspecifiedDimOnly(self):
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x = np.arange(1., 7.).reshape([6]).astype(np.float32)
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self._testBothReshape(x, [-1])
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def testFloatUnspecifiedDimBegin(self):
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x = np.arange(1., 7.).reshape([6]).astype(np.float32)
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self._testBothReshape(x, [-1, 2])
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def testFloatUnspecifiedDimEnd(self):
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x = np.arange(1., 7.).reshape([6]).astype(np.float32)
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self._testBothReshape(x, [3, -1])
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def testZeroDimBasic(self):
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x = np.zeros([0, 6]).astype(np.float32)
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self._testBothReshape(x, [0, 2, 3])
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def testZeroDimReshapeR1(self):
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x = np.zeros([0, 6]).astype(np.float32)
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self._testBothReshape(x, [-1])
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def testZeroDimReshapeR3(self):
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x = np.zeros([0, 6]).astype(np.float32)
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self._testBothReshape(x, [-1, 2, 3])
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# TODO(vrv): Add tests for failure conditions once python test_util
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# reports errors.
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def testFloatReshapeGradThreeDimensions(self):
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x = np.arange(1., 25.).reshape([2, 3, 4]).astype(np.float32)
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input_tensor = constant_op.constant(x)
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def reshape(x):
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return array_ops.reshape(x, [1, 8, 3])
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with self.cached_session():
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err = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(reshape, [input_tensor]))
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self.assertLess(err, 1e-3)
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def testFloatEmpty(self):
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x = np.empty((0, 0, 0, 0), dtype=np.float32)
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self._testBothReshape(x, [1, 2, 3, 0])
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self._testBothReshape(x, [1, 0, 0, 4])
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self._testBothReshape(x, [0, 0, 0, 0])
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self._testBothReshape(x, [1, 2, 0])
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self._testBothReshape(x, [0, 0, 0])
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self._testBothReshape(x, [1, -1, 5])
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def testZeroDimWithUnspecifiedDim(self):
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for use_gpu in (True, False):
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self._testZeroDimReshape(x=np.zeros([0, 6]).astype(np.float32),
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shape=[0, -1, 3],
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expected=(0, 2, 3),
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use_gpu=use_gpu)
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@test_util.run_deprecated_v1
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def testErrors(self):
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y = constant_op.constant(0.0, shape=[23, 29, 31])
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with self.assertRaisesRegex(ValueError, "must be evenly divisible by 17"):
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array_ops.reshape(y, [17, -1])
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z = constant_op.constant(0.0, shape=[32, 128])
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with self.assertRaisesRegex(ValueError,
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"Cannot reshape a tensor with 4096 elements"):
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array_ops.reshape(z, [4095])
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def testPartialShapes(self):
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# Testing unknown shapes in graph building.
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with ops.Graph().as_default():
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x = array_ops.placeholder(dtypes.float32)
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# Unknown input shape, partial new shape.
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y = array_ops.reshape(x, [1, 1, -1, 1])
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self.assertEqual([1, 1, None, 1], y.get_shape().as_list())
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# Unknown input shape, unknown new shape.
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y = array_ops.reshape(x, array_ops.placeholder(dtypes.int32))
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self.assertEqual(None, y.get_shape().ndims)
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# Unknown input shape, known rank for new shape.
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y = array_ops.reshape(x, array_ops.placeholder(dtypes.int32, shape=(3,)))
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self.assertEqual([None, None, None], y.get_shape().as_list())
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# Unknown input shape, partial new shape using `tf.stack()`.
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y = array_ops.reshape(x, [array_ops.placeholder(dtypes.int32), 37])
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self.assertEqual([None, 37], y.get_shape().as_list())
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# Unknown input shape, partial new shape using `tf.concat()`.
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y = array_ops.reshape(
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x,
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array_ops.concat(
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[array_ops.placeholder(
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dtypes.int32, shape=(2,)), [37, 42]], 0))
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self.assertEqual([None, None, 37, 42], y.get_shape().as_list())
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# Unknown input shape, partial new shape using `tf.shape()`.
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y = array_ops.reshape(
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x,
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array_ops.shape(
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array_ops.placeholder(
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dtypes.float32, shape=[None, 37, None])))
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self.assertEqual([None, 37, None], y.get_shape().as_list())
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def testTensorShape(self):
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x = array_ops.zeros([1, 100])
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y = array_ops.reshape(
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x, [tensor_shape.Dimension(100),
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tensor_shape.Dimension(1)])
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self.assertEqual([100, 1], y.get_shape().as_list())
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y = array_ops.reshape(x, tensor_shape.TensorShape([100, 1]))
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self.assertEqual([100, 1], y.get_shape().as_list())
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def testInt64Shape(self):
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with ops.device("/device:CPU:0"):
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x = array_ops.zeros([50000, 50000], dtype=dtypes.bool)
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# Provide dimension larger than int32
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y = array_ops.reshape(x, [50000**2])
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self.assertEqual([50000**2], y.get_shape().as_list())
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# Even if first dimension is within int32, ensure we correctly go to int64
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y = array_ops.reshape(x, [1, 50000**2])
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self.assertEqual([1, 50000**2], y.get_shape().as_list())
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if __name__ == "__main__":
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test.main()
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