621 lines
21 KiB
Python
621 lines
21 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 ConstantOp."""
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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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import tensorflow as tf
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class ConstantTest(tf.test.TestCase):
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def _testCpu(self, x):
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np_ans = np.array(x)
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with self.test_session(use_gpu=False):
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tf_ans = tf.convert_to_tensor(x).eval()
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if np_ans.dtype in [np.float32, np.float64, np.complex64]:
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self.assertAllClose(np_ans, tf_ans)
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else:
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self.assertAllEqual(np_ans, tf_ans)
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def _testGpu(self, x):
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np_ans = np.array(x)
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with self.test_session(use_gpu=True):
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tf_ans = tf.convert_to_tensor(x).eval()
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if np_ans.dtype in [np.float32, np.float64, np.complex64]:
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self.assertAllClose(np_ans, tf_ans)
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else:
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self.assertAllEqual(np_ans, tf_ans)
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def _testAll(self, x):
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self._testCpu(x)
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self._testGpu(x)
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def testFloat(self):
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self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(np.float32))
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self._testAll(
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np.random.normal(size=30).reshape([2, 3, 5]).astype(np.float32))
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self._testAll(np.empty((2, 0, 5)).astype(np.float32))
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def testDouble(self):
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self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(np.float64))
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self._testAll(
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np.random.normal(size=30).reshape([2, 3, 5]).astype(np.float64))
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self._testAll(np.empty((2, 0, 5)).astype(np.float64))
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def testInt32(self):
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self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(np.int32))
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self._testAll(
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(100 * np.random.normal(size=30)).reshape([2, 3, 5]).astype(np.int32))
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self._testAll(np.empty((2, 0, 5)).astype(np.int32))
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def testInt64(self):
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self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(np.int64))
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self._testAll(
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(100 * np.random.normal(size=30)).reshape([2, 3, 5]).astype(np.int64))
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self._testAll(np.empty((2, 0, 5)).astype(np.int64))
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def testSComplex(self):
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self._testAll(
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np.complex(1, 2) * np.arange(-15, 15).reshape([2, 3, 5]).astype(
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np.complex64))
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self._testAll(np.complex(
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1, 2) * np.random.normal(size=30).reshape([2, 3, 5]).astype(
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np.complex64))
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self._testAll(np.empty((2, 0, 5)).astype(np.complex64))
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def testString(self):
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self._testCpu(np.array([tf.compat.as_bytes(str(x))
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for x in np.arange(-15, 15)]).reshape([2, 3, 5]))
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self._testCpu(np.empty((2, 0, 5)).astype(np.str_))
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def testStringWithNulls(self):
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with self.test_session():
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val = tf.convert_to_tensor(b"\0\0\0\0").eval()
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self.assertEqual(len(val), 4)
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self.assertEqual(val, b"\0\0\0\0")
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with self.test_session():
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val = tf.convert_to_tensor(b"xx\0xx").eval()
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self.assertEqual(len(val), 5)
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self.assertAllEqual(val, b"xx\0xx")
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nested = [[b"\0\0\0\0", b"xx\0xx"], [b"\0_\0_\0_\0", b"\0"]]
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with self.test_session():
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val = tf.convert_to_tensor(nested).eval()
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# NOTE(mrry): Do not use assertAllEqual, because it converts nested to a
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# numpy array, which loses the null terminators.
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self.assertEqual(val.tolist(), nested)
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def testExplicitShapeNumPy(self):
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with tf.Graph().as_default():
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c = tf.constant(
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np.arange(-15, 15).reshape([2, 3, 5]).astype(np.float32),
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shape=[2, 3, 5])
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self.assertEqual(c.get_shape(), [2, 3, 5])
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def testImplicitShapeNumPy(self):
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with tf.Graph().as_default():
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c = tf.constant(
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np.arange(-15, 15).reshape([2, 3, 5]).astype(np.float32))
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self.assertEqual(c.get_shape(), [2, 3, 5])
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def testExplicitShapeList(self):
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with tf.Graph().as_default():
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c = tf.constant([1, 2, 3, 4, 5, 6, 7], shape=[7])
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self.assertEqual(c.get_shape(), [7])
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def testImplicitShapeList(self):
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with tf.Graph().as_default():
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c = tf.constant([1, 2, 3, 4, 5, 6, 7])
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self.assertEqual(c.get_shape(), [7])
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def testExplicitShapeNumber(self):
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with tf.Graph().as_default():
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c = tf.constant(1, shape=[1])
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self.assertEqual(c.get_shape(), [1])
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def testImplicitShapeNumber(self):
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with tf.Graph().as_default():
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c = tf.constant(1)
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self.assertEqual(c.get_shape(), [])
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def testShapeInconsistent(self):
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with tf.Graph().as_default():
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c = tf.constant([1, 2, 3, 4, 5, 6, 7], shape=[10])
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self.assertEqual(c.get_shape(), [10])
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# pylint: disable=g-long-lambda
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def testShapeWrong(self):
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with tf.Graph().as_default():
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with self.assertRaisesWithPredicateMatch(
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ValueError,
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lambda e: ("Too many elements provided. Needed at most 5, "
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"but received 7" == str(e))):
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tf.constant([1, 2, 3, 4, 5, 6, 7], shape=[5])
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# pylint: enable=g-long-lambda
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def testTooLargeConstant(self):
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with tf.Graph().as_default():
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large_array = np.zeros((512, 1024, 1024), dtype=np.float32)
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with self.assertRaisesRegexp(
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ValueError,
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"Cannot create a tensor proto whose content is larger than 2GB."):
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c = tf.constant(large_array)
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def testTooLargeGraph(self):
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with tf.Graph().as_default() as g:
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large_array = np.zeros((256, 1024, 1024), dtype=np.float32)
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c = tf.constant(large_array)
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d = tf.constant(large_array)
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with self.assertRaisesRegexp(
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ValueError, "GraphDef cannot be larger than 2GB."):
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g.as_graph_def()
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def testSparseValuesRaiseErrors(self):
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with self.assertRaisesRegexp(ValueError,
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"setting an array element with a sequence"):
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c = tf.constant([[1, 2], [3]], dtype=tf.int32)
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with self.assertRaisesRegexp(ValueError, "must be a dense"):
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c = tf.constant([[1, 2], [3]])
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with self.assertRaisesRegexp(ValueError, "must be a dense"):
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c = tf.constant([[1, 2], [3], [4, 5]])
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class AsTensorTest(tf.test.TestCase):
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def testAsTensorForTensorInput(self):
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with tf.Graph().as_default():
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t = tf.constant(10.0)
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x = tf.convert_to_tensor(t)
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self.assertIs(t, x)
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def testAsTensorForNonTensorInput(self):
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with tf.Graph().as_default():
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x = tf.convert_to_tensor(10.0)
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self.assertTrue(isinstance(x, tf.Tensor))
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def testAsTensorForShapeInput(self):
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with self.test_session():
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x = tf.convert_to_tensor(tf.TensorShape([]))
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self.assertEqual(tf.int32, x.dtype)
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self.assertAllEqual([], x.eval())
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x = tf.convert_to_tensor(tf.TensorShape([1, 2, 3]))
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self.assertEqual(tf.int32, x.dtype)
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self.assertAllEqual([1, 2, 3], x.eval())
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x = tf.convert_to_tensor(tf.TensorShape([1, 2, 3]), dtype=tf.int64)
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self.assertEqual(tf.int64, x.dtype)
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self.assertAllEqual([1, 2, 3], x.eval())
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x = tf.reshape(tf.zeros([6]), tf.TensorShape([2, 3]))
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self.assertAllEqual([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], x.eval())
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with self.assertRaisesRegexp(ValueError, "partially known"):
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tf.convert_to_tensor(tf.TensorShape(None))
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with self.assertRaisesRegexp(ValueError, "partially known"):
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tf.convert_to_tensor(tf.TensorShape([1, None, 64]))
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with self.assertRaises(TypeError):
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tf.convert_to_tensor(tf.TensorShape([1, 2, 3]), dtype=tf.float32)
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def testAsTensorForDimensionInput(self):
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with self.test_session():
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x = tf.convert_to_tensor(tf.TensorShape([1, 2, 3])[1])
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self.assertEqual(tf.int32, x.dtype)
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self.assertAllEqual(2, x.eval())
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x = tf.convert_to_tensor(tf.TensorShape([1, 2, 3])[1], dtype=tf.int64)
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self.assertEqual(tf.int64, x.dtype)
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self.assertAllEqual(2, x.eval())
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with self.assertRaisesRegexp(ValueError, "unknown Dimension"):
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tf.convert_to_tensor(tf.TensorShape(None)[1])
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with self.assertRaisesRegexp(ValueError, "unknown Dimension"):
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tf.convert_to_tensor(tf.TensorShape([1, None, 64])[1])
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with self.assertRaises(TypeError):
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tf.convert_to_tensor(tf.TensorShape([1, 2, 3])[1], dtype=tf.float32)
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class IdentityOpTest(tf.test.TestCase):
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def testIdTensor(self):
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with tf.Graph().as_default():
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x = tf.constant(2.0, shape=[6], name="input")
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id_op = tf.identity(x, name="id")
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self.assertTrue(isinstance(id_op.op.inputs[0], tf.Tensor))
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self.assertProtoEquals(
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"name: 'id' op: 'Identity' input: 'input' "
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"attr { key: 'T' value { type: DT_FLOAT } }", id_op.op.node_def)
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class ZerosTest(tf.test.TestCase):
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def _Zeros(self, shape):
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with self.test_session():
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ret = tf.zeros(shape)
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self.assertEqual(shape, ret.get_shape())
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return ret.eval()
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def testConst(self):
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self.assertTrue(np.array_equal(self._Zeros([2, 3]), np.array([[0] * 3] *
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2)))
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def testScalar(self):
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self.assertEqual(0, self._Zeros([]))
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self.assertEqual(0, self._Zeros(()))
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with self.test_session():
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scalar = tf.zeros(tf.constant([], dtype=tf.int32))
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self.assertEqual(0, scalar.eval())
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def testDynamicSizes(self):
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np_ans = np.array([[0] * 3] * 2)
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with self.test_session():
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# Creates a tensor of 2 x 3.
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d = tf.fill([2, 3], 12., name="fill")
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# Constructs a tensor of zeros of the same dimensions as "d".
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z = tf.zeros(tf.shape(d))
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out = z.eval()
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self.assertAllEqual(np_ans, out)
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self.assertShapeEqual(np_ans, d)
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self.assertShapeEqual(np_ans, z)
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def testDtype(self):
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with self.test_session():
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d = tf.fill([2, 3], 12., name="fill")
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self.assertEqual(d.get_shape(), [2, 3])
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# Test default type for both constant size and dynamic size
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z = tf.zeros([2, 3])
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self.assertEqual(z.dtype, tf.float32)
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self.assertEqual([2, 3], z.get_shape())
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z = tf.zeros(tf.shape(d))
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self.assertEqual(z.dtype, tf.float32)
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self.assertEqual([2, 3], z.get_shape())
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# Test explicit type control
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for dtype in [tf.float32, tf.float64, tf.int32,
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tf.uint8, tf.int16, tf.int8,
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tf.complex64, tf.int64]:
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z = tf.zeros([2, 3], dtype=dtype)
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self.assertEqual(z.dtype, dtype)
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self.assertEqual([2, 3], z.get_shape())
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z = tf.zeros(tf.shape(d), dtype=dtype)
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self.assertEqual(z.dtype, dtype)
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self.assertEqual([2, 3], z.get_shape())
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class ZerosLikeTest(tf.test.TestCase):
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def _compareZeros(self, dtype, use_gpu):
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with self.test_session(use_gpu=False):
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# Creates a tensor of non-zero values with shape 2 x 3.
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numpy_dtype = dtype.as_numpy_dtype
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d = tf.constant(np.ones((2, 3), dtype=numpy_dtype), dtype=dtype)
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# Constructs a tensor of zeros of the same dimensions and type as "d".
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z_var = tf.zeros_like(d)
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# Test that the type is correct
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self.assertEqual(z_var.dtype, dtype)
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z_value = z_var.eval()
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# Test that the value is correct
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self.assertTrue(np.array_equal(z_value, np.array([[0] * 3] * 2)))
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self.assertEqual([2, 3], z_var.get_shape())
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def testZerosLikeCPU(self):
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for dtype in [tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8,
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tf.complex64, tf.int64]:
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self._compareZeros(dtype, False)
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def testZerosLikeGPU(self):
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for dtype in [tf.float32, tf.float64, tf.int32]:
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self._compareZeros(dtype, True)
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def testZerosLikePartialShape(self):
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d = tf.placeholder(tf.float32, shape=[None, 4, None])
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z = tf.zeros_like(d)
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self.assertEqual(d.get_shape().as_list(), z.get_shape().as_list())
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def testZerosLikeDtype(self):
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# Make sure zeros_like works even for dtypes that cannot be cast between
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with self.test_session():
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shape = (3, 5)
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dtypes = np.float32, np.complex64
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for in_type in dtypes:
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x = np.arange(15).astype(in_type).reshape(*shape)
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for out_type in dtypes:
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y = tf.zeros_like(x, dtype=out_type).eval()
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self.assertEqual(y.dtype, out_type)
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self.assertEqual(y.shape, shape)
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self.assertAllEqual(y, np.zeros(shape, dtype=out_type))
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class OnesTest(tf.test.TestCase):
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def _Ones(self, shape):
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with self.test_session():
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ret = tf.ones(shape)
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self.assertEqual(shape, ret.get_shape())
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return ret.eval()
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def testConst(self):
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self.assertTrue(np.array_equal(self._Ones([2, 3]), np.array([[1] * 3] * 2)))
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def testScalar(self):
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self.assertEqual(1, self._Ones([]))
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self.assertEqual(1, self._Ones(()))
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with self.test_session():
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scalar = tf.ones(tf.constant([], dtype=tf.int32))
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self.assertEqual(1, scalar.eval())
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def testDynamicSizes(self):
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np_ans = np.array([[1] * 3] * 2)
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with self.test_session():
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# Creates a tensor of 2 x 3.
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d = tf.fill([2, 3], 12., name="fill")
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# Constructs a tensor of ones of the same dimensions as "d".
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z = tf.ones(tf.shape(d))
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out = z.eval()
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self.assertAllEqual(np_ans, out)
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self.assertShapeEqual(np_ans, d)
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self.assertShapeEqual(np_ans, z)
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def testAutoPack(self):
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with self.test_session():
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h = tf.placeholder(tf.int32, shape=[])
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w = tf.placeholder(tf.int32, shape=[])
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z = tf.ones([h, w])
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out = z.eval(feed_dict={h: 4, w: 16})
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self.assertAllEqual(out, np.array([[1] * 16] * 4))
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def testDtype(self):
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with self.test_session():
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d = tf.fill([2, 3], 12., name="fill")
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self.assertEqual(d.get_shape(), [2, 3])
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# Test default type for both constant size and dynamic size
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z = tf.ones([2, 3])
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self.assertEqual(z.dtype, tf.float32)
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self.assertEqual([2, 3], z.get_shape())
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z = tf.ones(tf.shape(d))
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self.assertEqual(z.dtype, tf.float32)
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self.assertEqual([2, 3], z.get_shape())
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# Test explicit type control
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for dtype in [tf.float32, tf.float64, tf.int32,
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tf.uint8, tf.int16, tf.int8,
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tf.complex64, tf.int64]:
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z = tf.ones([2, 3], dtype=dtype)
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self.assertEqual(z.dtype, dtype)
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self.assertEqual([2, 3], z.get_shape())
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z = tf.ones(tf.shape(d), dtype=dtype)
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self.assertEqual(z.dtype, dtype)
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self.assertEqual([2, 3], z.get_shape())
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class OnesLikeTest(tf.test.TestCase):
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def testOnesLike(self):
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for dtype in [tf.float32, tf.float64, tf.int32,
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tf.uint8, tf.int16, tf.int8,
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tf.complex64, tf.int64]:
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numpy_dtype = dtype.as_numpy_dtype
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with self.test_session():
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# Creates a tensor of non-zero values with shape 2 x 3.
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d = tf.constant(np.ones((2, 3), dtype=numpy_dtype), dtype=dtype)
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# Constructs a tensor of zeros of the same dimensions and type as "d".
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z_var = tf.ones_like(d)
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# Test that the type is correct
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self.assertEqual(z_var.dtype, dtype)
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z_value = z_var.eval()
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# Test that the value is correct
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self.assertTrue(np.array_equal(z_value, np.array([[1] * 3] * 2)))
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self.assertEqual([2, 3], z_var.get_shape())
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def testOnesLikePartialShape(self):
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d = tf.placeholder(tf.float32, shape=[None, 4, None])
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z = tf.ones_like(d)
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self.assertEqual(d.get_shape().as_list(), z.get_shape().as_list())
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class FillTest(tf.test.TestCase):
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def _compare(self, dims, val, np_ans, use_gpu):
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with self.test_session(use_gpu=use_gpu):
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tf_ans = tf.fill(dims, val, name="fill")
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out = tf_ans.eval()
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self.assertAllClose(np_ans, out)
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# Fill does not set the shape.
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# self.assertShapeEqual(np_ans, tf_ans)
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def _compareAll(self, dims, val, np_ans):
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self._compare(dims, val, np_ans, False)
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self._compare(dims, val, np_ans, True)
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def testFillFloat(self):
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np_ans = np.array([[3.1415] * 3] * 2).astype(np.float32)
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self._compareAll([2, 3], np_ans[0][0], np_ans)
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def testFillDouble(self):
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np_ans = np.array([[3.1415] * 3] * 2).astype(np.float64)
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self._compareAll([2, 3], np_ans[0][0], np_ans)
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def testFillInt32(self):
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np_ans = np.array([[42] * 3] * 2).astype(np.int32)
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self._compareAll([2, 3], np_ans[0][0], np_ans)
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def testFillInt64(self):
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np_ans = np.array([[-42] * 3] * 2).astype(np.int64)
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self._compareAll([2, 3], np_ans[0][0], np_ans)
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def testFillComplex(self):
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np_ans = np.array([[0.15] * 3] * 2).astype(np.complex64)
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self._compare([2, 3], np_ans[0][0], np_ans, use_gpu=False)
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def testFillString(self):
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np_ans = np.array([[b"yolo"] * 3] * 2)
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with self.test_session(use_gpu=False):
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tf_ans = tf.fill([2, 3], np_ans[0][0], name="fill").eval()
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self.assertAllEqual(np_ans, tf_ans)
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def testFillNegative(self):
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with self.test_session():
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for shape in (-1,), (2, -1), (-1, 2):
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with self.assertRaises(ValueError):
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tf.fill(shape, 7)
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# Using a placeholder so this won't be caught in Python.
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dims = tf.placeholder(tf.int32)
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fill_t = tf.fill(dims, 3.0)
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for shape in (-1,), (2, -1), (-1, 2):
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with self.assertRaises(tf.errors.InvalidArgumentError):
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fill_t.eval({dims: shape})
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def testShapeFunctionEdgeCases(self):
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# Non-vector dimensions.
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with self.assertRaises(ValueError):
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tf.fill([[0, 1], [2, 3]], 1.0)
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# Non-scalar value.
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with self.assertRaises(ValueError):
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tf.fill([3, 2], [1.0, 2.0])
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# Partial dimension information.
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f = tf.fill(
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tf.placeholder(tf.int32, shape=(4,)), 3.0)
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self.assertEqual([None, None, None, None], f.get_shape().as_list())
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def testGradient(self):
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with self.test_session():
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in_v = tf.constant(5.0)
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out_shape = [3, 2]
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out_filled = tf.fill(out_shape, in_v)
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err = tf.test.compute_gradient_error(in_v, [],
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out_filled, out_shape)
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self.assertLess(err, 1e-3)
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class PlaceholderTest(tf.test.TestCase):
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def testDtype(self):
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with self.test_session():
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p = tf.placeholder(tf.float32, name="p")
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p_identity = tf.identity(p)
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feed_array = np.random.rand(10, 10)
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self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}),
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feed_array)
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with self.assertRaisesOpError(
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"must feed a value for placeholder tensor 'p' with dtype float"):
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p_identity.eval()
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def testShape(self):
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with self.test_session():
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p = tf.placeholder(tf.float32, shape=(10, 10), name="p")
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p_identity = tf.identity(p)
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feed_array = np.random.rand(10, 10)
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self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}),
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feed_array)
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with self.assertRaisesOpError(
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"must feed a value for placeholder tensor 'p' with dtype float and "
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r"shape \[10,10\]"):
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p_identity.eval()
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with self.assertRaisesWithPredicateMatch(
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ValueError, lambda e: "Cannot feed value of shape" in str(e)):
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p_identity.eval(feed_dict={p: feed_array[:5, :5]})
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def testPartialShape(self):
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with self.test_session():
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p = tf.placeholder(tf.float32, shape=[None, 3], name="p")
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p_identity = tf.identity(p)
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feed_array = np.random.rand(10, 3)
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self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}),
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feed_array)
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with self.assertRaisesWithPredicateMatch(
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ValueError, lambda e: "Cannot feed value of shape" in str(e)):
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p_identity.eval(feed_dict={p: feed_array[:5, :2]})
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def testControlDependency(self):
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with self.test_session():
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p = tf.placeholder(tf.int32, shape=[], name="p")
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with tf.control_dependencies([p]):
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c = tf.constant(5, tf.int32)
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d = tf.mul(p, c)
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self.assertEqual(10, d.eval(feed_dict={p: 2}))
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def testBadShape(self):
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with self.assertRaises(ValueError):
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tf.placeholder(tf.float32, shape=(-1, 10))
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def testTensorStr(self):
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a = tf.placeholder(tf.float32, name="a")
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self.assertEqual("<tf.Tensor 'a:0' shape=<unknown> dtype=float32>", repr(a))
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b = tf.placeholder(tf.int32, shape=(32, 40), name="b")
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self.assertEqual(
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"<tf.Tensor 'b:0' shape=(32, 40) dtype=int32>",
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repr(b))
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c = tf.placeholder(tf.qint32, shape=(32, None, 2), name="c")
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self.assertEqual(
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"<tf.Tensor 'c:0' shape=(32, ?, 2) dtype=qint32>",
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repr(c))
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class PlaceholderWithDefaultTest(tf.test.TestCase):
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def testFullShape(self):
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with self.test_session():
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p = tf.placeholder_with_default([[2, 2], [2, 2]], shape=[2, 2])
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a = tf.identity(p)
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self.assertAllEqual([[2, 2], [2, 2]], a.eval())
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self.assertAllEqual([[3, 3], [3, 3]],
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a.eval(feed_dict={p: [[3, 3], [3, 3]]}))
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with self.assertRaises(ValueError):
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a.eval(feed_dict={p: [[6, 6, 6], [6, 6, 6]]})
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def testPartialShape(self):
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with self.test_session():
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p = tf.placeholder_with_default([1, 2, 3], shape=[None])
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a = tf.identity(p)
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self.assertAllEqual([1, 2, 3], a.eval())
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self.assertAllEqual([3, 37], a.eval(feed_dict={p: [3, 37]}))
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with self.assertRaises(ValueError):
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a.eval(feed_dict={p: [[2, 2], [2, 2]]})
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def testNoShape(self):
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with self.test_session():
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p = tf.placeholder_with_default([17], shape=None)
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a = tf.identity(p)
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self.assertAllEqual([17], a.eval())
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self.assertAllEqual([3, 37], a.eval(feed_dict={p: [3, 37]}))
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self.assertAllEqual([[3, 3], [3, 3]],
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a.eval(feed_dict={p: [[3, 3], [3, 3]]}))
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if __name__ == "__main__":
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tf.test.main()
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