本文整理汇总了Python中tensorflow.python.framework.tensor_util.make_tensor_proto方法的典型用法代码示例。如果您正苦于以下问题:Python tensor_util.make_tensor_proto方法的具体用法?Python tensor_util.make_tensor_proto怎么用?Python tensor_util.make_tensor_proto使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.framework.tensor_util
的用法示例。
在下文中一共展示了tensor_util.make_tensor_proto方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testHalf
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testHalf(self):
t = tensor_util.make_tensor_proto(np.array([10.0, 20.0], dtype=np.float16))
self.assertProtoEquals("""
dtype: DT_HALF
tensor_shape {
dim {
size: 2
}
}
half_val: 18688
half_val: 19712
""", t)
a = tensor_util.MakeNdarray(t)
self.assertEquals(np.float16, a.dtype)
self.assertAllClose(np.array([10.0, 20.0], dtype=np.float16), a)
示例2: testIntTypes
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testIntTypes(self):
for dtype, nptype in [
(tf.int32, np.int32),
(tf.uint8, np.uint8),
(tf.uint16, np.uint16),
(tf.int16, np.int16),
(tf.int8, np.int8)]:
# Test with array.
t = tensor_util.make_tensor_proto([10, 20, 30], dtype=dtype)
self.assertEquals(dtype, t.dtype)
self.assertProtoEquals("dim { size: 3 }", t.tensor_shape)
a = tensor_util.MakeNdarray(t)
self.assertEquals(nptype, a.dtype)
self.assertAllClose(np.array([10, 20, 30], dtype=nptype), a)
# Test with ndarray.
t = tensor_util.make_tensor_proto(np.array([10, 20, 30], dtype=nptype))
self.assertEquals(dtype, t.dtype)
self.assertProtoEquals("dim { size: 3 }", t.tensor_shape)
a = tensor_util.MakeNdarray(t)
self.assertEquals(nptype, a.dtype)
self.assertAllClose(np.array([10, 20, 30], dtype=nptype), a)
示例3: testComplex64N
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testComplex64N(self):
t = tensor_util.make_tensor_proto([(1+2j), (3+4j), (5+6j)], shape=[1, 3],
dtype=tf.complex64)
self.assertProtoEquals("""
dtype: DT_COMPLEX64
tensor_shape { dim { size: 1 } dim { size: 3 } }
scomplex_val: 1
scomplex_val: 2
scomplex_val: 3
scomplex_val: 4
scomplex_val: 5
scomplex_val: 6
""", t)
a = tensor_util.MakeNdarray(t)
self.assertEquals(np.complex64, a.dtype)
self.assertAllEqual(np.array([[(1+2j), (3+4j), (5+6j)]]), a)
示例4: testComplex64NpArray
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testComplex64NpArray(self):
t = tensor_util.make_tensor_proto(
np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), dtype=tf.complex64)
# scomplex_val are real_0, imag_0, real_1, imag_1, ...
self.assertProtoEquals("""
dtype: DT_COMPLEX64
tensor_shape { dim { size: 2 } dim { size: 2 } }
scomplex_val: 1
scomplex_val: 2
scomplex_val: 3
scomplex_val: 4
scomplex_val: 5
scomplex_val: 6
scomplex_val: 7
scomplex_val: 8
""", t)
a = tensor_util.MakeNdarray(t)
self.assertEquals(np.complex64, a.dtype)
self.assertAllEqual(np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), a)
示例5: testComplex128NpArray
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testComplex128NpArray(self):
t = tensor_util.make_tensor_proto(
np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), dtype=tf.complex128)
# scomplex_val are real_0, imag_0, real_1, imag_1, ...
self.assertProtoEquals("""
dtype: DT_COMPLEX128
tensor_shape { dim { size: 2 } dim { size: 2 } }
dcomplex_val: 1
dcomplex_val: 2
dcomplex_val: 3
dcomplex_val: 4
dcomplex_val: 5
dcomplex_val: 6
dcomplex_val: 7
dcomplex_val: 8
""", t)
a = tensor_util.MakeNdarray(t)
self.assertEquals(np.complex128, a.dtype)
self.assertAllEqual(np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), a)
示例6: _populate_const_op
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def _populate_const_op(output_node, node_name, dtype, data, data_shape):
"""Creates a Const op.
Args:
output_node: TensorFlow NodeDef.
node_name: str node name.
dtype: AttrValue with a populated .type field.
data: numpy data value.
data_shape: Tuple of integers containing data shape.
"""
output_node.op = "Const"
output_node.name = node_name
output_node.attr["dtype"].CopyFrom(dtype)
tensor = tensor_util.make_tensor_proto(
data, dtype=dtype.type, shape=data_shape)
output_node.attr["value"].tensor.CopyFrom(tensor)
示例7: set_attr_tensor
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def set_attr_tensor(self, node, key, value, dtype, shape=None):
node.attr[key].CopyFrom(
attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
value, dtype=dtype, shape=shape)))
示例8: testToFloat32
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testToFloat32(self):
with self.test_session():
expected = np.random.rand(3, 4, 5).astype(np.float32)
tensor_proto = tensor_util.make_tensor_proto(expected)
serialized = tf.placeholder(tf.string)
tensor = tf.parse_tensor(serialized, tf.float32)
result = tensor.eval(
feed_dict={serialized: tensor_proto.SerializeToString()})
self.assertAllEqual(expected, result)
示例9: testToUint8
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testToUint8(self):
with self.test_session():
expected = np.random.rand(3, 4, 5).astype(np.uint8)
tensor_proto = tensor_util.make_tensor_proto(expected)
serialized = tf.placeholder(tf.string)
tensor = tf.parse_tensor(serialized, tf.uint8)
result = tensor.eval(
feed_dict={serialized: tensor_proto.SerializeToString()})
self.assertAllEqual(expected, result)
示例10: testTypeMismatch
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testTypeMismatch(self):
with self.test_session():
expected = np.random.rand(3, 4, 5).astype(np.uint8)
tensor_proto = tensor_util.make_tensor_proto(expected)
serialized = tf.placeholder(tf.string)
tensor = tf.parse_tensor(serialized, tf.uint16)
with self.assertRaisesOpError(
r"Type mismatch between parsed tensor \(uint8\) and dtype "
r"\(uint16\)"):
tensor.eval(feed_dict={serialized: tensor_proto.SerializeToString()})
示例11: set_attr_tensor
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def set_attr_tensor(self, node, key, value, dtype, shape=None):
node.attr[key].CopyFrom(tf.AttrValue(
tensor=tensor_util.make_tensor_proto(value,
dtype=dtype,
shape=shape)))
示例12: testFloat
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testFloat(self):
value = 10.0
t = tensor_util.make_tensor_proto(value)
self.assertProtoEquals("""
dtype: DT_FLOAT
tensor_shape {}
float_val: %.1f
""" % value, t)
a = tensor_util.MakeNdarray(t)
self.assertEquals(np.float32, a.dtype)
self.assertAllClose(np.array(value, dtype=np.float32), a)
示例13: testFloatTyped
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testFloatTyped(self):
t = tensor_util.make_tensor_proto([10.0, 20.0, 30.0], dtype=tf.float32)
self.assertProtoEquals("""
dtype: DT_FLOAT
tensor_shape { dim { size: 3 } }
tensor_content: "\000\000 A\000\000\240A\000\000\360A"
""", t)
a = tensor_util.MakeNdarray(t)
self.assertEquals(np.float32, a.dtype)
self.assertAllClose(np.array([10.0, 20.0, 30.0], dtype=np.float32), a)
示例14: testFloatTypeCoerce
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testFloatTypeCoerce(self):
t = tensor_util.make_tensor_proto([10, 20, 30], dtype=tf.float32)
self.assertProtoEquals("""
dtype: DT_FLOAT
tensor_shape { dim { size: 3 } }
tensor_content: "\000\000 A\000\000\240A\000\000\360A"
""", t)
a = tensor_util.MakeNdarray(t)
self.assertEquals(np.float32, a.dtype)
self.assertAllClose(np.array([10.0, 20.0, 30.0], dtype=np.float32), a)
示例15: testFloatTypeCoerceNdarray
# 需要导入模块: from tensorflow.python.framework import tensor_util [as 别名]
# 或者: from tensorflow.python.framework.tensor_util import make_tensor_proto [as 别名]
def testFloatTypeCoerceNdarray(self):
arr = np.asarray([10, 20, 30], dtype="int")
t = tensor_util.make_tensor_proto(arr, dtype=tf.float32)
self.assertProtoEquals("""
dtype: DT_FLOAT
tensor_shape { dim { size: 3 } }
tensor_content: "\000\000 A\000\000\240A\000\000\360A"
""", t)
a = tensor_util.MakeNdarray(t)
self.assertEquals(np.float32, a.dtype)
self.assertAllClose(np.array([10.0, 20.0, 30.0], dtype=np.float32), a)