我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用tensorflow.python.framework.dtypes.int16()。
def _convert_string_dtype(dtype): """Get the type from a string. Arguments: dtype: A string representation of a type. Returns: The type requested. Raises: ValueError: if `dtype` is not supported. """ if dtype == 'float16': return dtypes_module.float16 if dtype == 'float32': return dtypes_module.float32 elif dtype == 'float64': return dtypes_module.float64 elif dtype == 'int16': return dtypes_module.int16 elif dtype == 'int32': return dtypes_module.int32 elif dtype == 'int64': return dtypes_module.int64 elif dtype == 'uint8': return dtypes_module.int8 elif dtype == 'uint16': return dtypes_module.uint16 else: raise ValueError('Unsupported dtype:', dtype)
def testFromCSVWithFeatureSpec(self): if not HAS_PANDAS: return num_batches = 100 batch_size = 8 data_path = _make_test_csv_sparse() feature_spec = { "int": tf.FixedLenFeature(None, dtypes.int16, np.nan), "float": tf.VarLenFeature(dtypes.float16), "bool": tf.VarLenFeature(dtypes.bool), "string": tf.FixedLenFeature(None, dtypes.string, "") } pandas_df = pd.read_csv(data_path, dtype={"string": object}) # Pandas insanely uses NaN for empty cells in a string column. # And, we can't use Pandas replace() to fix them because nan != nan s = pandas_df["string"] for i in range(0, len(s)): if isinstance(s[i], float) and math.isnan(s[i]): pandas_df.set_value(i, "string", "") tensorflow_df = df.TensorFlowDataFrame.from_csv_with_feature_spec( [data_path], batch_size=batch_size, shuffle=False, feature_spec=feature_spec) # These columns were sparse; re-densify them for comparison tensorflow_df["float"] = densify.Densify(np.nan)(tensorflow_df["float"]) tensorflow_df["bool"] = densify.Densify(np.nan)(tensorflow_df["bool"]) self._assert_pandas_equals_tensorflow(pandas_df, tensorflow_df, num_batches=num_batches, batch_size=batch_size)
def testConvertBetweenInteger(self): try: # Make sure converting to between integer types scales appropriately with self.test_session(): self._convert([0, 255], dtypes.uint8, dtypes.int16, [0, 255 * 128]) self._convert([0, 32767], dtypes.int16, dtypes.uint8, [0, 255]) # itensor is tf.int32, but attr "T" is set to tf.int16 except: import pdb; pdb.post_mortem()
def test_input_int16(self): data = np.matrix([[1, 2], [3, 4]], dtype=np.int16) self._assert_dtype(np.int16, dtypes.int16, data) self._assert_dtype(np.int16, dtypes.int16, self._wrap_dict(data))
def testFromCSVWithFeatureSpec(self): if not HAS_PANDAS: return num_batches = 100 batch_size = 8 data_path = _make_test_csv_sparse() feature_spec = { "int": parsing_ops.FixedLenFeature(None, dtypes.int16, np.nan), "float": parsing_ops.VarLenFeature(dtypes.float16), "bool": parsing_ops.VarLenFeature(dtypes.bool), "string": parsing_ops.FixedLenFeature(None, dtypes.string, "") } pandas_df = pd.read_csv(data_path, dtype={"string": object}) # Pandas insanely uses NaN for empty cells in a string column. # And, we can't use Pandas replace() to fix them because nan != nan s = pandas_df["string"] for i in range(0, len(s)): if isinstance(s[i], float) and math.isnan(s[i]): pandas_df.set_value(i, "string", "") tensorflow_df = df.TensorFlowDataFrame.from_csv_with_feature_spec( [data_path], batch_size=batch_size, shuffle=False, feature_spec=feature_spec) # These columns were sparse; re-densify them for comparison tensorflow_df["float"] = densify.Densify(np.nan)(tensorflow_df["float"]) tensorflow_df["bool"] = densify.Densify(np.nan)(tensorflow_df["bool"]) self._assert_pandas_equals_tensorflow( pandas_df, tensorflow_df, num_batches=num_batches, batch_size=batch_size)
