def test_input_fn(self):
dataset = census_dataset.input_fn(self.input_csv, 1, False, 1)
features, labels = dataset.make_one_shot_iterator().get_next()
with self.test_session() as sess:
features, labels = sess.run((features, labels))
# Compare the two features dictionaries.
for key in TEST_INPUT_VALUES:
self.assertTrue(key in features)
self.assertEqual(len(features[key]), 1)
feature_value = features[key][0]
# Convert from bytes to string for Python 3.
if isinstance(feature_value, bytes):
feature_value = feature_value.decode()
self.assertEqual(TEST_INPUT_VALUES[key], feature_value)
self.assertFalse(labels)
def eval_input_fn():
return census_dataset.input_fn(test_file, 1, False,
flags_obj.batch_size)
def train_input_fn():
return census_dataset.input_fn(train_file,
flags_obj.epochs_between_evals, True,
flags_obj.batch_size)
def eval_input_fn(): return census_dataset.input_fn(test_file, 1, False, flags_obj.batch_size)
def train_input_fn():
return census_dataset.input_fn(
train_file, flags_obj.epochs_between_evals, True, flags_obj.batch_size)
def input_fn():
return census_dataset.input_fn(
TEST_CSV, num_epochs=num_epochs, shuffle=shuffle,
batch_size=batch_size)
def easy_input_function(df, label_key, num_epochs, shuffle, batch_size):
label = df[label_key]
ds = tf.data.Dataset.from_tensor_slices((dict(df),label))
if shuffle:
ds = ds.shuffle(10000)
ds = ds.batch(batch_size).repeat(num_epochs)
return ds
import inspect
print(inspect.getsource(census_dataset.input_fn))
ds = census_dataset.input_fn(train_file, num_epochs=5, shuffle=True, batch_size=10)
for feature_batch, label_batch in ds.take(1):
print('Feature keys:', list(feature_batch.keys())[:5])
print()
print('Age batch :', feature_batch['age'])
print()
print('Label batch :', label_batch )
import functools
train_inpf = functools.partial(census_dataset.input_fn, train_file, num_epochs=2, shuffle=True, batch_size=64)
test_inpf = functools.partial(census_dataset.input_fn, test_file, num_epochs=1, shuffle=False, batch_size=64)
def print_ds(ds):
for feature_batch, label_batch in ds.take(1):
print("some feature keys: ", list(feature_batch.keys()))
print()
print("A batch of Ages: ", feature_batch["age"])
print()
print("A batch of labels: ", label_batch)
ds = easy_input_function(train_df,
label_key='income_bracket',
num_epochs=5,
shuffle=True,
batch_size=10)
print_ds(ds)
ds = census_dataset.input_fn(train_file, 5, True, 10)
print_ds(ds)
# using input functions
print("################# input functions")
import functools
train_inpf = functools.partial(census_dataset.input_fn, train_file, 5, True,
64)
test_inpf = functools.partial(census_dataset.input_fn, test_file, 1, False, 64)
# feature columns
print("################# feature columns")
age = tf.feature_column.numeric_column("age")
ds = train_inpf()
feature_batch = next(iter(ds.take(1)))[0]
print(tf.feature_column.input_layer(feature_batch, [age]).numpy())
