diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py
index 19b2b1abadf..727d8e40eff 100644
--- a/tensorflow/python/keras/callbacks.py
+++ b/tensorflow/python/keras/callbacks.py
@@ -21,6 +21,7 @@ from __future__ import division
from __future__ import print_function
import collections
+import collections.abc as collections_abc
import copy
import csv
import io
@@ -2608,7 +2609,7 @@ class CSVLogger(Callback):
is_zero_dim_ndarray = isinstance(k, np.ndarray) and k.ndim == 0
if isinstance(k, six.string_types):
return k
- elif isinstance(k, collections.Iterable) and not is_zero_dim_ndarray:
+ elif isinstance(k, collections_abc.Iterable) and not is_zero_dim_ndarray:
return '"[%s]"' % (', '.join(map(str, k)))
else:
return k
diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py
index 6492e039406..b4fb582f759 100644
--- a/tensorflow/python/keras/engine/training.py
+++ b/tensorflow/python/keras/engine/training.py
@@ -968,7 +968,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
form of datasets, generators, or `keras.utils.Sequence` instances
(since they generate batches).
validation_freq: Only relevant if validation data is provided. Integer
- or `collections.Container` instance (e.g. list, tuple, etc.).
+ or `collections.abc.Container` instance (e.g. list, tuple, etc.).
If an integer, specifies how many training epochs to run before a
new validation run is performed, e.g. `validation_freq=2` runs
validation every 2 epochs. If a Container, specifies the epochs on
diff --git a/tensorflow/python/keras/engine/training_arrays_v1.py b/tensorflow/python/keras/engine/training_arrays_v1.py
index 91ebbefd998..b08d6dc59bc 100644
--- a/tensorflow/python/keras/engine/training_arrays_v1.py
+++ b/tensorflow/python/keras/engine/training_arrays_v1.py
@@ -94,7 +94,7 @@ def model_iteration(model,
validation from data tensors). Ignored with the default value of
`None`.
validation_freq: Only relevant if validation data is provided. Integer or
- `collections.Container` instance (e.g. list, tuple, etc.). If an
+ `collections.abc.Container` instance (e.g. list, tuple, etc.). If an
integer, specifies how many training epochs to run before a new
validation run is performed, e.g. `validation_freq=2` runs
validation every 2 epochs. If a Container, specifies the epochs on
diff --git a/tensorflow/python/keras/engine/training_utils_v1.py b/tensorflow/python/keras/engine/training_utils_v1.py
index 6c2669b4b43..58bcaf84985 100644
--- a/tensorflow/python/keras/engine/training_utils_v1.py
+++ b/tensorflow/python/keras/engine/training_utils_v1.py
@@ -20,6 +20,7 @@ from __future__ import print_function
import abc
import atexit
import collections
+import collections.abc as collections_abc
import functools
import multiprocessing.pool
import threading
@@ -584,7 +585,7 @@ def standardize_sample_or_class_weights(x_weight, output_names, weight_type):
'You should provide one `' + weight_type + '`'
'array per model output.')
return x_weight
- if isinstance(x_weight, collections.Mapping):
+ if isinstance(x_weight, collections_abc.Mapping):
generic_utils.check_for_unexpected_keys(weight_type, x_weight, output_names)
x_weights = []
for name in output_names:
@@ -771,7 +772,7 @@ def collect_per_output_metric_info(metrics,
[metrics_module.clone_metric(m) for m in metrics])
else:
nested_metrics = [metrics]
- elif isinstance(metrics, collections.Mapping):
+ elif isinstance(metrics, collections_abc.Mapping):
generic_utils.check_for_unexpected_keys('metrics', metrics, output_names)
nested_metrics = []
for name in output_names:
@@ -1087,7 +1088,7 @@ def get_loss_function(loss):
'before passing them to Model.compile.'.format(loss))
# Deserialize loss configuration, if needed.
- if isinstance(loss, collections.Mapping):
+ if isinstance(loss, collections_abc.Mapping):
loss = losses.get(loss)
# Custom callable class.
@@ -1304,7 +1305,7 @@ def prepare_sample_weight_modes(training_endpoints, sample_weight_mode):
ValueError: In case of invalid `sample_weight_mode` input.
"""
- if isinstance(sample_weight_mode, collections.Mapping):
+ if isinstance(sample_weight_mode, collections_abc.Mapping):
generic_utils.check_for_unexpected_keys(
'sample_weight_mode', sample_weight_mode,
[e.output_name for e in training_endpoints])
@@ -1351,7 +1352,7 @@ def prepare_loss_functions(loss, output_names):
ValueError: If loss is a dict with keys not in model output names,
or if loss is a list with len not equal to model outputs.
"""
- if isinstance(loss, collections.Mapping):
+ if isinstance(loss, collections_abc.Mapping):
generic_utils.check_for_unexpected_keys('loss', loss, output_names)
loss_functions = []
for name in output_names:
@@ -1363,7 +1364,7 @@ def prepare_loss_functions(loss, output_names):
loss_functions.append(get_loss_function(loss.get(name, None)))
elif isinstance(loss, six.string_types):
loss_functions = [get_loss_function(loss) for _ in output_names]
- elif isinstance(loss, collections.Sequence):
+ elif isinstance(loss, collections_abc.Sequence):
if len(loss) != len(output_names):
raise ValueError('When passing a list as loss, it should have one entry '
'per model outputs. The model has {} outputs, but you '
@@ -1397,7 +1398,7 @@ def prepare_loss_weights(training_endpoints, loss_weights=None):
if loss_weights is None:
for e in training_endpoints:
e.loss_weight = 1.
- elif isinstance(loss_weights, collections.Mapping):
+ elif isinstance(loss_weights, collections_abc.Mapping):
generic_utils.check_for_unexpected_keys(
'loss_weights', loss_weights,
[e.output_name for e in training_endpoints])
@@ -1704,9 +1705,9 @@ def should_run_validation(validation_freq, epoch):
raise ValueError('`validation_freq` can not be less than 1.')
return one_indexed_epoch % validation_freq == 0
- if not isinstance(validation_freq, collections.Container):
+ if not isinstance(validation_freq, collections_abc.Container):
raise ValueError('`validation_freq` must be an Integer or '
- '`collections.Container` (e.g. list, tuple, etc.)')
+ '`collections.abc.Container` (e.g. list, tuple, etc.)')
return one_indexed_epoch in validation_freq
diff --git a/tensorflow/python/keras/engine/training_v1.py b/tensorflow/python/keras/engine/training_v1.py
index 5ee6d0e858b..cea6b1b7609 100644
--- a/tensorflow/python/keras/engine/training_v1.py
+++ b/tensorflow/python/keras/engine/training_v1.py
@@ -18,6 +18,7 @@ from __future__ import division
from __future__ import print_function
import collections
+import collections.abc as collections_abc
import warnings
import numpy as np
@@ -745,7 +746,7 @@ class Model(training_lib.Model):
the dataset at each epoch. This ensures that the same validation
samples are used every time.
validation_freq: Only relevant if validation data is provided. Integer
- or `collections.Container` instance (e.g. list, tuple, etc.).
+ or `collections.abc.Container` instance (e.g. list, tuple, etc.).
If an integer, specifies how many training epochs to run before a
new validation run is performed, e.g. `validation_freq=2` runs
validation every 2 epochs. If a Container, specifies the epochs on
@@ -1207,7 +1208,7 @@ class Model(training_lib.Model):
# at this point.
if self.run_eagerly or self._distribution_strategy:
inputs = training_utils_v1.cast_if_floating_dtype(inputs)
- if isinstance(inputs, collections.Sequence):
+ if isinstance(inputs, collections_abc.Sequence):
# Unwrap lists with only one input, as we do when training on batch
if len(inputs) == 1:
inputs = inputs[0]
diff --git a/tensorflow/python/keras/keras_parameterized.py b/tensorflow/python/keras/keras_parameterized.py
index 125b935b807..43e7b2d270b 100644
--- a/tensorflow/python/keras/keras_parameterized.py
+++ b/tensorflow/python/keras/keras_parameterized.py
@@ -18,7 +18,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
-import collections
+import collections.abc as collections_abc
import functools
import itertools
import unittest
@@ -473,7 +473,7 @@ def _test_or_class_decorator(test_or_class, single_method_decorator):
The decorated result.
"""
def _decorate_test_or_class(obj):
- if isinstance(obj, collections.Iterable):
+ if isinstance(obj, collections_abc.Iterable):
return itertools.chain.from_iterable(
single_method_decorator(method) for method in obj)
if isinstance(obj, type):
diff --git a/tensorflow/python/keras/layers/preprocessing/preprocessing_test_utils.py b/tensorflow/python/keras/layers/preprocessing/preprocessing_test_utils.py
index d93dcc21d5e..a698761ed61 100644
--- a/tensorflow/python/keras/layers/preprocessing/preprocessing_test_utils.py
+++ b/tensorflow/python/keras/layers/preprocessing/preprocessing_test_utils.py
@@ -18,7 +18,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
-import collections
+import collections.abc as collections_abc
import numpy as np
from tensorflow.python.platform import test
@@ -37,7 +37,7 @@ class PreprocessingLayerTest(test.TestCase):
self.assertEqual(len(a), len(b))
for a_value, b_value in zip(a, b):
self.assertAllCloseOrEqual(a_value, b_value, msg=msg)
- elif isinstance(a, collections.Mapping):
+ elif isinstance(a, collections_abc.Mapping):
self.assertEqual(len(a), len(b))
for key, a_value in a.items():
b_value = b[key]
diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py
index fcb60a8050c..d5387a94644 100644
--- a/tensorflow/python/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/layers/recurrent.py
@@ -19,7 +19,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
-import collections
+import collections.abc as collections_abc
import warnings
import numpy as np
@@ -47,6 +47,7 @@ from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.training.tracking import data_structures
from tensorflow.python.util import nest
+from tensorflow.python.util.compat import collections_abc
from tensorflow.python.util.tf_export import keras_export
from tensorflow.tools.docs import doc_controls
@@ -828,7 +829,7 @@ class RNN(Layer):
# input shape: `(samples, time (padded with zeros), input_dim)`
# note that the .build() method of subclasses MUST define
# self.input_spec and self.state_spec with complete input shapes.
- if (isinstance(inputs, collections.Sequence)
+ if (isinstance(inputs, collections_abc.Sequence)
and not isinstance(inputs, tuple)):
# get initial_state from full input spec
# as they could be copied to multiple GPU.
diff --git a/tensorflow/python/keras/saving/saved_model/json_utils.py b/tensorflow/python/keras/saving/saved_model/json_utils.py
index b0834eb0c32..cc79a777e79 100644
--- a/tensorflow/python/keras/saving/saved_model/json_utils.py
+++ b/tensorflow/python/keras/saving/saved_model/json_utils.py
@@ -25,7 +25,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
-import collections
+import collections.abc as collections_abc
import json
import numpy as np
import wrapt
@@ -117,7 +117,7 @@ def get_json_type(obj):
if isinstance(obj, dtypes.DType):
return obj.name
- if isinstance(obj, collections.Mapping):
+ if isinstance(obj, collections_abc.Mapping):
return dict(obj)
if obj is Ellipsis:
diff --git a/tensorflow/python/keras/saving/saving_utils.py b/tensorflow/python/keras/saving/saving_utils.py
index ea15ef9d539..d16f69bd89d 100644
--- a/tensorflow/python/keras/saving/saving_utils.py
+++ b/tensorflow/python/keras/saving/saving_utils.py
@@ -17,7 +17,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
-import collections
+import collections.abc as collections_abc
import copy
import os
import six
@@ -82,7 +82,7 @@ def model_input_signature(model, keep_original_batch_size=False):
input_specs = _enforce_names_consistency(input_specs)
# Return a list with a single element as the model's input signature.
if isinstance(input_specs,
- collections.Sequence) and len(input_specs) == 1:
+ collections_abc.Sequence) and len(input_specs) == 1:
# Note that the isinstance check filters out single-element dictionaries,
# which should also be wrapped as a single-element list.
return input_specs