Move tf.layers to keras folder and export from there.
PiperOrigin-RevId: 300878712 Change-Id: I3648257c5e49f03a6d163110f4cfed10b57444ea
This commit is contained in:
Scott Zhu
TensorFlower Gardener
parent
62b6926417
commit
2c6bfdd663
@ -2062,8 +2062,8 @@ tf_py_test(
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main = "framework/registry_test.py",
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main = "framework/registry_test.py",
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python_version = "PY3",
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python_version = "PY3",
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deps = [
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deps = [
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":client_testlib",
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":framework_for_generated_wrappers",
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":framework_for_generated_wrappers",
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"//tensorflow/python:client_testlib",
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"@absl_py//absl/testing:parameterized",
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"@absl_py//absl/testing:parameterized",
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],
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],
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)
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)
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@ -3895,8 +3895,8 @@ cuda_py_test(
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srcs = ["training/experimental/mixed_precision_test.py"],
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srcs = ["training/experimental/mixed_precision_test.py"],
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python_version = "PY3",
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python_version = "PY3",
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deps = [
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deps = [
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":client_testlib",
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":mixed_precision",
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":mixed_precision",
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"//tensorflow/python:client_testlib",
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"@absl_py//absl/testing:parameterized",
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"@absl_py//absl/testing:parameterized",
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],
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],
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)
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)
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@ -5879,7 +5879,7 @@ filegroup(
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"//tensorflow/core/util:port", # util_port
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"//tensorflow/core/util:port", # util_port
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"//tensorflow/core/util/tensor_bundle", # checkpoint_reader
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"//tensorflow/core/util/tensor_bundle", # checkpoint_reader
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"//tensorflow/lite/toco/python:toco_python_api", # toco
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"//tensorflow/lite/toco/python:toco_python_api", # toco
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"//tensorflow/python:tf_session_helper", # tf_session
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":tf_session_helper", # tf_session
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"//tensorflow/python/eager:pywrap_tfe_lib", # pywrap_tfe_lib
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"//tensorflow/python/eager:pywrap_tfe_lib", # pywrap_tfe_lib
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"//tensorflow/stream_executor:stream_executor_pimpl", # stat_summarizer
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"//tensorflow/stream_executor:stream_executor_pimpl", # stat_summarizer
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"//tensorflow/tools/graph_transforms:transform_graph_lib", # transform_graph
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"//tensorflow/tools/graph_transforms:transform_graph_lib", # transform_graph
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@ -7021,6 +7021,7 @@ py_tests(
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],
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],
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)
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)
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# TODO(scottzhu): Move all the tf.layer related targets.
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py_library(
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py_library(
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name = "layers_base",
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name = "layers_base",
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srcs = [
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srcs = [
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@ -7029,19 +7030,7 @@ py_library(
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],
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],
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srcs_version = "PY2AND3",
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srcs_version = "PY2AND3",
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deps = [
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deps = [
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":array_ops",
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"//tensorflow/python/keras/legacy_tf_layers:layers_base",
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":control_flow_ops",
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":framework_for_generated_wrappers",
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":layers_util",
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":platform",
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":smart_cond",
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":tensor_util",
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":util",
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":variable_scope",
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":variables",
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"//tensorflow/python/eager:context",
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"//tensorflow/python/keras:engine",
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"//third_party/py/numpy",
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],
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],
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)
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)
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@ -7070,134 +7059,12 @@ py_library(
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],
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],
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srcs_version = "PY2AND3",
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srcs_version = "PY2AND3",
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deps = [
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deps = [
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":array_ops",
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":array_ops_gen",
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":control_flow_ops",
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":framework",
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":framework_for_generated_wrappers",
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":init_ops",
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":layers_base",
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":layers_base",
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":math_ops",
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"//tensorflow/python/keras/engine:input_spec",
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":nn",
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"//tensorflow/python/keras/legacy_tf_layers:convolutional",
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":nn_ops",
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"//tensorflow/python/keras/legacy_tf_layers:core",
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":platform",
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"//tensorflow/python/keras/legacy_tf_layers:normalization",
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":resource_variable_ops",
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"//tensorflow/python/keras/legacy_tf_layers:pooling",
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":resource_variable_ops_gen",
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":standard_ops",
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":state_ops",
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":training",
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":util",
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":variable_scope",
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":variables",
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"//tensorflow/python/eager:context",
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"//tensorflow/python/keras/layers",
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"//third_party/py/numpy",
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"@six_archive//:six",
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],
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)
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tf_py_test(
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name = "layers_base_test",
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size = "small",
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srcs = ["layers/base_test.py"],
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main = "layers/base_test.py",
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python_version = "PY3",
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deps = [
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":array_ops",
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":client_testlib",
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":framework_for_generated_wrappers",
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":framework_test_lib",
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":init_ops",
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":layers",
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":layers_base",
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":math_ops",
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":random_ops",
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":variable_scope",
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"//tensorflow/python/eager:context",
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],
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)
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tf_py_test(
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name = "layers_core_test",
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size = "small",
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srcs = ["layers/core_test.py"],
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main = "layers/core_test.py",
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python_version = "PY3",
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deps = [
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":array_ops",
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":client_testlib",
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":framework_for_generated_wrappers",
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":framework_test_lib",
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":layers",
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":math_ops",
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":nn_ops",
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":random_ops",
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":variable_scope",
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":variables",
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"//third_party/py/numpy",
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],
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)
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tf_py_test(
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name = "layers_convolutional_test",
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size = "small",
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srcs = ["layers/convolutional_test.py"],
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main = "layers/convolutional_test.py",
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python_version = "PY3",
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deps = [
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":client_testlib",
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":framework_for_generated_wrappers",
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":framework_test_lib",
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":layers",
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":math_ops",
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":nn_ops",
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":random_ops",
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],
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)
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tf_py_test(
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name = "layers_utils_test",
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size = "small",
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srcs = ["layers/utils_test.py"],
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main = "layers/utils_test.py",
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python_version = "PY3",
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deps = [
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":client_testlib",
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":layers",
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],
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)
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tf_py_test(
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name = "layers_pooling_test",
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size = "small",
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srcs = ["layers/pooling_test.py"],
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main = "layers/pooling_test.py",
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python_version = "PY3",
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deps = [
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":client_testlib",
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":framework_test_lib",
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":layers",
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":random_ops",
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],
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)
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cuda_py_test(
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name = "layers_normalization_test",
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size = "medium",
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srcs = ["layers/normalization_test.py"],
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main = "layers/normalization_test.py",
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python_version = "PY3",
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shard_count = 10,
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deps = [
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":array_ops",
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":client_testlib",
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":framework_for_generated_wrappers",
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":framework_test_lib",
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":layers",
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":math_ops",
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":random_ops",
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":variables",
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"//third_party/py/numpy",
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],
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],
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)
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)
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@ -7711,7 +7578,7 @@ tf_py_test(
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deps = [
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deps = [
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":client_testlib",
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":client_testlib",
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":graph_placer",
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":graph_placer",
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"//tensorflow/python:math_ops",
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":math_ops",
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],
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],
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)
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)
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@ -8145,6 +8012,6 @@ cuda_py_test(
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srcs = ["ops/raw_ops_test.py"],
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srcs = ["ops/raw_ops_test.py"],
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python_version = "PY3",
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python_version = "PY3",
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deps = [
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deps = [
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"//tensorflow/python:client_testlib",
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":client_testlib",
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],
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],
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)
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)
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195
tensorflow/python/keras/legacy_tf_layers/BUILD
Normal file
195
tensorflow/python/keras/legacy_tf_layers/BUILD
Normal file
@ -0,0 +1,195 @@
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# Description:
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# Contains the legacy TF layers (internal TensorFlow version).
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load("//tensorflow:tensorflow.bzl", "tf_py_test")
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load("//tensorflow:tensorflow.bzl", "cuda_py_test")
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package(
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default_visibility = ["//tensorflow:__subpackages__"],
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licenses = ["notice"], # Apache 2.0
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)
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py_library(
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name = "layers_base",
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srcs = [
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"__init__.py",
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"base.py",
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],
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srcs_version = "PY2AND3",
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deps = [
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"//tensorflow/python:dtypes",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:util",
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"//tensorflow/python:variable_scope",
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"//tensorflow/python:variables",
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"//tensorflow/python/eager:context",
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"//tensorflow/python/keras:backend",
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"//tensorflow/python/keras/engine:base_layer",
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"//tensorflow/python/keras/mixed_precision/experimental:policy",
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"//tensorflow/python/training/tracking:base",
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],
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)
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py_library(
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name = "convolutional",
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srcs = ["convolutional.py"],
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deps = [
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":layers_base",
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"//tensorflow/python:init_ops",
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"//tensorflow/python:util",
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"//tensorflow/python/keras/layers",
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],
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)
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py_library(
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name = "core",
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srcs = ["core.py"],
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deps = [
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":layers_base",
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"//tensorflow/python:init_ops",
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"//tensorflow/python:util",
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"//tensorflow/python/keras/layers",
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],
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)
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py_library(
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name = "normalization",
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srcs = ["normalization.py"],
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deps = [
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":layers_base",
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"//tensorflow/python:init_ops",
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"//tensorflow/python:util",
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"//tensorflow/python/keras/layers:normalization",
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],
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)
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py_library(
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name = "pooling",
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srcs = ["pooling.py"],
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deps = [
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":layers_base",
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"//tensorflow/python:util",
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"//tensorflow/python/keras/layers",
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],
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)
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tf_py_test(
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name = "base_test",
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size = "small",
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srcs = ["base_test.py"],
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main = "base_test.py",
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python_version = "PY3",
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deps = [
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":core",
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":layers_base",
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"//tensorflow/python:array_ops",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:constant_op",
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"//tensorflow/python:dtypes",
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"//tensorflow/python:extra_py_tests_deps",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:framework_test_lib",
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"//tensorflow/python:init_ops",
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"//tensorflow/python:math_ops",
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"//tensorflow/python:partitioned_variables",
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"//tensorflow/python:random_ops",
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"//tensorflow/python:state_ops",
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"//tensorflow/python:variable_scope",
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"//tensorflow/python/eager:context",
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"//tensorflow/python/eager:def_function",
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"//tensorflow/python/keras/engine:base_layer",
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"//tensorflow/python/keras/engine:input_spec",
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],
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)
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tf_py_test(
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name = "core_test",
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size = "small",
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srcs = ["core_test.py"],
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main = "core_test.py",
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python_version = "PY3",
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deps = [
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":core",
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"//tensorflow/python:array_ops",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:constant_op",
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"//tensorflow/python:dtypes",
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"//tensorflow/python:extra_py_tests_deps",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:framework_test_lib",
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"//tensorflow/python:init_ops",
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"//tensorflow/python:math_ops",
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"//tensorflow/python:nn_ops",
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"//tensorflow/python:random_ops",
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"//tensorflow/python:tensor_shape",
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"//tensorflow/python:variable_scope",
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"//tensorflow/python:variables",
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"//tensorflow/python/eager:context",
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],
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)
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tf_py_test(
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name = "convolutional_test",
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size = "small",
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srcs = ["convolutional_test.py"],
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main = "convolutional_test.py",
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python_version = "PY3",
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deps = [
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":convolutional",
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"//tensorflow/python:array_ops",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:dtypes",
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"//tensorflow/python:extra_py_tests_deps",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:framework_test_lib",
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"//tensorflow/python:init_ops",
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"//tensorflow/python:math_ops",
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"//tensorflow/python:nn_ops",
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"//tensorflow/python:random_ops",
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"//tensorflow/python:variable_scope",
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"//tensorflow/python:variables",
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],
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)
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tf_py_test(
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name = "pooling_test",
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size = "small",
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srcs = ["pooling_test.py"],
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main = "pooling_test.py",
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python_version = "PY3",
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deps = [
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":pooling",
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"//tensorflow/python:array_ops",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:extra_py_tests_deps",
|
||||||
|
"//tensorflow/python:framework_test_lib",
|
||||||
|
"//tensorflow/python:random_ops",
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
cuda_py_test(
|
||||||
|
name = "normalization_test",
|
||||||
|
size = "medium",
|
||||||
|
srcs = ["normalization_test.py"],
|
||||||
|
main = "normalization_test.py",
|
||||||
|
python_version = "PY3",
|
||||||
|
shard_count = 10,
|
||||||
|
deps = [
|
||||||
|
":convolutional",
|
||||||
|
":normalization",
|
||||||
|
"//tensorflow/core:protos_all_py",
|
||||||
|
"//tensorflow/python:array_ops",
|
||||||
|
"//tensorflow/python:client_testlib",
|
||||||
|
"//tensorflow/python:dtypes",
|
||||||
|
"//tensorflow/python:extra_py_tests_deps",
|
||||||
|
"//tensorflow/python:framework_ops",
|
||||||
|
"//tensorflow/python:framework_test_lib",
|
||||||
|
"//tensorflow/python:init_ops",
|
||||||
|
"//tensorflow/python:math_ops",
|
||||||
|
"//tensorflow/python:random_ops",
|
||||||
|
"//tensorflow/python:saver",
|
||||||
|
"//tensorflow/python:training_lib",
|
||||||
|
"//tensorflow/python:variable_scope",
|
||||||
|
"//tensorflow/python:variables",
|
||||||
|
],
|
||||||
|
)
|
||||||
593
tensorflow/python/keras/legacy_tf_layers/base.py
Normal file
593
tensorflow/python/keras/legacy_tf_layers/base.py
Normal file
@ -0,0 +1,593 @@
|
|||||||
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
# =============================================================================
|
||||||
|
"""Contains the base Layer class, from which all layers inherit."""
|
||||||
|
from __future__ import absolute_import
|
||||||
|
from __future__ import division
|
||||||
|
from __future__ import print_function
|
||||||
|
|
||||||
|
import copy
|
||||||
|
|
||||||
|
from tensorflow.python.eager import context
|
||||||
|
from tensorflow.python.framework import dtypes
|
||||||
|
from tensorflow.python.framework import ops
|
||||||
|
from tensorflow.python.keras import backend
|
||||||
|
from tensorflow.python.keras.engine import base_layer
|
||||||
|
from tensorflow.python.keras.mixed_precision.experimental import policy
|
||||||
|
from tensorflow.python.ops import variable_scope as vs
|
||||||
|
from tensorflow.python.ops import variables as tf_variables
|
||||||
|
from tensorflow.python.training.tracking import base as trackable
|
||||||
|
from tensorflow.python.util import deprecation
|
||||||
|
from tensorflow.python.util import function_utils
|
||||||
|
from tensorflow.python.util import nest
|
||||||
|
from tensorflow.python.util import tf_contextlib
|
||||||
|
from tensorflow.python.util.tf_export import tf_export
|
||||||
|
|
||||||
|
# Avoid breaking users who directly import this symbol from this file.
|
||||||
|
# TODO(fchollet): remove this.
|
||||||
|
InputSpec = base_layer.InputSpec # pylint: disable=invalid-name
|
||||||
|
|
||||||
|
_KERAS_STYLE_SCOPE = False
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.experimental.keras_style_scope'])
|
||||||
|
@tf_contextlib.contextmanager
|
||||||
|
def keras_style_scope():
|
||||||
|
"""Use Keras-style variable management.
|
||||||
|
|
||||||
|
All tf.layers and tf RNN cells created in this scope use Keras-style
|
||||||
|
variable management. Creating such layers with a scope= argument is
|
||||||
|
disallowed, and reuse=True is disallowed.
|
||||||
|
|
||||||
|
The purpose of this scope is to allow users of existing layers to
|
||||||
|
slowly transition to a Keras layers API without breaking existing
|
||||||
|
functionality.
|
||||||
|
|
||||||
|
One example of this is when using TensorFlow's RNN classes with Keras
|
||||||
|
Models or Networks. Because Keras models do not properly set variable
|
||||||
|
scopes, users of RNNs may either accidentally share scopes between two
|
||||||
|
different models, or get errors about variables that already exist.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
|
||||||
|
```python
|
||||||
|
class RNNModel(tf.keras.Model):
|
||||||
|
|
||||||
|
def __init__(self, name):
|
||||||
|
super(RNNModel, self).__init__(name=name)
|
||||||
|
self.rnn = tf.compat.v1.nn.rnn_cell.MultiRNNCell(
|
||||||
|
[tf.compat.v1.nn.rnn_cell.LSTMCell(64) for _ in range(2)])
|
||||||
|
|
||||||
|
def call(self, input, state):
|
||||||
|
return self.rnn(input, state)
|
||||||
|
|
||||||
|
model_1 = RNNModel("model_1")
|
||||||
|
model_2 = RNNModel("model_2")
|
||||||
|
|
||||||
|
# OK
|
||||||
|
output_1, next_state_1 = model_1(input, state)
|
||||||
|
# Raises an error about trying to create an already existing variable.
|
||||||
|
output_2, next_state_2 = model_2(input, state)
|
||||||
|
```
|
||||||
|
|
||||||
|
The solution is to wrap the model construction and execution in a keras-style
|
||||||
|
scope:
|
||||||
|
|
||||||
|
```python
|
||||||
|
with keras_style_scope():
|
||||||
|
model_1 = RNNModel("model_1")
|
||||||
|
model_2 = RNNModel("model_2")
|
||||||
|
|
||||||
|
# model_1 and model_2 are guaranteed to create their own variables.
|
||||||
|
output_1, next_state_1 = model_1(input, state)
|
||||||
|
output_2, next_state_2 = model_2(input, state)
|
||||||
|
|
||||||
|
assert len(model_1.weights) > 0
|
||||||
|
assert len(model_2.weights) > 0
|
||||||
|
assert(model_1.weights != model_2.weights)
|
||||||
|
```
|
||||||
|
|
||||||
|
Yields:
|
||||||
|
A keras layer style scope.
|
||||||
|
"""
|
||||||
|
global _KERAS_STYLE_SCOPE
|
||||||
|
stack = _KERAS_STYLE_SCOPE
|
||||||
|
_KERAS_STYLE_SCOPE = True
|
||||||
|
try:
|
||||||
|
yield
|
||||||
|
finally:
|
||||||
|
_KERAS_STYLE_SCOPE = stack
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.experimental.set_keras_style'])
|
||||||
|
def set_keras_style():
|
||||||
|
"""Use Keras-style variable management.
|
||||||
|
|
||||||
|
All tf.layers and tf RNN cells created after keras style ha been enabled
|
||||||
|
use Keras-style variable management. Creating such layers with a
|
||||||
|
scope= argument is disallowed, and reuse=True is disallowed.
|
||||||
|
|
||||||
|
The purpose of this function is to allow users of existing layers to
|
||||||
|
slowly transition to Keras layers API without breaking existing
|
||||||
|
functionality.
|
||||||
|
|
||||||
|
For more details, see the documentation for `keras_style_scope`.
|
||||||
|
|
||||||
|
Note, once keras style has been set, it is set globally for the entire
|
||||||
|
program and cannot be unset.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
|
||||||
|
```python
|
||||||
|
set_keras_style()
|
||||||
|
|
||||||
|
model_1 = RNNModel(name="model_1")
|
||||||
|
model_2 = RNNModel(name="model_2")
|
||||||
|
|
||||||
|
# model_1 and model_2 are guaranteed to create their own variables.
|
||||||
|
output_1, next_state_1 = model_1(input, state)
|
||||||
|
output_2, next_state_2 = model_2(input, state)
|
||||||
|
|
||||||
|
assert len(model_1.weights) > 0
|
||||||
|
assert len(model_2.weights) > 0
|
||||||
|
assert(model_1.weights != model_2.weights)
|
||||||
|
```
|
||||||
|
"""
|
||||||
|
global _KERAS_STYLE_SCOPE
|
||||||
|
_KERAS_STYLE_SCOPE = True
|
||||||
|
|
||||||
|
|
||||||
|
def _is_in_keras_style_scope():
|
||||||
|
global _KERAS_STYLE_SCOPE
|
||||||
|
return _KERAS_STYLE_SCOPE
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.Layer'])
|
||||||
|
class Layer(base_layer.Layer):
|
||||||
|
"""Base layer class.
|
||||||
|
|
||||||
|
It is considered legacy, and we recommend the use of `tf.keras.layers.Layer`
|
||||||
|
instead.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
trainable: Boolean, whether the layer's variables should be trainable.
|
||||||
|
name: String name of the layer.
|
||||||
|
dtype: Default dtype of the layer's weights (default of `None` means use the
|
||||||
|
type of the first input).
|
||||||
|
|
||||||
|
Read-only properties:
|
||||||
|
name: The name of the layer (string).
|
||||||
|
dtype: Default dtype of the layer's weights (default of `None` means use the
|
||||||
|
type of the first input).
|
||||||
|
trainable_variables: List of trainable variables.
|
||||||
|
non_trainable_variables: List of non-trainable variables.
|
||||||
|
variables: List of all variables of this layer, trainable and
|
||||||
|
non-trainable.
|
||||||
|
updates: List of update ops of this layer.
|
||||||
|
losses: List of losses added by this layer.
|
||||||
|
trainable_weights: List of variables to be included in backprop.
|
||||||
|
non_trainable_weights: List of variables that should not be
|
||||||
|
included in backprop.
|
||||||
|
weights: The concatenation of the lists trainable_weights and
|
||||||
|
non_trainable_weights (in this order).
|
||||||
|
|
||||||
|
Mutable properties:
|
||||||
|
trainable: Whether the layer should be trained (boolean).
|
||||||
|
input_spec: Optional (list of) `InputSpec` object(s) specifying the
|
||||||
|
constraints on inputs that can be accepted by the layer.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, trainable=True, name=None, dtype=None,
|
||||||
|
**kwargs):
|
||||||
|
# For backwards compatibility, legacy layers do not use `ResourceVariable`
|
||||||
|
# by default.
|
||||||
|
self._use_resource_variables = False
|
||||||
|
scope = kwargs.pop('_scope', None)
|
||||||
|
self._reuse = kwargs.pop('_reuse', None)
|
||||||
|
|
||||||
|
# Avoid an incorrect lint error
|
||||||
|
self._trainable_weights = []
|
||||||
|
self.built = False
|
||||||
|
|
||||||
|
if dtype is None:
|
||||||
|
# Indicates to infer dtype from inputs. When the V2 dtype behavior is
|
||||||
|
# enabled, Keras layers default their dtype to floatx instead, so we pass
|
||||||
|
# an "_infer" policy to keep the old V1 behavior.
|
||||||
|
dtype = policy.Policy('_infer')
|
||||||
|
|
||||||
|
if 'autocast' not in kwargs:
|
||||||
|
kwargs['autocast'] = False
|
||||||
|
|
||||||
|
super(Layer, self).__init__(trainable=trainable, name=name, dtype=dtype,
|
||||||
|
**kwargs)
|
||||||
|
|
||||||
|
if _is_in_keras_style_scope():
|
||||||
|
if scope is not None:
|
||||||
|
raise ValueError(
|
||||||
|
'scope argument not allowed when keras style layers are enabled, '
|
||||||
|
'but saw: {}'.format(scope))
|
||||||
|
if self._reuse is not None:
|
||||||
|
raise ValueError(
|
||||||
|
'reuse argument not allowed when keras style layers are enabled, '
|
||||||
|
'but saw: {}'.format(self._reuse))
|
||||||
|
self._keras_style = True
|
||||||
|
else:
|
||||||
|
self._keras_style = False
|
||||||
|
|
||||||
|
self._call_has_scope_arg = 'scope' in self._call_fn_args
|
||||||
|
if scope:
|
||||||
|
with vs.variable_scope(scope) as captured_scope:
|
||||||
|
self._scope = captured_scope
|
||||||
|
else:
|
||||||
|
self._scope = None
|
||||||
|
self._current_scope = None
|
||||||
|
|
||||||
|
# We no longer track graph in tf.layers layers. This property is only kept to
|
||||||
|
# maintain API backward compatibility.
|
||||||
|
@property
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None,
|
||||||
|
instructions='Stop using this property because tf.layers layers no '
|
||||||
|
'longer track their graph.')
|
||||||
|
def graph(self):
|
||||||
|
if context.executing_eagerly():
|
||||||
|
raise RuntimeError('Layer.graph not supported when executing eagerly.')
|
||||||
|
return None
|
||||||
|
|
||||||
|
def _init_set_name(self, name):
|
||||||
|
# Determine layer name (non-unique).
|
||||||
|
if isinstance(name, vs.VariableScope):
|
||||||
|
base_name = name.name
|
||||||
|
self._name, _ = self._make_unique_name()
|
||||||
|
else:
|
||||||
|
base_name = name
|
||||||
|
self._name = name
|
||||||
|
if not name:
|
||||||
|
self._name, base_name = self._make_unique_name()
|
||||||
|
self._base_name = base_name
|
||||||
|
|
||||||
|
def _make_unique_name(self, name_uid_map=None, avoid_names=None,
|
||||||
|
namespace='', zero_based=False):
|
||||||
|
base_name = base_layer.to_snake_case(self.__class__.__name__)
|
||||||
|
name = backend.unique_object_name(
|
||||||
|
base_name,
|
||||||
|
name_uid_map=name_uid_map,
|
||||||
|
avoid_names=avoid_names,
|
||||||
|
namespace=namespace,
|
||||||
|
zero_based=zero_based)
|
||||||
|
return (name, base_name)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def scope_name(self):
|
||||||
|
if not self._scope:
|
||||||
|
raise ValueError('No name available for layer scope because the layer "' +
|
||||||
|
self._name + '" has not been used yet. The scope name ' +
|
||||||
|
' is determined the first time the layer instance is ' +
|
||||||
|
'called. You must therefore call the layer before ' +
|
||||||
|
'querying `scope_name`.')
|
||||||
|
return self._scope.name
|
||||||
|
|
||||||
|
def add_loss(self, losses, inputs=None):
|
||||||
|
previous_losses_length = len(self._losses)
|
||||||
|
previous_callable_losses_length = len(self._callable_losses)
|
||||||
|
super(Layer, self).add_loss(losses, inputs=inputs)
|
||||||
|
if not context.executing_eagerly():
|
||||||
|
# TODO(fchollet): deprecate collection below.
|
||||||
|
new_losses = self._losses[previous_losses_length:]
|
||||||
|
new_callable_losses = self._callable_losses[
|
||||||
|
previous_callable_losses_length:]
|
||||||
|
for regularizer in new_callable_losses:
|
||||||
|
loss_tensor = regularizer()
|
||||||
|
if loss_tensor is not None:
|
||||||
|
new_losses.append(loss_tensor)
|
||||||
|
_add_elements_to_collection(
|
||||||
|
new_losses,
|
||||||
|
ops.GraphKeys.REGULARIZATION_LOSSES)
|
||||||
|
|
||||||
|
def _name_scope(self):
|
||||||
|
"""Determines op naming for the Layer."""
|
||||||
|
if self._keras_style:
|
||||||
|
return super(Layer, self)._name_scope()
|
||||||
|
return self._current_scope.original_name_scope
|
||||||
|
|
||||||
|
def _set_scope(self, scope=None):
|
||||||
|
if self._scope is None:
|
||||||
|
# If constructed with _scope=None, lazy setting of scope.
|
||||||
|
if self._reuse:
|
||||||
|
with vs.variable_scope(
|
||||||
|
scope if scope is not None else self._base_name) as captured_scope:
|
||||||
|
self._scope = captured_scope
|
||||||
|
else:
|
||||||
|
with vs.variable_scope(
|
||||||
|
scope, default_name=self._base_name) as captured_scope:
|
||||||
|
self._scope = captured_scope
|
||||||
|
|
||||||
|
def add_weight(self,
|
||||||
|
name,
|
||||||
|
shape,
|
||||||
|
dtype=None,
|
||||||
|
initializer=None,
|
||||||
|
regularizer=None,
|
||||||
|
trainable=None,
|
||||||
|
constraint=None,
|
||||||
|
use_resource=None,
|
||||||
|
synchronization=vs.VariableSynchronization.AUTO,
|
||||||
|
aggregation=vs.VariableAggregation.NONE,
|
||||||
|
partitioner=None,
|
||||||
|
**kwargs):
|
||||||
|
"""Adds a new variable to the layer, or gets an existing one; returns it.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
name: variable name.
|
||||||
|
shape: variable shape.
|
||||||
|
dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
|
||||||
|
initializer: initializer instance (callable).
|
||||||
|
regularizer: regularizer instance (callable).
|
||||||
|
trainable: whether the variable should be part of the layer's
|
||||||
|
"trainable_variables" (e.g. variables, biases)
|
||||||
|
or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
|
||||||
|
Note, if the current variable scope is marked as non-trainable
|
||||||
|
then this parameter is ignored and any added variables are also
|
||||||
|
marked as non-trainable. `trainable` defaults to `True` unless
|
||||||
|
`synchronization` is set to `ON_READ`.
|
||||||
|
constraint: constraint instance (callable).
|
||||||
|
use_resource: Whether to use `ResourceVariable`.
|
||||||
|
synchronization: Indicates when a distributed a variable will be
|
||||||
|
aggregated. Accepted values are constants defined in the class
|
||||||
|
`tf.VariableSynchronization`. By default the synchronization is set to
|
||||||
|
`AUTO` and the current `DistributionStrategy` chooses
|
||||||
|
when to synchronize. If `synchronization` is set to `ON_READ`,
|
||||||
|
`trainable` must not be set to `True`.
|
||||||
|
aggregation: Indicates how a distributed variable will be aggregated.
|
||||||
|
Accepted values are constants defined in the class
|
||||||
|
`tf.VariableAggregation`.
|
||||||
|
partitioner: (optional) partitioner instance (callable). If
|
||||||
|
provided, when the requested variable is created it will be split
|
||||||
|
into multiple partitions according to `partitioner`. In this case,
|
||||||
|
an instance of `PartitionedVariable` is returned. Available
|
||||||
|
partitioners include `tf.compat.v1.fixed_size_partitioner` and
|
||||||
|
`tf.compat.v1.variable_axis_size_partitioner`. For more details, see
|
||||||
|
the documentation of `tf.compat.v1.get_variable` and the "Variable
|
||||||
|
Partitioners and Sharding" section of the API guide.
|
||||||
|
**kwargs: Additional keyword arguments.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The created variable. Usually either a `Variable` or `ResourceVariable`
|
||||||
|
instance. If `partitioner` is not `None`, a `PartitionedVariable`
|
||||||
|
instance is returned.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
RuntimeError: If called with partitioned variable regularization and
|
||||||
|
eager execution is enabled.
|
||||||
|
ValueError: When trainable has been set to True with synchronization
|
||||||
|
set as `ON_READ`.
|
||||||
|
"""
|
||||||
|
for kwarg in kwargs:
|
||||||
|
if kwarg != 'experimental_autocast':
|
||||||
|
raise TypeError('Unknown keyword argument:', kwarg)
|
||||||
|
if self._keras_style:
|
||||||
|
return super(Layer, self).add_weight(
|
||||||
|
name=name,
|
||||||
|
shape=shape,
|
||||||
|
dtype=dtype,
|
||||||
|
initializer=initializer,
|
||||||
|
regularizer=regularizer,
|
||||||
|
trainable=trainable and self.trainable,
|
||||||
|
constraint=constraint,
|
||||||
|
use_resource=use_resource,
|
||||||
|
synchronization=vs.VariableSynchronization.AUTO,
|
||||||
|
aggregation=vs.VariableAggregation.NONE,
|
||||||
|
partitioner=partitioner,
|
||||||
|
**kwargs)
|
||||||
|
|
||||||
|
if synchronization == vs.VariableSynchronization.ON_READ:
|
||||||
|
if trainable:
|
||||||
|
raise ValueError(
|
||||||
|
'Synchronization value can be set to '
|
||||||
|
'VariableSynchronization.ON_READ only for non-trainable variables. '
|
||||||
|
'You have specified trainable=True and '
|
||||||
|
'synchronization=VariableSynchronization.ON_READ.')
|
||||||
|
else:
|
||||||
|
# Set trainable to be false when variable is to be synced on read.
|
||||||
|
trainable = False
|
||||||
|
elif trainable is None:
|
||||||
|
trainable = True
|
||||||
|
|
||||||
|
def _should_add_regularizer(variable, existing_variable_set):
|
||||||
|
if isinstance(variable, tf_variables.PartitionedVariable):
|
||||||
|
for var in variable:
|
||||||
|
if var in existing_variable_set:
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
else:
|
||||||
|
return variable not in existing_variable_set
|
||||||
|
|
||||||
|
init_graph = None
|
||||||
|
if not context.executing_eagerly():
|
||||||
|
default_graph = ops.get_default_graph()
|
||||||
|
if default_graph.building_function:
|
||||||
|
with ops.init_scope():
|
||||||
|
# Retrieve the variables from the graph into which variables
|
||||||
|
# will be lifted; if initialization ops will be lifted into
|
||||||
|
# the eager context, then there is nothing to retrieve, since variable
|
||||||
|
# collections are not supported when eager execution is enabled.
|
||||||
|
if not context.executing_eagerly():
|
||||||
|
init_graph = ops.get_default_graph()
|
||||||
|
existing_variables = set(tf_variables.global_variables())
|
||||||
|
else:
|
||||||
|
# Initialization ops will not be lifted out of the default graph.
|
||||||
|
init_graph = default_graph
|
||||||
|
existing_variables = set(tf_variables.global_variables())
|
||||||
|
|
||||||
|
if dtype is None:
|
||||||
|
dtype = self.dtype or dtypes.float32
|
||||||
|
|
||||||
|
self._set_scope(None)
|
||||||
|
reuse = self.built or self._reuse
|
||||||
|
prev_len_trainable = len(self._trainable_weights)
|
||||||
|
with vs.variable_scope(
|
||||||
|
self._scope, reuse=reuse, auxiliary_name_scope=False) as scope:
|
||||||
|
self._current_scope = scope
|
||||||
|
with ops.name_scope(self._name_scope(), skip_on_eager=False):
|
||||||
|
use_resource = (use_resource or
|
||||||
|
self._use_resource_variables or
|
||||||
|
scope.use_resource)
|
||||||
|
if initializer is None:
|
||||||
|
initializer = scope.initializer
|
||||||
|
variable = super(Layer, self).add_weight(
|
||||||
|
name,
|
||||||
|
shape,
|
||||||
|
dtype=dtypes.as_dtype(dtype),
|
||||||
|
initializer=initializer,
|
||||||
|
trainable=trainable and self.trainable,
|
||||||
|
constraint=constraint,
|
||||||
|
partitioner=partitioner,
|
||||||
|
use_resource=use_resource,
|
||||||
|
synchronization=synchronization,
|
||||||
|
aggregation=aggregation,
|
||||||
|
getter=vs.get_variable,
|
||||||
|
**kwargs)
|
||||||
|
|
||||||
|
if regularizer:
|
||||||
|
if (ops.executing_eagerly_outside_functions()
|
||||||
|
or _should_add_regularizer(variable, existing_variables)):
|
||||||
|
self._handle_weight_regularization(name, variable, regularizer)
|
||||||
|
|
||||||
|
if init_graph is not None:
|
||||||
|
# Handle edge case where a custom getter has overridden `trainable`.
|
||||||
|
# There is one known occurrence of this, in unit test
|
||||||
|
# testBasicRNNCellNotTrainable in
|
||||||
|
# contrib.rnn.python.kernel_tests.core_rnn_cell_test
|
||||||
|
with init_graph.as_default():
|
||||||
|
trainable_variables = tf_variables.trainable_variables()
|
||||||
|
if (trainable and self.trainable and
|
||||||
|
variable not in trainable_variables):
|
||||||
|
# A custom getter / variable scope overrode the trainable flag.
|
||||||
|
extra_trainable_vars = self._trainable_weights[prev_len_trainable:]
|
||||||
|
self._trainable_weights = self._trainable_weights[
|
||||||
|
:prev_len_trainable]
|
||||||
|
self._non_trainable_weights += extra_trainable_vars
|
||||||
|
return variable
|
||||||
|
|
||||||
|
def __call__(self, inputs, *args, **kwargs):
|
||||||
|
"""Wraps `call`, applying pre- and post-processing steps.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: input tensor(s).
|
||||||
|
*args: additional positional arguments to be passed to `self.call`.
|
||||||
|
**kwargs: additional keyword arguments to be passed to `self.call`.
|
||||||
|
**Note**: kwarg `scope` is reserved for use by the layer.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Output tensor(s).
|
||||||
|
|
||||||
|
Note:
|
||||||
|
- If the layer's `call` method takes a `scope` keyword argument,
|
||||||
|
this argument will be automatically set to the current variable scope.
|
||||||
|
- If the layer's `call` method takes a `mask` argument (as some Keras
|
||||||
|
layers do), its default value will be set to the mask generated
|
||||||
|
for `inputs` by the previous layer (if `input` did come from
|
||||||
|
a layer that generated a corresponding mask, i.e. if it came from
|
||||||
|
a Keras layer with masking support.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if the layer's `call` method returns None (an invalid value).
|
||||||
|
"""
|
||||||
|
scope = kwargs.pop('scope', None)
|
||||||
|
|
||||||
|
if self._keras_style:
|
||||||
|
if scope is not None:
|
||||||
|
raise ValueError(
|
||||||
|
'scope argument not allowed when keras style layers are enabled, '
|
||||||
|
'but saw: {}'.format(scope))
|
||||||
|
return super(Layer, self).__call__(inputs, *args, **kwargs)
|
||||||
|
|
||||||
|
self._set_scope(scope)
|
||||||
|
|
||||||
|
if self.built:
|
||||||
|
try:
|
||||||
|
# Some classes which inherit from Layer do not use its constructor, so
|
||||||
|
# rather than initializing to None we check for an AttributeError.
|
||||||
|
scope_context_manager = self._always_reuse_variable_scope
|
||||||
|
except AttributeError:
|
||||||
|
# From this point we will always set reuse=True, so create a "final"
|
||||||
|
# variable scope with this setting. We avoid re-creating variable scopes
|
||||||
|
# after this point as an optimization.
|
||||||
|
self._always_reuse_variable_scope = vs.variable_scope(
|
||||||
|
self._scope, reuse=True, auxiliary_name_scope=False)
|
||||||
|
scope_context_manager = self._always_reuse_variable_scope
|
||||||
|
else:
|
||||||
|
scope_context_manager = vs.variable_scope(
|
||||||
|
self._scope, reuse=self._reuse, auxiliary_name_scope=False)
|
||||||
|
|
||||||
|
with scope_context_manager as scope:
|
||||||
|
self._current_scope = scope
|
||||||
|
|
||||||
|
try:
|
||||||
|
call_has_scope_arg = self._call_has_scope_arg
|
||||||
|
except AttributeError:
|
||||||
|
self._call_fn_args = function_utils.fn_args(self.call)
|
||||||
|
self._call_has_scope_arg = 'scope' in self._call_fn_args
|
||||||
|
call_has_scope_arg = self._call_has_scope_arg
|
||||||
|
if call_has_scope_arg:
|
||||||
|
kwargs['scope'] = scope
|
||||||
|
|
||||||
|
# Actually call layer
|
||||||
|
outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
|
||||||
|
|
||||||
|
if not context.executing_eagerly():
|
||||||
|
# Update global default collections.
|
||||||
|
_add_elements_to_collection(self.updates, ops.GraphKeys.UPDATE_OPS)
|
||||||
|
return outputs
|
||||||
|
|
||||||
|
def __deepcopy__(self, memo):
|
||||||
|
no_copy = set(['_graph', '_thread_local', '_metrics_lock'])
|
||||||
|
shallow_copy = set(['_scope', '_always_reuse_variable_scope'])
|
||||||
|
cls = self.__class__
|
||||||
|
result = cls.__new__(cls)
|
||||||
|
memo[id(self)] = result
|
||||||
|
for k, v in self.__dict__.items():
|
||||||
|
if k in no_copy:
|
||||||
|
setattr(result, k, v)
|
||||||
|
elif k in shallow_copy:
|
||||||
|
setattr(result, k, copy.copy(v))
|
||||||
|
elif base_layer.is_tensor_or_tensor_list(v):
|
||||||
|
setattr(result, k, v)
|
||||||
|
else:
|
||||||
|
setattr(result, k, copy.deepcopy(v, memo))
|
||||||
|
return result
|
||||||
|
|
||||||
|
def __setattr__(self, value, name):
|
||||||
|
# By-pass the automatic dependency tracking performed by the parent Layer.
|
||||||
|
super(trackable.Trackable, self).__setattr__(value, name)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def _is_legacy_layer(self):
|
||||||
|
"""Used by keras to check compatibility. This should not be overridden."""
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
def _add_elements_to_collection(elements, collection_list):
|
||||||
|
if context.executing_eagerly():
|
||||||
|
raise RuntimeError('Using collections from Layers not supported in Eager '
|
||||||
|
'mode. Tried to add %s to %s' % (elements,
|
||||||
|
collection_list))
|
||||||
|
elements = nest.flatten(elements)
|
||||||
|
collection_list = nest.flatten(collection_list)
|
||||||
|
for name in collection_list:
|
||||||
|
collection = ops.get_collection_ref(name)
|
||||||
|
collection_set = {id(e) for e in collection}
|
||||||
|
for element in elements:
|
||||||
|
if id(element) not in collection_set:
|
||||||
|
collection.append(element)
|
||||||
@ -30,8 +30,8 @@ from tensorflow.python.framework import ops
|
|||||||
from tensorflow.python.framework import test_util
|
from tensorflow.python.framework import test_util
|
||||||
from tensorflow.python.keras.engine import base_layer as keras_base_layer
|
from tensorflow.python.keras.engine import base_layer as keras_base_layer
|
||||||
from tensorflow.python.keras.engine import input_spec
|
from tensorflow.python.keras.engine import input_spec
|
||||||
from tensorflow.python.layers import base as base_layers
|
from tensorflow.python.keras.legacy_tf_layers import base as base_layers
|
||||||
from tensorflow.python.layers import core as core_layers
|
from tensorflow.python.keras.legacy_tf_layers import core as core_layers
|
||||||
from tensorflow.python.ops import array_ops
|
from tensorflow.python.ops import array_ops
|
||||||
from tensorflow.python.ops import init_ops
|
from tensorflow.python.ops import init_ops
|
||||||
from tensorflow.python.ops import math_ops
|
from tensorflow.python.ops import math_ops
|
||||||
1469
tensorflow/python/keras/legacy_tf_layers/convolutional.py
Normal file
1469
tensorflow/python/keras/legacy_tf_layers/convolutional.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -23,7 +23,7 @@ import numpy as np
|
|||||||
from tensorflow.python.framework import dtypes
|
from tensorflow.python.framework import dtypes
|
||||||
from tensorflow.python.framework import ops
|
from tensorflow.python.framework import ops
|
||||||
from tensorflow.python.framework import test_util
|
from tensorflow.python.framework import test_util
|
||||||
from tensorflow.python.layers import convolutional as conv_layers
|
from tensorflow.python.keras.legacy_tf_layers import convolutional as conv_layers
|
||||||
from tensorflow.python.ops import array_ops
|
from tensorflow.python.ops import array_ops
|
||||||
from tensorflow.python.ops import init_ops
|
from tensorflow.python.ops import init_ops
|
||||||
from tensorflow.python.ops import math_ops
|
from tensorflow.python.ops import math_ops
|
||||||
338
tensorflow/python/keras/legacy_tf_layers/core.py
Normal file
338
tensorflow/python/keras/legacy_tf_layers/core.py
Normal file
@ -0,0 +1,338 @@
|
|||||||
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
# =============================================================================
|
||||||
|
|
||||||
|
"""Contains the core layers: Dense, Dropout.
|
||||||
|
|
||||||
|
Also contains their functional aliases.
|
||||||
|
"""
|
||||||
|
from __future__ import absolute_import
|
||||||
|
from __future__ import division
|
||||||
|
from __future__ import print_function
|
||||||
|
|
||||||
|
|
||||||
|
from tensorflow.python.keras import layers as keras_layers
|
||||||
|
from tensorflow.python.keras.legacy_tf_layers import base
|
||||||
|
from tensorflow.python.ops import init_ops
|
||||||
|
from tensorflow.python.util import deprecation
|
||||||
|
from tensorflow.python.util.tf_export import tf_export
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.Dense'])
|
||||||
|
class Dense(keras_layers.Dense, base.Layer):
|
||||||
|
"""Densely-connected layer class.
|
||||||
|
|
||||||
|
This layer implements the operation:
|
||||||
|
`outputs = activation(inputs * kernel + bias)`
|
||||||
|
Where `activation` is the activation function passed as the `activation`
|
||||||
|
argument (if not `None`), `kernel` is a weights matrix created by the layer,
|
||||||
|
and `bias` is a bias vector created by the layer
|
||||||
|
(only if `use_bias` is `True`).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
units: Integer or Long, dimensionality of the output space.
|
||||||
|
activation: Activation function (callable). Set it to None to maintain a
|
||||||
|
linear activation.
|
||||||
|
use_bias: Boolean, whether the layer uses a bias.
|
||||||
|
kernel_initializer: Initializer function for the weight matrix.
|
||||||
|
If `None` (default), weights are initialized using the default
|
||||||
|
initializer used by `tf.compat.v1.get_variable`.
|
||||||
|
bias_initializer: Initializer function for the bias.
|
||||||
|
kernel_regularizer: Regularizer function for the weight matrix.
|
||||||
|
bias_regularizer: Regularizer function for the bias.
|
||||||
|
activity_regularizer: Regularizer function for the output.
|
||||||
|
kernel_constraint: An optional projection function to be applied to the
|
||||||
|
kernel after being updated by an `Optimizer` (e.g. used to implement
|
||||||
|
norm constraints or value constraints for layer weights). The function
|
||||||
|
must take as input the unprojected variable and must return the
|
||||||
|
projected variable (which must have the same shape). Constraints are
|
||||||
|
not safe to use when doing asynchronous distributed training.
|
||||||
|
bias_constraint: An optional projection function to be applied to the
|
||||||
|
bias after being updated by an `Optimizer`.
|
||||||
|
trainable: Boolean, if `True` also add variables to the graph collection
|
||||||
|
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
|
||||||
|
name: String, the name of the layer. Layers with the same name will
|
||||||
|
share weights, but to avoid mistakes we require reuse=True in such cases.
|
||||||
|
_reuse: Boolean, whether to reuse the weights of a previous layer
|
||||||
|
by the same name.
|
||||||
|
|
||||||
|
Properties:
|
||||||
|
units: Python integer, dimensionality of the output space.
|
||||||
|
activation: Activation function (callable).
|
||||||
|
use_bias: Boolean, whether the layer uses a bias.
|
||||||
|
kernel_initializer: Initializer instance (or name) for the kernel matrix.
|
||||||
|
bias_initializer: Initializer instance (or name) for the bias.
|
||||||
|
kernel_regularizer: Regularizer instance for the kernel matrix (callable)
|
||||||
|
bias_regularizer: Regularizer instance for the bias (callable).
|
||||||
|
activity_regularizer: Regularizer instance for the output (callable)
|
||||||
|
kernel_constraint: Constraint function for the kernel matrix.
|
||||||
|
bias_constraint: Constraint function for the bias.
|
||||||
|
kernel: Weight matrix (TensorFlow variable or tensor).
|
||||||
|
bias: Bias vector, if applicable (TensorFlow variable or tensor).
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, units,
|
||||||
|
activation=None,
|
||||||
|
use_bias=True,
|
||||||
|
kernel_initializer=None,
|
||||||
|
bias_initializer=init_ops.zeros_initializer(),
|
||||||
|
kernel_regularizer=None,
|
||||||
|
bias_regularizer=None,
|
||||||
|
activity_regularizer=None,
|
||||||
|
kernel_constraint=None,
|
||||||
|
bias_constraint=None,
|
||||||
|
trainable=True,
|
||||||
|
name=None,
|
||||||
|
**kwargs):
|
||||||
|
super(Dense, self).__init__(units=units,
|
||||||
|
activation=activation,
|
||||||
|
use_bias=use_bias,
|
||||||
|
kernel_initializer=kernel_initializer,
|
||||||
|
bias_initializer=bias_initializer,
|
||||||
|
kernel_regularizer=kernel_regularizer,
|
||||||
|
bias_regularizer=bias_regularizer,
|
||||||
|
activity_regularizer=activity_regularizer,
|
||||||
|
kernel_constraint=kernel_constraint,
|
||||||
|
bias_constraint=bias_constraint,
|
||||||
|
trainable=trainable,
|
||||||
|
name=name,
|
||||||
|
**kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None, instructions='Use keras.layers.Dense instead.')
|
||||||
|
@tf_export(v1=['layers.dense'])
|
||||||
|
def dense(
|
||||||
|
inputs, units,
|
||||||
|
activation=None,
|
||||||
|
use_bias=True,
|
||||||
|
kernel_initializer=None,
|
||||||
|
bias_initializer=init_ops.zeros_initializer(),
|
||||||
|
kernel_regularizer=None,
|
||||||
|
bias_regularizer=None,
|
||||||
|
activity_regularizer=None,
|
||||||
|
kernel_constraint=None,
|
||||||
|
bias_constraint=None,
|
||||||
|
trainable=True,
|
||||||
|
name=None,
|
||||||
|
reuse=None):
|
||||||
|
"""Functional interface for the densely-connected layer.
|
||||||
|
|
||||||
|
This layer implements the operation:
|
||||||
|
`outputs = activation(inputs * kernel + bias)`
|
||||||
|
where `activation` is the activation function passed as the `activation`
|
||||||
|
argument (if not `None`), `kernel` is a weights matrix created by the layer,
|
||||||
|
and `bias` is a bias vector created by the layer
|
||||||
|
(only if `use_bias` is `True`).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: Tensor input.
|
||||||
|
units: Integer or Long, dimensionality of the output space.
|
||||||
|
activation: Activation function (callable). Set it to None to maintain a
|
||||||
|
linear activation.
|
||||||
|
use_bias: Boolean, whether the layer uses a bias.
|
||||||
|
kernel_initializer: Initializer function for the weight matrix.
|
||||||
|
If `None` (default), weights are initialized using the default
|
||||||
|
initializer used by `tf.compat.v1.get_variable`.
|
||||||
|
bias_initializer: Initializer function for the bias.
|
||||||
|
kernel_regularizer: Regularizer function for the weight matrix.
|
||||||
|
bias_regularizer: Regularizer function for the bias.
|
||||||
|
activity_regularizer: Regularizer function for the output.
|
||||||
|
kernel_constraint: An optional projection function to be applied to the
|
||||||
|
kernel after being updated by an `Optimizer` (e.g. used to implement
|
||||||
|
norm constraints or value constraints for layer weights). The function
|
||||||
|
must take as input the unprojected variable and must return the
|
||||||
|
projected variable (which must have the same shape). Constraints are
|
||||||
|
not safe to use when doing asynchronous distributed training.
|
||||||
|
bias_constraint: An optional projection function to be applied to the
|
||||||
|
bias after being updated by an `Optimizer`.
|
||||||
|
trainable: Boolean, if `True` also add variables to the graph collection
|
||||||
|
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
|
||||||
|
name: String, the name of the layer.
|
||||||
|
reuse: Boolean, whether to reuse the weights of a previous layer
|
||||||
|
by the same name.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Output tensor the same shape as `inputs` except the last dimension is of
|
||||||
|
size `units`.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if eager execution is enabled.
|
||||||
|
"""
|
||||||
|
layer = Dense(units,
|
||||||
|
activation=activation,
|
||||||
|
use_bias=use_bias,
|
||||||
|
kernel_initializer=kernel_initializer,
|
||||||
|
bias_initializer=bias_initializer,
|
||||||
|
kernel_regularizer=kernel_regularizer,
|
||||||
|
bias_regularizer=bias_regularizer,
|
||||||
|
activity_regularizer=activity_regularizer,
|
||||||
|
kernel_constraint=kernel_constraint,
|
||||||
|
bias_constraint=bias_constraint,
|
||||||
|
trainable=trainable,
|
||||||
|
name=name,
|
||||||
|
_scope=name,
|
||||||
|
_reuse=reuse)
|
||||||
|
return layer.apply(inputs)
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.Dropout'])
|
||||||
|
class Dropout(keras_layers.Dropout, base.Layer):
|
||||||
|
"""Applies Dropout to the input.
|
||||||
|
|
||||||
|
Dropout consists in randomly setting a fraction `rate` of input units to 0
|
||||||
|
at each update during training time, which helps prevent overfitting.
|
||||||
|
The units that are kept are scaled by `1 / (1 - rate)`, so that their
|
||||||
|
sum is unchanged at training time and inference time.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out
|
||||||
|
10% of input units.
|
||||||
|
noise_shape: 1D tensor of type `int32` representing the shape of the
|
||||||
|
binary dropout mask that will be multiplied with the input.
|
||||||
|
For instance, if your inputs have shape
|
||||||
|
`(batch_size, timesteps, features)`, and you want the dropout mask
|
||||||
|
to be the same for all timesteps, you can use
|
||||||
|
`noise_shape=[batch_size, 1, features]`.
|
||||||
|
seed: A Python integer. Used to create random seeds. See
|
||||||
|
`tf.compat.v1.set_random_seed`.
|
||||||
|
for behavior.
|
||||||
|
name: The name of the layer (string).
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, rate=0.5,
|
||||||
|
noise_shape=None,
|
||||||
|
seed=None,
|
||||||
|
name=None,
|
||||||
|
**kwargs):
|
||||||
|
super(Dropout, self).__init__(rate=rate,
|
||||||
|
noise_shape=noise_shape,
|
||||||
|
seed=seed,
|
||||||
|
name=name,
|
||||||
|
**kwargs)
|
||||||
|
|
||||||
|
def call(self, inputs, training=False):
|
||||||
|
return super(Dropout, self).call(inputs, training=training)
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None,
|
||||||
|
instructions='Use keras.layers.dropout instead.')
|
||||||
|
@tf_export(v1=['layers.dropout'])
|
||||||
|
def dropout(inputs,
|
||||||
|
rate=0.5,
|
||||||
|
noise_shape=None,
|
||||||
|
seed=None,
|
||||||
|
training=False,
|
||||||
|
name=None):
|
||||||
|
"""Applies Dropout to the input.
|
||||||
|
|
||||||
|
Dropout consists in randomly setting a fraction `rate` of input units to 0
|
||||||
|
at each update during training time, which helps prevent overfitting.
|
||||||
|
The units that are kept are scaled by `1 / (1 - rate)`, so that their
|
||||||
|
sum is unchanged at training time and inference time.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: Tensor input.
|
||||||
|
rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
|
||||||
|
10% of input units.
|
||||||
|
noise_shape: 1D tensor of type `int32` representing the shape of the
|
||||||
|
binary dropout mask that will be multiplied with the input.
|
||||||
|
For instance, if your inputs have shape
|
||||||
|
`(batch_size, timesteps, features)`, and you want the dropout mask
|
||||||
|
to be the same for all timesteps, you can use
|
||||||
|
`noise_shape=[batch_size, 1, features]`.
|
||||||
|
seed: A Python integer. Used to create random seeds. See
|
||||||
|
`tf.compat.v1.set_random_seed`
|
||||||
|
for behavior.
|
||||||
|
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
|
||||||
|
(e.g. a placeholder). Whether to return the output in training mode
|
||||||
|
(apply dropout) or in inference mode (return the input untouched).
|
||||||
|
name: The name of the layer (string).
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Output tensor.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if eager execution is enabled.
|
||||||
|
"""
|
||||||
|
layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
|
||||||
|
return layer.apply(inputs, training=training)
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.Flatten'])
|
||||||
|
class Flatten(keras_layers.Flatten, base.Layer):
|
||||||
|
"""Flattens an input tensor while preserving the batch axis (axis 0).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
||||||
|
The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, ..., channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, ...)`.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
|
||||||
|
```
|
||||||
|
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
|
||||||
|
y = Flatten()(x)
|
||||||
|
# now `y` has shape `(None, 16)`
|
||||||
|
|
||||||
|
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
|
||||||
|
y = Flatten()(x)
|
||||||
|
# now `y` has shape `(None, None)`
|
||||||
|
```
|
||||||
|
"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None,
|
||||||
|
instructions='Use keras.layers.Flatten instead.')
|
||||||
|
@tf_export(v1=['layers.flatten'])
|
||||||
|
def flatten(inputs, name=None, data_format='channels_last'):
|
||||||
|
"""Flattens an input tensor while preserving the batch axis (axis 0).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: Tensor input.
|
||||||
|
name: The name of the layer (string).
|
||||||
|
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
||||||
|
The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, height, width, channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, height, width)`.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Reshaped tensor.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
|
||||||
|
```
|
||||||
|
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
|
||||||
|
y = flatten(x)
|
||||||
|
# now `y` has shape `(None, 16)`
|
||||||
|
|
||||||
|
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
|
||||||
|
y = flatten(x)
|
||||||
|
# now `y` has shape `(None, None)`
|
||||||
|
```
|
||||||
|
"""
|
||||||
|
layer = Flatten(name=name, data_format=data_format)
|
||||||
|
return layer.apply(inputs)
|
||||||
|
|
||||||
|
|
||||||
|
# Aliases
|
||||||
|
|
||||||
|
FullyConnected = Dense
|
||||||
|
fully_connected = dense
|
||||||
@ -29,7 +29,7 @@ from tensorflow.python.framework import dtypes
|
|||||||
from tensorflow.python.framework import ops
|
from tensorflow.python.framework import ops
|
||||||
from tensorflow.python.framework import tensor_shape
|
from tensorflow.python.framework import tensor_shape
|
||||||
from tensorflow.python.framework import test_util
|
from tensorflow.python.framework import test_util
|
||||||
from tensorflow.python.layers import core as core_layers
|
from tensorflow.python.keras.legacy_tf_layers import core as core_layers
|
||||||
from tensorflow.python.ops import array_ops
|
from tensorflow.python.ops import array_ops
|
||||||
from tensorflow.python.ops import init_ops
|
from tensorflow.python.ops import init_ops
|
||||||
from tensorflow.python.ops import math_ops
|
from tensorflow.python.ops import math_ops
|
||||||
342
tensorflow/python/keras/legacy_tf_layers/normalization.py
Normal file
342
tensorflow/python/keras/legacy_tf_layers/normalization.py
Normal file
@ -0,0 +1,342 @@
|
|||||||
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
# =============================================================================
|
||||||
|
|
||||||
|
"""Contains the normalization layer classes and their functional aliases.
|
||||||
|
"""
|
||||||
|
from __future__ import absolute_import
|
||||||
|
from __future__ import division
|
||||||
|
from __future__ import print_function
|
||||||
|
|
||||||
|
|
||||||
|
from tensorflow.python.keras.layers import normalization as keras_normalization
|
||||||
|
from tensorflow.python.keras.legacy_tf_layers import base
|
||||||
|
from tensorflow.python.ops import init_ops
|
||||||
|
from tensorflow.python.util import deprecation
|
||||||
|
from tensorflow.python.util.tf_export import tf_export
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.BatchNormalization'])
|
||||||
|
class BatchNormalization(keras_normalization.BatchNormalization, base.Layer):
|
||||||
|
"""Batch Normalization layer from (Ioffe et al., 2015).
|
||||||
|
|
||||||
|
Keras APIs handle BatchNormalization updates to the moving_mean and
|
||||||
|
moving_variance as part of their `fit()` and `evaluate()` loops. However, if a
|
||||||
|
custom training loop is used with an instance of `Model`, these updates need
|
||||||
|
to be explicitly included. Here's a simple example of how it can be done:
|
||||||
|
|
||||||
|
```python
|
||||||
|
# model is an instance of Model that contains BatchNormalization layer.
|
||||||
|
update_ops = model.get_updates_for(None) + model.get_updates_for(features)
|
||||||
|
train_op = optimizer.minimize(loss)
|
||||||
|
train_op = tf.group([train_op, update_ops])
|
||||||
|
```
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
axis: An `int` or list of `int`, the axis or axes that should be normalized,
|
||||||
|
typically the features axis/axes. For instance, after a `Conv2D` layer
|
||||||
|
with `data_format="channels_first"`, set `axis=1`. If a list of axes is
|
||||||
|
provided, each axis in `axis` will be normalized
|
||||||
|
simultaneously. Default is `-1` which uses the last axis. Note: when
|
||||||
|
using multi-axis batch norm, the `beta`, `gamma`, `moving_mean`, and
|
||||||
|
`moving_variance` variables are the same rank as the input Tensor,
|
||||||
|
with dimension size 1 in all reduced (non-axis) dimensions).
|
||||||
|
momentum: Momentum for the moving average.
|
||||||
|
epsilon: Small float added to variance to avoid dividing by zero.
|
||||||
|
center: If True, add offset of `beta` to normalized tensor. If False, `beta`
|
||||||
|
is ignored.
|
||||||
|
scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
|
||||||
|
next layer is linear (also e.g. `nn.relu`), this can be disabled since the
|
||||||
|
scaling can be done by the next layer.
|
||||||
|
beta_initializer: Initializer for the beta weight.
|
||||||
|
gamma_initializer: Initializer for the gamma weight.
|
||||||
|
moving_mean_initializer: Initializer for the moving mean.
|
||||||
|
moving_variance_initializer: Initializer for the moving variance.
|
||||||
|
beta_regularizer: Optional regularizer for the beta weight.
|
||||||
|
gamma_regularizer: Optional regularizer for the gamma weight.
|
||||||
|
beta_constraint: An optional projection function to be applied to the `beta`
|
||||||
|
weight after being updated by an `Optimizer` (e.g. used to implement norm
|
||||||
|
constraints or value constraints for layer weights). The function must
|
||||||
|
take as input the unprojected variable and must return the projected
|
||||||
|
variable (which must have the same shape). Constraints are not safe to use
|
||||||
|
when doing asynchronous distributed training.
|
||||||
|
gamma_constraint: An optional projection function to be applied to the
|
||||||
|
`gamma` weight after being updated by an `Optimizer`.
|
||||||
|
renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds extra
|
||||||
|
variables during training. The inference is the same for either value of
|
||||||
|
this parameter.
|
||||||
|
renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
|
||||||
|
scalar `Tensors` used to clip the renorm correction. The correction `(r,
|
||||||
|
d)` is used as `corrected_value = normalized_value * r + d`, with `r`
|
||||||
|
clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
|
||||||
|
dmax are set to inf, 0, inf, respectively.
|
||||||
|
renorm_momentum: Momentum used to update the moving means and standard
|
||||||
|
deviations with renorm. Unlike `momentum`, this affects training and
|
||||||
|
should be neither too small (which would add noise) nor too large (which
|
||||||
|
would give stale estimates). Note that `momentum` is still applied to get
|
||||||
|
the means and variances for inference.
|
||||||
|
fused: if `None` or `True`, use a faster, fused implementation if possible.
|
||||||
|
If `False`, use the system recommended implementation.
|
||||||
|
trainable: Boolean, if `True` also add variables to the graph collection
|
||||||
|
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
|
||||||
|
virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
|
||||||
|
which means batch normalization is performed across the whole batch. When
|
||||||
|
`virtual_batch_size` is not `None`, instead perform "Ghost Batch
|
||||||
|
Normalization", which creates virtual sub-batches which are each
|
||||||
|
normalized separately (with shared gamma, beta, and moving statistics).
|
||||||
|
Must divide the actual batch size during execution.
|
||||||
|
adjustment: A function taking the `Tensor` containing the (dynamic) shape of
|
||||||
|
the input tensor and returning a pair (scale, bias) to apply to the
|
||||||
|
normalized values (before gamma and beta), only during training. For
|
||||||
|
example, if axis==-1,
|
||||||
|
`adjustment = lambda shape: (
|
||||||
|
tf.random.uniform(shape[-1:], 0.93, 1.07),
|
||||||
|
tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
|
||||||
|
value by up to 7% up or down, then shift the result by up to 0.1
|
||||||
|
(with independent scaling and bias for each feature but shared
|
||||||
|
across all examples), and finally apply gamma and/or beta. If
|
||||||
|
`None`, no adjustment is applied. Cannot be specified if
|
||||||
|
virtual_batch_size is specified.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
References:
|
||||||
|
Batch Normalization - Accelerating Deep Network Training by Reducing
|
||||||
|
Internal Covariate Shift:
|
||||||
|
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
|
||||||
|
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
|
||||||
|
Batch Renormalization - Towards Reducing Minibatch Dependence in
|
||||||
|
Batch-Normalized Models:
|
||||||
|
[Ioffe,
|
||||||
|
2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
|
||||||
|
([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self,
|
||||||
|
axis=-1,
|
||||||
|
momentum=0.99,
|
||||||
|
epsilon=1e-3,
|
||||||
|
center=True,
|
||||||
|
scale=True,
|
||||||
|
beta_initializer=init_ops.zeros_initializer(),
|
||||||
|
gamma_initializer=init_ops.ones_initializer(),
|
||||||
|
moving_mean_initializer=init_ops.zeros_initializer(),
|
||||||
|
moving_variance_initializer=init_ops.ones_initializer(),
|
||||||
|
beta_regularizer=None,
|
||||||
|
gamma_regularizer=None,
|
||||||
|
beta_constraint=None,
|
||||||
|
gamma_constraint=None,
|
||||||
|
renorm=False,
|
||||||
|
renorm_clipping=None,
|
||||||
|
renorm_momentum=0.99,
|
||||||
|
fused=None,
|
||||||
|
trainable=True,
|
||||||
|
virtual_batch_size=None,
|
||||||
|
adjustment=None,
|
||||||
|
name=None,
|
||||||
|
**kwargs):
|
||||||
|
super(BatchNormalization, self).__init__(
|
||||||
|
axis=axis,
|
||||||
|
momentum=momentum,
|
||||||
|
epsilon=epsilon,
|
||||||
|
center=center,
|
||||||
|
scale=scale,
|
||||||
|
beta_initializer=beta_initializer,
|
||||||
|
gamma_initializer=gamma_initializer,
|
||||||
|
moving_mean_initializer=moving_mean_initializer,
|
||||||
|
moving_variance_initializer=moving_variance_initializer,
|
||||||
|
beta_regularizer=beta_regularizer,
|
||||||
|
gamma_regularizer=gamma_regularizer,
|
||||||
|
beta_constraint=beta_constraint,
|
||||||
|
gamma_constraint=gamma_constraint,
|
||||||
|
renorm=renorm,
|
||||||
|
renorm_clipping=renorm_clipping,
|
||||||
|
renorm_momentum=renorm_momentum,
|
||||||
|
fused=fused,
|
||||||
|
trainable=trainable,
|
||||||
|
virtual_batch_size=virtual_batch_size,
|
||||||
|
adjustment=adjustment,
|
||||||
|
name=name,
|
||||||
|
**kwargs)
|
||||||
|
|
||||||
|
def call(self, inputs, training=False):
|
||||||
|
return super(BatchNormalization, self).call(inputs, training=training)
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None, instructions='Use keras.layers.BatchNormalization instead. In '
|
||||||
|
'particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not '
|
||||||
|
'be used (consult the `tf.keras.layers.BatchNormalization` '
|
||||||
|
'documentation).')
|
||||||
|
@tf_export(v1=['layers.batch_normalization'])
|
||||||
|
def batch_normalization(inputs,
|
||||||
|
axis=-1,
|
||||||
|
momentum=0.99,
|
||||||
|
epsilon=1e-3,
|
||||||
|
center=True,
|
||||||
|
scale=True,
|
||||||
|
beta_initializer=init_ops.zeros_initializer(),
|
||||||
|
gamma_initializer=init_ops.ones_initializer(),
|
||||||
|
moving_mean_initializer=init_ops.zeros_initializer(),
|
||||||
|
moving_variance_initializer=init_ops.ones_initializer(),
|
||||||
|
beta_regularizer=None,
|
||||||
|
gamma_regularizer=None,
|
||||||
|
beta_constraint=None,
|
||||||
|
gamma_constraint=None,
|
||||||
|
training=False,
|
||||||
|
trainable=True,
|
||||||
|
name=None,
|
||||||
|
reuse=None,
|
||||||
|
renorm=False,
|
||||||
|
renorm_clipping=None,
|
||||||
|
renorm_momentum=0.99,
|
||||||
|
fused=None,
|
||||||
|
virtual_batch_size=None,
|
||||||
|
adjustment=None):
|
||||||
|
"""Functional interface for the batch normalization layer from_config(Ioffe et al., 2015).
|
||||||
|
|
||||||
|
Note: when training, the moving_mean and moving_variance need to be updated.
|
||||||
|
By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
|
||||||
|
need to be executed alongside the `train_op`. Also, be sure to add any
|
||||||
|
batch_normalization ops before getting the update_ops collection. Otherwise,
|
||||||
|
update_ops will be empty, and training/inference will not work properly. For
|
||||||
|
example:
|
||||||
|
|
||||||
|
```python
|
||||||
|
x_norm = tf.compat.v1.layers.batch_normalization(x, training=training)
|
||||||
|
|
||||||
|
# ...
|
||||||
|
|
||||||
|
update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
|
||||||
|
train_op = optimizer.minimize(loss)
|
||||||
|
train_op = tf.group([train_op, update_ops])
|
||||||
|
```
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: Tensor input.
|
||||||
|
axis: An `int`, the axis that should be normalized (typically the features
|
||||||
|
axis). For instance, after a `Convolution2D` layer with
|
||||||
|
`data_format="channels_first"`, set `axis=1` in `BatchNormalization`.
|
||||||
|
momentum: Momentum for the moving average.
|
||||||
|
epsilon: Small float added to variance to avoid dividing by zero.
|
||||||
|
center: If True, add offset of `beta` to normalized tensor. If False, `beta`
|
||||||
|
is ignored.
|
||||||
|
scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
|
||||||
|
next layer is linear (also e.g. `nn.relu`), this can be disabled since the
|
||||||
|
scaling can be done by the next layer.
|
||||||
|
beta_initializer: Initializer for the beta weight.
|
||||||
|
gamma_initializer: Initializer for the gamma weight.
|
||||||
|
moving_mean_initializer: Initializer for the moving mean.
|
||||||
|
moving_variance_initializer: Initializer for the moving variance.
|
||||||
|
beta_regularizer: Optional regularizer for the beta weight.
|
||||||
|
gamma_regularizer: Optional regularizer for the gamma weight.
|
||||||
|
beta_constraint: An optional projection function to be applied to the `beta`
|
||||||
|
weight after being updated by an `Optimizer` (e.g. used to implement norm
|
||||||
|
constraints or value constraints for layer weights). The function must
|
||||||
|
take as input the unprojected variable and must return the projected
|
||||||
|
variable (which must have the same shape). Constraints are not safe to use
|
||||||
|
when doing asynchronous distributed training.
|
||||||
|
gamma_constraint: An optional projection function to be applied to the
|
||||||
|
`gamma` weight after being updated by an `Optimizer`.
|
||||||
|
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
|
||||||
|
(e.g. a placeholder). Whether to return the output in training mode
|
||||||
|
(normalized with statistics of the current batch) or in inference mode
|
||||||
|
(normalized with moving statistics). **NOTE**: make sure to set this
|
||||||
|
parameter correctly, or else your training/inference will not work
|
||||||
|
properly.
|
||||||
|
trainable: Boolean, if `True` also add variables to the graph collection
|
||||||
|
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
|
||||||
|
name: String, the name of the layer.
|
||||||
|
reuse: Boolean, whether to reuse the weights of a previous layer by the same
|
||||||
|
name.
|
||||||
|
renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds extra
|
||||||
|
variables during training. The inference is the same for either value of
|
||||||
|
this parameter.
|
||||||
|
renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
|
||||||
|
scalar `Tensors` used to clip the renorm correction. The correction `(r,
|
||||||
|
d)` is used as `corrected_value = normalized_value * r + d`, with `r`
|
||||||
|
clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
|
||||||
|
dmax are set to inf, 0, inf, respectively.
|
||||||
|
renorm_momentum: Momentum used to update the moving means and standard
|
||||||
|
deviations with renorm. Unlike `momentum`, this affects training and
|
||||||
|
should be neither too small (which would add noise) nor too large (which
|
||||||
|
would give stale estimates). Note that `momentum` is still applied to get
|
||||||
|
the means and variances for inference.
|
||||||
|
fused: if `None` or `True`, use a faster, fused implementation if possible.
|
||||||
|
If `False`, use the system recommended implementation.
|
||||||
|
virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
|
||||||
|
which means batch normalization is performed across the whole batch. When
|
||||||
|
`virtual_batch_size` is not `None`, instead perform "Ghost Batch
|
||||||
|
Normalization", which creates virtual sub-batches which are each
|
||||||
|
normalized separately (with shared gamma, beta, and moving statistics).
|
||||||
|
Must divide the actual batch size during execution.
|
||||||
|
adjustment: A function taking the `Tensor` containing the (dynamic) shape of
|
||||||
|
the input tensor and returning a pair (scale, bias) to apply to the
|
||||||
|
normalized values (before gamma and beta), only during training. For
|
||||||
|
example, if axis==-1,
|
||||||
|
`adjustment = lambda shape: (
|
||||||
|
tf.random.uniform(shape[-1:], 0.93, 1.07),
|
||||||
|
tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
|
||||||
|
value by up to 7% up or down, then shift the result by up to 0.1
|
||||||
|
(with independent scaling and bias for each feature but shared
|
||||||
|
across all examples), and finally apply gamma and/or beta. If
|
||||||
|
`None`, no adjustment is applied. Cannot be specified if
|
||||||
|
virtual_batch_size is specified.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Output tensor.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if eager execution is enabled.
|
||||||
|
|
||||||
|
References:
|
||||||
|
Batch Normalization - Accelerating Deep Network Training by Reducing
|
||||||
|
Internal Covariate Shift:
|
||||||
|
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
|
||||||
|
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
|
||||||
|
Batch Renormalization - Towards Reducing Minibatch Dependence in
|
||||||
|
Batch-Normalized Models:
|
||||||
|
[Ioffe,
|
||||||
|
2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
|
||||||
|
([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
|
||||||
|
"""
|
||||||
|
layer = BatchNormalization(
|
||||||
|
axis=axis,
|
||||||
|
momentum=momentum,
|
||||||
|
epsilon=epsilon,
|
||||||
|
center=center,
|
||||||
|
scale=scale,
|
||||||
|
beta_initializer=beta_initializer,
|
||||||
|
gamma_initializer=gamma_initializer,
|
||||||
|
moving_mean_initializer=moving_mean_initializer,
|
||||||
|
moving_variance_initializer=moving_variance_initializer,
|
||||||
|
beta_regularizer=beta_regularizer,
|
||||||
|
gamma_regularizer=gamma_regularizer,
|
||||||
|
beta_constraint=beta_constraint,
|
||||||
|
gamma_constraint=gamma_constraint,
|
||||||
|
renorm=renorm,
|
||||||
|
renorm_clipping=renorm_clipping,
|
||||||
|
renorm_momentum=renorm_momentum,
|
||||||
|
fused=fused,
|
||||||
|
trainable=trainable,
|
||||||
|
virtual_batch_size=virtual_batch_size,
|
||||||
|
adjustment=adjustment,
|
||||||
|
name=name,
|
||||||
|
_reuse=reuse,
|
||||||
|
_scope=name)
|
||||||
|
return layer.apply(inputs, training=training)
|
||||||
|
|
||||||
|
|
||||||
|
# Aliases
|
||||||
|
|
||||||
|
BatchNorm = BatchNormalization
|
||||||
|
batch_norm = batch_normalization
|
||||||
@ -26,8 +26,8 @@ from tensorflow.core.protobuf import saver_pb2
|
|||||||
from tensorflow.python.framework import dtypes
|
from tensorflow.python.framework import dtypes
|
||||||
from tensorflow.python.framework import ops
|
from tensorflow.python.framework import ops
|
||||||
from tensorflow.python.framework import test_util
|
from tensorflow.python.framework import test_util
|
||||||
from tensorflow.python.layers import convolutional as conv_layers
|
from tensorflow.python.keras.legacy_tf_layers import convolutional as conv_layers
|
||||||
from tensorflow.python.layers import normalization as normalization_layers
|
from tensorflow.python.keras.legacy_tf_layers import normalization as normalization_layers
|
||||||
from tensorflow.python.ops import array_ops
|
from tensorflow.python.ops import array_ops
|
||||||
from tensorflow.python.ops import init_ops
|
from tensorflow.python.ops import init_ops
|
||||||
from tensorflow.python.ops import math_ops
|
from tensorflow.python.ops import math_ops
|
||||||
470
tensorflow/python/keras/legacy_tf_layers/pooling.py
Normal file
470
tensorflow/python/keras/legacy_tf_layers/pooling.py
Normal file
@ -0,0 +1,470 @@
|
|||||||
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
# =============================================================================
|
||||||
|
|
||||||
|
"""Contains the pooling layer classes and their functional aliases.
|
||||||
|
"""
|
||||||
|
from __future__ import absolute_import
|
||||||
|
from __future__ import division
|
||||||
|
from __future__ import print_function
|
||||||
|
|
||||||
|
from tensorflow.python.keras import layers as keras_layers
|
||||||
|
from tensorflow.python.keras.legacy_tf_layers import base
|
||||||
|
from tensorflow.python.util import deprecation
|
||||||
|
from tensorflow.python.util.tf_export import tf_export
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.AveragePooling1D'])
|
||||||
|
class AveragePooling1D(keras_layers.AveragePooling1D, base.Layer):
|
||||||
|
"""Average Pooling layer for 1D inputs.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
pool_size: An integer or tuple/list of a single integer,
|
||||||
|
representing the size of the pooling window.
|
||||||
|
strides: An integer or tuple/list of a single integer, specifying the
|
||||||
|
strides of the pooling operation.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
||||||
|
The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, length, channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, length)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None, **kwargs):
|
||||||
|
if strides is None:
|
||||||
|
raise ValueError('Argument `strides` must not be None.')
|
||||||
|
super(AveragePooling1D, self).__init__(
|
||||||
|
pool_size=pool_size,
|
||||||
|
strides=strides,
|
||||||
|
padding=padding,
|
||||||
|
data_format=data_format,
|
||||||
|
name=name,
|
||||||
|
**kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None, instructions='Use keras.layers.AveragePooling1D instead.')
|
||||||
|
@tf_export(v1=['layers.average_pooling1d'])
|
||||||
|
def average_pooling1d(inputs, pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None):
|
||||||
|
"""Average Pooling layer for 1D inputs.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: The tensor over which to pool. Must have rank 3.
|
||||||
|
pool_size: An integer or tuple/list of a single integer,
|
||||||
|
representing the size of the pooling window.
|
||||||
|
strides: An integer or tuple/list of a single integer, specifying the
|
||||||
|
strides of the pooling operation.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
||||||
|
The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, length, channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, length)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The output tensor, of rank 3.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if eager execution is enabled.
|
||||||
|
"""
|
||||||
|
layer = AveragePooling1D(pool_size=pool_size,
|
||||||
|
strides=strides,
|
||||||
|
padding=padding,
|
||||||
|
data_format=data_format,
|
||||||
|
name=name)
|
||||||
|
return layer.apply(inputs)
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.MaxPooling1D'])
|
||||||
|
class MaxPooling1D(keras_layers.MaxPooling1D, base.Layer):
|
||||||
|
"""Max Pooling layer for 1D inputs.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
pool_size: An integer or tuple/list of a single integer,
|
||||||
|
representing the size of the pooling window.
|
||||||
|
strides: An integer or tuple/list of a single integer, specifying the
|
||||||
|
strides of the pooling operation.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
||||||
|
The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, length, channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, length)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None, **kwargs):
|
||||||
|
if strides is None:
|
||||||
|
raise ValueError('Argument `strides` must not be None.')
|
||||||
|
super(MaxPooling1D, self).__init__(
|
||||||
|
pool_size=pool_size,
|
||||||
|
strides=strides,
|
||||||
|
padding=padding,
|
||||||
|
data_format=data_format,
|
||||||
|
name=name,
|
||||||
|
**kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None, instructions='Use keras.layers.MaxPooling1D instead.')
|
||||||
|
@tf_export(v1=['layers.max_pooling1d'])
|
||||||
|
def max_pooling1d(inputs, pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None):
|
||||||
|
"""Max Pooling layer for 1D inputs.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: The tensor over which to pool. Must have rank 3.
|
||||||
|
pool_size: An integer or tuple/list of a single integer,
|
||||||
|
representing the size of the pooling window.
|
||||||
|
strides: An integer or tuple/list of a single integer, specifying the
|
||||||
|
strides of the pooling operation.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
||||||
|
The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, length, channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, length)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The output tensor, of rank 3.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if eager execution is enabled.
|
||||||
|
"""
|
||||||
|
layer = MaxPooling1D(pool_size=pool_size,
|
||||||
|
strides=strides,
|
||||||
|
padding=padding,
|
||||||
|
data_format=data_format,
|
||||||
|
name=name)
|
||||||
|
return layer.apply(inputs)
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.AveragePooling2D'])
|
||||||
|
class AveragePooling2D(keras_layers.AveragePooling2D, base.Layer):
|
||||||
|
"""Average pooling layer for 2D inputs (e.g. images).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
|
||||||
|
specifying the size of the pooling window.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
strides: An integer or tuple/list of 2 integers,
|
||||||
|
specifying the strides of the pooling operation.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` (default) and `channels_first` are supported.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, height, width, channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, height, width)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None, **kwargs):
|
||||||
|
if strides is None:
|
||||||
|
raise ValueError('Argument `strides` must not be None.')
|
||||||
|
super(AveragePooling2D, self).__init__(
|
||||||
|
pool_size=pool_size, strides=strides,
|
||||||
|
padding=padding, data_format=data_format, name=name, **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None, instructions='Use keras.layers.AveragePooling2D instead.')
|
||||||
|
@tf_export(v1=['layers.average_pooling2d'])
|
||||||
|
def average_pooling2d(inputs,
|
||||||
|
pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None):
|
||||||
|
"""Average pooling layer for 2D inputs (e.g. images).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: The tensor over which to pool. Must have rank 4.
|
||||||
|
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
|
||||||
|
specifying the size of the pooling window.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
strides: An integer or tuple/list of 2 integers,
|
||||||
|
specifying the strides of the pooling operation.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` (default) and `channels_first` are supported.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, height, width, channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, height, width)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Output tensor.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if eager execution is enabled.
|
||||||
|
"""
|
||||||
|
layer = AveragePooling2D(pool_size=pool_size, strides=strides,
|
||||||
|
padding=padding, data_format=data_format,
|
||||||
|
name=name)
|
||||||
|
return layer.apply(inputs)
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.MaxPooling2D'])
|
||||||
|
class MaxPooling2D(keras_layers.MaxPooling2D, base.Layer):
|
||||||
|
"""Max pooling layer for 2D inputs (e.g. images).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
|
||||||
|
specifying the size of the pooling window.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
strides: An integer or tuple/list of 2 integers,
|
||||||
|
specifying the strides of the pooling operation.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` (default) and `channels_first` are supported.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, height, width, channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, height, width)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None, **kwargs):
|
||||||
|
if strides is None:
|
||||||
|
raise ValueError('Argument `strides` must not be None.')
|
||||||
|
super(MaxPooling2D, self).__init__(
|
||||||
|
pool_size=pool_size, strides=strides,
|
||||||
|
padding=padding, data_format=data_format, name=name, **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None, instructions='Use keras.layers.MaxPooling2D instead.')
|
||||||
|
@tf_export(v1=['layers.max_pooling2d'])
|
||||||
|
def max_pooling2d(inputs,
|
||||||
|
pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None):
|
||||||
|
"""Max pooling layer for 2D inputs (e.g. images).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: The tensor over which to pool. Must have rank 4.
|
||||||
|
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
|
||||||
|
specifying the size of the pooling window.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
strides: An integer or tuple/list of 2 integers,
|
||||||
|
specifying the strides of the pooling operation.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` (default) and `channels_first` are supported.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, height, width, channels)` while `channels_first` corresponds to
|
||||||
|
inputs with shape `(batch, channels, height, width)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Output tensor.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if eager execution is enabled.
|
||||||
|
"""
|
||||||
|
layer = MaxPooling2D(pool_size=pool_size, strides=strides,
|
||||||
|
padding=padding, data_format=data_format,
|
||||||
|
name=name)
|
||||||
|
return layer.apply(inputs)
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.AveragePooling3D'])
|
||||||
|
class AveragePooling3D(keras_layers.AveragePooling3D, base.Layer):
|
||||||
|
"""Average pooling layer for 3D inputs (e.g. volumes).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
pool_size: An integer or tuple/list of 3 integers:
|
||||||
|
(pool_depth, pool_height, pool_width)
|
||||||
|
specifying the size of the pooling window.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
strides: An integer or tuple/list of 3 integers,
|
||||||
|
specifying the strides of the pooling operation.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` (default) and `channels_first` are supported.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, depth, height, width, channels)` while `channels_first`
|
||||||
|
corresponds to inputs with shape
|
||||||
|
`(batch, channels, depth, height, width)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None, **kwargs):
|
||||||
|
if strides is None:
|
||||||
|
raise ValueError('Argument `strides` must not be None.')
|
||||||
|
super(AveragePooling3D, self).__init__(
|
||||||
|
pool_size=pool_size, strides=strides,
|
||||||
|
padding=padding, data_format=data_format, name=name, **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None, instructions='Use keras.layers.AveragePooling3D instead.')
|
||||||
|
@tf_export(v1=['layers.average_pooling3d'])
|
||||||
|
def average_pooling3d(inputs,
|
||||||
|
pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None):
|
||||||
|
"""Average pooling layer for 3D inputs (e.g. volumes).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: The tensor over which to pool. Must have rank 5.
|
||||||
|
pool_size: An integer or tuple/list of 3 integers:
|
||||||
|
(pool_depth, pool_height, pool_width)
|
||||||
|
specifying the size of the pooling window.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
strides: An integer or tuple/list of 3 integers,
|
||||||
|
specifying the strides of the pooling operation.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` (default) and `channels_first` are supported.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, depth, height, width, channels)` while `channels_first`
|
||||||
|
corresponds to inputs with shape
|
||||||
|
`(batch, channels, depth, height, width)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Output tensor.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if eager execution is enabled.
|
||||||
|
"""
|
||||||
|
layer = AveragePooling3D(pool_size=pool_size, strides=strides,
|
||||||
|
padding=padding, data_format=data_format,
|
||||||
|
name=name)
|
||||||
|
return layer.apply(inputs)
|
||||||
|
|
||||||
|
|
||||||
|
@tf_export(v1=['layers.MaxPooling3D'])
|
||||||
|
class MaxPooling3D(keras_layers.MaxPooling3D, base.Layer):
|
||||||
|
"""Max pooling layer for 3D inputs (e.g. volumes).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
pool_size: An integer or tuple/list of 3 integers:
|
||||||
|
(pool_depth, pool_height, pool_width)
|
||||||
|
specifying the size of the pooling window.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
strides: An integer or tuple/list of 3 integers,
|
||||||
|
specifying the strides of the pooling operation.
|
||||||
|
Can be a single integer to specify the same value for
|
||||||
|
all spatial dimensions.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` (default) and `channels_first` are supported.
|
||||||
|
`channels_last` corresponds to inputs with shape
|
||||||
|
`(batch, depth, height, width, channels)` while `channels_first`
|
||||||
|
corresponds to inputs with shape
|
||||||
|
`(batch, channels, depth, height, width)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None, **kwargs):
|
||||||
|
if strides is None:
|
||||||
|
raise ValueError('Argument `strides` must not be None.')
|
||||||
|
super(MaxPooling3D, self).__init__(
|
||||||
|
pool_size=pool_size, strides=strides,
|
||||||
|
padding=padding, data_format=data_format, name=name, **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
@deprecation.deprecated(
|
||||||
|
date=None, instructions='Use keras.layers.MaxPooling3D instead.')
|
||||||
|
@tf_export(v1=['layers.max_pooling3d'])
|
||||||
|
def max_pooling3d(inputs,
|
||||||
|
pool_size, strides,
|
||||||
|
padding='valid', data_format='channels_last',
|
||||||
|
name=None):
|
||||||
|
"""Max pooling layer for 3D inputs (e.g.
|
||||||
|
|
||||||
|
volumes).
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
inputs: The tensor over which to pool. Must have rank 5.
|
||||||
|
pool_size: An integer or tuple/list of 3 integers: (pool_depth, pool_height,
|
||||||
|
pool_width) specifying the size of the pooling window. Can be a single
|
||||||
|
integer to specify the same value for all spatial dimensions.
|
||||||
|
strides: An integer or tuple/list of 3 integers, specifying the strides of
|
||||||
|
the pooling operation. Can be a single integer to specify the same value
|
||||||
|
for all spatial dimensions.
|
||||||
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||||||
|
Case-insensitive.
|
||||||
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||||||
|
`channels_last` (default) and `channels_first` are supported.
|
||||||
|
`channels_last` corresponds to inputs with shape `(batch, depth, height,
|
||||||
|
width, channels)` while `channels_first` corresponds to inputs with shape
|
||||||
|
`(batch, channels, depth, height, width)`.
|
||||||
|
name: A string, the name of the layer.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Output tensor.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if eager execution is enabled.
|
||||||
|
"""
|
||||||
|
layer = MaxPooling3D(pool_size=pool_size, strides=strides,
|
||||||
|
padding=padding, data_format=data_format,
|
||||||
|
name=name)
|
||||||
|
return layer.apply(inputs)
|
||||||
|
|
||||||
|
# Aliases
|
||||||
|
|
||||||
|
AvgPool2D = AveragePooling2D
|
||||||
|
MaxPool2D = MaxPooling2D
|
||||||
|
max_pool2d = max_pooling2d
|
||||||
|
avg_pool2d = average_pooling2d
|
||||||
@ -19,7 +19,7 @@ from __future__ import division
|
|||||||
from __future__ import print_function
|
from __future__ import print_function
|
||||||
|
|
||||||
from tensorflow.python.framework import test_util
|
from tensorflow.python.framework import test_util
|
||||||
from tensorflow.python.layers import pooling as pooling_layers
|
from tensorflow.python.keras.legacy_tf_layers import pooling as pooling_layers
|
||||||
from tensorflow.python.ops import array_ops
|
from tensorflow.python.ops import array_ops
|
||||||
from tensorflow.python.ops import random_ops
|
from tensorflow.python.ops import random_ops
|
||||||
from tensorflow.python.platform import test
|
from tensorflow.python.platform import test
|
||||||
@ -17,577 +17,10 @@ from __future__ import absolute_import
|
|||||||
from __future__ import division
|
from __future__ import division
|
||||||
from __future__ import print_function
|
from __future__ import print_function
|
||||||
|
|
||||||
import copy
|
from tensorflow.python.keras.legacy_tf_layers import base
|
||||||
|
|
||||||
from tensorflow.python.eager import context
|
InputSpec = base.InputSpec
|
||||||
from tensorflow.python.framework import dtypes
|
|
||||||
from tensorflow.python.framework import ops
|
|
||||||
from tensorflow.python.keras import backend
|
|
||||||
from tensorflow.python.keras.engine import base_layer
|
|
||||||
from tensorflow.python.keras.mixed_precision.experimental import policy
|
|
||||||
from tensorflow.python.ops import variable_scope as vs
|
|
||||||
from tensorflow.python.ops import variables as tf_variables
|
|
||||||
from tensorflow.python.training.tracking import base as trackable
|
|
||||||
from tensorflow.python.util import deprecation
|
|
||||||
from tensorflow.python.util import function_utils
|
|
||||||
from tensorflow.python.util import nest
|
|
||||||
from tensorflow.python.util import tf_contextlib
|
|
||||||
from tensorflow.python.util.tf_export import tf_export
|
|
||||||
|
|
||||||
# Avoid breaking users who directly import this symbol from this file.
|
keras_style_scope = base.keras_style_scope
|
||||||
# TODO(fchollet): remove this.
|
set_keras_style = base.set_keras_style
|
||||||
InputSpec = base_layer.InputSpec # pylint: disable=invalid-name
|
Layer = base.Layer
|
||||||
|
|
||||||
_KERAS_STYLE_SCOPE = False
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.experimental.keras_style_scope'])
|
|
||||||
@tf_contextlib.contextmanager
|
|
||||||
def keras_style_scope():
|
|
||||||
"""Use Keras-style variable management.
|
|
||||||
|
|
||||||
All tf.layers and tf RNN cells created in this scope use Keras-style
|
|
||||||
variable management. Creating such layers with a scope= argument is
|
|
||||||
disallowed, and reuse=True is disallowed.
|
|
||||||
|
|
||||||
The purpose of this scope is to allow users of existing layers to
|
|
||||||
slowly transition to a Keras layers API without breaking existing
|
|
||||||
functionality.
|
|
||||||
|
|
||||||
One example of this is when using TensorFlow's RNN classes with Keras
|
|
||||||
Models or Networks. Because Keras models do not properly set variable
|
|
||||||
scopes, users of RNNs may either accidentally share scopes between two
|
|
||||||
different models, or get errors about variables that already exist.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
|
|
||||||
```python
|
|
||||||
class RNNModel(tf.keras.Model):
|
|
||||||
|
|
||||||
def __init__(self, name):
|
|
||||||
super(RNNModel, self).__init__(name=name)
|
|
||||||
self.rnn = tf.compat.v1.nn.rnn_cell.MultiRNNCell(
|
|
||||||
[tf.compat.v1.nn.rnn_cell.LSTMCell(64) for _ in range(2)])
|
|
||||||
|
|
||||||
def call(self, input, state):
|
|
||||||
return self.rnn(input, state)
|
|
||||||
|
|
||||||
model_1 = RNNModel("model_1")
|
|
||||||
model_2 = RNNModel("model_2")
|
|
||||||
|
|
||||||
# OK
|
|
||||||
output_1, next_state_1 = model_1(input, state)
|
|
||||||
# Raises an error about trying to create an already existing variable.
|
|
||||||
output_2, next_state_2 = model_2(input, state)
|
|
||||||
```
|
|
||||||
|
|
||||||
The solution is to wrap the model construction and execution in a keras-style
|
|
||||||
scope:
|
|
||||||
|
|
||||||
```python
|
|
||||||
with keras_style_scope():
|
|
||||||
model_1 = RNNModel("model_1")
|
|
||||||
model_2 = RNNModel("model_2")
|
|
||||||
|
|
||||||
# model_1 and model_2 are guaranteed to create their own variables.
|
|
||||||
output_1, next_state_1 = model_1(input, state)
|
|
||||||
output_2, next_state_2 = model_2(input, state)
|
|
||||||
|
|
||||||
assert len(model_1.weights) > 0
|
|
||||||
assert len(model_2.weights) > 0
|
|
||||||
assert(model_1.weights != model_2.weights)
|
|
||||||
```
|
|
||||||
|
|
||||||
Yields:
|
|
||||||
A keras layer style scope.
|
|
||||||
"""
|
|
||||||
global _KERAS_STYLE_SCOPE
|
|
||||||
stack = _KERAS_STYLE_SCOPE
|
|
||||||
_KERAS_STYLE_SCOPE = True
|
|
||||||
try:
|
|
||||||
yield
|
|
||||||
finally:
|
|
||||||
_KERAS_STYLE_SCOPE = stack
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.experimental.set_keras_style'])
|
|
||||||
def set_keras_style():
|
|
||||||
"""Use Keras-style variable management.
|
|
||||||
|
|
||||||
All tf.layers and tf RNN cells created after keras style ha been enabled
|
|
||||||
use Keras-style variable management. Creating such layers with a
|
|
||||||
scope= argument is disallowed, and reuse=True is disallowed.
|
|
||||||
|
|
||||||
The purpose of this function is to allow users of existing layers to
|
|
||||||
slowly transition to Keras layers API without breaking existing
|
|
||||||
functionality.
|
|
||||||
|
|
||||||
For more details, see the documentation for `keras_style_scope`.
|
|
||||||
|
|
||||||
Note, once keras style has been set, it is set globally for the entire
|
|
||||||
program and cannot be unset.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
|
|
||||||
```python
|
|
||||||
set_keras_style()
|
|
||||||
|
|
||||||
model_1 = RNNModel(name="model_1")
|
|
||||||
model_2 = RNNModel(name="model_2")
|
|
||||||
|
|
||||||
# model_1 and model_2 are guaranteed to create their own variables.
|
|
||||||
output_1, next_state_1 = model_1(input, state)
|
|
||||||
output_2, next_state_2 = model_2(input, state)
|
|
||||||
|
|
||||||
assert len(model_1.weights) > 0
|
|
||||||
assert len(model_2.weights) > 0
|
|
||||||
assert(model_1.weights != model_2.weights)
|
|
||||||
```
|
|
||||||
"""
|
|
||||||
global _KERAS_STYLE_SCOPE
|
|
||||||
_KERAS_STYLE_SCOPE = True
|
|
||||||
|
|
||||||
|
|
||||||
def _is_in_keras_style_scope():
|
|
||||||
global _KERAS_STYLE_SCOPE
|
|
||||||
return _KERAS_STYLE_SCOPE
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.Layer'])
|
|
||||||
class Layer(base_layer.Layer):
|
|
||||||
"""Base layer class.
|
|
||||||
|
|
||||||
It is considered legacy, and we recommend the use of `tf.keras.layers.Layer`
|
|
||||||
instead.
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
trainable: Boolean, whether the layer's variables should be trainable.
|
|
||||||
name: String name of the layer.
|
|
||||||
dtype: Default dtype of the layer's weights (default of `None` means use the
|
|
||||||
type of the first input).
|
|
||||||
|
|
||||||
Read-only properties:
|
|
||||||
name: The name of the layer (string).
|
|
||||||
dtype: Default dtype of the layer's weights (default of `None` means use the
|
|
||||||
type of the first input).
|
|
||||||
trainable_variables: List of trainable variables.
|
|
||||||
non_trainable_variables: List of non-trainable variables.
|
|
||||||
variables: List of all variables of this layer, trainable and
|
|
||||||
non-trainable.
|
|
||||||
updates: List of update ops of this layer.
|
|
||||||
losses: List of losses added by this layer.
|
|
||||||
trainable_weights: List of variables to be included in backprop.
|
|
||||||
non_trainable_weights: List of variables that should not be
|
|
||||||
included in backprop.
|
|
||||||
weights: The concatenation of the lists trainable_weights and
|
|
||||||
non_trainable_weights (in this order).
|
|
||||||
|
|
||||||
Mutable properties:
|
|
||||||
trainable: Whether the layer should be trained (boolean).
|
|
||||||
input_spec: Optional (list of) `InputSpec` object(s) specifying the
|
|
||||||
constraints on inputs that can be accepted by the layer.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, trainable=True, name=None, dtype=None,
|
|
||||||
**kwargs):
|
|
||||||
# For backwards compatibility, legacy layers do not use `ResourceVariable`
|
|
||||||
# by default.
|
|
||||||
self._use_resource_variables = False
|
|
||||||
scope = kwargs.pop('_scope', None)
|
|
||||||
self._reuse = kwargs.pop('_reuse', None)
|
|
||||||
|
|
||||||
# Avoid an incorrect lint error
|
|
||||||
self._trainable_weights = []
|
|
||||||
self.built = False
|
|
||||||
|
|
||||||
if dtype is None:
|
|
||||||
# Indicates to infer dtype from inputs. When the V2 dtype behavior is
|
|
||||||
# enabled, Keras layers default their dtype to floatx instead, so we pass
|
|
||||||
# an "_infer" policy to keep the old V1 behavior.
|
|
||||||
dtype = policy.Policy('_infer')
|
|
||||||
|
|
||||||
if 'autocast' not in kwargs:
|
|
||||||
kwargs['autocast'] = False
|
|
||||||
|
|
||||||
super(Layer, self).__init__(trainable=trainable, name=name, dtype=dtype,
|
|
||||||
**kwargs)
|
|
||||||
|
|
||||||
if _is_in_keras_style_scope():
|
|
||||||
if scope is not None:
|
|
||||||
raise ValueError(
|
|
||||||
'scope argument not allowed when keras style layers are enabled, '
|
|
||||||
'but saw: {}'.format(scope))
|
|
||||||
if self._reuse is not None:
|
|
||||||
raise ValueError(
|
|
||||||
'reuse argument not allowed when keras style layers are enabled, '
|
|
||||||
'but saw: {}'.format(self._reuse))
|
|
||||||
self._keras_style = True
|
|
||||||
else:
|
|
||||||
self._keras_style = False
|
|
||||||
|
|
||||||
self._call_has_scope_arg = 'scope' in self._call_fn_args
|
|
||||||
if scope:
|
|
||||||
with vs.variable_scope(scope) as captured_scope:
|
|
||||||
self._scope = captured_scope
|
|
||||||
else:
|
|
||||||
self._scope = None
|
|
||||||
self._current_scope = None
|
|
||||||
|
|
||||||
# We no longer track graph in tf.layers layers. This property is only kept to
|
|
||||||
# maintain API backward compatibility.
|
|
||||||
@property
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None,
|
|
||||||
instructions='Stop using this property because tf.layers layers no '
|
|
||||||
'longer track their graph.')
|
|
||||||
def graph(self):
|
|
||||||
if context.executing_eagerly():
|
|
||||||
raise RuntimeError('Layer.graph not supported when executing eagerly.')
|
|
||||||
return None
|
|
||||||
|
|
||||||
def _init_set_name(self, name):
|
|
||||||
# Determine layer name (non-unique).
|
|
||||||
if isinstance(name, vs.VariableScope):
|
|
||||||
base_name = name.name
|
|
||||||
self._name, _ = self._make_unique_name()
|
|
||||||
else:
|
|
||||||
base_name = name
|
|
||||||
self._name = name
|
|
||||||
if not name:
|
|
||||||
self._name, base_name = self._make_unique_name()
|
|
||||||
self._base_name = base_name
|
|
||||||
|
|
||||||
def _make_unique_name(self, name_uid_map=None, avoid_names=None,
|
|
||||||
namespace='', zero_based=False):
|
|
||||||
base_name = base_layer.to_snake_case(self.__class__.__name__)
|
|
||||||
name = backend.unique_object_name(
|
|
||||||
base_name,
|
|
||||||
name_uid_map=name_uid_map,
|
|
||||||
avoid_names=avoid_names,
|
|
||||||
namespace=namespace,
|
|
||||||
zero_based=zero_based)
|
|
||||||
return (name, base_name)
|
|
||||||
|
|
||||||
@property
|
|
||||||
def scope_name(self):
|
|
||||||
if not self._scope:
|
|
||||||
raise ValueError('No name available for layer scope because the layer "' +
|
|
||||||
self._name + '" has not been used yet. The scope name ' +
|
|
||||||
' is determined the first time the layer instance is ' +
|
|
||||||
'called. You must therefore call the layer before ' +
|
|
||||||
'querying `scope_name`.')
|
|
||||||
return self._scope.name
|
|
||||||
|
|
||||||
def add_loss(self, losses, inputs=None):
|
|
||||||
previous_losses_length = len(self._losses)
|
|
||||||
previous_callable_losses_length = len(self._callable_losses)
|
|
||||||
super(Layer, self).add_loss(losses, inputs=inputs)
|
|
||||||
if not context.executing_eagerly():
|
|
||||||
# TODO(fchollet): deprecate collection below.
|
|
||||||
new_losses = self._losses[previous_losses_length:]
|
|
||||||
new_callable_losses = self._callable_losses[
|
|
||||||
previous_callable_losses_length:]
|
|
||||||
for regularizer in new_callable_losses:
|
|
||||||
loss_tensor = regularizer()
|
|
||||||
if loss_tensor is not None:
|
|
||||||
new_losses.append(loss_tensor)
|
|
||||||
_add_elements_to_collection(
|
|
||||||
new_losses,
|
|
||||||
ops.GraphKeys.REGULARIZATION_LOSSES)
|
|
||||||
|
|
||||||
def _name_scope(self):
|
|
||||||
"""Determines op naming for the Layer."""
|
|
||||||
if self._keras_style:
|
|
||||||
return super(Layer, self)._name_scope()
|
|
||||||
return self._current_scope.original_name_scope
|
|
||||||
|
|
||||||
def _set_scope(self, scope=None):
|
|
||||||
if self._scope is None:
|
|
||||||
# If constructed with _scope=None, lazy setting of scope.
|
|
||||||
if self._reuse:
|
|
||||||
with vs.variable_scope(
|
|
||||||
scope if scope is not None else self._base_name) as captured_scope:
|
|
||||||
self._scope = captured_scope
|
|
||||||
else:
|
|
||||||
with vs.variable_scope(
|
|
||||||
scope, default_name=self._base_name) as captured_scope:
|
|
||||||
self._scope = captured_scope
|
|
||||||
|
|
||||||
def add_weight(self,
|
|
||||||
name,
|
|
||||||
shape,
|
|
||||||
dtype=None,
|
|
||||||
initializer=None,
|
|
||||||
regularizer=None,
|
|
||||||
trainable=None,
|
|
||||||
constraint=None,
|
|
||||||
use_resource=None,
|
|
||||||
synchronization=vs.VariableSynchronization.AUTO,
|
|
||||||
aggregation=vs.VariableAggregation.NONE,
|
|
||||||
partitioner=None,
|
|
||||||
**kwargs):
|
|
||||||
"""Adds a new variable to the layer, or gets an existing one; returns it.
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
name: variable name.
|
|
||||||
shape: variable shape.
|
|
||||||
dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
|
|
||||||
initializer: initializer instance (callable).
|
|
||||||
regularizer: regularizer instance (callable).
|
|
||||||
trainable: whether the variable should be part of the layer's
|
|
||||||
"trainable_variables" (e.g. variables, biases)
|
|
||||||
or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
|
|
||||||
Note, if the current variable scope is marked as non-trainable
|
|
||||||
then this parameter is ignored and any added variables are also
|
|
||||||
marked as non-trainable. `trainable` defaults to `True` unless
|
|
||||||
`synchronization` is set to `ON_READ`.
|
|
||||||
constraint: constraint instance (callable).
|
|
||||||
use_resource: Whether to use `ResourceVariable`.
|
|
||||||
synchronization: Indicates when a distributed a variable will be
|
|
||||||
aggregated. Accepted values are constants defined in the class
|
|
||||||
`tf.VariableSynchronization`. By default the synchronization is set to
|
|
||||||
`AUTO` and the current `DistributionStrategy` chooses
|
|
||||||
when to synchronize. If `synchronization` is set to `ON_READ`,
|
|
||||||
`trainable` must not be set to `True`.
|
|
||||||
aggregation: Indicates how a distributed variable will be aggregated.
|
|
||||||
Accepted values are constants defined in the class
|
|
||||||
`tf.VariableAggregation`.
|
|
||||||
partitioner: (optional) partitioner instance (callable). If
|
|
||||||
provided, when the requested variable is created it will be split
|
|
||||||
into multiple partitions according to `partitioner`. In this case,
|
|
||||||
an instance of `PartitionedVariable` is returned. Available
|
|
||||||
partitioners include `tf.compat.v1.fixed_size_partitioner` and
|
|
||||||
`tf.compat.v1.variable_axis_size_partitioner`. For more details, see
|
|
||||||
the documentation of `tf.compat.v1.get_variable` and the "Variable
|
|
||||||
Partitioners and Sharding" section of the API guide.
|
|
||||||
**kwargs: Additional keyword arguments.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
The created variable. Usually either a `Variable` or `ResourceVariable`
|
|
||||||
instance. If `partitioner` is not `None`, a `PartitionedVariable`
|
|
||||||
instance is returned.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
RuntimeError: If called with partitioned variable regularization and
|
|
||||||
eager execution is enabled.
|
|
||||||
ValueError: When trainable has been set to True with synchronization
|
|
||||||
set as `ON_READ`.
|
|
||||||
"""
|
|
||||||
for kwarg in kwargs:
|
|
||||||
if kwarg != 'experimental_autocast':
|
|
||||||
raise TypeError('Unknown keyword argument:', kwarg)
|
|
||||||
if self._keras_style:
|
|
||||||
return super(Layer, self).add_weight(
|
|
||||||
name=name,
|
|
||||||
shape=shape,
|
|
||||||
dtype=dtype,
|
|
||||||
initializer=initializer,
|
|
||||||
regularizer=regularizer,
|
|
||||||
trainable=trainable and self.trainable,
|
|
||||||
constraint=constraint,
|
|
||||||
use_resource=use_resource,
|
|
||||||
synchronization=vs.VariableSynchronization.AUTO,
|
|
||||||
aggregation=vs.VariableAggregation.NONE,
|
|
||||||
partitioner=partitioner,
|
|
||||||
**kwargs)
|
|
||||||
|
|
||||||
if synchronization == vs.VariableSynchronization.ON_READ:
|
|
||||||
if trainable:
|
|
||||||
raise ValueError(
|
|
||||||
'Synchronization value can be set to '
|
|
||||||
'VariableSynchronization.ON_READ only for non-trainable variables. '
|
|
||||||
'You have specified trainable=True and '
|
|
||||||
'synchronization=VariableSynchronization.ON_READ.')
|
|
||||||
else:
|
|
||||||
# Set trainable to be false when variable is to be synced on read.
|
|
||||||
trainable = False
|
|
||||||
elif trainable is None:
|
|
||||||
trainable = True
|
|
||||||
|
|
||||||
def _should_add_regularizer(variable, existing_variable_set):
|
|
||||||
if isinstance(variable, tf_variables.PartitionedVariable):
|
|
||||||
for var in variable:
|
|
||||||
if var in existing_variable_set:
|
|
||||||
return False
|
|
||||||
return True
|
|
||||||
else:
|
|
||||||
return variable not in existing_variable_set
|
|
||||||
|
|
||||||
init_graph = None
|
|
||||||
if not context.executing_eagerly():
|
|
||||||
default_graph = ops.get_default_graph()
|
|
||||||
if default_graph.building_function:
|
|
||||||
with ops.init_scope():
|
|
||||||
# Retrieve the variables from the graph into which variables
|
|
||||||
# will be lifted; if initialization ops will be lifted into
|
|
||||||
# the eager context, then there is nothing to retrieve, since variable
|
|
||||||
# collections are not supported when eager execution is enabled.
|
|
||||||
if not context.executing_eagerly():
|
|
||||||
init_graph = ops.get_default_graph()
|
|
||||||
existing_variables = set(tf_variables.global_variables())
|
|
||||||
else:
|
|
||||||
# Initialization ops will not be lifted out of the default graph.
|
|
||||||
init_graph = default_graph
|
|
||||||
existing_variables = set(tf_variables.global_variables())
|
|
||||||
|
|
||||||
if dtype is None:
|
|
||||||
dtype = self.dtype or dtypes.float32
|
|
||||||
|
|
||||||
self._set_scope(None)
|
|
||||||
reuse = self.built or self._reuse
|
|
||||||
prev_len_trainable = len(self._trainable_weights)
|
|
||||||
with vs.variable_scope(
|
|
||||||
self._scope, reuse=reuse, auxiliary_name_scope=False) as scope:
|
|
||||||
self._current_scope = scope
|
|
||||||
with ops.name_scope(self._name_scope(), skip_on_eager=False):
|
|
||||||
use_resource = (use_resource or
|
|
||||||
self._use_resource_variables or
|
|
||||||
scope.use_resource)
|
|
||||||
if initializer is None:
|
|
||||||
initializer = scope.initializer
|
|
||||||
variable = super(Layer, self).add_weight(
|
|
||||||
name,
|
|
||||||
shape,
|
|
||||||
dtype=dtypes.as_dtype(dtype),
|
|
||||||
initializer=initializer,
|
|
||||||
trainable=trainable and self.trainable,
|
|
||||||
constraint=constraint,
|
|
||||||
partitioner=partitioner,
|
|
||||||
use_resource=use_resource,
|
|
||||||
synchronization=synchronization,
|
|
||||||
aggregation=aggregation,
|
|
||||||
getter=vs.get_variable,
|
|
||||||
**kwargs)
|
|
||||||
|
|
||||||
if regularizer:
|
|
||||||
if (ops.executing_eagerly_outside_functions()
|
|
||||||
or _should_add_regularizer(variable, existing_variables)):
|
|
||||||
self._handle_weight_regularization(name, variable, regularizer)
|
|
||||||
|
|
||||||
if init_graph is not None:
|
|
||||||
# Handle edge case where a custom getter has overridden `trainable`.
|
|
||||||
# There is one known occurrence of this, in unit test
|
|
||||||
# testBasicRNNCellNotTrainable in
|
|
||||||
# contrib.rnn.python.kernel_tests.core_rnn_cell_test
|
|
||||||
with init_graph.as_default():
|
|
||||||
trainable_variables = tf_variables.trainable_variables()
|
|
||||||
if (trainable and self.trainable and
|
|
||||||
variable not in trainable_variables):
|
|
||||||
# A custom getter / variable scope overrode the trainable flag.
|
|
||||||
extra_trainable_vars = self._trainable_weights[prev_len_trainable:]
|
|
||||||
self._trainable_weights = self._trainable_weights[
|
|
||||||
:prev_len_trainable]
|
|
||||||
self._non_trainable_weights += extra_trainable_vars
|
|
||||||
return variable
|
|
||||||
|
|
||||||
def __call__(self, inputs, *args, **kwargs):
|
|
||||||
"""Wraps `call`, applying pre- and post-processing steps.
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: input tensor(s).
|
|
||||||
*args: additional positional arguments to be passed to `self.call`.
|
|
||||||
**kwargs: additional keyword arguments to be passed to `self.call`.
|
|
||||||
**Note**: kwarg `scope` is reserved for use by the layer.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Output tensor(s).
|
|
||||||
|
|
||||||
Note:
|
|
||||||
- If the layer's `call` method takes a `scope` keyword argument,
|
|
||||||
this argument will be automatically set to the current variable scope.
|
|
||||||
- If the layer's `call` method takes a `mask` argument (as some Keras
|
|
||||||
layers do), its default value will be set to the mask generated
|
|
||||||
for `inputs` by the previous layer (if `input` did come from
|
|
||||||
a layer that generated a corresponding mask, i.e. if it came from
|
|
||||||
a Keras layer with masking support.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if the layer's `call` method returns None (an invalid value).
|
|
||||||
"""
|
|
||||||
scope = kwargs.pop('scope', None)
|
|
||||||
|
|
||||||
if self._keras_style:
|
|
||||||
if scope is not None:
|
|
||||||
raise ValueError(
|
|
||||||
'scope argument not allowed when keras style layers are enabled, '
|
|
||||||
'but saw: {}'.format(scope))
|
|
||||||
return super(Layer, self).__call__(inputs, *args, **kwargs)
|
|
||||||
|
|
||||||
self._set_scope(scope)
|
|
||||||
|
|
||||||
if self.built:
|
|
||||||
try:
|
|
||||||
# Some classes which inherit from Layer do not use its constructor, so
|
|
||||||
# rather than initializing to None we check for an AttributeError.
|
|
||||||
scope_context_manager = self._always_reuse_variable_scope
|
|
||||||
except AttributeError:
|
|
||||||
# From this point we will always set reuse=True, so create a "final"
|
|
||||||
# variable scope with this setting. We avoid re-creating variable scopes
|
|
||||||
# after this point as an optimization.
|
|
||||||
self._always_reuse_variable_scope = vs.variable_scope(
|
|
||||||
self._scope, reuse=True, auxiliary_name_scope=False)
|
|
||||||
scope_context_manager = self._always_reuse_variable_scope
|
|
||||||
else:
|
|
||||||
scope_context_manager = vs.variable_scope(
|
|
||||||
self._scope, reuse=self._reuse, auxiliary_name_scope=False)
|
|
||||||
|
|
||||||
with scope_context_manager as scope:
|
|
||||||
self._current_scope = scope
|
|
||||||
|
|
||||||
try:
|
|
||||||
call_has_scope_arg = self._call_has_scope_arg
|
|
||||||
except AttributeError:
|
|
||||||
self._call_fn_args = function_utils.fn_args(self.call)
|
|
||||||
self._call_has_scope_arg = 'scope' in self._call_fn_args
|
|
||||||
call_has_scope_arg = self._call_has_scope_arg
|
|
||||||
if call_has_scope_arg:
|
|
||||||
kwargs['scope'] = scope
|
|
||||||
|
|
||||||
# Actually call layer
|
|
||||||
outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
|
|
||||||
|
|
||||||
if not context.executing_eagerly():
|
|
||||||
# Update global default collections.
|
|
||||||
_add_elements_to_collection(self.updates, ops.GraphKeys.UPDATE_OPS)
|
|
||||||
return outputs
|
|
||||||
|
|
||||||
def __deepcopy__(self, memo):
|
|
||||||
no_copy = set(['_graph', '_thread_local', '_metrics_lock'])
|
|
||||||
shallow_copy = set(['_scope', '_always_reuse_variable_scope'])
|
|
||||||
cls = self.__class__
|
|
||||||
result = cls.__new__(cls)
|
|
||||||
memo[id(self)] = result
|
|
||||||
for k, v in self.__dict__.items():
|
|
||||||
if k in no_copy:
|
|
||||||
setattr(result, k, v)
|
|
||||||
elif k in shallow_copy:
|
|
||||||
setattr(result, k, copy.copy(v))
|
|
||||||
elif base_layer.is_tensor_or_tensor_list(v):
|
|
||||||
setattr(result, k, v)
|
|
||||||
else:
|
|
||||||
setattr(result, k, copy.deepcopy(v, memo))
|
|
||||||
return result
|
|
||||||
|
|
||||||
def __setattr__(self, value, name):
|
|
||||||
# By-pass the automatic dependency tracking performed by the parent Layer.
|
|
||||||
super(trackable.Trackable, self).__setattr__(value, name)
|
|
||||||
|
|
||||||
@property
|
|
||||||
def _is_legacy_layer(self):
|
|
||||||
"""Used by keras to check compatibility. This should not be overridden."""
|
|
||||||
return True
|
|
||||||
|
|
||||||
|
|
||||||
def _add_elements_to_collection(elements, collection_list):
|
|
||||||
if context.executing_eagerly():
|
|
||||||
raise RuntimeError('Using collections from Layers not supported in Eager '
|
|
||||||
'mode. Tried to add %s to %s' % (elements,
|
|
||||||
collection_list))
|
|
||||||
elements = nest.flatten(elements)
|
|
||||||
collection_list = nest.flatten(collection_list)
|
|
||||||
for name in collection_list:
|
|
||||||
collection = ops.get_collection_ref(name)
|
|
||||||
collection_set = {id(e) for e in collection}
|
|
||||||
for element in elements:
|
|
||||||
if id(element) not in collection_set:
|
|
||||||
collection.append(element)
|
|
||||||
|
|||||||
File diff suppressed because it is too large
Load Diff
@ -21,316 +21,15 @@ from __future__ import absolute_import
|
|||||||
from __future__ import division
|
from __future__ import division
|
||||||
from __future__ import print_function
|
from __future__ import print_function
|
||||||
|
|
||||||
|
from tensorflow.python.keras.legacy_tf_layers import core
|
||||||
from tensorflow.python.keras import layers as keras_layers
|
|
||||||
from tensorflow.python.layers import base
|
|
||||||
from tensorflow.python.ops import init_ops
|
|
||||||
from tensorflow.python.util import deprecation
|
|
||||||
from tensorflow.python.util.tf_export import tf_export
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.Dense'])
|
Dense = core.Dense
|
||||||
class Dense(keras_layers.Dense, base.Layer):
|
dense = core.dense
|
||||||
"""Densely-connected layer class.
|
Dropout = core.Dropout
|
||||||
|
dropout = core.dropout
|
||||||
This layer implements the operation:
|
Flatten = core.Flatten
|
||||||
`outputs = activation(inputs * kernel + bias)`
|
flatten = core.flatten
|
||||||
Where `activation` is the activation function passed as the `activation`
|
|
||||||
argument (if not `None`), `kernel` is a weights matrix created by the layer,
|
|
||||||
and `bias` is a bias vector created by the layer
|
|
||||||
(only if `use_bias` is `True`).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
units: Integer or Long, dimensionality of the output space.
|
|
||||||
activation: Activation function (callable). Set it to None to maintain a
|
|
||||||
linear activation.
|
|
||||||
use_bias: Boolean, whether the layer uses a bias.
|
|
||||||
kernel_initializer: Initializer function for the weight matrix.
|
|
||||||
If `None` (default), weights are initialized using the default
|
|
||||||
initializer used by `tf.compat.v1.get_variable`.
|
|
||||||
bias_initializer: Initializer function for the bias.
|
|
||||||
kernel_regularizer: Regularizer function for the weight matrix.
|
|
||||||
bias_regularizer: Regularizer function for the bias.
|
|
||||||
activity_regularizer: Regularizer function for the output.
|
|
||||||
kernel_constraint: An optional projection function to be applied to the
|
|
||||||
kernel after being updated by an `Optimizer` (e.g. used to implement
|
|
||||||
norm constraints or value constraints for layer weights). The function
|
|
||||||
must take as input the unprojected variable and must return the
|
|
||||||
projected variable (which must have the same shape). Constraints are
|
|
||||||
not safe to use when doing asynchronous distributed training.
|
|
||||||
bias_constraint: An optional projection function to be applied to the
|
|
||||||
bias after being updated by an `Optimizer`.
|
|
||||||
trainable: Boolean, if `True` also add variables to the graph collection
|
|
||||||
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
|
|
||||||
name: String, the name of the layer. Layers with the same name will
|
|
||||||
share weights, but to avoid mistakes we require reuse=True in such cases.
|
|
||||||
_reuse: Boolean, whether to reuse the weights of a previous layer
|
|
||||||
by the same name.
|
|
||||||
|
|
||||||
Properties:
|
|
||||||
units: Python integer, dimensionality of the output space.
|
|
||||||
activation: Activation function (callable).
|
|
||||||
use_bias: Boolean, whether the layer uses a bias.
|
|
||||||
kernel_initializer: Initializer instance (or name) for the kernel matrix.
|
|
||||||
bias_initializer: Initializer instance (or name) for the bias.
|
|
||||||
kernel_regularizer: Regularizer instance for the kernel matrix (callable)
|
|
||||||
bias_regularizer: Regularizer instance for the bias (callable).
|
|
||||||
activity_regularizer: Regularizer instance for the output (callable)
|
|
||||||
kernel_constraint: Constraint function for the kernel matrix.
|
|
||||||
bias_constraint: Constraint function for the bias.
|
|
||||||
kernel: Weight matrix (TensorFlow variable or tensor).
|
|
||||||
bias: Bias vector, if applicable (TensorFlow variable or tensor).
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, units,
|
|
||||||
activation=None,
|
|
||||||
use_bias=True,
|
|
||||||
kernel_initializer=None,
|
|
||||||
bias_initializer=init_ops.zeros_initializer(),
|
|
||||||
kernel_regularizer=None,
|
|
||||||
bias_regularizer=None,
|
|
||||||
activity_regularizer=None,
|
|
||||||
kernel_constraint=None,
|
|
||||||
bias_constraint=None,
|
|
||||||
trainable=True,
|
|
||||||
name=None,
|
|
||||||
**kwargs):
|
|
||||||
super(Dense, self).__init__(units=units,
|
|
||||||
activation=activation,
|
|
||||||
use_bias=use_bias,
|
|
||||||
kernel_initializer=kernel_initializer,
|
|
||||||
bias_initializer=bias_initializer,
|
|
||||||
kernel_regularizer=kernel_regularizer,
|
|
||||||
bias_regularizer=bias_regularizer,
|
|
||||||
activity_regularizer=activity_regularizer,
|
|
||||||
kernel_constraint=kernel_constraint,
|
|
||||||
bias_constraint=bias_constraint,
|
|
||||||
trainable=trainable,
|
|
||||||
name=name,
|
|
||||||
**kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None, instructions='Use keras.layers.Dense instead.')
|
|
||||||
@tf_export(v1=['layers.dense'])
|
|
||||||
def dense(
|
|
||||||
inputs, units,
|
|
||||||
activation=None,
|
|
||||||
use_bias=True,
|
|
||||||
kernel_initializer=None,
|
|
||||||
bias_initializer=init_ops.zeros_initializer(),
|
|
||||||
kernel_regularizer=None,
|
|
||||||
bias_regularizer=None,
|
|
||||||
activity_regularizer=None,
|
|
||||||
kernel_constraint=None,
|
|
||||||
bias_constraint=None,
|
|
||||||
trainable=True,
|
|
||||||
name=None,
|
|
||||||
reuse=None):
|
|
||||||
"""Functional interface for the densely-connected layer.
|
|
||||||
|
|
||||||
This layer implements the operation:
|
|
||||||
`outputs = activation(inputs * kernel + bias)`
|
|
||||||
where `activation` is the activation function passed as the `activation`
|
|
||||||
argument (if not `None`), `kernel` is a weights matrix created by the layer,
|
|
||||||
and `bias` is a bias vector created by the layer
|
|
||||||
(only if `use_bias` is `True`).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: Tensor input.
|
|
||||||
units: Integer or Long, dimensionality of the output space.
|
|
||||||
activation: Activation function (callable). Set it to None to maintain a
|
|
||||||
linear activation.
|
|
||||||
use_bias: Boolean, whether the layer uses a bias.
|
|
||||||
kernel_initializer: Initializer function for the weight matrix.
|
|
||||||
If `None` (default), weights are initialized using the default
|
|
||||||
initializer used by `tf.compat.v1.get_variable`.
|
|
||||||
bias_initializer: Initializer function for the bias.
|
|
||||||
kernel_regularizer: Regularizer function for the weight matrix.
|
|
||||||
bias_regularizer: Regularizer function for the bias.
|
|
||||||
activity_regularizer: Regularizer function for the output.
|
|
||||||
kernel_constraint: An optional projection function to be applied to the
|
|
||||||
kernel after being updated by an `Optimizer` (e.g. used to implement
|
|
||||||
norm constraints or value constraints for layer weights). The function
|
|
||||||
must take as input the unprojected variable and must return the
|
|
||||||
projected variable (which must have the same shape). Constraints are
|
|
||||||
not safe to use when doing asynchronous distributed training.
|
|
||||||
bias_constraint: An optional projection function to be applied to the
|
|
||||||
bias after being updated by an `Optimizer`.
|
|
||||||
trainable: Boolean, if `True` also add variables to the graph collection
|
|
||||||
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
|
|
||||||
name: String, the name of the layer.
|
|
||||||
reuse: Boolean, whether to reuse the weights of a previous layer
|
|
||||||
by the same name.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Output tensor the same shape as `inputs` except the last dimension is of
|
|
||||||
size `units`.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if eager execution is enabled.
|
|
||||||
"""
|
|
||||||
layer = Dense(units,
|
|
||||||
activation=activation,
|
|
||||||
use_bias=use_bias,
|
|
||||||
kernel_initializer=kernel_initializer,
|
|
||||||
bias_initializer=bias_initializer,
|
|
||||||
kernel_regularizer=kernel_regularizer,
|
|
||||||
bias_regularizer=bias_regularizer,
|
|
||||||
activity_regularizer=activity_regularizer,
|
|
||||||
kernel_constraint=kernel_constraint,
|
|
||||||
bias_constraint=bias_constraint,
|
|
||||||
trainable=trainable,
|
|
||||||
name=name,
|
|
||||||
_scope=name,
|
|
||||||
_reuse=reuse)
|
|
||||||
return layer.apply(inputs)
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.Dropout'])
|
|
||||||
class Dropout(keras_layers.Dropout, base.Layer):
|
|
||||||
"""Applies Dropout to the input.
|
|
||||||
|
|
||||||
Dropout consists in randomly setting a fraction `rate` of input units to 0
|
|
||||||
at each update during training time, which helps prevent overfitting.
|
|
||||||
The units that are kept are scaled by `1 / (1 - rate)`, so that their
|
|
||||||
sum is unchanged at training time and inference time.
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out
|
|
||||||
10% of input units.
|
|
||||||
noise_shape: 1D tensor of type `int32` representing the shape of the
|
|
||||||
binary dropout mask that will be multiplied with the input.
|
|
||||||
For instance, if your inputs have shape
|
|
||||||
`(batch_size, timesteps, features)`, and you want the dropout mask
|
|
||||||
to be the same for all timesteps, you can use
|
|
||||||
`noise_shape=[batch_size, 1, features]`.
|
|
||||||
seed: A Python integer. Used to create random seeds. See
|
|
||||||
`tf.compat.v1.set_random_seed`.
|
|
||||||
for behavior.
|
|
||||||
name: The name of the layer (string).
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, rate=0.5,
|
|
||||||
noise_shape=None,
|
|
||||||
seed=None,
|
|
||||||
name=None,
|
|
||||||
**kwargs):
|
|
||||||
super(Dropout, self).__init__(rate=rate,
|
|
||||||
noise_shape=noise_shape,
|
|
||||||
seed=seed,
|
|
||||||
name=name,
|
|
||||||
**kwargs)
|
|
||||||
|
|
||||||
def call(self, inputs, training=False):
|
|
||||||
return super(Dropout, self).call(inputs, training=training)
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None,
|
|
||||||
instructions='Use keras.layers.dropout instead.')
|
|
||||||
@tf_export(v1=['layers.dropout'])
|
|
||||||
def dropout(inputs,
|
|
||||||
rate=0.5,
|
|
||||||
noise_shape=None,
|
|
||||||
seed=None,
|
|
||||||
training=False,
|
|
||||||
name=None):
|
|
||||||
"""Applies Dropout to the input.
|
|
||||||
|
|
||||||
Dropout consists in randomly setting a fraction `rate` of input units to 0
|
|
||||||
at each update during training time, which helps prevent overfitting.
|
|
||||||
The units that are kept are scaled by `1 / (1 - rate)`, so that their
|
|
||||||
sum is unchanged at training time and inference time.
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: Tensor input.
|
|
||||||
rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
|
|
||||||
10% of input units.
|
|
||||||
noise_shape: 1D tensor of type `int32` representing the shape of the
|
|
||||||
binary dropout mask that will be multiplied with the input.
|
|
||||||
For instance, if your inputs have shape
|
|
||||||
`(batch_size, timesteps, features)`, and you want the dropout mask
|
|
||||||
to be the same for all timesteps, you can use
|
|
||||||
`noise_shape=[batch_size, 1, features]`.
|
|
||||||
seed: A Python integer. Used to create random seeds. See
|
|
||||||
`tf.compat.v1.set_random_seed`
|
|
||||||
for behavior.
|
|
||||||
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
|
|
||||||
(e.g. a placeholder). Whether to return the output in training mode
|
|
||||||
(apply dropout) or in inference mode (return the input untouched).
|
|
||||||
name: The name of the layer (string).
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Output tensor.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if eager execution is enabled.
|
|
||||||
"""
|
|
||||||
layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
|
|
||||||
return layer.apply(inputs, training=training)
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.Flatten'])
|
|
||||||
class Flatten(keras_layers.Flatten, base.Layer):
|
|
||||||
"""Flattens an input tensor while preserving the batch axis (axis 0).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
|
||||||
The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, ..., channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, ...)`.
|
|
||||||
|
|
||||||
Examples:
|
|
||||||
|
|
||||||
```
|
|
||||||
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
|
|
||||||
y = Flatten()(x)
|
|
||||||
# now `y` has shape `(None, 16)`
|
|
||||||
|
|
||||||
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
|
|
||||||
y = Flatten()(x)
|
|
||||||
# now `y` has shape `(None, None)`
|
|
||||||
```
|
|
||||||
"""
|
|
||||||
pass
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None,
|
|
||||||
instructions='Use keras.layers.Flatten instead.')
|
|
||||||
@tf_export(v1=['layers.flatten'])
|
|
||||||
def flatten(inputs, name=None, data_format='channels_last'):
|
|
||||||
"""Flattens an input tensor while preserving the batch axis (axis 0).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: Tensor input.
|
|
||||||
name: The name of the layer (string).
|
|
||||||
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
|
||||||
The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, height, width, channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, height, width)`.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Reshaped tensor.
|
|
||||||
|
|
||||||
Examples:
|
|
||||||
|
|
||||||
```
|
|
||||||
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
|
|
||||||
y = flatten(x)
|
|
||||||
# now `y` has shape `(None, 16)`
|
|
||||||
|
|
||||||
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
|
|
||||||
y = flatten(x)
|
|
||||||
# now `y` has shape `(None, None)`
|
|
||||||
```
|
|
||||||
"""
|
|
||||||
layer = Flatten(name=name, data_format=data_format)
|
|
||||||
return layer.apply(inputs)
|
|
||||||
|
|
||||||
|
|
||||||
# Aliases
|
# Aliases
|
||||||
|
|
||||||
|
|||||||
@ -20,322 +20,11 @@ from __future__ import division
|
|||||||
from __future__ import print_function
|
from __future__ import print_function
|
||||||
|
|
||||||
|
|
||||||
from tensorflow.python.keras.layers import normalization as keras_normalization
|
from tensorflow.python.keras.legacy_tf_layers import normalization
|
||||||
from tensorflow.python.layers import base
|
|
||||||
from tensorflow.python.ops import init_ops
|
|
||||||
from tensorflow.python.util import deprecation
|
|
||||||
from tensorflow.python.util.tf_export import tf_export
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.BatchNormalization'])
|
|
||||||
class BatchNormalization(keras_normalization.BatchNormalization, base.Layer):
|
|
||||||
"""Batch Normalization layer from (Ioffe et al., 2015).
|
|
||||||
|
|
||||||
Keras APIs handle BatchNormalization updates to the moving_mean and
|
|
||||||
moving_variance as part of their `fit()` and `evaluate()` loops. However, if a
|
|
||||||
custom training loop is used with an instance of `Model`, these updates need
|
|
||||||
to be explicitly included. Here's a simple example of how it can be done:
|
|
||||||
|
|
||||||
```python
|
|
||||||
# model is an instance of Model that contains BatchNormalization layer.
|
|
||||||
update_ops = model.get_updates_for(None) + model.get_updates_for(features)
|
|
||||||
train_op = optimizer.minimize(loss)
|
|
||||||
train_op = tf.group([train_op, update_ops])
|
|
||||||
```
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
axis: An `int` or list of `int`, the axis or axes that should be normalized,
|
|
||||||
typically the features axis/axes. For instance, after a `Conv2D` layer
|
|
||||||
with `data_format="channels_first"`, set `axis=1`. If a list of axes is
|
|
||||||
provided, each axis in `axis` will be normalized
|
|
||||||
simultaneously. Default is `-1` which uses the last axis. Note: when
|
|
||||||
using multi-axis batch norm, the `beta`, `gamma`, `moving_mean`, and
|
|
||||||
`moving_variance` variables are the same rank as the input Tensor,
|
|
||||||
with dimension size 1 in all reduced (non-axis) dimensions).
|
|
||||||
momentum: Momentum for the moving average.
|
|
||||||
epsilon: Small float added to variance to avoid dividing by zero.
|
|
||||||
center: If True, add offset of `beta` to normalized tensor. If False, `beta`
|
|
||||||
is ignored.
|
|
||||||
scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
|
|
||||||
next layer is linear (also e.g. `nn.relu`), this can be disabled since the
|
|
||||||
scaling can be done by the next layer.
|
|
||||||
beta_initializer: Initializer for the beta weight.
|
|
||||||
gamma_initializer: Initializer for the gamma weight.
|
|
||||||
moving_mean_initializer: Initializer for the moving mean.
|
|
||||||
moving_variance_initializer: Initializer for the moving variance.
|
|
||||||
beta_regularizer: Optional regularizer for the beta weight.
|
|
||||||
gamma_regularizer: Optional regularizer for the gamma weight.
|
|
||||||
beta_constraint: An optional projection function to be applied to the `beta`
|
|
||||||
weight after being updated by an `Optimizer` (e.g. used to implement norm
|
|
||||||
constraints or value constraints for layer weights). The function must
|
|
||||||
take as input the unprojected variable and must return the projected
|
|
||||||
variable (which must have the same shape). Constraints are not safe to use
|
|
||||||
when doing asynchronous distributed training.
|
|
||||||
gamma_constraint: An optional projection function to be applied to the
|
|
||||||
`gamma` weight after being updated by an `Optimizer`.
|
|
||||||
renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds extra
|
|
||||||
variables during training. The inference is the same for either value of
|
|
||||||
this parameter.
|
|
||||||
renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
|
|
||||||
scalar `Tensors` used to clip the renorm correction. The correction `(r,
|
|
||||||
d)` is used as `corrected_value = normalized_value * r + d`, with `r`
|
|
||||||
clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
|
|
||||||
dmax are set to inf, 0, inf, respectively.
|
|
||||||
renorm_momentum: Momentum used to update the moving means and standard
|
|
||||||
deviations with renorm. Unlike `momentum`, this affects training and
|
|
||||||
should be neither too small (which would add noise) nor too large (which
|
|
||||||
would give stale estimates). Note that `momentum` is still applied to get
|
|
||||||
the means and variances for inference.
|
|
||||||
fused: if `None` or `True`, use a faster, fused implementation if possible.
|
|
||||||
If `False`, use the system recommended implementation.
|
|
||||||
trainable: Boolean, if `True` also add variables to the graph collection
|
|
||||||
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
|
|
||||||
virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
|
|
||||||
which means batch normalization is performed across the whole batch. When
|
|
||||||
`virtual_batch_size` is not `None`, instead perform "Ghost Batch
|
|
||||||
Normalization", which creates virtual sub-batches which are each
|
|
||||||
normalized separately (with shared gamma, beta, and moving statistics).
|
|
||||||
Must divide the actual batch size during execution.
|
|
||||||
adjustment: A function taking the `Tensor` containing the (dynamic) shape of
|
|
||||||
the input tensor and returning a pair (scale, bias) to apply to the
|
|
||||||
normalized values (before gamma and beta), only during training. For
|
|
||||||
example, if axis==-1,
|
|
||||||
`adjustment = lambda shape: (
|
|
||||||
tf.random.uniform(shape[-1:], 0.93, 1.07),
|
|
||||||
tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
|
|
||||||
value by up to 7% up or down, then shift the result by up to 0.1
|
|
||||||
(with independent scaling and bias for each feature but shared
|
|
||||||
across all examples), and finally apply gamma and/or beta. If
|
|
||||||
`None`, no adjustment is applied. Cannot be specified if
|
|
||||||
virtual_batch_size is specified.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
References:
|
|
||||||
Batch Normalization - Accelerating Deep Network Training by Reducing
|
|
||||||
Internal Covariate Shift:
|
|
||||||
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
|
|
||||||
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
|
|
||||||
Batch Renormalization - Towards Reducing Minibatch Dependence in
|
|
||||||
Batch-Normalized Models:
|
|
||||||
[Ioffe,
|
|
||||||
2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
|
|
||||||
([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self,
|
|
||||||
axis=-1,
|
|
||||||
momentum=0.99,
|
|
||||||
epsilon=1e-3,
|
|
||||||
center=True,
|
|
||||||
scale=True,
|
|
||||||
beta_initializer=init_ops.zeros_initializer(),
|
|
||||||
gamma_initializer=init_ops.ones_initializer(),
|
|
||||||
moving_mean_initializer=init_ops.zeros_initializer(),
|
|
||||||
moving_variance_initializer=init_ops.ones_initializer(),
|
|
||||||
beta_regularizer=None,
|
|
||||||
gamma_regularizer=None,
|
|
||||||
beta_constraint=None,
|
|
||||||
gamma_constraint=None,
|
|
||||||
renorm=False,
|
|
||||||
renorm_clipping=None,
|
|
||||||
renorm_momentum=0.99,
|
|
||||||
fused=None,
|
|
||||||
trainable=True,
|
|
||||||
virtual_batch_size=None,
|
|
||||||
adjustment=None,
|
|
||||||
name=None,
|
|
||||||
**kwargs):
|
|
||||||
super(BatchNormalization, self).__init__(
|
|
||||||
axis=axis,
|
|
||||||
momentum=momentum,
|
|
||||||
epsilon=epsilon,
|
|
||||||
center=center,
|
|
||||||
scale=scale,
|
|
||||||
beta_initializer=beta_initializer,
|
|
||||||
gamma_initializer=gamma_initializer,
|
|
||||||
moving_mean_initializer=moving_mean_initializer,
|
|
||||||
moving_variance_initializer=moving_variance_initializer,
|
|
||||||
beta_regularizer=beta_regularizer,
|
|
||||||
gamma_regularizer=gamma_regularizer,
|
|
||||||
beta_constraint=beta_constraint,
|
|
||||||
gamma_constraint=gamma_constraint,
|
|
||||||
renorm=renorm,
|
|
||||||
renorm_clipping=renorm_clipping,
|
|
||||||
renorm_momentum=renorm_momentum,
|
|
||||||
fused=fused,
|
|
||||||
trainable=trainable,
|
|
||||||
virtual_batch_size=virtual_batch_size,
|
|
||||||
adjustment=adjustment,
|
|
||||||
name=name,
|
|
||||||
**kwargs)
|
|
||||||
|
|
||||||
def call(self, inputs, training=False):
|
|
||||||
return super(BatchNormalization, self).call(inputs, training=training)
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None, instructions='Use keras.layers.BatchNormalization instead. In '
|
|
||||||
'particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not '
|
|
||||||
'be used (consult the `tf.keras.layers.BatchNormalization` '
|
|
||||||
'documentation).')
|
|
||||||
@tf_export(v1=['layers.batch_normalization'])
|
|
||||||
def batch_normalization(inputs,
|
|
||||||
axis=-1,
|
|
||||||
momentum=0.99,
|
|
||||||
epsilon=1e-3,
|
|
||||||
center=True,
|
|
||||||
scale=True,
|
|
||||||
beta_initializer=init_ops.zeros_initializer(),
|
|
||||||
gamma_initializer=init_ops.ones_initializer(),
|
|
||||||
moving_mean_initializer=init_ops.zeros_initializer(),
|
|
||||||
moving_variance_initializer=init_ops.ones_initializer(),
|
|
||||||
beta_regularizer=None,
|
|
||||||
gamma_regularizer=None,
|
|
||||||
beta_constraint=None,
|
|
||||||
gamma_constraint=None,
|
|
||||||
training=False,
|
|
||||||
trainable=True,
|
|
||||||
name=None,
|
|
||||||
reuse=None,
|
|
||||||
renorm=False,
|
|
||||||
renorm_clipping=None,
|
|
||||||
renorm_momentum=0.99,
|
|
||||||
fused=None,
|
|
||||||
virtual_batch_size=None,
|
|
||||||
adjustment=None):
|
|
||||||
"""Functional interface for the batch normalization layer from_config(Ioffe et al., 2015).
|
|
||||||
|
|
||||||
Note: when training, the moving_mean and moving_variance need to be updated.
|
|
||||||
By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
|
|
||||||
need to be executed alongside the `train_op`. Also, be sure to add any
|
|
||||||
batch_normalization ops before getting the update_ops collection. Otherwise,
|
|
||||||
update_ops will be empty, and training/inference will not work properly. For
|
|
||||||
example:
|
|
||||||
|
|
||||||
```python
|
|
||||||
x_norm = tf.compat.v1.layers.batch_normalization(x, training=training)
|
|
||||||
|
|
||||||
# ...
|
|
||||||
|
|
||||||
update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
|
|
||||||
train_op = optimizer.minimize(loss)
|
|
||||||
train_op = tf.group([train_op, update_ops])
|
|
||||||
```
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: Tensor input.
|
|
||||||
axis: An `int`, the axis that should be normalized (typically the features
|
|
||||||
axis). For instance, after a `Convolution2D` layer with
|
|
||||||
`data_format="channels_first"`, set `axis=1` in `BatchNormalization`.
|
|
||||||
momentum: Momentum for the moving average.
|
|
||||||
epsilon: Small float added to variance to avoid dividing by zero.
|
|
||||||
center: If True, add offset of `beta` to normalized tensor. If False, `beta`
|
|
||||||
is ignored.
|
|
||||||
scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
|
|
||||||
next layer is linear (also e.g. `nn.relu`), this can be disabled since the
|
|
||||||
scaling can be done by the next layer.
|
|
||||||
beta_initializer: Initializer for the beta weight.
|
|
||||||
gamma_initializer: Initializer for the gamma weight.
|
|
||||||
moving_mean_initializer: Initializer for the moving mean.
|
|
||||||
moving_variance_initializer: Initializer for the moving variance.
|
|
||||||
beta_regularizer: Optional regularizer for the beta weight.
|
|
||||||
gamma_regularizer: Optional regularizer for the gamma weight.
|
|
||||||
beta_constraint: An optional projection function to be applied to the `beta`
|
|
||||||
weight after being updated by an `Optimizer` (e.g. used to implement norm
|
|
||||||
constraints or value constraints for layer weights). The function must
|
|
||||||
take as input the unprojected variable and must return the projected
|
|
||||||
variable (which must have the same shape). Constraints are not safe to use
|
|
||||||
when doing asynchronous distributed training.
|
|
||||||
gamma_constraint: An optional projection function to be applied to the
|
|
||||||
`gamma` weight after being updated by an `Optimizer`.
|
|
||||||
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
|
|
||||||
(e.g. a placeholder). Whether to return the output in training mode
|
|
||||||
(normalized with statistics of the current batch) or in inference mode
|
|
||||||
(normalized with moving statistics). **NOTE**: make sure to set this
|
|
||||||
parameter correctly, or else your training/inference will not work
|
|
||||||
properly.
|
|
||||||
trainable: Boolean, if `True` also add variables to the graph collection
|
|
||||||
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
|
|
||||||
name: String, the name of the layer.
|
|
||||||
reuse: Boolean, whether to reuse the weights of a previous layer by the same
|
|
||||||
name.
|
|
||||||
renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds extra
|
|
||||||
variables during training. The inference is the same for either value of
|
|
||||||
this parameter.
|
|
||||||
renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
|
|
||||||
scalar `Tensors` used to clip the renorm correction. The correction `(r,
|
|
||||||
d)` is used as `corrected_value = normalized_value * r + d`, with `r`
|
|
||||||
clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
|
|
||||||
dmax are set to inf, 0, inf, respectively.
|
|
||||||
renorm_momentum: Momentum used to update the moving means and standard
|
|
||||||
deviations with renorm. Unlike `momentum`, this affects training and
|
|
||||||
should be neither too small (which would add noise) nor too large (which
|
|
||||||
would give stale estimates). Note that `momentum` is still applied to get
|
|
||||||
the means and variances for inference.
|
|
||||||
fused: if `None` or `True`, use a faster, fused implementation if possible.
|
|
||||||
If `False`, use the system recommended implementation.
|
|
||||||
virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
|
|
||||||
which means batch normalization is performed across the whole batch. When
|
|
||||||
`virtual_batch_size` is not `None`, instead perform "Ghost Batch
|
|
||||||
Normalization", which creates virtual sub-batches which are each
|
|
||||||
normalized separately (with shared gamma, beta, and moving statistics).
|
|
||||||
Must divide the actual batch size during execution.
|
|
||||||
adjustment: A function taking the `Tensor` containing the (dynamic) shape of
|
|
||||||
the input tensor and returning a pair (scale, bias) to apply to the
|
|
||||||
normalized values (before gamma and beta), only during training. For
|
|
||||||
example, if axis==-1,
|
|
||||||
`adjustment = lambda shape: (
|
|
||||||
tf.random.uniform(shape[-1:], 0.93, 1.07),
|
|
||||||
tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
|
|
||||||
value by up to 7% up or down, then shift the result by up to 0.1
|
|
||||||
(with independent scaling and bias for each feature but shared
|
|
||||||
across all examples), and finally apply gamma and/or beta. If
|
|
||||||
`None`, no adjustment is applied. Cannot be specified if
|
|
||||||
virtual_batch_size is specified.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Output tensor.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if eager execution is enabled.
|
|
||||||
|
|
||||||
References:
|
|
||||||
Batch Normalization - Accelerating Deep Network Training by Reducing
|
|
||||||
Internal Covariate Shift:
|
|
||||||
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
|
|
||||||
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
|
|
||||||
Batch Renormalization - Towards Reducing Minibatch Dependence in
|
|
||||||
Batch-Normalized Models:
|
|
||||||
[Ioffe,
|
|
||||||
2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
|
|
||||||
([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
|
|
||||||
"""
|
|
||||||
layer = BatchNormalization(
|
|
||||||
axis=axis,
|
|
||||||
momentum=momentum,
|
|
||||||
epsilon=epsilon,
|
|
||||||
center=center,
|
|
||||||
scale=scale,
|
|
||||||
beta_initializer=beta_initializer,
|
|
||||||
gamma_initializer=gamma_initializer,
|
|
||||||
moving_mean_initializer=moving_mean_initializer,
|
|
||||||
moving_variance_initializer=moving_variance_initializer,
|
|
||||||
beta_regularizer=beta_regularizer,
|
|
||||||
gamma_regularizer=gamma_regularizer,
|
|
||||||
beta_constraint=beta_constraint,
|
|
||||||
gamma_constraint=gamma_constraint,
|
|
||||||
renorm=renorm,
|
|
||||||
renorm_clipping=renorm_clipping,
|
|
||||||
renorm_momentum=renorm_momentum,
|
|
||||||
fused=fused,
|
|
||||||
trainable=trainable,
|
|
||||||
virtual_batch_size=virtual_batch_size,
|
|
||||||
adjustment=adjustment,
|
|
||||||
name=name,
|
|
||||||
_reuse=reuse,
|
|
||||||
_scope=name)
|
|
||||||
return layer.apply(inputs, training=training)
|
|
||||||
|
|
||||||
|
|
||||||
|
BatchNormalization = normalization.BatchNormalization
|
||||||
|
batch_normalization = normalization.batch_normalization
|
||||||
# Aliases
|
# Aliases
|
||||||
|
|
||||||
BatchNorm = BatchNormalization
|
BatchNorm = BatchNormalization
|
||||||
|
|||||||
@ -19,448 +19,21 @@ from __future__ import absolute_import
|
|||||||
from __future__ import division
|
from __future__ import division
|
||||||
from __future__ import print_function
|
from __future__ import print_function
|
||||||
|
|
||||||
from tensorflow.python.keras import layers as keras_layers
|
from tensorflow.python.keras.legacy_tf_layers import pooling
|
||||||
from tensorflow.python.layers import base
|
|
||||||
from tensorflow.python.util import deprecation
|
|
||||||
from tensorflow.python.util.tf_export import tf_export
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.AveragePooling1D'])
|
AveragePooling1D = pooling.AveragePooling1D
|
||||||
class AveragePooling1D(keras_layers.AveragePooling1D, base.Layer):
|
average_pooling1d = pooling.average_pooling1d
|
||||||
"""Average Pooling layer for 1D inputs.
|
MaxPooling1D = pooling.MaxPooling1D
|
||||||
|
max_pooling1d = pooling.max_pooling1d
|
||||||
Arguments:
|
AveragePooling2D = pooling.AveragePooling2D
|
||||||
pool_size: An integer or tuple/list of a single integer,
|
average_pooling2d = pooling.average_pooling2d
|
||||||
representing the size of the pooling window.
|
MaxPooling2D = pooling.MaxPooling2D
|
||||||
strides: An integer or tuple/list of a single integer, specifying the
|
max_pooling2d = pooling.max_pooling2d
|
||||||
strides of the pooling operation.
|
AveragePooling3D = pooling.AveragePooling3D
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
average_pooling3d = pooling.average_pooling3d
|
||||||
Case-insensitive.
|
MaxPooling3D = pooling.MaxPooling3D
|
||||||
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
max_pooling3d = pooling.max_pooling3d
|
||||||
The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, length, channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, length)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None, **kwargs):
|
|
||||||
if strides is None:
|
|
||||||
raise ValueError('Argument `strides` must not be None.')
|
|
||||||
super(AveragePooling1D, self).__init__(
|
|
||||||
pool_size=pool_size,
|
|
||||||
strides=strides,
|
|
||||||
padding=padding,
|
|
||||||
data_format=data_format,
|
|
||||||
name=name,
|
|
||||||
**kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None, instructions='Use keras.layers.AveragePooling1D instead.')
|
|
||||||
@tf_export(v1=['layers.average_pooling1d'])
|
|
||||||
def average_pooling1d(inputs, pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None):
|
|
||||||
"""Average Pooling layer for 1D inputs.
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: The tensor over which to pool. Must have rank 3.
|
|
||||||
pool_size: An integer or tuple/list of a single integer,
|
|
||||||
representing the size of the pooling window.
|
|
||||||
strides: An integer or tuple/list of a single integer, specifying the
|
|
||||||
strides of the pooling operation.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
|
||||||
The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, length, channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, length)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
The output tensor, of rank 3.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if eager execution is enabled.
|
|
||||||
"""
|
|
||||||
layer = AveragePooling1D(pool_size=pool_size,
|
|
||||||
strides=strides,
|
|
||||||
padding=padding,
|
|
||||||
data_format=data_format,
|
|
||||||
name=name)
|
|
||||||
return layer.apply(inputs)
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.MaxPooling1D'])
|
|
||||||
class MaxPooling1D(keras_layers.MaxPooling1D, base.Layer):
|
|
||||||
"""Max Pooling layer for 1D inputs.
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
pool_size: An integer or tuple/list of a single integer,
|
|
||||||
representing the size of the pooling window.
|
|
||||||
strides: An integer or tuple/list of a single integer, specifying the
|
|
||||||
strides of the pooling operation.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
|
||||||
The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, length, channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, length)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None, **kwargs):
|
|
||||||
if strides is None:
|
|
||||||
raise ValueError('Argument `strides` must not be None.')
|
|
||||||
super(MaxPooling1D, self).__init__(
|
|
||||||
pool_size=pool_size,
|
|
||||||
strides=strides,
|
|
||||||
padding=padding,
|
|
||||||
data_format=data_format,
|
|
||||||
name=name,
|
|
||||||
**kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None, instructions='Use keras.layers.MaxPooling1D instead.')
|
|
||||||
@tf_export(v1=['layers.max_pooling1d'])
|
|
||||||
def max_pooling1d(inputs, pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None):
|
|
||||||
"""Max Pooling layer for 1D inputs.
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: The tensor over which to pool. Must have rank 3.
|
|
||||||
pool_size: An integer or tuple/list of a single integer,
|
|
||||||
representing the size of the pooling window.
|
|
||||||
strides: An integer or tuple/list of a single integer, specifying the
|
|
||||||
strides of the pooling operation.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
|
||||||
The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, length, channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, length)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
The output tensor, of rank 3.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if eager execution is enabled.
|
|
||||||
"""
|
|
||||||
layer = MaxPooling1D(pool_size=pool_size,
|
|
||||||
strides=strides,
|
|
||||||
padding=padding,
|
|
||||||
data_format=data_format,
|
|
||||||
name=name)
|
|
||||||
return layer.apply(inputs)
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.AveragePooling2D'])
|
|
||||||
class AveragePooling2D(keras_layers.AveragePooling2D, base.Layer):
|
|
||||||
"""Average pooling layer for 2D inputs (e.g. images).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
|
|
||||||
specifying the size of the pooling window.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
strides: An integer or tuple/list of 2 integers,
|
|
||||||
specifying the strides of the pooling operation.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string. The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` (default) and `channels_first` are supported.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, height, width, channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, height, width)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None, **kwargs):
|
|
||||||
if strides is None:
|
|
||||||
raise ValueError('Argument `strides` must not be None.')
|
|
||||||
super(AveragePooling2D, self).__init__(
|
|
||||||
pool_size=pool_size, strides=strides,
|
|
||||||
padding=padding, data_format=data_format, name=name, **kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None, instructions='Use keras.layers.AveragePooling2D instead.')
|
|
||||||
@tf_export(v1=['layers.average_pooling2d'])
|
|
||||||
def average_pooling2d(inputs,
|
|
||||||
pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None):
|
|
||||||
"""Average pooling layer for 2D inputs (e.g. images).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: The tensor over which to pool. Must have rank 4.
|
|
||||||
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
|
|
||||||
specifying the size of the pooling window.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
strides: An integer or tuple/list of 2 integers,
|
|
||||||
specifying the strides of the pooling operation.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string. The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` (default) and `channels_first` are supported.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, height, width, channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, height, width)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Output tensor.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if eager execution is enabled.
|
|
||||||
"""
|
|
||||||
layer = AveragePooling2D(pool_size=pool_size, strides=strides,
|
|
||||||
padding=padding, data_format=data_format,
|
|
||||||
name=name)
|
|
||||||
return layer.apply(inputs)
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.MaxPooling2D'])
|
|
||||||
class MaxPooling2D(keras_layers.MaxPooling2D, base.Layer):
|
|
||||||
"""Max pooling layer for 2D inputs (e.g. images).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
|
|
||||||
specifying the size of the pooling window.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
strides: An integer or tuple/list of 2 integers,
|
|
||||||
specifying the strides of the pooling operation.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string. The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` (default) and `channels_first` are supported.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, height, width, channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, height, width)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None, **kwargs):
|
|
||||||
if strides is None:
|
|
||||||
raise ValueError('Argument `strides` must not be None.')
|
|
||||||
super(MaxPooling2D, self).__init__(
|
|
||||||
pool_size=pool_size, strides=strides,
|
|
||||||
padding=padding, data_format=data_format, name=name, **kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None, instructions='Use keras.layers.MaxPooling2D instead.')
|
|
||||||
@tf_export(v1=['layers.max_pooling2d'])
|
|
||||||
def max_pooling2d(inputs,
|
|
||||||
pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None):
|
|
||||||
"""Max pooling layer for 2D inputs (e.g. images).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: The tensor over which to pool. Must have rank 4.
|
|
||||||
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
|
|
||||||
specifying the size of the pooling window.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
strides: An integer or tuple/list of 2 integers,
|
|
||||||
specifying the strides of the pooling operation.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string. The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` (default) and `channels_first` are supported.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, height, width, channels)` while `channels_first` corresponds to
|
|
||||||
inputs with shape `(batch, channels, height, width)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Output tensor.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if eager execution is enabled.
|
|
||||||
"""
|
|
||||||
layer = MaxPooling2D(pool_size=pool_size, strides=strides,
|
|
||||||
padding=padding, data_format=data_format,
|
|
||||||
name=name)
|
|
||||||
return layer.apply(inputs)
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.AveragePooling3D'])
|
|
||||||
class AveragePooling3D(keras_layers.AveragePooling3D, base.Layer):
|
|
||||||
"""Average pooling layer for 3D inputs (e.g. volumes).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
pool_size: An integer or tuple/list of 3 integers:
|
|
||||||
(pool_depth, pool_height, pool_width)
|
|
||||||
specifying the size of the pooling window.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
strides: An integer or tuple/list of 3 integers,
|
|
||||||
specifying the strides of the pooling operation.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string. The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` (default) and `channels_first` are supported.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, depth, height, width, channels)` while `channels_first`
|
|
||||||
corresponds to inputs with shape
|
|
||||||
`(batch, channels, depth, height, width)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None, **kwargs):
|
|
||||||
if strides is None:
|
|
||||||
raise ValueError('Argument `strides` must not be None.')
|
|
||||||
super(AveragePooling3D, self).__init__(
|
|
||||||
pool_size=pool_size, strides=strides,
|
|
||||||
padding=padding, data_format=data_format, name=name, **kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None, instructions='Use keras.layers.AveragePooling3D instead.')
|
|
||||||
@tf_export(v1=['layers.average_pooling3d'])
|
|
||||||
def average_pooling3d(inputs,
|
|
||||||
pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None):
|
|
||||||
"""Average pooling layer for 3D inputs (e.g. volumes).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: The tensor over which to pool. Must have rank 5.
|
|
||||||
pool_size: An integer or tuple/list of 3 integers:
|
|
||||||
(pool_depth, pool_height, pool_width)
|
|
||||||
specifying the size of the pooling window.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
strides: An integer or tuple/list of 3 integers,
|
|
||||||
specifying the strides of the pooling operation.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string. The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` (default) and `channels_first` are supported.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, depth, height, width, channels)` while `channels_first`
|
|
||||||
corresponds to inputs with shape
|
|
||||||
`(batch, channels, depth, height, width)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Output tensor.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if eager execution is enabled.
|
|
||||||
"""
|
|
||||||
layer = AveragePooling3D(pool_size=pool_size, strides=strides,
|
|
||||||
padding=padding, data_format=data_format,
|
|
||||||
name=name)
|
|
||||||
return layer.apply(inputs)
|
|
||||||
|
|
||||||
|
|
||||||
@tf_export(v1=['layers.MaxPooling3D'])
|
|
||||||
class MaxPooling3D(keras_layers.MaxPooling3D, base.Layer):
|
|
||||||
"""Max pooling layer for 3D inputs (e.g. volumes).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
pool_size: An integer or tuple/list of 3 integers:
|
|
||||||
(pool_depth, pool_height, pool_width)
|
|
||||||
specifying the size of the pooling window.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
strides: An integer or tuple/list of 3 integers,
|
|
||||||
specifying the strides of the pooling operation.
|
|
||||||
Can be a single integer to specify the same value for
|
|
||||||
all spatial dimensions.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string. The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` (default) and `channels_first` are supported.
|
|
||||||
`channels_last` corresponds to inputs with shape
|
|
||||||
`(batch, depth, height, width, channels)` while `channels_first`
|
|
||||||
corresponds to inputs with shape
|
|
||||||
`(batch, channels, depth, height, width)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None, **kwargs):
|
|
||||||
if strides is None:
|
|
||||||
raise ValueError('Argument `strides` must not be None.')
|
|
||||||
super(MaxPooling3D, self).__init__(
|
|
||||||
pool_size=pool_size, strides=strides,
|
|
||||||
padding=padding, data_format=data_format, name=name, **kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
@deprecation.deprecated(
|
|
||||||
date=None, instructions='Use keras.layers.MaxPooling3D instead.')
|
|
||||||
@tf_export(v1=['layers.max_pooling3d'])
|
|
||||||
def max_pooling3d(inputs,
|
|
||||||
pool_size, strides,
|
|
||||||
padding='valid', data_format='channels_last',
|
|
||||||
name=None):
|
|
||||||
"""Max pooling layer for 3D inputs (e.g.
|
|
||||||
|
|
||||||
volumes).
|
|
||||||
|
|
||||||
Arguments:
|
|
||||||
inputs: The tensor over which to pool. Must have rank 5.
|
|
||||||
pool_size: An integer or tuple/list of 3 integers: (pool_depth, pool_height,
|
|
||||||
pool_width) specifying the size of the pooling window. Can be a single
|
|
||||||
integer to specify the same value for all spatial dimensions.
|
|
||||||
strides: An integer or tuple/list of 3 integers, specifying the strides of
|
|
||||||
the pooling operation. Can be a single integer to specify the same value
|
|
||||||
for all spatial dimensions.
|
|
||||||
padding: A string. The padding method, either 'valid' or 'same'.
|
|
||||||
Case-insensitive.
|
|
||||||
data_format: A string. The ordering of the dimensions in the inputs.
|
|
||||||
`channels_last` (default) and `channels_first` are supported.
|
|
||||||
`channels_last` corresponds to inputs with shape `(batch, depth, height,
|
|
||||||
width, channels)` while `channels_first` corresponds to inputs with shape
|
|
||||||
`(batch, channels, depth, height, width)`.
|
|
||||||
name: A string, the name of the layer.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Output tensor.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: if eager execution is enabled.
|
|
||||||
"""
|
|
||||||
layer = MaxPooling3D(pool_size=pool_size, strides=strides,
|
|
||||||
padding=padding, data_format=data_format,
|
|
||||||
name=name)
|
|
||||||
return layer.apply(inputs)
|
|
||||||
|
|
||||||
# Aliases
|
# Aliases
|
||||||
|
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.AveragePooling1D"
|
path: "tensorflow.layers.AveragePooling1D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.AveragePooling1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.AveragePooling1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.AveragePooling1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.AveragePooling2D"
|
path: "tensorflow.layers.AveragePooling2D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.AveragePooling2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.AveragePooling2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.AveragePooling2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.AveragePooling3D"
|
path: "tensorflow.layers.AveragePooling3D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling3D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.AveragePooling3D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.AveragePooling3D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.AveragePooling3D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling3D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling3D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.BatchNormalization"
|
path: "tensorflow.layers.BatchNormalization"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.normalization.BatchNormalization\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.normalization.BatchNormalization\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.normalization.BatchNormalization\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.normalization.BatchNormalization\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.normalization.BatchNormalizationBase\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.normalization.BatchNormalizationBase\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.Conv1D"
|
path: "tensorflow.layers.Conv1D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,10 +1,10 @@
|
|||||||
path: "tensorflow.layers.Conv2DTranspose"
|
path: "tensorflow.layers.Conv2DTranspose"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2DTranspose\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv2DTranspose\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv2DTranspose\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv2DTranspose\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.Conv2D"
|
path: "tensorflow.layers.Conv2D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,10 +1,10 @@
|
|||||||
path: "tensorflow.layers.Conv3DTranspose"
|
path: "tensorflow.layers.Conv3DTranspose"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3DTranspose\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv3DTranspose\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv3DTranspose\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv3DTranspose\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv3D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv3D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.Conv3D"
|
path: "tensorflow.layers.Conv3D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv3D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv3D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv3D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,8 +1,8 @@
|
|||||||
path: "tensorflow.layers.Dense"
|
path: "tensorflow.layers.Dense"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.core.Dense\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.core.Dense\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.core.Dense\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.core.Dense\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,8 +1,8 @@
|
|||||||
path: "tensorflow.layers.Dropout"
|
path: "tensorflow.layers.Dropout"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.core.Dropout\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.core.Dropout\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.core.Dropout\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,8 +1,8 @@
|
|||||||
path: "tensorflow.layers.Flatten"
|
path: "tensorflow.layers.Flatten"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.core.Flatten\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.core.Flatten\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.core.Flatten\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.core.Flatten\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,6 +1,6 @@
|
|||||||
path: "tensorflow.layers.Layer"
|
path: "tensorflow.layers.Layer"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.MaxPooling1D"
|
path: "tensorflow.layers.MaxPooling1D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.MaxPooling1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.MaxPooling1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.MaxPooling1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.MaxPooling2D"
|
path: "tensorflow.layers.MaxPooling2D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.MaxPooling2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.MaxPooling2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.MaxPooling2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,9 +1,9 @@
|
|||||||
path: "tensorflow.layers.MaxPooling3D"
|
path: "tensorflow.layers.MaxPooling3D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling3D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.MaxPooling3D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.MaxPooling3D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.MaxPooling3D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling3D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling3D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,10 +1,10 @@
|
|||||||
path: "tensorflow.layers.SeparableConv1D"
|
path: "tensorflow.layers.SeparableConv1D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.convolutional.SeparableConv1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.SeparableConv1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv1D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv1D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,10 +1,10 @@
|
|||||||
path: "tensorflow.layers.SeparableConv2D"
|
path: "tensorflow.layers.SeparableConv2D"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.layers.convolutional.SeparableConv2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.SeparableConv2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv2D\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv2D\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -3,7 +3,7 @@ tf_class {
|
|||||||
is_instance: "<class \'tensorflow.lite.experimental.examples.lstm.rnn_cell.TFLiteLSTMCell\'>"
|
is_instance: "<class \'tensorflow.lite.experimental.examples.lstm.rnn_cell.TFLiteLSTMCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -3,7 +3,7 @@ tf_class {
|
|||||||
is_instance: "<class \'tensorflow.lite.experimental.examples.lstm.rnn_cell.TfLiteRNNCell\'>"
|
is_instance: "<class \'tensorflow.lite.experimental.examples.lstm.rnn_cell.TfLiteRNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -3,7 +3,7 @@ tf_class {
|
|||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -3,7 +3,7 @@ tf_class {
|
|||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.BasicRNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.BasicRNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -4,7 +4,7 @@ tf_class {
|
|||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_wrapper_impl.DeviceWrapperBase\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_wrapper_impl.DeviceWrapperBase\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -4,7 +4,7 @@ tf_class {
|
|||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_wrapper_impl.DropoutWrapperBase\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_wrapper_impl.DropoutWrapperBase\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -3,7 +3,7 @@ tf_class {
|
|||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.GRUCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.GRUCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -3,7 +3,7 @@ tf_class {
|
|||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LSTMCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LSTMCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -2,7 +2,7 @@ path: "tensorflow.nn.rnn_cell.MultiRNNCell"
|
|||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.MultiRNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.MultiRNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -1,7 +1,7 @@
|
|||||||
path: "tensorflow.nn.rnn_cell.RNNCell"
|
path: "tensorflow.nn.rnn_cell.RNNCell"
|
||||||
tf_class {
|
tf_class {
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
@ -4,7 +4,7 @@ tf_class {
|
|||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_wrapper_impl.ResidualWrapperBase\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_wrapper_impl.ResidualWrapperBase\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
|
||||||
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
|
||||||
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user