Squashed commit master (merge) to resolve conflicts

Author: Frightera

.github/bot_config.yml

@@ -16,4 +16,3 @@
 # A list of assignees
 assignees:
    - tilakrayal
-   - sushreebarsa

keras/activations.py

@@ -63,12 +63,12 @@ def softmax(x, axis=-1):
     The input values in are the log-odds of the resulting probability.
 
     Args:
-      x : Input tensor.
-      axis: Integer, axis along which the softmax normalization is applied.
+        x : Input tensor.
+        axis: Integer, axis along which the softmax normalization is applied.
 
     Returns:
-      Tensor, output of softmax transformation (all values are non-negative
-        and sum to 1).
+        Tensor, output of softmax transformation (all values are non-negative
+            and sum to 1).
 
     Examples:
 
@@ -84,22 +84,7 @@ def softmax(x, axis=-1):
     >>> layer = tf.keras.layers.Dense(32,
     ...                               activation=tf.keras.activations.softmax)
     """
-    if x.shape.rank <= 1:
-        raise ValueError(
-            f"Cannot apply softmax to a tensor that is 1D. Received input: {x}"
-        )
-
-    if isinstance(axis, int):
-        output = tf.nn.softmax(x, axis=axis)
-    else:
-        # nn.softmax does not support tuple axis.
-        numerator = tf.exp(x - tf.reduce_max(x, axis=axis, keepdims=True))
-        denominator = tf.reduce_sum(numerator, axis=axis, keepdims=True)
-        output = numerator / denominator
-
-    # Cache the logits to use for crossentropy loss.
-    output._keras_logits = x
-    return output
+    return backend.softmax(x, axis)
 
 
 @keras_export("keras.activations.elu")
@@ -138,11 +123,11 @@ def elu(x, alpha=1.0):
     Args:
         x: Input tensor.
         alpha: A scalar, slope of negative section. `alpha` controls the value
-          to which an ELU saturates for negative net inputs.
+            to which an ELU saturates for negative net inputs.
 
     Returns:
         The exponential linear unit (ELU) activation function: `x` if `x > 0`
-          and `alpha * (exp(x) - 1)` if `x < 0`.
+            and `alpha * (exp(x) - 1)` if `x < 0`.
 
 
     Reference:
@@ -196,9 +181,9 @@ def selu(x):
 
     Notes:
         - To be used together with the
-          `tf.keras.initializers.LecunNormal` initializer.
+            `tf.keras.initializers.LecunNormal` initializer.
         - To be used together with the dropout variant
-          `tf.keras.layers.AlphaDropout` (not regular dropout).
+            `tf.keras.layers.AlphaDropout` (not regular dropout).
 
     References:
         - [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515)
@@ -275,7 +260,7 @@ def swish(x):
         The swish activation applied to `x` (see reference paper for details).
 
     Reference:
-      - [Ramachandran et al., 2017](https://arxiv.org/abs/1710.05941)
+        - [Ramachandran et al., 2017](https://arxiv.org/abs/1710.05941)
     """
     return tf.nn.silu(x)
 
@@ -307,16 +292,16 @@ def relu(x, alpha=0.0, max_value=None, threshold=0.0):
     Args:
         x: Input `tensor` or `variable`.
         alpha: A `float` that governs the slope for values lower than the
-          threshold.
+            threshold.
         max_value: A `float` that sets the saturation threshold (the largest
-          value the function will return).
+            value the function will return).
         threshold: A `float` giving the threshold value of the activation
-          function below which values will be damped or set to zero.
+            function below which values will be damped or set to zero.
 
     Returns:
-        A `Tensor` representing the input tensor,
-        transformed by the relu activation function.
-        Tensor will be of the same shape and dtype of input `x`.
+        A `Tensor` representing the input tensor, transformed by the relu
+        activation function. Tensor will be of the same shape and dtype of
+        input `x`.
     """
     return backend.relu(
         x, alpha=alpha, max_value=max_value, threshold=threshold
@@ -358,8 +343,8 @@ def gelu(x, approximate=False):
         if `approximate` is `False`.
 
     Reference:
-      - [Gaussian Error Linear Units (GELUs)](https://arxiv.org/abs/1606.08415)
-    """
+        - [Gaussian Error Linear Units (GELUs)](https://arxiv.org/abs/1606.08415)
+    """  # noqa: E501
     return tf.nn.gelu(x, approximate)
 
 
@@ -412,10 +397,7 @@ def sigmoid(x):
     Returns:
         Tensor with the sigmoid activation: `1 / (1 + exp(-x))`.
     """
-    output = tf.sigmoid(x)
-    # Cache the logits to use for crossentropy loss.
-    output._keras_logits = x
-    return output
+    return backend.sigmoid(x)
 
 
 @keras_export("keras.activations.exponential")
@@ -459,11 +441,11 @@ def hard_sigmoid(x):
         x: Input tensor.
 
     Returns:
-      The hard sigmoid activation, defined as:
+        The hard sigmoid activation, defined as:
 
-        - `if x < -2.5: return 0`
-        - `if x > 2.5: return 1`
-        - `if -2.5 <= x <= 2.5: return 0.2 * x + 0.5`
+            - `if x < -2.5: return 0`
+            - `if x > 2.5: return 1`
+            - `if -2.5 <= x <= 2.5: return 0.2 * x + 0.5`
     """
     return backend.hard_sigmoid(x)
 
@@ -535,6 +517,8 @@ def serialize(activation, use_legacy_format=False):
 
     Args:
         activation : Function object.
+        use_legacy_format: Boolean, whether to use the legacy format for
+            serialization. Defaults to False.
 
     Returns:
         String denoting the name attribute of the input function
@@ -608,9 +592,11 @@ def deserialize(name, custom_objects=None, use_legacy_format=False):
     """Returns activation function given a string identifier.
 
     Args:
-      name: The name of the activation function.
-      custom_objects: Optional `{function_name: function_obj}`
-        dictionary listing user-provided activation functions.
+        name: The name of the activation function.
+        custom_objects: Optional `{function_name: function_obj}`
+            dictionary listing user-provided activation functions.
+        use_legacy_format: Boolean, whether to use the legacy format for
+            deserialization. Defaults to False.
 
     Returns:
         Corresponding activation function.

keras/api/BUILD

@@ -5,6 +5,7 @@ load("//keras/api:api_gen.bzl", "gen_api_init_files")
 load("//keras/api:api_init_files.bzl", "KERAS_API_INIT_FILES", "KERAS_API_INIT_FILES_V1")
 
 package(
+    # copybara:uncomment default_applicable_licenses = ["//keras:license"],
     default_visibility = [
         "//keras:friends",
         "//third_party/py/tensorflow:__subpackages__",

keras/api/api_gen.bzl

@@ -119,7 +119,7 @@ def gen_api_init_files(
             _make_cmd(api_gen_binary_target, flags, loading = "default"),
         ),
         srcs = srcs,
-        exec_tools = [":" + api_gen_binary_target],
+        tools = [":" + api_gen_binary_target],
         visibility = ["//visibility:public"],
     )
 

keras/api/golden/BUILD

@@ -1,6 +1,7 @@
 # TensorFlow API backwards compatibility test goldens.
 
 package(
+    # copybara:uncomment default_applicable_licenses = ["//keras:license"],
     default_visibility = ["//visibility:public"],
     licenses = ["notice"],  # Apache 2.0
 )

keras/api/golden/v1/tensorflow.keras.-model.pbtxt

@@ -36,6 +36,10 @@ tf_class {
     name: "dynamic"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "enable_tune_steps_per_execution"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "inbound_nodes"
     mtype: "<type \'property\'>"

keras/api/golden/v1/tensorflow.keras.-sequential.pbtxt

@@ -38,6 +38,10 @@ tf_class {
     name: "dynamic"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "enable_tune_steps_per_execution"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "inbound_nodes"
     mtype: "<type \'property\'>"

keras/api/golden/v1/tensorflow.keras.experimental.-linear-model.pbtxt

@@ -37,6 +37,10 @@ tf_class {
     name: "dynamic"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "enable_tune_steps_per_execution"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "inbound_nodes"
     mtype: "<type \'property\'>"

keras/api/golden/v1/tensorflow.keras.experimental.-wide-deep-model.pbtxt

@@ -37,6 +37,10 @@ tf_class {
     name: "dynamic"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "enable_tune_steps_per_execution"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "inbound_nodes"
     mtype: "<type \'property\'>"

keras/api/golden/v1/tensorflow.keras.models.-linear-model.pbtxt

@@ -37,6 +37,10 @@ tf_class {
     name: "dynamic"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "enable_tune_steps_per_execution"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "inbound_nodes"
     mtype: "<type \'property\'>"