Revert "Squashed commit master (merge) to resolve conflicts"

This reverts commit 0af059c.

Author: Frightera

.github/bot_config.yml

@@ -16,3 +16,4 @@
 # 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:
 
@@ -84,7 +84,22 @@ def softmax(x, axis=-1):
     >>> layer = tf.keras.layers.Dense(32,
     ...                               activation=tf.keras.activations.softmax)
     """
-    return backend.softmax(x, axis)
+    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
 
 
 @keras_export("keras.activations.elu")
@@ -123,11 +138,11 @@ 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:
@@ -181,9 +196,9 @@ 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)
@@ -260,7 +275,7 @@ 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)
 
@@ -292,16 +307,16 @@ 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
@@ -343,8 +358,8 @@ def gelu(x, approximate=False):
         if `approximate` is `False`.
 
     Reference:
-        - [Gaussian Error Linear Units (GELUs)](https://arxiv.org/abs/1606.08415)
-    """  # noqa: E501
+      - [Gaussian Error Linear Units (GELUs)](https://arxiv.org/abs/1606.08415)
+    """
     return tf.nn.gelu(x, approximate)
 
 
@@ -397,7 +412,10 @@ def sigmoid(x):
     Returns:
         Tensor with the sigmoid activation: `1 / (1 + exp(-x))`.
     """
-    return backend.sigmoid(x)
+    output = tf.sigmoid(x)
+    # Cache the logits to use for crossentropy loss.
+    output._keras_logits = x
+    return output
 
 
 @keras_export("keras.activations.exponential")
@@ -441,11 +459,11 @@ 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)
 
@@ -517,8 +535,6 @@ 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
@@ -592,11 +608,9 @@ 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.
-        use_legacy_format: Boolean, whether to use the legacy format for
-            deserialization. Defaults to False.
+      name: The name of the activation function.
+      custom_objects: Optional `{function_name: function_obj}`
+        dictionary listing user-provided activation functions.
 
     Returns:
         Corresponding activation function.

keras/api/BUILD

@@ -5,7 +5,6 @@ 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,
-        tools = [":" + api_gen_binary_target],
+        exec_tools = [":" + api_gen_binary_target],
         visibility = ["//visibility:public"],
     )
 

keras/api/golden/BUILD

@@ -1,7 +1,6 @@
 # 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

@@ -36,10 +36,6 @@ 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

@@ -38,10 +38,6 @@ 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

@@ -37,10 +37,6 @@ 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

@@ -37,10 +37,6 @@ 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

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