Improve docs for consistency as Markdown format (keras-team#12533)

examples/lstm_stateful.py

@@ -86,7 +86,7 @@ def gen_uniform_amp(amp=1, xn=10000):
     -amp and +amp
     and of length xn
 
-    Arguments:
+    # Arguments
         amp: maximum/minimum range of uniform data
         xn: length of series
     """

keras/activations.py

@@ -175,7 +175,7 @@ def hard_sigmoid(x):
 def exponential(x):
     """Exponential (base e) activation function.
 
-    # Arguments:
+    # Arguments
         x: Input tensor.
 
     # Returns

keras/engine/network.py

@@ -960,7 +960,7 @@ def from_config(cls, config, custom_objects=None):
         def add_unprocessed_node(layer, node_data):
             """Add node to layer list
 
-            Args:
+            # Arguments
                 layer: layer object
                 node_data: Node data specifying layer call
             """
@@ -972,11 +972,11 @@ def add_unprocessed_node(layer, node_data):
         def process_node(layer, node_data):
             """Reconstruct node by linking to inbound layers
 
-            Args:
+            # Arguments
                 layer: Layer to process
                 node_data: List of layer configs
 
-            Raises:
+            # Raises
                 ValueError: For incorrect layer config
                 LookupError: If layer required is not found
             """

keras/engine/training_utils.py

@@ -621,18 +621,18 @@ def is_sequence(seq):
 def should_run_validation(validation_freq, epoch):
     """Checks if validation should be run this epoch.
 
-    Arguments:
-    validation_freq: Integer or list. If an integer, specifies how many training
-      epochs to run before a new validation run is performed. If a list,
-      specifies the epochs on which to run validation.
-    epoch: Integer, the number of the training epoch just completed.
-
-    Returns:
-    Bool, True if validation should be run.
-
-    Raises:
-    ValueError: if `validation_freq` is an Integer and less than 1, or if
-    it is neither an Integer nor a Sequence.
+    # Arguments
+        validation_freq: Integer or list. If an integer, specifies how many training
+          epochs to run before a new validation run is performed. If a list,
+          specifies the epochs on which to run validation.
+        epoch: Integer, the number of the training epoch just completed.
+
+    # Returns
+        Bool, True if validation should be run.
+
+    # Raises
+        ValueError: if `validation_freq` is an Integer and less than 1, or if
+        it is neither an Integer nor a Sequence.
     """
     # `epoch` is 0-indexed internally but 1-indexed in the public API.
     one_indexed_epoch = epoch + 1

keras/utils/test_utils.py

@@ -153,14 +153,14 @@ class tf_file_io_proxy(object):
     recommended to use method `get_filepath(filename)` in tests to make them
     pass with and without a real GCS bucket during testing. See example below.
 
-    Arguments:
+    # Arguments
         file_io_module: String identifier of the file_io module import to patch. E.g
             'keras.engine.saving.tf_file_io'
         bucket_name: String identifier of *a real* GCS bucket (with or without the
             'gs://' prefix). A bucket name provided with argument precedes what is
             specified using the GCS_TEST_BUCKET environment variable.
 
-    Example:
+    # Example
     ```python
     model = Sequential()
     model.add(Dense(2, input_shape=(3,)))

keras/utils/vis_utils.py

@@ -169,7 +169,7 @@ def plot_model(model,
         expand_nested: whether to expand nested models into clusters.
         dpi: dot DPI.
 
-    # Returns:
+    # Returns
         A Jupyter notebook Image object if Jupyter is installed.
         This enables in-line display of the model plots in notebooks.
     """