PR submission feedback

PR review: alecglassford feedback
 - Move l = tf.keras.layers -> Dense = tf.keras.layers.Dense
 - Add explanation for how to use Hyper parameter tuning with Keras
 - Update notebooks

census/tf-keras/trainer/model.py

@@ -63,15 +63,15 @@ def create_keras_model(input_dim, learning_rate):
   Returns:
     The compiled Keras model (still needs to be trained)
   """
-  l = tf.keras.layers
+  Dense = tf.keras.layers.Dense
   model = tf.keras.Sequential(
       [
-          l.Dense(100, activation=tf.nn.relu, kernel_initializer='uniform',
+          Dense(100, activation=tf.nn.relu, kernel_initializer='uniform',
                   input_shape=(input_dim,)),
-          l.Dense(75, activation=tf.nn.relu),
-          l.Dense(50, activation=tf.nn.relu),
-          l.Dense(25, activation=tf.nn.relu),
-          l.Dense(1, activation=tf.nn.sigmoid)
+          Dense(75, activation=tf.nn.relu),
+          Dense(50, activation=tf.nn.relu),
+          Dense(25, activation=tf.nn.relu),
+          Dense(1, activation=tf.nn.sigmoid)
       ])
 
   # Custom Optimizer:

census/tf-keras/trainer/task.py

@@ -116,8 +116,7 @@ def train_and_evaluate(args):
       validation_data=validation_dataset,
       validation_steps=1,
       verbose=1,
-      callbacks=[lr_decay_cb, tensorboard_cb]
-  )
+      callbacks=[lr_decay_cb, tensorboard_cb])
 
   export_path = tf.contrib.saved_model.save_keras_model(
       keras_model, os.path.join(args.job_dir, 'keras_export'))