Removed from_dataset(), let make_dataset() return input_fn as provider of dataset

samples/cookbook/regression/automobile_data.py

@@ -110,11 +110,10 @@ def load_data(y_name="price", train_fraction=0.7, seed=None):
   return (x_train, y_train), (x_test, y_test)
 
 
-def from_dataset(dataset): return lambda: dataset.make_one_shot_iterator().get_next()
-
-
 def make_dataset(batch_sz, x, y=None, shuffle=False, shuffle_buffer_size=1000):
     """Create a slice Dataset from a pandas DataFrame and labels"""
+
+    def input_fn():
         if y is not None:
             dataset = tf.data.Dataset.from_tensor_slices((dict(x), y))
         else:
@@ -123,4 +122,6 @@ def make_dataset(batch_sz, x, y=None, shuffle=False, shuffle_buffer_size=1000):
             dataset = dataset.shuffle(shuffle_buffer_size).batch(batch_sz).repeat()
         else:
             dataset = dataset.batch(batch_sz)
-    return dataset
+        return dataset.make_one_shot_iterator().get_next()
+
+    return input_fn

samples/cookbook/regression/custom_regression.py

@@ -101,11 +101,11 @@ def main(argv):
   train_y /= args.price_norm_factor
   test_y /= args.price_norm_factor
 
-  # Build the training dataset.
-  train = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
+  # Provide the training input dataset.
+  train_input_fn = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
 
   # Build the validation dataset.
-  test = automobile_data.make_dataset(args.batch_size, test_x, test_y)
+  test_input_fn = automobile_data.make_dataset(args.batch_size, test_x, test_y)
 
   # The first way assigns a unique weight to each category. To do this you must
   # specify the category's vocabulary (values outside this specification will
@@ -144,10 +144,10 @@ def main(argv):
       })
 
   # Train the model.
-  model.train(input_fn=automobile_data.from_dataset(train), steps=args.train_steps)
+  model.train(input_fn=train_input_fn, steps=args.train_steps)
 
   # Evaluate how the model performs on data it has not yet seen.
-  eval_result = model.evaluate(input_fn=automobile_data.from_dataset(test))
+  eval_result = model.evaluate(input_fn=test_input_fn)
 
   # Print the Root Mean Square Error (RMSE).
   print("\n" + 80 * "*")

samples/cookbook/regression/dnn_regression.py

@@ -41,11 +41,11 @@ def main(argv):
   train_y /= args.price_norm_factor
   test_y /= args.price_norm_factor
 
-  # Build the training dataset.
-  train = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
+  # Provide the training input dataset.
+  train_input_fn = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
 
-  # Build the validation dataset.
-  test = automobile_data.make_dataset(args.batch_size, test_x, test_y)
+  # Provide the validation input dataset.
+  test_input_fn = automobile_data.make_dataset(args.batch_size, test_x, test_y)
 
   # Use the same categorical columns as in `linear_regression_categorical`
   body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
@@ -74,10 +74,10 @@ def main(argv):
 
   # Train the model.
   # By default, the Estimators log output every 100 steps.
-  model.train(input_fn=automobile_data.from_dataset(train), steps=args.train_steps)
+  model.train(input_fn=train_input_fn, steps=args.train_steps)
 
   # Evaluate how the model performs on data it has not yet seen.
-  eval_result = model.evaluate(input_fn=automobile_data.from_dataset(test))
+  eval_result = model.evaluate(input_fn=test_input_fn)
 
   # The evaluation returns a Python dictionary. The "average_loss" key holds the
   # Mean Squared Error (MSE).

samples/cookbook/regression/linear_regression.py

@@ -42,11 +42,11 @@ def main(argv):
   train_y /= args.price_norm_factor
   test_y /= args.price_norm_factor
 
-  # Build the training dataset.
-  train = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
+  # Provide the training input dataset.
+  train_input_fn = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
 
-  # Build the validation dataset.
-  test = automobile_data.make_dataset(args.batch_size, test_x, test_y)
+  # Provide the validation input dataset.
+  test_input_fn = automobile_data.make_dataset(args.batch_size, test_x, test_y)
 
   feature_columns = [
       # "curb-weight" and "highway-mpg" are numeric columns.
@@ -59,10 +59,10 @@ def main(argv):
 
   # Train the model.
   # By default, the Estimators log output every 100 steps.
-  model.train(input_fn=automobile_data.from_dataset(train), steps=args.train_steps)
+  model.train(input_fn=train_input_fn, steps=args.train_steps)
 
   # Evaluate how the model performs on data it has not yet seen.
-  eval_result = model.evaluate(input_fn=automobile_data.from_dataset(test))
+  eval_result = model.evaluate(input_fn=test_input_fn)
 
   # The evaluation returns a Python dictionary. The "average_loss" key holds the
   # Mean Squared Error (MSE).
@@ -79,8 +79,9 @@ def main(argv):
       "highway-mpg": np.array([30, 40])
   }
 
-  predict = automobile_data.make_dataset(1, input_dict)
-  predict_results = model.predict(input_fn=automobile_data.from_dataset(predict))
+  # Provide the predict input dataset.
+  predict_input_fn = automobile_data.make_dataset(1, input_dict)
+  predict_results = model.predict(input_fn=predict_input_fn)
 
   # Print the prediction results.
   print("\nPrediction results:")

samples/cookbook/regression/linear_regression_categorical.py

@@ -41,11 +41,11 @@ def main(argv):
   train_y /= args.price_norm_factor
   test_y /= args.price_norm_factor
 
-  # Build the training dataset.
-  train = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
+  # Provide the training input dataset.
+  train_input_fn = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
 
-  # Build the validation dataset.
-  test = automobile_data.make_dataset(args.batch_size, test_x, test_y)
+  # Provide the validation input dataset.
+  test_input_fn = automobile_data.make_dataset(args.batch_size, test_x, test_y)
 
   # The following code demonstrates two of the ways that `feature_columns` can
   # be used to build a model with categorical inputs.
@@ -83,10 +83,10 @@ def main(argv):
 
   # Train the model.
   # By default, the Estimators log output every 100 steps.
-  model.train(input_fn=automobile_data.from_dataset(train), steps=args.train_steps)
+  model.train(input_fn=train_input_fn, steps=args.train_steps)
 
   # Evaluate how the model performs on data it has not yet seen.
-  eval_result = model.evaluate(input_fn=automobile_data.from_dataset(test))
+  eval_result = model.evaluate(input_fn=test_input_fn)
 
   # The evaluation returns a Python dictionary. The "average_loss" key holds the
   # Mean Squared Error (MSE).