added tabular data example

tabular/dnn_housing.py

@@ -0,0 +1,56 @@
+# from fastestimator.pipeline.static.preprocess import Reshape
+from fastestimator.estimator.estimator import Estimator
+from fastestimator.pipeline.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras import layers
+import tensorflow as tf
+import numpy as np
+
+class Network:
+    def __init__(self):
+        self.model = self.create_dnn()
+        self.optimizer = tf.optimizers.Adam(learning_rate=0.1)
+        self.loss = tf.losses.MeanSquaredError()
+
+    def train_op(self, batch):
+        with tf.GradientTape() as tape:
+            predictions = self.model(batch["x"])
+            loss = self.loss(batch["y"], predictions)
+        gradients = tape.gradient(loss, self.model.trainable_variables)
+        self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))
+        return predictions, loss
+
+    def eval_op(self, batch):
+        predictions = self.model(batch["x"], training=False)
+        loss = self.loss(batch["y"], predictions)
+        return predictions, loss
+
+    def create_dnn(self):
+        model = tf.keras.Sequential()
+        model.add(layers.Dense(10, activation="relu"))
+        model.add(layers.Dropout(0.5))
+        model.add(layers.Dense(10, activation="relu"))
+        model.add(layers.Dropout(0.5))
+        model.add(layers.Dense(10, activation="relu"))
+        model.add(layers.Dropout(0.5))
+        model.add(layers.Dense(1, activation="linear"))
+        return model
+
+def get_estimator(epochs=30, batch_size=32):
+
+    (x_train, y_train), (x_eval, y_eval) = tf.keras.datasets.boston_housing.load_data()
+    scaler = StandardScaler()
+    x_train = scaler.fit_transform(x_train)
+    x_eval = scaler.transform(x_eval)
+
+    pipeline = Pipeline(batch_size=batch_size,
+                        feature_name=["x", "y"],
+                        train_data={"x": x_train, "y": y_train},
+                        validation_data={"x": x_eval, "y": y_eval},
+                        transform_train= [[], []])
+
+    estimator = Estimator(network= Network(),
+                          pipeline=pipeline,
+                          epochs= epochs,
+                          log_steps=10)
+    return estimator