update name

04-Linear-Regression/main.py

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+import  tensorflow as tf
+import  numpy as np
+from    tensorflow import keras
+import  os
+
+
+class Regressor(keras.layers.Layer):
+
+    def __init__(self):
+        super(Regressor, self).__init__()
+
+        # here must specify shape instead of tensor !
+        # name here is meanless !
+        # [dim_in, dim_out]
+        self.w = self.add_variable('meanless-name', [13, 1])
+        # [dim_out]
+        self.b = self.add_variable('meanless-name', [1])
+
+        print(self.w.shape, self.b.shape)
+        print(type(self.w), tf.is_tensor(self.w), self.w.name)
+        print(type(self.b), tf.is_tensor(self.b), self.b.name)
+
+
+    def call(self, x):
+
+        x = tf.matmul(x, self.w) + self.b
+
+        return x
+
+def main():
+
+    tf.random.set_seed(22)
+    np.random.seed(22)
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+    assert tf.__version__.startswith('2.')
+
+
+    (x_train, y_train), (x_val, y_val) = keras.datasets.boston_housing.load_data()
+    #
+    x_train, x_val = x_train.astype(np.float32), x_val.astype(np.float32)
+    # (404, 13) (404,) (102, 13) (102,)
+    print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)
+    # Here has two mis-leading issues:
+    # 1. (x_train, y_train) cant be written as [x_train, y_train]
+    # 2.
+    db_train = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(64)
+    db_val = tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(102)
+
+
+    model = Regressor()
+    criteon = keras.losses.MeanSquaredError()
+    optimizer = keras.optimizers.Adam(learning_rate=1e-2)
+
+    for epoch in range(200):
+
+        for step, (x, y) in enumerate(db_train):
+
+            with tf.GradientTape() as tape:
+                # [b, 1]
+                logits = model(x)
+                # [b]
+                logits = tf.squeeze(logits, axis=1)
+                # [b] vs [b]
+                loss = criteon(y, logits)
+
+            grads = tape.gradient(loss, model.trainable_variables)
+            optimizer.apply_gradients(zip(grads, model.trainable_variables))
+
+        print(epoch, 'loss:', loss.numpy())
+
+
+        if epoch % 10 == 0:
+
+            for x, y in db_val:
+                # [b, 1]
+                logits = model(x)
+                # [b]
+                logits = tf.squeeze(logits, axis=1)
+                # [b] vs [b]
+                loss = criteon(y, logits)
+
+                print(epoch, 'val loss:', loss.numpy())
+
+
+
+
+
+if __name__ == '__main__':
+    main()