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ML/TensorFlow/Basics/tutorial9-custom-layers.py

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+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.datasets import mnist
+
+# To Avoid GPU errors
+physical_devices = tf.config.list_physical_devices("GPU")
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train = x_train.reshape(-1, 28 * 28).astype("float32") / 255.0
+x_test = x_test.reshape(-1, 28 * 28).astype("float32") / 255.0
+
+
+class Dense(layers.Layer):
+    def __init__(self, units, input_dim):
+        super(Dense, self).__init__()
+        self.w = self.add_weight(
+            name="w",
+            shape=(input_dim, units),
+            initializer="random_normal",
+            trainable=True,
+        )
+        self.b = self.add_weight(
+            name="b", shape=(units,), initializer="zeros", trainable=True
+        )
+
+    def call(self, inputs):
+        return tf.matmul(inputs, self.w) + self.b
+
+
+class Dense(layers.Layer):
+    def __init__(self, units):
+        super(Dense, self).__init__()
+        self.units = units
+
+    def build(self, input_shape):
+        self.w = self.add_weight(
+            name="w",
+            shape=(input_shape[-1], self.units),
+            initializer="random_normal",
+            trainable=True,
+        )
+        self.b = self.add_weight(
+            name="b", shape=(self.units,), initializer="random_normal", trainable=True,
+        )
+
+    def call(self, inputs):
+        return tf.matmul(inputs, self.w) + self.b
+
+
+class MyReLU(layers.Layer):
+    def __init__(self):
+        super(MyReLU, self).__init__()
+
+    def call(self, x):
+        return tf.math.maximum(x, 0)
+
+
+class MyModel(Model):  # model.fit, model.evalute, model.predict
+    def __init__(self, num_classes=10):
+        super(MyModel, self).__init__()
+        self.dense1 = Dense(64)
+        self.dense2 = Dense(num_classes)
+        self.relu = MyReLU()
+
+        # self.dense1 = layers.Dense(64)
+        # self.dense3 = layers.Dense(num_classes)
+
+    def call(self, x):
+        x = self.relu(self.dense1(x))
+        return self.dense2(x)
+
+
+model = MyModel()
+model.compile(
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    optimizer=keras.optimizers.Adam(),
+    metrics=["accuracy"],
+)
+
+model.fit(x_train, y_train, batch_size=32, epochs=2, verbose=2)
+model.evaluate(x_test, y_test, batch_size=32, verbose=2)