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ML/TensorFlow/Basics/tutorial10-save-model.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
+
+# Alright, so here have some code which should feel familiar from previous tutorials,
+# here is what we want to cover
+# 1. How to save and load model weights
+# 2. Save and loading entire model (Serializing model)
+#   - Saves weights
+#   - Model architecture
+#   - Training Configuration (model.compile())
+#   - Optimizer and states
+
+model1 = keras.Sequential([layers.Dense(64, activation="relu"), layers.Dense(10)])
+
+inputs = keras.Input(784)
+x = layers.Dense(64, activation="relu")(inputs)
+outputs = layers.Dense(10)(x)
+model2 = keras.Model(inputs=inputs, outputs=outputs)
+
+
+class MyModel(keras.Model):
+    def __init__(self):
+        super(MyModel, self).__init__()
+        self.dense1 = layers.Dense(64, activation="relu")
+        self.dense2 = layers.Dense(10)
+
+    def call(self, input_tensor):
+        x = tf.nn.relu(self.dense1(input_tensor))
+        return self.dense2(x)
+
+
+# SavedModel format or HDF5 format
+model3 = MyModel()
+# model = keras.models.load_model('saved_model/')
+# model.load_weights('checkpoint_folder/')
+
+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)
+# model.save_weights('checkpoint_folder/')
+model.save("saved_model/")