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ML/TensorFlow/Basics/tutorial11-transfer-learning.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
+import tensorflow_hub as hub
+
+# To Avoid GPU errors
+physical_devices = tf.config.list_physical_devices("GPU")
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+# ================================================ #
+#                  Pretrained-Model                #
+# ================================================ #
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train = x_train.reshape(-1, 28, 28, 1).astype("float32") / 255.0
+x_test = x_test.reshape(-1, 28, 28, 1).astype("float32") / 255.0
+
+model = keras.models.load_model("pretrained")
+
+# Freeze all model layer weights
+model.trainable = False
+
+# Can also set trainable for specific layers
+for layer in model.layers:
+    # assert should be true because of one-liner above
+    assert layer.trainable == False
+    layer.trainable = False
+
+print(model.summary())  # for finding base input and output
+base_inputs = model.layers[0].input
+base_output = model.layers[-2].output
+output = layers.Dense(10)(base_output)
+new_model = keras.Model(base_inputs, output)
+
+# This model is actually identical to model we
+# loaded (this is just for demonstration and
+# and not something you would do in practice).
+print(new_model.summary())
+
+# As usual we do compile and fit, this time on new_model
+new_model.compile(
+    optimizer=keras.optimizers.Adam(),
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    metrics=["accuracy"],
+)
+
+new_model.fit(x_train, y_train, batch_size=32, epochs=3, verbose=2)
+
+# =================================================== #
+#                Pretrained Keras Model               #
+# =================================================== #
+
+# Random data for demonstration (3 examples w. 3 classes)
+x = tf.random.normal(shape=(3, 299, 299, 3))
+y = tf.constant([0, 1, 2])
+
+model = keras.applications.InceptionV3(include_top=True)
+print(model.summary())
+
+# for input you can also do model.input,
+# then for base_outputs you can obviously
+# choose other than simply removing the last one :)
+base_inputs = model.layers[0].input
+base_outputs = model.layers[-2].output
+classifier = layers.Dense(3)(base_outputs)
+new_model = keras.Model(inputs=base_inputs, outputs=classifier)
+new_model.compile(
+    optimizer=keras.optimizers.Adam(),
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    metrics=["accuracy"],
+)
+
+print(new_model.summary())
+new_model.fit(x, y, epochs=15, verbose=2)
+
+# ================================================= #
+#                Pretrained Hub Model               #
+# ================================================= #
+
+# Random data for demonstration (3 examples w. 3 classes)
+x = tf.random.normal(shape=(3, 299, 299, 3))
+y = tf.constant([0, 1, 2])
+
+url = "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/4"
+
+base_model = hub.KerasLayer(url, input_shape=(299, 299, 3))
+model = keras.Sequential(
+    [
+        base_model,
+        layers.Dense(128, activation="relu"),
+        layers.Dense(64, activation="relu"),
+        layers.Dense(10),
+    ]
+)
+
+model.compile(
+    optimizer=keras.optimizers.Adam(),
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    metrics=["accuracy"],
+)
+
+model.fit(x, y, batch_size=32, epochs=15, verbose=2)