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for image_batch, label_batch in train_generator:
  break
image_batch.shape, label_batch.shape

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print (train_generator.class_indices)

labels = '\n'.join(sorted(train_generator.class_indices.keys()))

with open('labels.txt', 'w') as f:
  f.write(labels)

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IMG_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)

# Create the base model from the pre-trained model MobileNet V2
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
                                              include_top=False, 
                                              weights='imagenet')

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model = tf.keras.Sequential([
  base_model,
  tf.keras.layers.Conv2D(32, 3, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.GlobalAveragePooling2D(),
  tf.keras.layers.Dense(5, activation='softmax')
])

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model.compile(optimizer=tf.keras.optimizers.Adam(), 
              loss='categorical_crossentropy', 
              metrics=['accuracy'])

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epochs = 10

history = model.fit(train_generator, 
                    epochs=epochs, 
                    validation_data=val_generator)

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acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')

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# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))

# Fine tune from this layer onwards
fine_tune_at = 100

# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
  layer.trainable =  False

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model.compile(loss='categorical_crossentropy',
              optimizer = tf.keras.optimizers.Adam(1e-5),
              metrics=['accuracy'])

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saved_model_dir = 'save/fine_tuning'
tf.saved_model.save(model, saved_model_dir)

converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()

with open('model.tflite', 'wb') as f:
  f.write(tflite_model)
  
# download the model & labels