frogermcs

tflite_interpreter.resize_tensor_input(input_details[0]['index'], (32, 224, 224, 3))
tflite_interpreter.resize_tensor_input(output_details[0]['index'], (32, 5))
tflite_interpreter.allocate_tensors()

input_details = tflite_interpreter.get_input_details()
output_details = tflite_interpreter.get_output_details()

print("== Input details ==")
print("shape:", input_details[0]['shape'])
print("\n== Output details ==")

frogermcs

tflite_interpreter = tf.lite.Interpreter(model_path=TFLITE_MODEL)

input_details = tflite_interpreter.get_input_details()
output_details = tflite_interpreter.get_output_details()

print("== Input details ==")
print("shape:", input_details[0]['shape'])
print("type:", input_details[0]['dtype'])
print("\n== Output details ==")
print("shape:", output_details[0]['shape'])

frogermcs

!mkdir "tflite_models"
TFLITE_MODEL = "tflite_models/flowers.tflite"
TFLITE_QUANT_MODEL = "tflite_models/flowers_quant.tflite"

# Get the concrete function from the Keras model.
run_model = tf.function(lambda x : flowers_model(x))

# Save the concrete function.
concrete_func = run_model.get_concrete_function(
    tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype)

frogermcs

# Get predictions for each image
predicted_ids = np.argmax(tf_model_predictions, axis=-1)
predicted_labels = dataset_labels[predicted_ids]

# Print images batch and labels predictions
plt.figure(figsize=(10,9))
plt.subplots_adjust(hspace=0.5)
for n in range(30):
  plt.subplot(6,5,n+1)
  plt.imshow(val_image_batch[n])

frogermcs

# Get images and labels batch from validation dataset generator
val_image_batch, val_label_batch = next(iter(valid_generator))
true_label_ids = np.argmax(val_label_batch, axis=-1)
print("Validation batch shape:", val_image_batch.shape)
# >> Validation batch shape: (32, 224, 224, 3)

# Get predictions for images batch
tf_model_predictions = flowers_model.predict(val_image_batch)
print("Prediction results shape:", tf_model_predictions.shape)
# >> Prediction results shape: (32, 5)

frogermcs

flowers_model = tf.keras.experimental.load_from_saved_model(FLOWERS_SAVED_MODEL, 
                                                            custom_objects={'KerasLayer':hub.KerasLayer})

frogermcs

FLOWERS_SAVED_MODEL = "saved_models/flowers"
tf.keras.experimental.export_saved_model(model, FLOWERS_SAVED_MODEL)

frogermcs

steps_per_epoch = np.ceil(train_generator.samples/train_generator.batch_size)
val_steps_per_epoch = np.ceil(valid_generator.samples/valid_generator.batch_size)

hist = model.fit(
    train_generator, 
    epochs=10,
    verbose=1,
    steps_per_epoch=steps_per_epoch,
    validation_data=valid_generator,
    validation_steps=val_steps_per_epoch).history

frogermcs

# Get Flowers dataset
data_root = tf.keras.utils.get_file(
  'flower_photos','https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
   untar=True)
   
 # Create data generator for training and validation
IMAGE_SHAPE = (224, 224)
TRAINING_DATA_DIR = str(data_root)

datagen_kwargs = dict(rescale=1./255, validation_split=.20)

frogermcs

model = tf.keras.Sequential([
  hub.KerasLayer("https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4", 
                 output_shape=[1280],
                 trainable=False),
  tf.keras.layers.Dropout(0.4),
  tf.keras.layers.Dense(train_generator.num_classes, activation='softmax')
])