# -*- coding: utf-8 -*-

"""

Created on Sun Mar 21 11:08:26 2021

@author: alex

"""

# Install if missing

# ! pip install tensorflow_hub

import os, shutil

import matplotlib.pylab as plt

from sklearn.metrics import confusion_matrix

import numpy as np

import tensorflow as tf

import tensorflow_hub as hub

print("TF version:", tf.__version__)

print("Hub version:", hub.__version__)

print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE")

# Download data from Kaggle or Git to local directory ('/test')

# https://github.com/nicolas-gervais/predicting-car-price-from-scraped-data/tree/master/picture-scraper

# subfolder erstellen

sourcefile = 'C:/Users/alex/CAS_ML_local/B_Deeplearning/03_Project/test'

'''

# Filename auslesen und Orderstruktur anlegen

for filename in os.listdir(sourcefile):

brand = filename.rsplit('_', 17)[0]

try:

os.mkdir(os.path.join(sourcefile, brand)) # Ordner erstellen wenn nötig...

except WindowsError:

pass # ...sonst weiter und Bild moven

shutil.move(os.path.join(sourcefile, filename), os.path.join(sourcefile, brand, filename))

# Anzahl Bilder zählen

def get_nr_files(sourcefile):

file_count = 0

for r, d, files in os.walk(sourcefile):

file_count += len(files)

return file_count

# shoud return 64467

'''

# Daten in TensorFlow laden und spliten

para_kwargs = dict(

follow_links=False)

train_ds = tf.keras.preprocessing.image_dataset_from_directory(

**para_kwargs,

subset='training')

valid_ds = tf.keras.preprocessing.image_dataset_from_directory(

**para_kwargs,

subset='validation')

for image_batch, labels_batch in train_ds:

print(image_batch.shape)

print(labels_batch.shape)

# Visualisieren der eingelesenen Daten

class_names = train_ds.class_names

image_batch, label_batch = next(iter(train_ds))

image_batch / 255

plt.figure(figsize=(10, 10))

for i in range(16):

ax = plt.subplot(4, 4, i + 1)

plt.imshow(image_batch[i].numpy().astype("uint8"))

label = label_batch[i]

plt.title(class_names[label])

plt.axis("off")

# Pre-trained Model auswählen (aus TF-Hub)

model_name = "mobilenet_v2_100_224"

model_handle_map = {"mobilenet_v2_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4",}

model_handle = model_handle_map.get(model_name)

IMAGE_SIZE = (224, 224, 3)

BATCH_SIZE = 32

print(f"\nAusgewaehltes model: {model_name} : {model_handle}")

print(f"\nBild-Groesse {IMAGE_SIZE}")

# CNN zusammenstellen mit ANzahl Klassen wie im Datenset

print("\nModell erstellen mit", model_handle)

model = tf.keras.Sequential([

])

model.build((None,)+IMAGE_SIZE)

model.summary()

# Trainieren des Modells

model.compile(

optimizer=tf.keras.optimizers.SGD(lr=0.005, momentum=0.9),

loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=0.1),

metrics=['accuracy'])

history = model.fit(

train_ds,

epochs=25,

validation_data=valid_ds).history

# plot the development of the accuracy and loss during training

plt.figure(figsize=(12,4))

plt.subplot(1,2,(1))

plt.plot(history['accuracy'],linestyle='-.')

plt.plot(history['val_accuracy'])

plt.title('model accuracy')

plt.ylabel('accuracy')

plt.xlabel('epoch')

plt.legend(['train', 'valid'], loc='lower right')

plt.subplot(1,2,(2))

plt.plot(history['loss'],linestyle='-.')

plt.plot(history['val_loss'])

plt.title('model loss')

plt.ylabel('loss')

plt.xlabel('epoch')

plt.legend(['train', 'valid'], loc='upper right')

plt.show()

pred=model.predict(valid_ds)

'''

Wie machen wir Test datenset?

Wie zeigen wir confusionmatrix ?

Wie zeigen wir predicted foto?

Cool wäre:

eigenes modell

alles auf colab migrieren

print(confusion_matrix(np.argmax(valid_ds.image_batch,axis=1),np.argmax(pred,axis=1)))

acc_fc = np.sum(np.argmax(valid_ds,axis=1)==np.argmax(pred,axis=1))/len(pred)

print("Acc = " , acc_fc)

def show_batch(image_batch, label_batch):

plt.figure(figsize=(10,10))

for n in range(25):

ax = plt.subplot(5,5,n+1)

plt.imshow(image_batch[n])

plt.title(class_names[label_batch[n]==1][0].title())

plt.axis('off')

show_batch(train_ds, train_ds)

'''