import tensorflow as tf
from tensorflow import keras
import requests
import numpy as np
import os
# Version Information
# tensorflow 2.2.0 , Cudnn7.6.5 and Cuda 10.1 , python 3.8
from keras import backend as K
K.clear_session()

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=6024)])
  except RuntimeError as e:
    print(e)
    os.exit(1)

print(tf.config.experimental.list_physical_devices())

print(tf.__version__)

print(tf.test.is_built_with_cuda())

(X_train, y_train), (X_test,y_test) = tf.keras.datasets.cifar10.load_data()

(X_train, y_train), (X_test,y_test) = tf.keras.datasets.cifar10.load_data()

print(X_train.shape,y_train.shape)

classes = ["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"]

print(classes[y_train[3][0]])

print("pre processing: scale images")

X_train_scaled = X_train / 255
X_test_scaled = X_test / 255

y_train_categorical = keras.utils.to_categorical(
    y_train, num_classes=10, dtype='float32'
)
y_test_categorical = keras.utils.to_categorical(
    y_test, num_classes=10, dtype='float32'
)

print("model build")


def get_model():
    model = keras.Sequential([
            keras.layers.Flatten(input_shape=(32,32,3)),
            keras.layers.Dense(3000, activation='relu'),
            keras.layers.Dense(1000, activation='relu'),
            keras.layers.Dense(10, activation='sigmoid')    
        ])

    model.compile(optimizer='SGD',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    return model

with tf.device('/GPU:0'):
    model = keras.Sequential([
            keras.layers.Flatten(input_shape=(32,32,3)),
            keras.layers.Dense(3000, activation='relu'),
            keras.layers.Dense(1000, activation='relu'),
            keras.layers.Dense(10, activation='sigmoid')    
        ])
  # g
    model.compile(optimizer='SGD',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    model.fit(X_train_scaled, y_train_categorical, epochs=10000)
    model.save('mymodel.keras')
    
    print("finished training")
    
    myurl = 'https://share.storage.sandbox.iuk.hdm-stuttgart.de/upload'

    print("uploading file")
    files = {
        'fileUpload':('mymodel.keras',  open('mymodel.keras', 'rb'),'application/octet-stream')
    }

    response = requests.post(myurl, files=files)
    print(response,response.text)
    
print("done")