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def autoencoder_old(path_train, path_valid, batch_size, epochs, path_save_model, path_weights_model, plot_name, option, reduction_divisor):
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#!/usr/bin/env python3
# -*- coding: utf8 -*-
import ctypes
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
os.environ['TF_ENABLE_XLA'] = '1'
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
os.environ['TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32'] = '1'
os.environ['TF_DISABLE_CUDNN_TENSOR_OP_MATH'] = '1'
os.environ['TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32'] = '1'
import numpy as np
import tensorflow as tf
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=4096)])
except RuntimeError as e:
print(e)
from keras.models import Sequential
from keras.layers import InputLayer, Dense, Flatten, Dropout, Conv2D, MaxPooling2D, Conv2DTranspose, LeakyReLU, Reshape, Activation, BatchNormalization, UpSampling2D
from keras.activations import selu
from keras.constraints import max_norm
from keras.optimizers import Adam, SGD
from keras.utils import to_categorical, normalize
from keras.callbacks import ModelCheckpoint
from keras.datasets import mnist
from keras import losses
from keras import backend as K
import matplotlib.pyplot as plt
import numpy as np
import random
from Generators.DataGenerator_whole import DataGenerator
train = sorted(train,key=lambda x: ((int(x.split("_")[2])),(int(x.split("_")[3])),(int(x.split("_")[4])),(x.split("_")[5])))
valid = sorted(valid,key=lambda x: ((int(x.split("_")[2])),(int(x.split("_")[3])),(int(x.split("_")[4])),(x.split("_")[5])))
train_generator = DataGenerator(path_train, option, reduction_divisor, batch_size, True)
valid_generator = DataGenerator(path_valid, option, reduction_divisor, batch_size, True)
# BUILD MODEL
max_norm_value = 100.0
model = Sequential([
InputLayer(input_shape=(260,128,1)),
BatchNormalization(),
Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="enc_conv1"),
LeakyReLU(),
Dropout(0.3, name="enc_drop1"),
MaxPooling2D(pool_size=(2,2), name="enc_pool1"),
Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="enc_conv2"),
LeakyReLU(),
MaxPooling2D(pool_size=(2,2), name="enc_pool2"),
Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="enc_conv3"),
LeakyReLU(),
MaxPooling2D(pool_size=(2,2), name="enc_pool3"),
Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="enc_conv4"),
LeakyReLU(),
MaxPooling2D(pool_size=(2,2), name="enc_pool4"),
Flatten(),
UpSampling2D(size=(2,2), name="dec_up1"),
Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="dec_conv1"),
LeakyReLU(),
UpSampling2D(size=(2,2), name="dec_up2"),
Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="dec_conv2"),
LeakyReLU(),
UpSampling2D(size=(2,2), name="dec_up3"),
Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="dec_conv3"),
LeakyReLU(),
Dropout(0.3, name="dec_drop1"),
UpSampling2D(size=(2,2), name="dec_up4"),
Conv2D(filters=1, kernel_size=(3,3), strides=1, padding="same", activation="linear", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="dec_conv4")
model.compile(loss='mse', optimizer='adam', metrics=['mape', 'accuracy'])
except Exception as e:
print("Konnte Ordner für Gewichte nicht erstellen" + str(e))
filepath = path_weights_model + "/weights-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(
filepath,
monitor='loss',
verbose=0,
save_best_only=True,
mode='min'
)
model.summary()
history = model.fit(train_generator, steps_per_epoch = int(np.floor(len(train) // reduction_divisor / batch_size)), epochs = epochs, validation_data=valid_generator, validation_steps = int(np.floor(len(valid) // reduction_divisor / batch_size)), callbacks=[checkpoint], use_multiprocessing=True)
model.save(path_save_model)
index = path_save_model.split("/")
path_save_nomodel = '/'.join(index[0:-1])
loss_history = history.history["loss"]
mape_history = history.history["mape"]
accuracy_history = history.history["accuracy"]
val_loss_history = history.history["val_loss"]
val_mape_history = history.history["val_mape"]
val_accuracy_history = history.history["val_accuracy"]
dict_history = {'loss':loss_history, 'mape':mape_history, 'accuracy':accuracy_history, 'val_loss':val_loss_history, 'val_mape':val_mape_history, 'val_accuracy':val_accuracy_history}
np.save(path_save_nomodel + '/' + plot_name, dict_history)
plt.style.use("ggplot")
plt.figure()
plt.plot(history.history["loss"], label="train_loss")
plt.plot(history.history["val_loss"], label="val_loss")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
index = path_save_model.split("/")
path_save_nomodel = '/'.join(index[0:-1])
plt.savefig(path_save_nomodel + '/' + plot_name + '.png', dpi=400)