Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
#!/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
from tensorflow.keras.models import Sequential
import tensorflow.keras
import os
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 tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Dropout, Conv2D, MaxPooling2D, Conv2DTranspose, LeakyReLU, Reshape, Activation, BatchNormalization, UpSampling2D
from tensorflow.keras.activations import selu
from tensorflow.keras.constraints import max_norm
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.utils import to_categorical, normalize
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.datasets import mnist
from tensorflow.keras import losses
from tensorflow.keras import backend as K
import matplotlib.pyplot as plt
import numpy as np
# GET DATA
# def getData():
# Datasets
path_train = '/media/aneumann/Harddisk/Datenbanken/PythonTest/test/Preprocessing/Train'
path_valid = '/media/aneumann/Harddisk/Datenbanken/PythonTest/test/Preprocessing/Validation'
train = os.listdir(path_train)
train = sorted(train)
print(len(train))
valid = os.listdir(path_valid)
valid = sorted(valid)
print(len(valid))
# partition = {'train': train,
# 'validation': valid}
# labels = []
# for file in train:
# np.load(path_train + '/' + file)
# print(partition)
# train_file = np.load(path_train + '/' + train[0])
# ids_train = np.arange(np.size(train_file['x'],0))
# print(ids_train)
batch_size = 10
def batch_generator(files, path, batch_size):
i = 0
# while True:
# for file in files:
file = files[i]
print(file)
loadfile = np.load(path + '/' + file)
ids = np.arange(np.size(loadfile['x'],0))
batch = []
batch_counter = 0
np.random.shuffle(ids)
# while batch_counter < np.floor(len(ids)/batch_size):
while True:
for i in ids:
batch.append(i)
if len(batch) == batch_size:
yield load_data(loadfile,batch)
batch = []
batch_counter += batch_size
# i += 1
# def batch_valid_generator(ids, batch_size):
def load_data(loadfile,ids):
X = []
Y = []
for i in ids:
x = loadfile['x'][i,:,:,:]
y = loadfile['y'][i,:,:,:]
X.append(x)
Y.append(x)
return np.array(X), np.array(Y)
# train_generator = batch_generator(ids, batch_size = 10)
train_generator = batch_generator(train, path_train, batch_size)
# print(train_generator.get_next().shape)
# print(train_generator.get_next().shape)
# for X,Y in train_generator:
# print(X.shape,Y.shape)
# valid_generator = batch_valid_generator(ids_valid, batch_size)
valid_generator = batch_generator(valid, path_valid, batch_size)
# return train_generator, valid_generator
# BUILD MODEL
max_norm_value = 100.0
model = Sequential([
Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", input_shape=(256, 128, 1), 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(),
Flatten(),
Dense(units = 8192),
Dense(units = 8192),
Reshape((32,16,16)),
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(),
Dropout(0.3, name="dec_drop1"),
UpSampling2D(size=(2,2), name="dec_up3"),
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_conv3")
])
# opt = Adam(lr=0.000001)
model.compile(loss='mse', optimizer='adam', metrics=['mape', 'accuracy'])
# MODEL Train
if not os.path.exists("weights_cnn"):
try:
os.mkdir("weights_cnn")
except Exception as e:
print("Konnte Ordner für Gewichte nicht erstellen" + str(e))
filepath = "weights_cnn/weights-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(
filepath,
monitor='loss',
verbose=0,
save_best_only=True,
mode='min'
)
model.summary()
history = model.fit_generator(train_generator, steps_per_epoch = 1, epochs = 5, validation_data=valid_generator, validation_steps = 1, use_multiprocessing=True)
model.summary()
model.save('cnn_autoencoder_model.h5')