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net.py
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"""
@Author: Francesco Picetti - [email protected]
"""
import os
# change TensorFlow logging verbosity
# 0: all logs
# 1: filter out INFO logs
# 2: filter out WARNINGS logs
# 3: filter out ERROR logs
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# use the GPU with the lowest memory usage
import GPUtil
DEVICE_ID = str(GPUtil.getFirstAvailable(order='memory')[0])
os.environ["CUDA_VISIBLE_DEVICES"] = DEVICE_ID
print('GPU selected:', DEVICE_ID)
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
import keras.backend as K
K.set_session(session)
from keras.layers import Input, Conv2D, Conv2DTranspose, BatchNormalization, Flatten, Dense, MaxPooling2D, Lambda
from keras.models import Model
from keras.optimizers import Adam, SGD
class Settings:
def __init__(self):
self.patience = 10
self.epochs = 100
self.lr_factor = 0.1
self.batch_size = 128
def Auto1(patch_size, opt=Adam()):
"""
Autoencoder with a hidden representation of 32 elements
"""
img = Input(shape=patch_size, name='g_in_0')
x = Conv2D(16, (6, 6), strides=1, padding='same', name='g_conv_0')(img)
x = Conv2D(16, (5, 5), strides=2, padding='same', name='g_conv_1')(x)
x = Conv2D(16, (4, 4), strides=2, padding='same', name='g_conv_2')(x)
x = Conv2D(16, (3, 3), strides=2, padding='same', name='g_conv_3')(x)
enc = Conv2D(8, (1, 1), strides=2, padding='same', name='encoder')(x)
x = Conv2DTranspose(16, (2, 2), strides=2, padding='same')(enc)
x = Conv2DTranspose(16, (3, 3), strides=2, padding='same', name='g_deconv_1')(x)
x = Conv2DTranspose(16, (4, 4), strides=2, padding='same', name='g_deconv_2')(x)
x = Conv2DTranspose(16, (5, 5), strides=2, padding='same', name='g_deconv_3')(x)
dec = Conv2DTranspose(1, (6, 6), strides=1, padding='same',
activation='tanh', name='g_deconv_4')(x)
autoencoder = Model(inputs=img, outputs=dec)
encoder = Model(inputs=img, outputs=enc)
autoencoder.compile(loss='mean_squared_error', optimizer=opt)
return autoencoder, encoder
def Auto2(patch_size, opt=Adam()):
"""
Autoencoder with a hidden representation of 16 elements
"""
img = Input(shape=patch_size, name='g_in_0')
x = Conv2D(16, (6, 6), strides=1, padding='same', name='g_conv_0')(img)
x = Conv2D(16, (5, 5), strides=2, padding='same', name='g_conv_1')(x)
x = Conv2D(16, (4, 4), strides=2, padding='same', name='g_conv_2')(x)
x = Conv2D(16, (3, 3), strides=2, padding='same', name='g_conv_3')(x)
x = Conv2D(16, (2, 2), strides=2, padding='same')(x)
enc = Conv2D(16, (1, 1), strides=2, padding='same', name='encoder')(x)
x = Conv2DTranspose(16, (2, 2), strides=2, padding='same')(enc)
x = Conv2DTranspose(16, (2, 2), strides=2, padding='same', name='g_deconv_0')(x)
x = Conv2DTranspose(16, (3, 3), strides=2, padding='same', name='g_deconv_1')(x)
x = Conv2DTranspose(16, (4, 4), strides=2, padding='same', name='g_deconv_2')(x)
x = Conv2DTranspose(16, (5, 5), strides=2, padding='same', name='g_deconv_3')(x)
dec = Conv2DTranspose(1, (6, 6), strides=1, padding='same',
activation='tanh', name='g_deconv_4')(x)
autoencoder = Model(inputs=img, outputs=dec)
encoder = Model(inputs=img, outputs=enc)
autoencoder.compile(loss='mean_squared_error', optimizer=opt)
return autoencoder, encoder
def Auto3(patch_size, opt=Adam()):
"""
Autoencoder with a hidden representation of 64 elements
"""
img = Input(shape=patch_size, name='g_in_0')
x = Conv2D(16, (6, 6), strides=1, padding='same', name='g_conv_0')(img)
x = Conv2D(16, (5, 5), strides=2, padding='same', name='g_conv_1')(x)
x = Conv2D(16, (4, 4), strides=2, padding='same', name='g_conv_2')(x)
x = Conv2D(16, (3, 3), strides=2, padding='same', name='g_conv_3')(x)
enc = Conv2D(16, (2, 2), strides=2, padding='same', name='encoder')(x)
x = Conv2DTranspose(16, (2, 2), strides=2, padding='same', name='g_deconv_0')(enc)
x = Conv2DTranspose(16, (3, 3), strides=2, padding='same', name='g_deconv_1')(x)
x = Conv2DTranspose(16, (4, 4), strides=2, padding='same', name='g_deconv_2')(x)
x = Conv2DTranspose(16, (5, 5), strides=2, padding='same', name='g_deconv_3')(x)
dec = Conv2DTranspose(1, (6, 6), strides=1, padding='same',
activation='tanh', name='g_deconv_4')(x)
autoencoder = Model(inputs=img, outputs=dec)
encoder = Model(inputs=img, outputs=enc)
autoencoder.compile(loss='mean_squared_error', optimizer=opt)
return autoencoder, encoder
def Auto3D1(patch_size, opt=Adam()):
"""
Autoencoder with a hidden representation of 32 elements
"""
img = Input(shape=patch_size, name='g_in_0')
x = Conv2D(16, (6, 6), strides=1, padding='same', name='g_conv_0')(img)
x = Conv2D(16, (5, 5), strides=2, padding='same', name='g_conv_1')(x)
x = Conv2D(16, (4, 4), strides=2, padding='same', name='g_conv_2')(x)
x = Conv2D(16, (3, 3), strides=2, padding='same', name='g_conv_3')(x)
enc = Conv2D(8, (1, 1), strides=2, padding='same', name='encoder')(x)
x = Conv2DTranspose(16, (2, 2), strides=2, padding='same')(enc)
x = Conv2DTranspose(16, (3, 3), strides=2, padding='same', name='g_deconv_1')(x)
x = Conv2DTranspose(16, (4, 4), strides=2, padding='same', name='g_deconv_2')(x)
x = Conv2DTranspose(16, (5, 5), strides=2, padding='same', name='g_deconv_3')(x)
dec = Conv2DTranspose(3, (6, 6), strides=1, padding='same',
activation='tanh', name='g_deconv_4')(x)
autoencoder = Model(inputs=img, outputs=dec)
encoder = Model(inputs=img, outputs=enc)
autoencoder.compile(loss='mean_squared_error', optimizer=opt)
return autoencoder, encoder
def Auto3D2(patch_size, opt=Adam()):
"""
Autoencoder with a hidden representation of 16 elements
"""
img = Input(shape=patch_size, name='g_in_0')
x = Conv2D(16, (6, 6), strides=1, padding='same', name='g_conv_0')(img)
x = Conv2D(16, (5, 5), strides=2, padding='same', name='g_conv_1')(x)
x = Conv2D(16, (4, 4), strides=2, padding='same', name='g_conv_2')(x)
x = Conv2D(16, (3, 3), strides=2, padding='same', name='g_conv_3')(x)
x = Conv2D(16, (2, 2), strides=2, padding='same')(x)
enc = Conv2D(16, (1, 1), strides=2, padding='same', name='encoder')(x)
x = Conv2DTranspose(16, (2, 2), strides=2, padding='same')(enc)
x = Conv2DTranspose(16, (2, 2), strides=2, padding='same', name='g_deconv_0')(x)
x = Conv2DTranspose(16, (3, 3), strides=2, padding='same', name='g_deconv_1')(x)
x = Conv2DTranspose(16, (4, 4), strides=2, padding='same', name='g_deconv_2')(x)
x = Conv2DTranspose(16, (5, 5), strides=2, padding='same', name='g_deconv_3')(x)
dec = Conv2DTranspose(3, (6, 6), strides=1, padding='same',
activation='tanh', name='g_deconv_4')(x)
autoencoder = Model(inputs=img, outputs=dec)
encoder = Model(inputs=img, outputs=enc)
autoencoder.compile(loss='mean_squared_error', optimizer=opt)
return autoencoder, encoder
def Auto3D3(patch_size, opt=Adam()):
"""
Autoencoder with a hidden representation of 64 elements
"""
img = Input(shape=patch_size, name='g_in_0')
x = Conv2D(16, (6, 6), strides=1, padding='same', name='g_conv_0')(img)
x = Conv2D(16, (5, 5), strides=2, padding='same', name='g_conv_1')(x)
x = Conv2D(16, (4, 4), strides=2, padding='same', name='g_conv_2')(x)
x = Conv2D(16, (3, 3), strides=2, padding='same', name='g_conv_3')(x)
enc = Conv2D(16, (2, 2), strides=2, padding='same', name='encoder')(x)
x = Conv2DTranspose(16, (2, 2), strides=2, padding='same', name='g_deconv_0')(enc)
x = Conv2DTranspose(16, (3, 3), strides=2, padding='same', name='g_deconv_1')(x)
x = Conv2DTranspose(16, (4, 4), strides=2, padding='same', name='g_deconv_2')(x)
x = Conv2DTranspose(16, (5, 5), strides=2, padding='same', name='g_deconv_3')(x)
dec = Conv2DTranspose(3, (6, 6), strides=1, padding='same',
activation='tanh', name='g_deconv_4')(x)
autoencoder = Model(inputs=img, outputs=dec)
encoder = Model(inputs=img, outputs=enc)
autoencoder.compile(loss='mean_squared_error', optimizer=opt)
return autoencoder, encoder