71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
from itertools import filterfalse
|
|
import sys
|
|
import os
|
|
|
|
#from keras.layers.pooling import MaxPool2D
|
|
#from numpy.lib.shape_base import expand_dims
|
|
|
|
#sys.path.append('c:/git/keras')
|
|
#sys.path.append('c:/git/absl')
|
|
|
|
# installed
|
|
# py -mpip install numpy
|
|
# py -mpip show numpy
|
|
# py -mpip install tensorflow
|
|
# py -mpip show tensorflow
|
|
# py -mpip install matplotlib
|
|
# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
|
|
|
|
import keras
|
|
#from keras.optimizers import adam_v2
|
|
#from tensorflow.keras.optimizers import Adam
|
|
#from keras.datasets import cifar10
|
|
#from keras.preprocessing.image import ImageDataGenerator
|
|
#from keras.models import Sequential
|
|
#from keras.utils import np_utils
|
|
#from keras.layers import Dense
|
|
from keras.layers import Activation
|
|
#from keras.layers import Flatten
|
|
#from keras.layers import Dropout
|
|
from keras.layers import BatchNormalization
|
|
from keras.layers import Conv2D
|
|
#from keras.layers import MaxPooling2D
|
|
#from keras.callbacks import ModelCheckpoint
|
|
#from tensorflow.keras.callbacks import EarlyStopping
|
|
from keras import regularizers
|
|
from keras import optimizers
|
|
from matplotlib import pyplot
|
|
import numpy as np
|
|
import tensorflow
|
|
|
|
def bottleneck_residual_block(x,kernel_size,filters,reduce=False,s=2):
|
|
F1, F2, F3 = filters
|
|
x_shortcut=x
|
|
|
|
if reduce:
|
|
x_shortcut=Conv2D(filters=F3,kernel_size=(1,1),strides=(s,s))(x_shortcut)
|
|
x_shortcut=BatchNormalization(axis=3)(x_shortcut)
|
|
|
|
x=Conv2D(filters=F1,kernel_size=(1,1),strides=(s,s),padding='valid')(x)
|
|
x=BatchNormalization(axis=3)(x)
|
|
x=Activation('relu')(x)
|
|
else:
|
|
x=Conv2D(filters=F1,kernel_size=(1,1),strides=(1,1),padding='valid',kernel_initializer = keras.initializers.glorot_uniform(seed=0))(x)
|
|
x=BatchNormalization(axis=3)(x)
|
|
x=Activation('relu')(x)
|
|
|
|
x=Conv2D(filters=F2,kernel_size=kernel_size,strides=(1,1),padding='same',kernel_initializer = keras.initializers.glorot_uniform(seed=0))(x)
|
|
x=BatchNormalization(axis=3)(x)
|
|
x=Activation('relu')(x)
|
|
|
|
x=Conv2D(filters=F3,kernel_size=(1,1),strides=(1,1),padding='valid',kernel_initializer = keras.initializers.glorot_uniform(seed=0))(x)
|
|
x=BatchNormalization(axis=3)(x)
|
|
|
|
x=keras.layers.Add()([x,x_shortcut])
|
|
x=Activation('relu')(x)
|
|
|
|
return x
|
|
|
|
|
|
|