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