61 lines
2.4 KiB
Python
61 lines
2.4 KiB
Python
import sys
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import os
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from keras.layers.pooling import MaxPool2D
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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 inception_module(x,kernel_init,bias_init,filters_1x1,filters_3x3_reduce,filters_3x3,filters_5x5_reduce,filters_5x5,filters_pool_proj,name=None):
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#1x1 route
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conv_1x1=Conv2D(filters_1x1,kernel_size=(1,1),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(x)
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#3x3 route is 1x1 CONV + 3x3 CONV
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pre_conv_3x3=Conv2D(filters_3x3_reduce,kernel_size=(1,1),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(x)
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conv_3x3=Conv2D(filters_3x3,kernel_size=(3,3),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(pre_conv_3x3)
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#5x5 route is 1x1 CONV + 5x5 CONV
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pre_conv_5x5=Conv2D(filters_5x5_reduce,kernel_size=(1,1),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(x)
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conv_5x5=Conv2D(filters_5x5,kernel_size=(5,5),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(pre_conv_5x5)
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#POOL route=POOL + 1x1 CONV
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pool_proj=MaxPool2D((3,3),strides=(1,1),padding='same')(x)
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pool_proj=Conv2D(filters_pool_proj,(1,1),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(pool_proj)
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output=keras.layers.concatenate([conv_1x1,conv_3x3,conv_5x5,pool_proj],axis=3,name=name)
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return output
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