import sys
import os

from keras.layers.pooling import MaxPool2D 

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 inception_module(x,kernel_init,bias_init,filters_1x1,filters_3x3_reduce,filters_3x3,filters_5x5_reduce,filters_5x5,filters_pool_proj,name=None):
#1x1 route    
   conv_1x1=Conv2D(filters_1x1,kernel_size=(1,1),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(x)
#3x3 route is 1x1 CONV + 3x3 CONV
   pre_conv_3x3=Conv2D(filters_3x3_reduce,kernel_size=(1,1),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(x)
   conv_3x3=Conv2D(filters_3x3,kernel_size=(3,3),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(pre_conv_3x3)
#5x5 route is 1x1 CONV + 5x5 CONV
   pre_conv_5x5=Conv2D(filters_5x5_reduce,kernel_size=(1,1),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(x)
   conv_5x5=Conv2D(filters_5x5,kernel_size=(5,5),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(pre_conv_5x5)
#POOL route=POOL + 1x1 CONV
   pool_proj=MaxPool2D((3,3),strides=(1,1),padding='same')(x)
   pool_proj=Conv2D(filters_pool_proj,(1,1),padding='same',activation='relu',kernel_initializer=kernel_init,bias_initializer=bias_init)(pool_proj)

   output=keras.layers.concatenate([conv_1x1,conv_3x3,conv_5x5,pool_proj],axis=3,name=name)
   return output