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untitled1.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Nov 19 17:06:48 2020
@author: tonim
"""
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import keras
from keras.models import Model
from keras.layers import Input,Conv2D, MaxPool2D, concatenate, AvgPool2D, AveragePooling2D, Flatten, Dense
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing import image
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
def inception_module(x,
filters_1x1,
filters_3x3_reduce,
filters_3x3,
filters_5x5_reduce,
filters_5x5,
filters_pool_proj,
name=None):
conv_1x1 = Conv2D(filters_1x1, (1, 1), padding='same', activation='relu', kernel_initializer=kernel_init, bias_initializer=bias_init)(x)
conv_3x3 = Conv2D(filters_3x3_reduce, (1, 1), padding='same', activation='relu', kernel_initializer=kernel_init, bias_initializer=bias_init)(x)
conv_3x3 = Conv2D(filters_3x3, (3, 3), padding='same', activation='relu', kernel_initializer=kernel_init, bias_initializer=bias_init)(conv_3x3)
conv_5x5 = Conv2D(filters_5x5_reduce, (1, 1), padding='same', activation='relu', kernel_initializer=kernel_init, bias_initializer=bias_init)(x)
conv_5x5 = Conv2D(filters_5x5, (5, 5), padding='same', activation='relu', kernel_initializer=kernel_init, bias_initializer=bias_init)(conv_5x5)
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 = concatenate([conv_1x1, conv_3x3, conv_5x5, pool_proj], axis=3, name=name)
return output
kernel_init = keras.initializers.glorot_uniform()
bias_init = keras.initializers.Constant(value=0.2)
input_layer = Input(shape=(224, 224, 3))
x = Conv2D(64, (7, 7), padding='same', strides=(2, 2), activation='relu', name='conv_1_7x7/2', kernel_initializer=kernel_init, bias_initializer=bias_init)(input_layer)
x = MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_1_3x3/2')(x)
x = Conv2D(64, (1, 1), padding='same', strides=(1, 1), activation='relu', name='conv_2a_3x3/1')(x)
x = Conv2D(192, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv_2b_3x3/1')(x)
x = MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_2_3x3/2')(x)
x = inception_module(x,
filters_1x1=64,
filters_3x3_reduce=96,
filters_3x3=128,
filters_5x5_reduce=16,
filters_5x5=32,
filters_pool_proj=32,
name='inception_3a')
x = inception_module(x,
filters_1x1=128,
filters_3x3_reduce=128,
filters_3x3=192,
filters_5x5_reduce=32,
filters_5x5=96,
filters_pool_proj=64,
name='inception_3b')
x = MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_3_3x3/2')(x)
x = inception_module(x,
filters_1x1=192,
filters_3x3_reduce=96,
filters_3x3=208,
filters_5x5_reduce=16,
filters_5x5=48,
filters_pool_proj=64,
name='inception_4a')
x1 = AveragePooling2D((5, 5), strides=3)(x)
x1 = Conv2D(128, (1, 1), padding='same', activation='relu')(x1)
x1 = Flatten()(x1)
x1 = Dense(1024, activation='relu')(x1)
x1 = Dropout(0.7)(x1)
x1 = Dense(10, activation='softmax', name='auxilliary_output_1')(x1)
x = inception_module(x,
filters_1x1=160,
filters_3x3_reduce=112,
filters_3x3=224,
filters_5x5_reduce=24,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4b')
x = inception_module(x,
filters_1x1=128,
filters_3x3_reduce=128,
filters_3x3=256,
filters_5x5_reduce=24,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4c')
x = inception_module(x,
filters_1x1=112,
filters_3x3_reduce=144,
filters_3x3=288,
filters_5x5_reduce=32,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4d')
x2 = AveragePooling2D((5, 5), strides=3)(x)
x2 = Conv2D(128, (1, 1), padding='same', activation='relu')(x2)
x2 = Flatten()(x2)
x2 = Dense(1024, activation='relu')(x2)
x2 = Dropout(0.7)(x2)
x2 = Dense(10, activation='softmax', name='auxilliary_output_2')(x2)
x = inception_module(x,
filters_1x1=256,
filters_3x3_reduce=160,
filters_3x3=320,
filters_5x5_reduce=32,
filters_5x5=128,
filters_pool_proj=128,
name='inception_4e')
x = MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_4_3x3/2')(x)
x = inception_module(x,
filters_1x1=256,
filters_3x3_reduce=160,
filters_3x3=320,
filters_5x5_reduce=32,
filters_5x5=128,
filters_pool_proj=128,
name='inception_5a')
x = inception_module(x,
filters_1x1=384,
filters_3x3_reduce=192,
filters_3x3=384,
filters_5x5_reduce=48,
filters_5x5=128,
filters_pool_proj=128,
name='inception_5b')
x = GlobalAveragePooling2D(name='avg_pool_5_3x3/1')(x)
x = Dropout(0.4)(x)
x = Dense(10, activation='softmax', name='output')(x)
model = Model(input_layer, [x, x1, x2], name='inception_v1')
model.summary()