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G_AllFeatures_VGG16.py
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G_AllFeatures_VGG16.py
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#https://towardsdatascience.com/step-by-step-vgg16-implementation-in-keras-for-beginners-a833c686ae6c
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu May 13 11:13:38 2021
@author: yurifarod
"""
import keras
import os
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D, Flatten
import timeit
from PIL import Image
start = timeit.default_timer()
diretorio = './train/fold_0/fold_0/all/'
x_train = []
y_train = []
for diretorio, subpasta, arquivos in os.walk(diretorio):
for arquivo in arquivos:
dir_img = diretorio + arquivo
img = Image.open(dir_img)
img = img.resize((450,450))
img =np.asarray(img)
x_train.append(img)
y_train.append(1)
diretorio = './train/fold_1/fold_1/all/'
for diretorio, subpasta, arquivos in os.walk(diretorio):
for arquivo in arquivos:
dir_img = diretorio + arquivo
img = Image.open(dir_img)
img = img.resize((450,450))
img =np.asarray(img)
x_train.append(img)
y_train.append(1)
diretorio = './train/fold_2/fold_2/all/'
for diretorio, subpasta, arquivos in os.walk(diretorio):
for arquivo in arquivos:
dir_img = diretorio + arquivo
img = Image.open(dir_img)
img = img.resize((450,450))
img =np.asarray(img)
x_train.append(img)
y_train.append(1)
diretorio = './train/fold_2/fold_2/hem/'
for diretorio, subpasta, arquivos in os.walk(diretorio):
for arquivo in arquivos:
dir_img = diretorio + arquivo
img = Image.open(dir_img)
img = img.resize((450,450))
img =np.asarray(img)
x_train.append(img)
y_train.append(0)
diretorio = './train/fold_1/fold_1/hem/'
for diretorio, subpasta, arquivos in os.walk(diretorio):
for arquivo in arquivos:
dir_img = diretorio + arquivo
img = Image.open(dir_img)
img = img.resize((450,450))
img =np.asarray(img)
x_train.append(img)
y_train.append(0)
diretorio = './train/fold_0/fold_0/hem/'
for diretorio, subpasta, arquivos in os.walk(diretorio):
for arquivo in arquivos:
dir_img = diretorio + arquivo
img = Image.open(dir_img)
img = img.resize((450,450))
img =np.asarray(img)
x_train.append(img)
y_train.append(0)
x_train = np.array(x_train)
x_train = x_train.astype('float32')
x_train /= 255
y_train = np.array(y_train)
model = Sequential()
model.add(Conv2D(input_shape=(450, 450, 3), filters=64, kernel_size=(3,3),
padding='same', activation='relu'))
model.add(Conv2D(filters=64,kernel_size=(3,3),padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Flatten())
model.add(Dense(units=512,activation="relu"))
model.add(Dense(units=512,activation="relu"))
model.add(Dense(units=1, activation="softmax"))
opt = keras.optimizers.Adam(learning_rate=0.9)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
model.fit(x_train,
y_train,
batch_size=20,
epochs = 100)
stop = timeit.default_timer()
print('Time: ', stop - start)
qtd_param = model.count_params()
print('Parametros da Rede: ', qtd_param)