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June 11, 2020 19:26
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
Created on Thu Mar 29 09:57:55 2018 | |
@author: avsthiago | |
""" | |
import numpy as np | |
np.random.seed(42) | |
from keras.applications.mobilenet import MobileNet | |
from keras.layers import Input | |
from keras.preprocessing.image import ImageDataGenerator | |
from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard | |
from keras.layers import Dense, GlobalAveragePooling2D | |
from keras.models import Model, load_model | |
from keras.utils.multi_gpu_utils import multi_gpu_model | |
from keras.applications.imagenet_utils import preprocess_input | |
from keras.callbacks import ReduceLROnPlateau | |
import cv2 | |
import json | |
from tensorflow import set_random_seed | |
import os | |
import gc | |
import tensorflow as tf | |
import time | |
import keras | |
import keras.backend as K | |
PATH = '/home/user/full_224/augmented_dataset' | |
out_dict = 'output/path' | |
datasets = os.listdir(PATH) | |
def preprocess(img): | |
return img - np.array([ 117.27782207, 91.85453205, 57.67647879]) | |
class TimeHistory(keras.callbacks.Callback): | |
def on_train_begin(self, logs={}): | |
self.times = [] | |
def on_epoch_begin(self, batch, logs={}): | |
self.epoch_time_start = time.time() | |
def on_epoch_end(self, batch, logs={}): | |
self.times.append(time.time() - self.epoch_time_start) | |
size = 224 | |
name = 'mobilenet_224_data_agumentation' | |
set_random_seed(42) | |
input_tensor = Input(shape=(size, size, 3)) | |
base_model = MobileNet(input_tensor=input_tensor,input_shape=(size,size,3), weights='imagenet', include_top=False) | |
x = base_model.output | |
x = GlobalAveragePooling2D()(x) | |
x = Dense(1024, activation='relu')(x) | |
predictions = Dense(7, activation='softmax')(x) | |
model = Model(inputs=base_model.input, outputs=predictions) | |
model = multi_gpu_model(model,2) | |
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) | |
path_train = os.path.join(PATH, 'train') | |
path_val = os.path.join(PATH, 'validation') | |
path_test = os.path.join(PATH,'test') | |
train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input) | |
val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input) | |
test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input) | |
train_generator = train_datagen.flow_from_directory(path_train, target_size=(size, size), batch_size=40) | |
val_generator = val_datagen.flow_from_directory(path_val,target_size=(size, size), batch_size=40) | |
test_generator = test_datagen.flow_from_directory(path_test,target_size=(size, size), batch_size=40) | |
earlystopper = EarlyStopping(patience=5, verbose=1) | |
model_name = os.path.join('/home/user/multiple_arch', name+'.h5') | |
time_callback = TimeHistory() | |
checkpointer = ModelCheckpoint(model_name , verbose=1, save_best_only=True) | |
learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss', | |
patience=3, | |
verbose=1, | |
factor=0.5, | |
min_lr=0.000001) | |
results = model.fit_generator(train_generator, validation_data=val_generator, | |
epochs=50, callbacks=[earlystopper, checkpointer, | |
learning_rate_reduction, time_callback ]) |
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