[RISE]11週間の活動内容
12869 ワード
モデル改良結果
Flashから保存したモデルを読み込み予測
colab予測の結果:
colabコード:
from google.colab import drive
drive.mount('/content/drive')
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.optimizers import *
import cv2
Path = "./drive/MyDrive/"
model = keras.models.load_model(Path+"my_model_2.h5")
optimizer = Adam(lr=0.00001)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
test_data = Path+".jpg"
class_names = ['Chinese', 'Japanese', 'Korean']
img = cv2.imread(Path+'test_image4.jpg')
img = cv2.resize(img,(224,224))
img = np.reshape(img,[1,224,224,3])
classes = np.argmax(model.predict(img), axis = -1)
print(classes)
names = [class_names[i] for i in classes]
print(names)
フラスココード:from flask import Flask, render_template, request
import numpy as np
from tensorflow import keras
from tensorflow.keras.optimizers import *
import cv2
app = Flask(__name__)
@app.route('/')
def basic():
return render_template("upload.html")
@app.route('/predict', methods=['POST'])
def predict():
f = request.files['file']
Path = "./"
f.save(Path+f.filename)
model = keras.models.load_model(Path + "my_model_2.h5")
optimizer = Adam(lr=0.00001)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
test_data = Path + ".jpg"
class_names = ['Chinese', 'Japanese', 'Korean']
img = cv2.imread(Path + 'test_image4.jpg')
img = cv2.resize(img, (224, 224))
img = np.reshape(img, [1, 224, 224, 3])
classes = np.argmax(model.predict(img), axis=-1)
print(classes)
names = [class_names[i] for i in classes]
print(names)
def post():
value = request.form['input']
return render_template('default.html', name=value)
if __name__ == '__main__':
app.run(debug=True)
Reference
この問題について([RISE]11週間の活動内容), 我々は、より多くの情報をここで見つけました https://velog.io/@the_huistle/RISE-11주차-활동내용テキストは自由に共有またはコピーできます。ただし、このドキュメントのURLは参考URLとして残しておいてください。
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