Python TensorFlow Keras: الخطوات التسع الرئيسية للانتقال من الصفر إلى التشغيل

from tensorflow.keras.applications import MobileNetV2

base = MobileNetV2(
    input_shape=(224, 224, 3),
    include_top=False,    # إزالة رأس ImageNet
    weights="imagenet"
)
base.trainable = False     # تجميد الأوزان

print(base.summary())

from tensorflow.keras import models, layers

n_classes = 5   # فئاتك

model = models.Sequential([
    layers.Input(shape=(224, 224, 3)),
    
    # المعالجة المسبقة الخاصة بالنموذج
    tf.keras.applications.mobilenet_v2.preprocess_input,
    
    # القاعدة المجمدة
    base,
    
    # رأس جديد
    layers.GlobalAveragePooling2D(),
    layers.Dropout(0.3),
    layers.Dense(n_classes, activation="softmax")
])

model.compile(optimizer="adam",
              loss="sparse_categorical_crossentropy",
              metrics=["accuracy"])

# الخرج = تفعيل(مجموع(الأوزان * المدخلات) + التحيز)
y = activation(w1*x1 + w2*x2 + ... + w_n*x_n + b)

Input (28x28=784)
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   v
الطبقة المخفية 1 (128 خلية عصبية، ReLU)
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   v
الطبقة المخفية 2 (64 خلية عصبية، ReLU)
   |
   v
طبقة الخرج (10 خلايا عصبية، Softmax)
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   v
احتمالات الفئات العشر

mask-api/
  ⓜ── app/
  │   ⓜ── main.py          # FastAPI
  │   ⓜ── mask_final.keras # النموذج
  │   └── __init__.py
  ⓜ── requirements.txt
  ⓜ── Dockerfile
  └── README.md

fastapi==0.109.0
uvicorn[standard]==0.27.0
tensorflow==2.16.1
pillow==10.2.0
python-multipart==0.0.6

from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.responses import JSONResponse
import tensorflow as tf
import numpy as np
from PIL import Image
import io

app = FastAPI(title="Face Mask Detection", version="1.0")
model = tf.keras.models.load_model("app/mask_final.keras")
LABELS = ["with_mask", "without_mask"]

@app.get("/")
def root():
    return {"service": "face-mask-detection", "version": "1.0"}

@app.get("/health")
def health():
    return {"status": "ok", "model_loaded": True}

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    if file.content_type not in ["image/jpeg", "image/png"]:
        raise HTTPException(400, "Only JPEG/PNG allowed")
    
    contents = await file.read()
    img = Image.open(io.BytesIO(contents)).convert("RGB").resize((224, 224))
    arr = np.expand_dims(np.array(img), axis=0).astype("float32")
    
    proba = float(model.predict(arr, verbose=0)[0][0])
    label = LABELS[int(proba > 0.5)]
    confidence = proba if proba > 0.5 else 1 - proba
    
    return {
        "prediction": label,
        "confidence": round(confidence, 4),
        "with_mask_proba": round(1 - proba, 4),
        "without_mask_proba": round(proba, 4)
    }

uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

# التوثيق التلقائي على http://localhost:8000/docs

# اختبار curl
curl -X POST "http://localhost:8000/predict" \
     -F "file=@test.jpg"

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY app/ ./app/

EXPOSE 8000

HEALTHCHECK --interval=30s --timeout=10s \
    CMD curl -f http://localhost:8000/health || exit 1

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

docker build -t mask-detection:1.0 .
docker run -d -p 8000:8000 --name mask-api mask-detection:1.0

# التحقق
docker logs mask-api
curl http://localhost:8000/health