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import tensorflow as tf
import tensorflow.keras.applications as apps
import tensorflow.keras.preprocessing.image as kimage
from pathlib import Path
from tqdm import tqdm
import numpy as np
class TFDeepFeatureExtractor:
    """
    Keras‑style wrapper for extracting intermediate activations.
    """
    def __init__(self,
                 model_name: str = "ResNet50",
                 layer_name: str = "avg_pool",   # name of the desired layer
                 input_shape: tuple = (224, 224, 3)):
        # 1️⃣ Load the pretrained base model (include_top=False => no classification head)
        base = getattr(apps, model_name)(
            weights="imagenet",
            include_top=False,
            input_shape=input_shape
        )
        # 2️⃣ Build a new model that outputs the chosen layer
        layer_output = base.get_layer(layer_name).output
        self.model = tf.keras.Model(inputs=base.input, outputs=layer_output)
# 3️⃣ Pre‑processing function (matches the chosen architecture)
        self.preprocess = getattr(apps, f"model_name.lower()_preprocess_input")
        self.input_shape = input_shape
def __call__(self, img_path: Path) -> np.ndarray:
        """
        Return a 1‑D feature vector for a single image file.
        """
        img = kimage.load_img(img_path, target_size=self.input_shape[:2])
        x = kimage.img_to_array(img)               # (H, W, C)
        x = np.expand_dims(x, axis=0)              # (1, H, W, C)
        x = self.preprocess(x)
feats = self.model(x, training=False)     # (1, H', W', C')
        feats = tf.squeeze(feats).numpy()         # flatten spatial dims
        return feats.ravel()                      # (D,)
# ----------------------------------------------------------------------
# Example usage
if __name__ == "__main__":
    extractor = TFDeepFeatureExtractor(
        model_name="ResNet50",
        layer_name="avg_pool"   # shape = (1, 1, 2048)
    )
img_folder = Path("data/images")
    out_path   = Path("data/features_resnet50_tf.npy")
all_feats = []
    for img_path in tqdm(sorted(img_folder.glob("*.jpg"))):
        feats = extractor(img_path)
        all_feats.append(feats)
np.save(out_path, np.stack(all_feats))
    print(f"Saved len(all_feats) vectors to out_path")

Key points

If you just need a single line to grab the output of the global average‑pool of a ResNet‑50:

import torch, torchvision.models as models, torchvision.transforms as T
from PIL import Image
model = models.resnet50(pretrained=True).eval()
feat = torch.nn.Sequential(*list(model.children())[:-1])   # everything except the final FC
x = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor(),
              T.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])])
vec = feat(x(Image.open("my_image.jpg")).unsqueeze(0)).squeeze()
print(vec.shape)   # torch.Size([2048])

pip install torch torchvision tqdm

Summary

Interpretation

Contextual implications

Recommended actions

Next step

The examples are written in Python and use two of the most common libraries: PyTorch and TensorFlow/Keras. Pick the one that fits your workflow.

A deep feature is the activation vector produced by an intermediate layer of a trained deep network (CNN, RNN, Transformer, etc.).
Instead of using raw pixels, tokens, or handcrafted descriptors, you feed your input through the network and grab the output of a layer that captures semantic information (e.g., conv5 of ResNet‑50, the [CLS] token of BERT, etc.). These vectors can then be: choda choda chodi bf