add keras_example.py

keras_example.py

@@ -0,0 +1,102 @@
+"""Minimal Keras/TF training pipeline for the CropHelth dataset.
+
+Setup:
+    huggingface-cli download hansaka01/crophelth --repo-type dataset --local-dir .
+    pip install tensorflow pandas
+
+Run:
+    python keras_example.py
+
+Uses dataset_index.csv (file, label, class_index, split) for a stratified
+train/val tf.data pipeline. Output classes are your codes (potato_lb etc.),
+so the model's argmax maps straight to the treatment lookup in
+knowledge/treatments.json.
+"""
+import os
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+DATA_DIR = os.path.dirname(os.path.abspath(__file__))  # repo root (where dataset_index.csv is)
+IMG_SIZE = (256, 256)
+BATCH = 32
+EPOCHS = 10
+IMG_MEAN, IMG_STD = 127.5, 127.5  # ImageNet-style normalization
+
+df = pd.read_csv(os.path.join(DATA_DIR, "dataset_index.csv"))
+CLASSES = sorted(df["label"].unique())
+CLASS_TO_INT = {c: i for i, c in enumerate(CLASSES)}
+NUM_CLASSES = len(CLASSES)
+df["label_int"] = df["label"].map(CLASS_TO_INT)
+print(f"classes: {NUM_CLASSES}  rows: {len(df)}")
+
+
+def make_dataset(frame: pd.DataFrame, shuffle: bool) -> tf.data.Dataset:
+    files = tf.constant(frame["file"].to_numpy())
+    labels = tf.constant(frame["label_int"].to_numpy(), tf.int32)
+
+    def parse(file, label):
+        path = tf.strings.join([tf.constant(DATA_DIR), file], separator="/")
+        img = tf.io.read_file(path)
+        img = tf.io.decode_image(img, channels=3)          # jpg + png
+        img = tf.image.resize(img, IMG_SIZE)
+        img = (tf.cast(img, tf.float32) - IMG_MEAN) / IMG_STD
+        return img, label
+
+    ds = tf.data.Dataset.from_tensor_slices((files, labels))
+    if shuffle:
+        ds = ds.shuffle(len(frame))
+    ds = ds.map(parse, num_parallel_calls=tf.data.AUTOTUNE)
+    ds = ds.batch(BATCH)
+    ds = ds.prefetch(tf.data.AUTOTUNE)
+    return ds
+
+
+train_ds = make_dataset(df[df["split"] == "train"], shuffle=True)
+val_ds = make_dataset(df[df["split"] == "val"], shuffle=False)
+
+# --- model: EfficientNetB0 transfer learning ---
+backbone = tf.keras.applications.EfficientNetB0(
+    include_top=False, weights="imagenet", input_shape=(256, 256, 3))
+backbone.trainable = False
+
+model = tf.keras.Sequential([
+    backbone,
+    tf.keras.layers.GlobalAveragePooling2D(),
+    tf.keras.layers.Dropout(0.3),
+    tf.keras.layers.Dense(NUM_CLASSES, activation="softmax"),
+], name="crophelth")
+
+model.compile(
+    optimizer=tf.keras.optimizers.Adam(1e-3),
+    loss="sparse_categorical_crossentropy",
+    metrics=["accuracy"],
+)
+model.summary()
+
+# --- stage 1: train the head ---
+model.fit(train_ds, validation_data=val_ds, epochs=4)
+
+# --- stage 2: unfreeze top of backbone, fine-tune ---
+backbone.trainable = True
+for layer in backbone.layers[:-30]:
+    layer.trainable = False
+model.compile(
+    optimizer=tf.keras.optimizers.Adam(1e-5),
+    loss="sparse_categorical_crossentropy",
+    metrics=["accuracy"],
+)
+model.fit(train_ds, validation_data=val_ds, epochs=EPOCHS - 4)
+
+model.save(os.path.join(DATA_DIR, "crophelth_model.keras"))
+np.savez(os.path.join(DATA_DIR, "class_names.npz"), classes=np.array(CLASSES))
+print("saved crophelth_model.keras + class_names.npz")
+
+# --- inference: code-based output, ready for the treatment lookup ---
+import json
+x, y = next(iter(val_ds.batch(1)))
+pred = int(np.argmax(model.predict(x, verbose=0)[0]))
+print("predicted code:", CLASSES[pred])
+treatments = json.load(open(os.path.join(DATA_DIR, "knowledge", "treatments.json")))
+print("treatment:", treatments.get(CLASSES[pred]))