Dodanie modelu Keras

README.md

@@ -1,10 +1,126 @@
 ---
-library_name: keras
-pipeline_tag: tabular-classification
+language: en
+library_name: tensorflow
 tags:
-- keras
-- mlp
+  - keras
+  - tensorflow
+  - tabular
+  - iris
+  - multiclass-classification
+pipeline_tag: tabular-classification
+license: mit
+---
+
+# Iris MLP Classifier (Keras / TensorFlow)
+
+This repository contains a simple **multiclass classifier** for the classic **Iris** dataset, implemented as a small **MLP (Multi-Layer Perceptron)** in **TensorFlow / Keras**.  
+The model predicts one of three classes based on four numerical features.
+
+## Task
+
+**Tabular multiclass classification**  
+Given the 4 iris measurements, predict the class:
+
+- `0` → setosa  
+- `1` → versicolor  
+- `2` → virginica
+
+## Dataset
+
+**Iris dataset** (from `sklearn.datasets.load_iris`)
+
+### Input features (4)
+The model expects **4 float features** in this exact order:
+
+1. `sepal length (cm)`
+2. `sepal width (cm)`
+3. `petal length (cm)`
+4. `petal width (cm)`
+
+### Target (3 classes)
+- integer labels `y ∈ {0,1,2}`
+- no one-hot encoding required (training uses `sparse_categorical_crossentropy`)
+
 ---
 
-# Model Keras Klasyfikacja Iris
+## Model architecture
+
+A small feed-forward network with built-in feature normalization:
+
+
+### Why `Normalization` layer?
+The `tf.keras.layers.Normalization` layer learns feature-wise mean and variance from the training set via `adapt(...)`.  
+This makes inference easier and safer: **the same scaling used during training is embedded inside the saved model**.
+
+---
+
+## Training configuration
+
+- **Optimizer:** Adam (`learning_rate=1e-3`)
+- **Loss:** `sparse_categorical_crossentropy`
+- **Metric:** accuracy
+- **Train/test split:** 80/20 (`stratify=y`, `random_state=42`)
+- **Validation split (from train):** 20% (`validation_split=0.2`)
+- **Epochs:** 100
+- **Batch size:** 16
+- **Reproducibility:**
+  - `tf.random.set_seed(42)`
+  - `np.random.seed(42)`
+
+> Note: Exact accuracy may vary slightly across environments due to numerical differences and nondeterminism in some TF ops.
+
+---
+
+## Example: training script (reference)
+
+The model was trained using the following core logic (simplified):
+
+```python
+normalizer = tf.keras.layers.Normalization(axis=-1)
+normalizer.adapt(X_train.to_numpy())
+
+model = tf.keras.Sequential([
+    tf.keras.Input(shape=(4,)),
+    normalizer,
+    tf.keras.layers.Dense(16, activation="relu"),
+    tf.keras.layers.Dense(16, activation="relu"),
+    tf.keras.layers.Dense(3, activation="softmax"),
+])
+
+model.compile(
+    optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
+    loss="sparse_categorical_crossentropy",
+    metrics=["accuracy"]
+)
+
+model.fit(
+    X_train.to_numpy(), y_train.to_numpy(),
+    validation_split=0.2,
+    epochs=100,
+    batch_size=16,
+    verbose=0
+)
+
+```
+## Example
+
+```python
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+model = tf.keras.models.load_model("iris_mlp.keras")
+
+x_new = pd.DataFrame([{
+    "sepal length (cm)": 5.1,
+    "sepal width (cm)": 3.5,
+    "petal length (cm)": 1.4,
+    "petal width (cm)": 0.2,
+}])
+
+proba = model.predict(x_new.to_numpy(), verbose=0)[0]  # shape: (3,)
+pred  = int(np.argmax(proba))
 
+print("Probabilities:", proba)
+print("Predicted class:", pred)
+```