dnn_estimator_iris.py

import tensorflow as tf
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer

iris = load_iris()
X = iris.data
y = iris.target

encoder = LabelBinarizer()
y_encoded = encoder.fit_transform(y)

X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)

model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(4,)),
    tf.keras.layers.Dense(20, activation='relu'),
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(3, activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(X_train, y_train, epochs=50, validation_data=(X_test, y_test))

import numpy as np
sample = np.array([[5.1, 3.5, 1.4, 0.2]])
prediction = model.predict(sample)
print("Predykcja (klasa):", iris.target_names[np.argmax(prediction)])