import tensorflow as tf

import pandas as pd

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

iris = load_iris()

X = iris.data

y = iris.target

X_train, X_test, y_train, y_test = train_test_split(

X, y, test_size=0.2, random_state=42, stratify=y

)

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

model = tf.keras.Sequential([

tf.keras.layers.Dense(10, activation='relu', input_shape=(X_train.shape[1],)),

tf.keras.layers.Dense(8, activation='relu'),

tf.keras.layers.Dense(3, activation='softmax')

])

model.compile(optimizer='adam',

loss='sparse_categorical_crossentropy',

metrics=['accuracy'])

history = model.fit(X_train, y_train, epochs=50, batch_size=8, verbose=1)

loss, accuracy = model.evaluate(X_test, y_test, verbose=0)

print(f"Acurácia no conjunto de teste: {accuracy*100:.2f}%")

predictions = model.predict(X_test)

print("Primeiras previsões (probabilidades):")

print(predictions[:5])

pred_classes = predictions.argmax(axis=1)

print("Classes previstas:", pred_classes[:5])

print("Classes reais: ", y_test[:5])