neural_network_tensorflow.py

neural_network_tensorflow.py

import tensorflow as tf
import pandas as pd

# Ler o arquivo CSV
df = pd.read_csv('diabetes.csv')

# Extrair os dados para feature_set e labels
feature_set = df.drop('diabetico', axis=1).values
labels = df['diabetico'].values

model = tf.keras.Sequential([
    tf.keras.layers.Dense(4, activation='sigmoid', input_shape=(3,)),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])

model.fit(feature_set, labels, epochs=20000, batch_size=len(feature_set), verbose=0)

# Predição
single_point = tf.constant([[1, 0, 0]])  # ou [[1, 0, 0]]
result = model.predict(single_point)
limiar = 0.5
resultado = 1 if result[0][0] > limiar else 0
print(resultado)