imagens_treino = imagens_treino/float(250)
modelo = keras.Sequential([ keras.layers.Flatten(input_shape= (28,28)),
keras.layers.Dense(256, activation= tensorflow.nn.relu),
keras.layers.Dense(12, activation= tensorflow.nn.softmax )])
modelo.compile(optimizer= 'adam', loss= 'sparse_categorical_crossentropy', metrics= ['accuracy'])
modelo.fit(imagens_treino, identificacoes_treino, epochs= 5)
teste_1 = modelo.predict(imagens_teste)
modelo.evaluate(imagens_teste, identificacoes_teste)e o resultado é: 313/313 [==============================] - 1s 1ms/step - loss: 55.9517 - accuracy: 0.8604 [55.95166778564453, 0.8604000210762024]
