In [ ]:

import tensorflow as tf
from tensorflow import keras

import tensorflow as tf from tensorflow import keras

In [ ]:

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0

In [ ]:

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(10, activation='softmax') ])

In [ ]:

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

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

In [ ]:

model.fit(x_train, y_train, epochs=2, validation_split=0.1)

model.fit(x_train, y_train, epochs=2, validation_split=0.1)

salva o modelo no formato HDF5¶

model.save("mnist_model.h5") ## mais antigo

salve o modelo no formato keras¶

model.save("mnist_model.keras") ## mais novo

In [ ]:

# Salve o modelo no formato SavedModel (obrigatório pro TensorFlow Serving)
model.export("mnist_model/1")

# Salve o modelo no formato SavedModel (obrigatório pro TensorFlow Serving) model.export("mnist_model/1")