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import tensorflow as tf | |
import os | |
print("TensorFlow version:", tf.__version__) | |
mnist = tf.keras.datasets.mnist | |
CWD = '' if os.getcwd() == '/' else os.getcwd() | |
(x_train, y_train), (x_test, y_test) = mnist.load_data('/inputs/mnist.npz') | |
x_train, x_test = x_train / 255.0, x_test / 255.0 | |
model = tf.keras.models.Sequential([ | |
tf.keras.layers.Flatten(input_shape=(28, 28)), | |
tf.keras.layers.Dense(128, activation='relu'), | |
tf.keras.layers.Dropout(0.2), | |
tf.keras.layers.Dense(10) | |
]) | |
predictions = model(x_train[:1]).numpy() | |
tf.nn.softmax(predictions).numpy() | |
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) | |
loss_fn(y_train[:1], predictions).numpy() | |
model.compile(optimizer='adam', | |
loss=loss_fn, | |
metrics=['accuracy']) | |
model.fit(x_train, y_train, epochs=5) | |
model.evaluate(x_test, y_test, verbose=2) | |
probability_model = tf.keras.Sequential([ | |
model, | |
tf.keras.layers.Softmax() | |
]) | |
probability_model(x_test[:5]) | |
model.save('/outputs/my_model.keras') |
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