42 lines
957 B
Python
42 lines
957 B
Python
#!/usr/bin/env python3
|
|
|
|
# Taken from https://www.tensorflow.org/tutorials/quickstart/beginner
|
|
import tensorflow as tf
|
|
|
|
mnist = tf.keras.datasets.mnist
|
|
|
|
(x_train, y_train), (x_test, y_test) = mnist.load_data()
|
|
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()
|
|
])
|
|
|
|
print(probability_model(x_test[:5]))
|