fooflow/ne.py

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()
print(predictions)

predictions_softmax = tf.nn.softmax(predictions).numpy()
print(predictions_softmax)

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss = loss_fn(y_train[:1], predictions).numpy()
print(loss)

model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy'])
model.fit(x_train, y_train, epochs=25)
model.evaluate(x_test, y_test, verbose=2)
probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
test0 = probability_model(x_test[:5])
print(test0)

print("doen")