TF Apple Fork test on mnist data

cnn.py

import tensorflow as tf
from tensorflow.python.compiler.mlcompute import mlcompute

mlcompute.set_mlc_device(device_name='gpu')


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()

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=100)

ev = model.evaluate(x_test,  y_test, verbose=5)
print(ev)