frotms / mobilenetv3-tensorflow Goto Github PK

if __name__ == "__main__":
print("begin ...")
input_test = tf.zeros([2, 224, 224, 3])
num_classes = 1000

if 0:
    model, end_points = mobilenet_v3_small(input_test, num_classes, multiplier=1.0, is_training=True, reuse=None)
else:
    t_steps = 1000
    t_batch = 128
    tf.random.set_random_seed(1)
    input_rand = tf.random.uniform(shape=(t_batch, 224, 224, 3), minval=0, maxval=1)
    x_batch = input_rand
    y_batch = tf.random.uniform(shape=(t_batch,), minval=0, maxval=1000, dtype=tf.int32)

    logits, end_points = mobilenet_v3_small(x_batch, num_classes, multiplier=1.0, is_training=True, reuse=None)

    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_batch))
    #train_ops = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss)
    train_ops = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)

    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    for s in range(t_steps):
        _, loss_batch = sess.run([train_ops, loss])

        print("steps {:05d} loss {:03f}".format(s, loss_batch))


print("done !")

steps 00000 loss 6.914634
steps 00001 loss 6.907555
steps 00002 loss 6.905149
steps 00003 loss 6.905774
steps 00004 loss 6.904990