run_id = log.dl_run_start(dl_run, dl_network, dl_model_file_path, dl_data, hyper_dict)
print()
for i in range(EPOCHS):
X_train, y_train = shuffle(X_train, y_train)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = X_train[offset:end], y_train[offset:end]
sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})
validation_accuracy = evaluate(X_validation, y_validation)
print("EPOCH {} ...".format(i+1))
print("Validation Accuracy = {:.3f}".format(validation_accuracy))
print()
saver.save(sess, 'lenet')
print("Model saved")
log.dl_run_end(run_id, validation_accuracy)
# Evaluate the model with test data
dl_data = ('TF_MNIST', 'Test')
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
run_id = log.dl_run_start(dl_run, dl_network, dl_model_file_path, dl_data, hyper_dict)
test_accuracy = evaluate(X_test, y_test)
print("Test Accuracy = {:.3f}".format(test_accuracy))
log.dl_run_end(run_id, test_accuracy)
def log_run_end(id_run, accuracy, training_loss):
"""
Log the end info of a neural network run
"""
log.dl_run_end(id_run, accuracy, training_loss)
