def main():
""" Test an RNN for sequential (possibly permuted) MNIST recognition. """
args, params_str, layer_kwargs = parse_args()
outs = mnist.load_split(args.data_dir, val=False, permute=args.permute, normalize=True, seed=0)
_, _, test_images, test_labels = outs
# Flatten the images.
test_inputs = test_images.reshape([len(test_images), -1, INPUT_SIZE])
# Align sequence-level labels with the appropriate time steps by padding with NaNs,
# and to do so, first convert the labels to floats.
length = test_inputs.shape[1]
pad = lambda x: np.pad(x, [[0, 0], [length - 1, 0], [0, 0]], mode='constant', constant_values=np.nan)
test_labels = pad(test_labels.reshape([-1, 1, 1]).astype(np.float))
test_batches = utils.full_bptt_batch_generator(test_inputs, test_labels, TEST_BATCH_SIZE, num_epochs=1,
shuffle=False)
model = models.RNNClassificationModel(args.layer_type, INPUT_SIZE, TARGET_SIZE, args.num_hidden_units,
args.activation_type, **layer_kwargs)
def _error_rate(valid_predictions, valid_targets):
incorrect_mask = tf.logical_not(tf.equal(tf.argmax(valid_predictions, 1), tf.argmax(valid_targets, 1)))
return tf.reduce_mean(tf.to_float(incorrect_mask))
model.error_rate = _error_rate(model.valid_predictions, model.valid_targets)
config = tf.ConfigProto()
config.gpu_options.allow_growth = False
sess = tf.Session(config=config)
saver = tf.train.Saver()
saver.restore(sess, os.path.join(args.results_dir, 'model.ckpt'))
error_rates = []
for batch_inputs, batch_labels in test_batches:
batch_targets = utils.one_hot(np.squeeze(batch_labels, 2), TARGET_SIZE)
valid_predictions, valid_targets, batch_error_rates = sess.run(
[model.valid_predictions, model.valid_targets, model.error_rate],
feed_dict={model.inputs: batch_inputs,
model.targets: batch_targets}
)
error_rates.append(batch_error_rates)
error_rate = np.mean(error_rates, dtype=np.float)
print('%f' % error_rate)
with open(os.path.join(args.results_dir, 'test_result.txt'), 'w') as f:
print('%f' % error_rate, file=f)
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