local variable 'z' referenced before assignment

[emnab]

I have this error! can you help me, please?

WARNING: Logging before flag parsing goes to stderr. I0718 14:45:17.243686 140030963623808 tl_logging.py:99] [!] checkpoint exists ... I0718 14:45:17.247771 140030963623808 tl_logging.py:99] [!] samples exists ... W0718 14:45:19.318463 140030963623808 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py:505: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. Instructions for updating: tf.py_func is deprecated in TF V2. Instead, there are two options available in V2. - tf.py_function takes a python function which manipulates tf eager tensors instead of numpy arrays. It's easy to convert a tf eager tensor to an ndarray (just call tensor.numpy()) but having access to eager tensors means tf.py_functions can use accelerators such as GPUs as well as being differentiable using a gradient tape. - tf.numpy_function maintains the semantics of the deprecated tf.py_func (it is not differentiable, and manipulates numpy arrays). It drops the stateful argument making all functions stateful.

I0718 14:45:19.464441 140030963623808 tl_logging.py:99] Input _inputlayer_1: [None, 100] I0718 14:45:19.974161 140030963623808 tl_logging.py:99] Dense dense_1: 8192 No Activation I0718 14:45:20.649731 140030963623808 tl_logging.py:99] Reshape reshape_1 I0718 14:45:20.707117 140030963623808 tl_logging.py:99] BatchNorm batchnorm_1: decay: 0.900000 epsilon: 0.000010 act: relu is_train: False I0718 14:45:20.782280 140030963623808 tl_logging.py:99] DeConv2d deconv2d_1: n_filters: 256 strides: (2, 2) padding: SAME act: No Activation dilation: (1, 1) I0718 14:45:22.796400 140030963623808 tl_logging.py:99] BatchNorm batchnorm2d_1: decay: 0.900000 epsilon: 0.000010 act: relu is_train: False I0718 14:45:22.873876 140030963623808 tl_logging.py:99] DeConv2d deconv2d_2: n_filters: 128 strides: (2, 2) padding: SAME act: No Activation dilation: (1, 1) I0718 14:45:23.076492 140030963623808 tl_logging.py:99] BatchNorm batchnorm2d_2: decay: 0.900000 epsilon: 0.000010 act: relu is_train: False I0718 14:45:23.153287 140030963623808 tl_logging.py:99] DeConv2d deconv2d_3: n_filters: 64 strides: (2, 2) padding: SAME act: No Activation dilation: (1, 1) I0718 14:45:23.227763 140030963623808 tl_logging.py:99] BatchNorm batchnorm2d_3: decay: 0.900000 epsilon: 0.000010 act: relu is_train: False I0718 14:45:23.307783 140030963623808 tl_logging.py:99] DeConv2d deconv2d_4: n_filters: 3 strides: (2, 2) padding: SAME act: tanh dilation: (1, 1) I0718 14:45:23.390201 140030963623808 tl_logging.py:99] Input _inputlayer_2: [None, 64, 64, 3] I0718 14:45:23.456669 140030963623808 tl_logging.py:99] Conv2d conv2d_1: n_filter: 64 filter_size: (5, 5) strides: (2, 2) pad: SAME act: I0718 14:45:23.530629 140030963623808 tl_logging.py:99] Conv2d conv2d_2: n_filter: 128 filter_size: (5, 5) strides: (2, 2) pad: SAME act: No Activation I0718 14:45:23.608061 140030963623808 tl_logging.py:99] BatchNorm batchnorm2d_4: decay: 0.900000 epsilon: 0.000010 act: is_train: False I0718 14:45:23.691769 140030963623808 tl_logging.py:99] Conv2d conv2d_3: n_filter: 256 filter_size: (5, 5) strides: (2, 2) pad: SAME act: No Activation I0718 14:45:23.775271 140030963623808 tl_logging.py:99] BatchNorm batchnorm2d_5: decay: 0.900000 epsilon: 0.000010 act: is_train: False I0718 14:45:23.942049 140030963623808 tl_logging.py:99] Conv2d conv2d_4: n_filter: 512 filter_size: (5, 5) strides: (2, 2) pad: SAME act: No Activation I0718 14:45:24.041154 140030963623808 tl_logging.py:99] BatchNorm batchnorm2d_6: decay: 0.900000 epsilon: 0.000010 act: is_train: False I0718 14:45:24.114703 140030963623808 tl_logging.py:99] Flatten flatten_1: I0718 14:45:24.177696 140030963623808 tl_logging.py:99] Dense dense_2: 1 identity I0718 14:45:29.721007 140030963623808 tl_logging.py:99] [] Saving TL weights into checkpoint/G.npz I0718 14:45:31.268130 140030963623808 tl_logging.py:99] [] Saved I0718 14:45:31.274845 140030963623808 tl_logging.py:99] [] Saving TL weights into checkpoint/D.npz I0718 14:45:32.590787 140030963623808 tl_logging.py:99] [] Saved

UnboundLocalError Traceback (most recent call last) in () 58 59 if name == 'main': ---> 60 train()

in train() 53 D.save_weights('{}/D.npz'.format(flags.checkpoint_dir), format='npz') 54 G.eval() ---> 55 result = G(z) 56 G.train() 57 tl.visualize.save_images(result.numpy(), [num_tiles, num_tiles], '{}/train_{:02d}.png'.format(flags.sample_dir, epoch))

UnboundLocalError: local variable 'z' referenced before assignment

[jonluntzel]

I'm having the same issue. Did you end up fixing it?

[zsdonghao]

how is it possible? the z is generated during training

[KevinOfCathay]

z is just noise. You can simply add: z = np.random.normal(loc=0.0, scale=1.0, size=[flags.batch_size, flags.z_dim]).astype(np.float32) before Gen.save_weights('{}/Gen.npz'.format(flags.checkpoint_dir), format='npz')

[kilroyrlc]

I think this is caused by z first being assigned in the block that iterates over input images. If your input directory has no images, rather than reporting this as an error, it'll go right to reference z and fail on that.