Chapter2 code

[acho98]

the below errors occurred when i ran the Chapter_2_Autoencoder.ipynb
my env is the colab

Epoch 1/50

TypeError Traceback (most recent call last)
in ()
2 shuffle=True,
3 epochs=epochs,
----> 4 batch_size=batch_size)

9 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise

TypeError: in user code:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
    return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
    return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step  **
    outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:756 train_step
    y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:238 __call__
    total_loss_metric_value, sample_weight=batch_dim)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated
    update_op = update_state_fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:177 update_state_fn
    return ag_update_state(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:364 update_state  **
    sample_weight, values)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/weights_broadcast_ops.py:155 broadcast_weights
    values = ops.convert_to_tensor(values, name="values")
/usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/trace.py:163 wrapped
    return func(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1540 convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py:339 _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py:265 constant
    allow_broadcast=True)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py:283 _constant_impl
    allow_broadcast=allow_broadcast))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py:435 make_tensor_proto
    values = np.asarray(values)
/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py:83 asarray
    return array(a, dtype, copy=False, order=order)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/keras_tensor.py:274 __array__
    'Cannot convert a symbolic Keras input/output to a numpy array. '

TypeError: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.

[JakubBialy]

I tried to debug this, but all i found out is that the problem is in vae_loss function, especially in z_log_var=z_log_var, z_mean. Event tf.print(z_log_var) causes TypeError: Could not build a TypeSpec for <tf.Operation 'tf.print/PrintV2' type=PrintV2> with type Operation

[firmamentone]

I've also encountered this problem.
After checking several discussions on the internet,
I use the add_loss() to assign the loss function instead of the loss parameter in compile().

it seems to work.

However, I still don't know the difference between the methods and why the original code can not work correctly.

The link is my test notebook
https://colab.research.google.com/drive/1wbUljO1qKvuFeWx9rrl75gv58Is51wtB?usp=sharing

[Aligh1997]

I've also encountered this problem.
After checking several discussions on the internet,
I use the add_loss() to assign the loss function instead of the loss parameter in compile().

it seems to work.

However, I still don't know the difference between the methods and why the original code can not work correctly.

The link is my test notebook
https://colab.research.google.com/drive/1wbUljO1qKvuFeWx9rrl75gv58Is51wtB?usp=sharing
Worked for me!
Thanks

[IDAwaken]

Just find the same issue. Still don't understand what cause the problem and how to solve it.
Really need some help!
Here are the error in details.
2021-05-10 11:34:20.296013: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3799900000 Hz
Epoch 1/50
Traceback (most recent call last):
File "/home/zephyrus/Documents/Project/models/GAN/gans-in-action-master/chapter-2/c2.py", line 82, in
batch_size=batch_size)
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 1100, in fit
tmp_logs = self.train_function(iterator)
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in call
result = self._call(*args, **kwds)
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
*args, **kwds))
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
out = weak_wrapped_fn().wrapped(*args, **kwds)
File "/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 977, in wrapper
raise e.ag_error_metadata.to_exception(e)
TypeError: in user code:
/
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:756 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/engine/compile_utils.py:238 call
total_loss_metric_value, sample_weight=batch_dim)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated
update_op = update_state_fn(*args, **kwargs)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/metrics.py:177 update_state_fn
return ag_update_state(*args, **kwargs)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/metrics.py:364 update_state **
sample_weight, values)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/ops/weights_broadcast_ops.py:155 broadcast_weights
values = ops.convert_to_tensor(values, name="values")
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/profiler/trace.py:163 wrapped
return func(*args, **kwargs)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/framework/ops.py:1540 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py:339 _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py:265 constant
allow_broadcast=True)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py:283 _constant_impl
allow_broadcast=allow_broadcast))
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/framework/tensor_util.py:435 make_tensor_proto
values = np.asarray(values)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/numpy/core/_asarray.py:85 asarray
return array(a, dtype, copy=False, order=order)
/home/zephyrus/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/engine/keras_tensor.py:274 array
'Cannot convert a symbolic Keras input/output to a numpy array. '

TypeError: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.

Process finished with exit code 1

[wang-jinghui]

Try this : tf version 2.1.0

vae.compile(optimizer='rmsprop', loss=vae_loss, experimental_run_tf_function = False)
#vae.compile(loss=None,optimizer='rmsprop')
vae.summary()

[weichenxi87]

I've also encountered this problem. After checking several discussions on the internet, I use the add_loss() to assign the loss function instead of the loss parameter in compile().

it seems to work.

However, I still don't know the difference between the methods and why the original code can not work correctly.

The link is my test notebook https://colab.research.google.com/drive/1wbUljO1qKvuFeWx9rrl75gv58Is51wtB?usp=sharing

thank you! Without your comment , maybe I will drop this book !