Cannot convert a symbolic Keras input/output to a numpy array...
abrarum opened this issue · 1 comments
Tensorflow version: 2.4.0
Issue: I tried to reproduce this example code but results in an error (it works in Tensorflow version 2.2.0): "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."
Trace:
TypeError Traceback (most recent call last)
in
15 #print(x_test[idx])
16
---> 17 grads = visualize_saliency(model, layer_idx, filter_indices=class_idx, seed_input=x_test[idx])
18 # Plot with 'jet' colormap to visualize as a heatmap.
19 plt.imshow(grads, cmap='jet')
~/Abrar/miniconda3/lib/python3.8/site-packages/vis/visualization/saliency.py in visualize_saliency(model, layer_idx, filter_indices, seed_input, backprop_modifier, grad_modifier)
124 (ActivationMaximization(model.layers[layer_idx], filter_indices), -1)
125 ]
--> 126 return visualize_saliency_with_losses(model.input, losses, seed_input, grad_modifier)
127
128
~/Abrar/miniconda3/lib/python3.8/site-packages/vis/visualization/saliency.py in visualize_saliency_with_losses(input_tensor, losses, seed_input, grad_modifier)
71 weighted losses.
72 """
---> 73 opt = Optimizer(input_tensor, losses, norm_grads=False)
74 grads = opt.minimize(seed_input=seed_input, max_iter=1, grad_modifier=grad_modifier, verbose=False)[1]
75
~/Abrar/miniconda3/lib/python3.8/site-packages/vis/optimizer.py in init(self, input_tensor, losses, input_range, wrt_tensor, norm_grads)
50
51 # Compute gradient of overall with respect to wrt tensor.
---> 52 grads = K.gradients(overall_loss, self.wrt_tensor)[0]
53 if norm_grads:
54 grads = grads / (K.sqrt(K.mean(K.square(grads))) + K.epsilon())
~/Abrar/miniconda3/lib/python3.8/site-packages/keras/backend/tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
73 if _SYMBOLIC_SCOPE.value:
74 with get_graph().as_default():
---> 75 return func(*args, **kwargs)
76 else:
77 return func(*args, **kwargs)
~/Abrar/miniconda3/lib/python3.8/site-packages/keras/backend/tensorflow_backend.py in gradients(loss, variables)
3023 if _is_tf_1():
3024 return tf.gradients(loss, variables, colocate_gradients_with_ops=True)
-> 3025 return tf.gradients(loss, variables)
3026
3027
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/ops/gradients_impl.py in gradients_v2(ys, xs, grad_ys, name, gate_gradients, aggregation_method, stop_gradients, unconnected_gradients)
313 # pylint: disable=protected-access
314 with ops.get_default_graph()._mutation_lock():
--> 315 return gradients_util._GradientsHelper(
316 ys, xs, grad_ys, name, True, gate_gradients,
317 aggregation_method, stop_gradients,
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/ops/gradients_util.py in _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method, stop_gradients, unconnected_gradients, src_graph)
526 # cluster ops for compilation.
527 gradient_uid = ops.get_default_graph().unique_name("uid")
--> 528 ys = ops.convert_n_to_tensor_or_indexed_slices(ys, name="y")
529 xs = [
530 x.handle if resource_variable_ops.is_resource_variable(x) else x
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py in convert_n_to_tensor_or_indexed_slices(values, dtype, name)
383 value.
384 """
--> 385 return internal_convert_n_to_tensor_or_indexed_slices(
386 values=values, dtype=dtype, name=name, as_ref=False)
387
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py in internal_convert_n_to_tensor_or_indexed_slices(values, dtype, name, as_ref)
355 n = None if name is None else "%s_%d" % (name, i)
356 ret.append(
--> 357 internal_convert_to_tensor_or_indexed_slices(
358 value, dtype=dtype, name=n, as_ref=as_ref))
359 return ret
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py in internal_convert_to_tensor_or_indexed_slices(value, dtype, name, as_ref)
316 return value
317 else:
--> 318 return ops.convert_to_tensor(value, dtype=dtype, name=name, as_ref=as_ref)
319
320
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/profiler/trace.py in wrapped(*args, **kwargs)
161 with Trace(trace_name, **trace_kwargs):
162 return func(*args, **kwargs)
--> 163 return func(*args, **kwargs)
164
165 return wrapped
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1538
1539 if ret is None:
-> 1540 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1541
1542 if ret is NotImplemented:
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
337 as_ref=False):
338 _ = as_ref
--> 339 return constant(v, dtype=dtype, name=name)
340
341
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
262 ValueError: if called on a symbolic tensor.
263 """
--> 264 return _constant_impl(value, dtype, shape, name, verify_shape=False,
265 allow_broadcast=True)
266
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
279 tensor_value = attr_value_pb2.AttrValue()
280 tensor_value.tensor.CopyFrom(
--> 281 tensor_util.make_tensor_proto(
282 value, dtype=dtype, shape=shape, verify_shape=verify_shape,
283 allow_broadcast=allow_broadcast))
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
433
434 if _is_array_like(values):
--> 435 values = np.asarray(values)
436
437 # We first convert value to a numpy array or scalar.
~/Abrar/miniconda3/lib/python3.8/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
81
82 """
---> 83 return array(a, dtype, copy=False, order=order)
84
85
~/Abrar/miniconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/keras_tensor.py in array(self)
271
272 def array(self):
--> 273 raise TypeError(
274 'Cannot convert a symbolic Keras input/output to a numpy array. '
275 'This error may indicate that you're trying to pass a symbolic value '
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.
I have been getting the same issue, I have tried upgrading tensorflow, keras and the keras-vis packages to no avail.