[BUG?] analyzer.analyze() not working properly

I am running this code cell in a notebook

for i, ridx in enumerate(test_sample_paths):
    x = dset[ridx]['matrix']
    x = x.reshape((1, -1, 300,1))    
    x = x.numpy()
    prob = k_model.predict_on_batch(x)[0] 
    y_hat = prob.argmax()

    for aidx, analyzer in enumerate(analyzers):
        a = np.squeeze(analyzer.analyze(x))
        a = np.sum(a, axis=1)        ### Add the values along the embedding dimension
        analysis[i, aidx] = a

This code results in error in this line a = np.squeeze(analyzer.analyze(x)) (This is for Smoothgrad analyzer) . The error is given below. When I rerun the cell, I get the same error again(This is for integrated_gradient analyzer), but on 3rd time, it runs FINE!! The print the text heatmap later and they seem to be alright as well.

TypeError Traceback (most recent call last) ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape) 526 try: --> 527 str_values = [compat.as_bytes(x) for x in proto_values] 528 except TypeError:

~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in (.0) 526 try: --> 527 str_values = [compat.as_bytes(x) for x in proto_values] 528 except TypeError:

~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/util/compat.py in as_bytes(bytes_or_text, encoding) 60 raise TypeError('Expected binary or unicode string, got %r' % ---> 61 (bytes_or_text,)) 62

TypeError: Expected binary or unicode string, got -1

During handling of the above exception, another exception occurred:

TypeError Traceback (most recent call last)
in 18 for aidx, analyzer in enumerate(analyzers): 19 print(analyzer) ---> 20 a = np.squeeze(analyzer.analyze(x))#, neuron_selection=y_hat)) 21 print(a.shape) 22 a = np.sum(a, axis=1) ### Add the values along the embedding dimension ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/innvestigate/analyzer/wrapper.py in analyze(self, X, *args, **kwargs) 143 if self._keras_based_augment_reduce is True: 144 if not hasattr(self._subanalyzer, "_analyzer_model"): --> 145 self.create_analyzer_model() 146 147 ns_mode = self._neuron_selection_mode ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/innvestigate/analyzer/wrapper.py in create_analyzer_model(self) 129 "with this wrapper.") 130 --> 131 new_inputs = iutils.to_list(self._augment(inputs)) 132 # print(type(new_inputs), type(extra_inputs)) 133 tmp = iutils.to_list(model(new_inputs+extra_inputs)) ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/innvestigate/analyzer/wrapper.py in _augment(self, X) 290 tmp = super(PathIntegrator, self)._augment(X) 291 tmp = [ilayers.Reshape((-1, self._augment_by_n)+K.int_shape(x)[1:])(x) --> 292 for x in tmp] 293 294 difference = self._compute_difference(X) ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/innvestigate/analyzer/wrapper.py in (.0) 290 tmp = super(PathIntegrator, self)._augment(X) 291 tmp = [ilayers.Reshape((-1, self._augment_by_n)+K.int_shape(x)[1:])(x) --> 292 for x in tmp] 293 294 difference = self._compute_difference(X) ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs) 455 # Actually call the layer, 456 # collecting output(s), mask(s), and shape(s). --> 457 output = self.call(inputs, **kwargs) 458 output_mask = self.compute_mask(inputs, previous_mask) 459 ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/innvestigate/layers.py in call(self, x) 489 490 def call(self, x): --> 491 return K.reshape(x, self._shape) 492 493 def compute_output_shape(self, input_shapes): ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in reshape(x, shape) 1967 A tensor. 1968 """ -> 1969 return tf.reshape(x, shape) 1970 1971 ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py in reshape(tensor, shape, name) 6480 if _ctx is None or not _ctx._eager_context.is_eager: 6481 _, _, _op = _op_def_lib._apply_op_helper( -> 6482 "Reshape", tensor=tensor, shape=shape, name=name) 6483 _result = _op.outputs[:] 6484 _inputs_flat = _op.inputs ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords) 511 except TypeError as err: 512 if dtype is None: --> 513 raise err 514 else: 515 raise TypeError( ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords) 508 dtype=dtype, 509 as_ref=input_arg.is_ref, --> 510 preferred_dtype=default_dtype) 511 except TypeError as err: 512 if dtype is None: ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx) 1144 1145 if ret is None: -> 1146 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) 1147 1148 if ret is NotImplemented: ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref) 227 as_ref=False): 228 _ = as_ref --> 229 return constant(v, dtype=dtype, name=name) 230 231 ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name, verify_shape) 206 tensor_value.tensor.CopyFrom( 207 tensor_util.make_tensor_proto( --> 208 value, dtype=dtype, shape=shape, verify_shape=verify_shape)) 209 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype) 210 const_tensor = g.create_op( ~/anaconda3/envs/gpu_ptorch/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape) 529 raise TypeError("Failed to convert object of type %s to Tensor. " 530 "Contents: %s. Consider casting elements to a " --> 531 "supported type." % (type(values), values)) 532 tensor_proto.string_val.extend(str_values) 533 return tensor_proto TypeError: Failed to convert object of type to Tensor. Contents: (-1, 64, None, 300, 1). Consider casting elements to a supported type.

The analyzers are the following:

input_range = (-24.0467,25.3291)
analyzers = []
noise_scale = (input_range[1]-input_range[0]) * 0.1
ri = input_range[0]  # reference input

methods = [
    # NAME                    OPT.PARAMS         
    ("gradient",              {},           "Gradient"),
    ("smoothgrad",            {"noise_scale": noise_scale,"postprocess": "square"},     "SmoothGrad"),
    ("deconvnet",             {},           "Deconvnet"),
    ("guided_backprop",       {},           "Guided Backprop",),
    ("pattern.net",           {"pattern_type": "relu"}, "PatternNet"),
    ("pattern.attribution",   {"pattern_type": "relu"}, "PatternAttribution"),
    ("deep_taylor.bounded",   {"low": input_range[0],"high": input_range[1]}, "DeepTaylor"),
    ("input_t_gradient",      {},          "Input * Gradient"),
    ("integrated_gradients",  {"reference_inputs": ri},   "Integrated Gradients"),
    #("deep_lift.wrapper",     {"reference_inputs": ri},    "DeepLIFT Wrapper - Rescale"),
    #("deep_lift.wrapper",     {"reference_inputs": ri, "nonlinear_mode": "reveal_cancel"}, "DeepLIFT Wrapper - RevealCancel"),
    ("lrp.z",                 {},   "LRP-Z"),
    ("lrp.epsilon",           {"epsilon": 1},    "LRP-Epsilon"),
]
for method in methods:
    analyzer = innvestigate.create_analyzer(method[0], k_model,**method[1]) 
    analyzer.fit(train_data, batch_size=1024, verbose=1)
    analyzers.append(analyzer)

There is no error in this code cell

albermax / innvestigate

[BUG?] analyzer.analyze() not working properly #193