Hi Daniele and all,

thanks for creating and maintaining this great library!

I have been trying to use GNNExplainer, but I keep seeing the below error message. Still don't know if it's a bug or something i am doing wrong on my side, but there is no much documentation or examples around it.

I am able to run smoothly the sample code at https://github.com/danielegrattarola/spektral/blob/master/examples/other/explain_node_predictions.py.

But when applying to my dataset (where I can run successfully a GCN model), i get the below:

dataset
Out[66]: SADataset(n_graphs=1)

dataset[0]
Out[67]: Graph(n_nodes=1653, n_node_features=42, n_edge_features=None, n_labels=10)

x_exp, a_exp = dataset[0].x, dataset[0].a

x_exp.shape
Out[69]: (1653, 42)

a_exp.shape
Out[70]: TensorShape([1653, 1653])

explainer = GNNExplainer(model, preprocess=gcn_filter, verbose=True)
n_hops was automatically inferred to be 2

node_idx = 0

adj_mask, feat_mask = explainer.explain_node(x=x_exp, a=a_exp, node_idx=node_idx)

pred_loss: 1.097847819328308, a_size_loss: 0.5874298214912415, a_entropy_loss: 0.0692998617887497, smoothness_loss: [[0.]], x_size_loss: 2.0829315185546875, x_entropy_loss: 0.06919442862272263
pred_loss: 1.0877137184143066, a_size_loss: 0.5852940678596497, a_entropy_loss: 0.06929884105920792, smoothness_loss: [[0.]], x_size_loss: 2.075951099395752, x_entropy_loss: 0.06918510049581528
[... output removed]
pred_loss: 0.6421844959259033, a_size_loss: 0.3796449303627014, a_entropy_loss: 0.05964722856879234, smoothness_loss: [[0.]], x_size_loss: 1.379091501235962, x_entropy_loss: 0.05970795825123787
pred_loss: 0.6415124535560608, a_size_loss: 0.37782761454582214, a_entropy_loss: 0.05948375537991524, smoothness_loss: [[0.]], x_size_loss: 1.372214436531067, x_entropy_loss: 0.059564121067523956

adj_mask.shape
Out[75]: TensorShape([2349])

adj_mask
Out[76]: 
<tf.Variable 'Variable:0' shape=(2349,) dtype=float32, numpy=
array([ 0.8150444 ,  0.77765435, -0.9916512 , ..., -1.0242233 ,
       -0.9629407 , -0.9988212 ], dtype=float32)>


feat_mask.shape
Out[77]: TensorShape([1, 42])

feat_mask
Out[78]: 
<tf.Variable 'Variable:0' shape=(1, 42) dtype=float32, numpy=
array([[ 0.58385307, -1.3217939 , -1.0627872 , -0.00148061, -1.0020486 ,
        -0.9942789 , -0.97092587, -0.9922697 ,  0.3853194 , -0.83190703,
        -1.1318972 , -0.99104863, -1.0001428 , -0.9827519 , -0.9750702 ,
        -0.96384555, -0.890569  , -1.0193573 ,  0.4747884 , -0.91873515,
         0.7341433 , -0.97718424, -0.86869913, -0.9699511 ,  0.37709397,
        -1.0660834 , -0.92709947, -0.89111555, -1.0546191 , -1.0837208 ,
        -1.0699799 , -1.0806109 ,  0.61809593, -0.9817147 , -1.0526807 ,
        -0.95195514, -1.0162035 , -1.181156  , -1.0657567 , -1.0472083 ,
        -0.85559815, -1.0388821 ]], dtype=float32)>

G = explainer.plot_subgraph(adj_mask, feat_mask, node_idx)
Traceback (most recent call last):
  File ".pyenv/versions/3.7.6/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-74-182e1ffafc94>", line 1, in <module>
    G = explainer.plot_subgraph(adj_mask, feat_mask, node_idx)
  File ".pyenv/versions/3.7.6/lib/python3.7/site-packages/spektral/models/gnn_explainer.py", line 276, in plot_subgraph
    adj_mtx, top_ftrs = self._explainer_cleaning(a_mask, x_mask, node_idx, a_thresh)
  File ".pyenv/versions/3.7.6/lib/python3.7/site-packages/spektral/models/gnn_explainer.py", line 243, in _explainer_cleaning
    tf.multiply, self.comp_graph, selected_adj_mask
  File ".pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py", line 206, in wrapper
    return target(*args, **kwargs)
  File ".pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow/python/ops/sparse_ops.py", line 2931, in map_values
    op(*inner_args, **inner_kwargs),
  File ".pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py", line 206, in wrapper
    return target(*args, **kwargs)
  File ".pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py", line 530, in multiply
    return gen_math_ops.mul(x, y, name)
  File ".pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 6240, in mul
    _ops.raise_from_not_ok_status(e, name)
  File ".pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 6897, in raise_from_not_ok_status
    six.raise_from(core._status_to_exception(e.code, message), None)
  File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [2349] vs. [2589] [Op:Mul]

Status

As of now this is just an initial port of the Multi Head Attention code I was using. I temporary put it in its own file to make initial development easier.

Objective

The main objective will be to modify the GraphAttention class and multi_head_attention function (maybe merge them) as to be compatible with the current implementation.

BTW: I might be a good to eventually break up the convolutional module into a folder with each layer in its own file, it would make contribution easier.

I installed Spektral both ways, via pip install and from the source.

spektral==0.6.0

from tensorflow.python import keras
print(keras.__version__)
2.4.0

and

import tensorflow as tf 
print(tf.keras.__version__)
2.4.0

and

print(tf.__version__)
2.3.0

I am still getting import error.

import spektral

ImportError: cannot import name 'gen_sparse_ops' from 'tensorflow.python' (/home/abdul/anaconda3/envs/tf-gpu/lib/python3.8/site-packages/tensorflow/python/init.py)

Hi!

I am building a graph convolutional network which will be used in conjunction with a merged layer for a reinforcement learning task.

I have a technical question about the convolutional layer itself which is slightly confusing to me which is: why is the adjacency matrix passed in to each conv layer and not ONLY the first one? My code is as follows:

adj = nx.to_numpy_array(graph)

node_features = [] #just the degree of the graph nodes
node_degree = nx.degree(damage_graph)
for i in dict(node_degree).values():
    node_features.append(i / len(damage_graph))

node_features_final = np.array(node_features).reshape(-1, 1)


adj_normalised = normalized_adjacency(adj)
adj_normalised = sp_matrix_to_sp_tensor(adj_normalised)
node_feature_shape = 1


nodefeature_input = tf.keras.layers.Input(shape=(node_feature_shape,), name='node_features_input')
adjacency_input = tf.keras.layers.Input(shape=(None,), name='adjacency_input', sparse=True)

conv_layer_one = GCNConv(64, activation='relu')([nodefeature_input, adj_normalised])
conv_layer_one = tf.keras.layers.Dropout(0.2)(conv_layer_one)
conv_layer_two = GCNConv(32, activation='relu')([conv_layer_one, adj_normalised])
conv_layer_pool = GlobalAvgPool()(conv_layer_two)
dense_layer_graph = tf.keras.layers.Dense(128, activation='relu')(conv_layer_pool)

input_action_vector = tf.keras.layers.Input(shape=(action_vector,), name='action_vec_input')
action_vector_dense = tf.keras.layers.Dense(128, activation='relu', name='action_layer_dense')(input_action_vector)

merged_layer = tf.keras.layers.Concatenate()([dense_layer_graph, action_vector_dense])
#output_layer... etc
model = Model([nodefeature_input, adjacency_input], [output_layer])

and my second question is about the normalise_adjacency - it does not add self loops. Should self loops be added before or after normalising the matrix?

thank you!

I have a GNN that works when I specify the loader as:

loader = spektral.data.loaders.DisjointLoader(dataset, batch_size=1)

However, when I increase the batch size, e.g.:

loader = spektral.data.loaders.DisjointLoader(dataset, batch_size=2)

I get:

Traceback (most recent call last):
  File "/Users/hca/PycharmProjects/Switching%20notes/ai/tests/test.py", line 30, in <module>
    model.fit(loader.load(), steps_per_epoch=loader.steps_per_epoch, epochs=3)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1100, in fit
    tmp_logs = self.train_function(iterator)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 888, in _call
    return self._stateless_fn(*args, **kwds)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2942, in __call__
    return graph_function._call_flat(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1918, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 555, in call
    outputs = execute.execute(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError:  Incompatible shapes: [26,1] vs. [2,13]
	 [[node gradient_tape/binary_crossentropy/logistic_loss/mul/BroadcastGradientArgs (defined at Users/hca/PycharmProjects/Switching%20notes/ai/tests/test.py:30) ]] [Op:__inference_train_function_3011]

Function call stack:
train_function

Here it seems to me that the loader adds an additional dimension to the target variable, but this dimension is not expected in the model itself. I can specify a batch size inside model.fit(), and that works if I don't specify a batch size larger than 1 inside DisjointLoader, but I am not sure how these interact and whether that is a good idea? Is there something else that I should do when I want to run batches?

Also, regarding node-level classification, when I specify

loader = spektral.data.loaders.DisjointLoader(dataset, node_level=True)

I get:

Traceback (most recent call last):
  File "/Users/hca/PycharmProjects/Switching%20notes/ai/tests/test.py", line 30, in <module>
    model.fit(loader.load(), steps_per_epoch=loader.steps_per_epoch, epochs=3)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1100, in fit
    tmp_logs = self.train_function(iterator)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 888, in _call
    return self._stateless_fn(*args, **kwds)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2942, in __call__
    return graph_function._call_flat(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1918, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 555, in call
    outputs = execute.execute(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError:  TypeError: `generator` yielded an element of ((TensorSpec(shape=(13, 36), dtype=tf.float64, name=None), SparseTensorSpec(TensorShape([13, 13]), tf.int64), TensorSpec(shape=(13,), dtype=tf.int64, name=None)), TensorSpec(shape=(1, 13), dtype=tf.float64, name=None)) where an element of ((TensorSpec(shape=(None, 36), dtype=tf.float64, name=None), SparseTensorSpec(TensorShape([None, None]), tf.int64), TensorSpec(shape=(None,), dtype=tf.int64, name=None)), TensorSpec(shape=(None,), dtype=tf.float64, name=None)) was expected.
Traceback (most recent call last):

  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/script_ops.py", line 247, in __call__
    return func(device, token, args)

  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/script_ops.py", line 135, in __call__
    ret = self._func(*args)

  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 620, in wrapper
    return func(*args, **kwargs)

  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 976, in generator_py_func
    raise TypeError(

TypeError: `generator` yielded an element of ((TensorSpec(shape=(13, 36), dtype=tf.float64, name=None), SparseTensorSpec(TensorShape([13, 13]), tf.int64), TensorSpec(shape=(13,), dtype=tf.int64, name=None)), TensorSpec(shape=(1, 13), dtype=tf.float64, name=None)) where an element of ((TensorSpec(shape=(None, 36), dtype=tf.float64, name=None), SparseTensorSpec(TensorShape([None, None]), tf.int64), TensorSpec(shape=(None,), dtype=tf.int64, name=None)), TensorSpec(shape=(None,), dtype=tf.float64, name=None)) was expected.


	 [[{{node EagerPyFunc}}]]
	 [[IteratorGetNext]] [Op:__inference_train_function_3015]

Function call stack:
train_function

In this case, the shape of y is (1, 13), where (None,) is expected. However, in the dataset, y has the dimensions (13,), which seems to me could have been correct, had DisjointLoader not changed that to (1, 13). What am I missing here?

My GNN code is as follows:

import spektral
from dataset_class import GNN_Dataset
from spektral.data.dataset import Dataset
from spektral.layers import GraphSageConv
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.models import Model

class SN_GNN(Model):
    def __init__(self):
        super().__init__()
        self.X_in = Input(shape=(13, ),
                     name='X_in')
        self.A_in = Input(shape=(None,),
                     sparse=True,
                     name='A_in')
        self.GraphSage = GraphSageConv(32)
        self.output_layer = Dense(1, activation='softmax')

    def call(self, inputs):
        x, a = inputs[0], inputs[1]
        x = self.GraphSage([x, a])
        out = self.output_layer(x)
        return out
model = SN_GNN()
dataset = GNN_Dataset('/dataset/sn/')
loader = spektral.data.loaders.DisjointLoader(dataset, node_level=True)
model.compile(optimizer='Adam', loss='binary_crossentropy')
model.fit(loader.load(), steps_per_epoch=loader.steps_per_epoch, epochs=3)

Hello @danielegrattarola, may you please deliver in examples the use of GINConv layer? I have a problem during passing a tensor (output of Keras "Input Layer") to this layer (model definition). Its connected with propagate method in Message Passing class:

Model Structure:

X_in = Input(shape=(F, ))
A_in = Input(shape=(N, ), sparse=True)
gc1 = GINConv(channels=300, mlp_activation='relu',)([X_in, A_in])

Error relation: self.index_i = A.indices[:, 0]

Error Type: TypeError: 'SparseTensor' object is not subscriptable.

Hi,

It seems that this is now incompatible with the latest Keras release (2.2.5) from August 22, 2019. Here are the details:

Environment

Python 3.7 spektral==0.0.12 tensorflow==1.14.0 keras==2.2.5

How to reproduce

Run a python script that imports spektral:

import spektral

Expected behaviour

Everything runs smoothly and spektral is imported correctly.

Observed behaviour

We get an import error:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<path_to_virtualenv>/lib/python3.7/site-packages/spektral/__init__.py", line 5, in <module>
    from . import layers
  File "<path_to_virtualenv>/lib/python3.7/site-packages/spektral/layers/__init__.py", line 4, in <module>
    from .convolutional import *
  File "<path_to_virtualenv>/lib/python3.7/site-packages/spektral/layers/convolutional.py", line 5, in <module>
    from keras.backend import tf
ImportError: cannot import name 'tf' from 'keras.backend' (<path_to_virtualenv>/lib/python3.7/site-packages/keras/backend/__init__.py)

Suggested change

On the short run, I think it would suffice to pin the keras version in setup.py to 2.2.4 (This fixed the issue in my project for now)

Thanks, Tudor.

Hi, Thanks a lot for your great package.

As it says in the doc, GATConv supports batch mode. However, the basic GAT algorithm uses the full dataset to provide scores. So I was wondering how I could provide batches for GATConv?

Hi Daniele, thank you for this really useful package!

I have a question about the input to my GCN. I am attempting to merge a GCN with a CNN. Construction of the model works fine, however, when specifying a batch dimension (because the merged model requires this) I am confused about the Input layer for the GCN as it is throwing an error at the concatenation layer. Models is as follows:

def graph_cnn(state_adjacency,cnn_input_shape):
    '''create merged NN with GCN representing environment state
        and CNN representing agwnt position'''

    #CNN branch
    cnn_branch_input = tf.keras.layers.Input(shape=(4,4,1))
    cnn_branch_two = tf.keras.layers.Conv2D(32, (2, 2), activation='relu', padding='same')(cnn_branch_input)
    cnn_branch_three = tf.keras.layers.MaxPooling2D(1, 1)(cnn_branch_two)
    cnn_branch_four = tf.keras.layers.Conv2D(32, (2, 2), activation='relu', padding='same')(cnn_branch_three)
    cnn_branch_five = tf.keras.layers.Flatten()(cnn_branch_four)
    cnn_branch_six = tf.keras.layers.Dense(32, activation='relu')(cnn_branch_five)

    #GCN branch: Spektral library
    #node_features = 
    #preprocess adjacency matrix -- self loops

    node_feat_input = tf.keras.layers.Input(shape=(4,), name='node_feature_inp_layer')
    graph_input_adj = tf.keras.layers.Input(len(adjacency), sparse=True, name='graph_adj_layer')
    gnn_branch = GraphConv(16, 'relu')([node_feat_input, graph_input_adj])
    gnn_branch = tf.keras.layers.Dropout(0.5)(gnn_branch)
    gnn_branch_two = GraphConv(1, 'linear')([gnn_branch, graph_input_adj])
    gnn_branch_two = tf.keras.layers.Dense(32, activation='relu')(gnn_branch_two)

    #merged layer
    merged = tf.keras.layers.Concatenate(axis=1)([gnn_branch_two, cnn_branch_six ])

    #output layer: action prediciton
    output_layer = tf.keras.layers.Dense(7, activation = 'linear')(merged)
    #put model together
    merged_model = tf.keras.models.Model(inputs=[cnn_branch_input, node_feat_input, graph_input_adj],
                                        outputs=[output_layer])
    #compile mode
    merged_model.compile(optimizer='adam', 
                         weighted_metrics=['acc'],
                         loss='mse')

    return merged_model

The inputs (made smaller just to establish pipeline) are: input = np.array((adj)) -- adjacency matrix (4,4) node_features = input (just made input for the purpose of running pipeline)

and now reshaping (and where the error occurs). For CNN, the input shape will be the same size as the adjacency matrix but will be an array with different 0's and 1's to the graph (again for the purpose of establishing this i have just made it the adjacency matrix).

cnn_input = np.expand_dims(input, 2)
cnn_input = np.expand_dims(input, axis=0)
shape = (1,4,4,1)

gcn_input = input (adjacency matrix).
gcn_input.shape = (1,4,4)
node_feature.shape = (1,4,4)
y.shape = (1,4,7)

My confusion is the Input layer for the graph network. the current shape is shape(4,) and shape(4) (len(adjacency), as you can see. When i run this model just to see if i can get the model to start training, I receive the following error.

model.fit([x, gcn_input, node_feature], y,
    #batch_size=4,
    shuffle=False)

ValueError: A Concatenate layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 4, 32), (None, 32)]

the shapes of my inputs are as follows (they need to have 1 as the initial batch dimension but in real training they will be in batches of 16 or 32.

I'm not sure how to fix the None,4,32 dimension to be the required None,32 for the concatenation layer! any help is much aprpeciated!

thanks, and sorry for the long post, i hope the code is informative in diagnosing the problem

Hey, I have implementations of the following few graphical neural network components:

Convolutions:

• Diffusion based graph convolution (as part of my GARNN implementation)
• Graph convolutions in the sense of M. Zhang et al., 2018 (as part of my DGCNN implementation)

Pooling:

• SortPooling (Also as part my DGCNN implementation)

I would love to add these to your framework however, I am a bit lost with all the requirements that my implementations need to fulfil in order to integrate seamlessly. Is there any guide for what tests, features or properties layers need to have?

Best, Levi

Hi @danielegrattarola ,

I posted the other day about the adjacency matrix input - sorry for a second post, but i am now trying to predict on a batch of sparse tensors with no success - I won't post all of the code, however, i have tried feeding in the adj matrix by creating a dataset and then using the batchloader with no success.. as well as the following:

dummy network:

nodefeature_input = tf.keras.layers.Input(shape=(node_feature_shape,), name='node_features_input')
adjacency_input = tf.keras.layers.Input(shape=(None,), name='adjacency_input', sparse=True)

conv_layer_one = GCNConv(64, activation='relu')([nodefeature_input, adjacency_input])
conv_layer_one = tf.keras.layers.Dropout(0.2)(conv_layer_one)
conv_layer_two = GCNConv(32, activation='relu')([conv_layer_one, adjacency_input])
conv_layer_pool = GlobalAvgPool()(conv_layer_two)
dense_layer_graph = tf.keras.layers.Dense(128, activation='relu')(conv_layer_pool)

dummy_gnn = Model(inputs=[nodefeature_input ,adjacency_input], outputs=[dense_layer_graph])

if i create a dummy batch of data:

adj_matrix = nx.adjacency_matrix(nx_graph) 
x = []
for i in range(10):
    x.append(adj_matrix) #its the same graph for the batch, but this is just to try with a batch of data

practice_batch = [GCNConv.preprocess(i) for i in x]
practice_batch = [sp_matrix_to_sp_tensor(i) for i in new_list]

node_features = node_features #just node features
y = np.zeros((10, 176, 1))
for i in range(10):
    y[index] = node_features
    index +=1

if i predict on one sample (as discussed the other day) this works without issue

dummy_gnn([y[0], practice_batch[0])

however, if I now predict on a batch of sparse tensors.. this fails..

sp_batch = []
for i in practice_batch:
    sp_batch.append(tf.sparse.expand_dims(i, 0))
sp_batch = tf.sparse.concat(sp_inputs= sp_batch, axis=0)
dummy_gnn([y.reshape(1, 10, 176, 1), sp_batch])

this now produces an error AssertionError: Expected a of rank 2 or 3, got 4

If i create a dummy network with only an input for the node features (y), the batch is accepted without issue, so the problem seems to be with the batch of sparse tensors (adjacency matrix input) - I have tried so many different ways of feeding these tensors in, any help is appreciated!! thank you again :)

ImportError: cannot import name 'EdgeConditionedConv' from 'spektral.layers' (/usr/local/lib/python3.8/dist-packages/spektral/layers/init.py)

ImportError: cannot import name 'batch_iterator' from 'spektral.utils' (/usr/local/lib/python3.8/dist-packages/spektral/utils/init.py)

I could not solve this two Importerror. I install spektral successfully with all its packages. But on my local machine as well as google colab has not this two 'EdgeConditionedConv' and 'batch_iterator' name. Please help me to find out the solution.

Hi!

I updated setup.py for the package to reflect specific dependencies when running on Apple Silicon (it now installs tensorflow-macos instead of tensorflow when this is the case). It solved some problems I was having when integrating spektral into a package whose dependencies I'm managing using poetry. It may help others too :)

I tested it on an M1 MacBook Pro, installing both via pip and poetry. I'm open for suggestions regarding other implementations of the same fix, if you're interested.

Best! Lucas

I'm running into two issues when trying to load a dataset.

1. with the TUDataset, the clean URL doesn't exist anymore. I changed tudataset.py --> line 55: url_clean = ("https://www.chrsmrrs.com/graphkerneldatasets") and that seems to work.

2. This error happened with any TUDataset and with OGB When I load a dataset I get an error from dataset.py/

dataset = TUDataset(name='PROTEINS', clean=True)

Here's the error: ----> 2 dataset = TUDataset(name='PROTEINS', clean=False)

File ~/miniconda3/envs/GraphDLenv/lib/python3.8/site-packages/spektral/datasets/tudataset.py:66, in TUDataset.init(self, name, clean, **kwargs) 64 self.name = name 65 self.clean = clean ---> 66 super().init(**kwargs)

File ~/miniconda3/envs/GraphDLenv/lib/python3.8/site-packages/spektral/data/dataset.py:119, in Dataset.init(self, transforms, datainputs, **kwargs) 116 self.download() 118 # Read graphs --> 119 self.graphs = self.read(datainputs)

... TypeError: read() takes 1 positional argument but 2 were given

I'm not wise enough in the ways of Python to know why it's not loading. Any help is appreciated, thanks for this awesome library!

Hi,

I use the GeneralGNN model from Spektral with my own dataset. Training and evaluation works fine. When I try to load the trained model, I get different errors for different loading approaches for example, weights,SavedModel, model.to_json etc.. So, my question is how to save and load the trained GeneralGNN model, is there any way to do it?. Note: I do not make any changes to this model.

thanks.

Could someone suggest, how to add new node(s) to the single graph, that already has trained model for node classification, to make prediction(classification) for this new nodes with existing model?

Hi! I currently have a model (implemented via subclassing) that has 2 ECCConv layers, then aggregates the node embeddings with a global sum and runs the result through a NN to get a score value. I also use DisjointLoader to batch graphs together.

I'd like to try generating that score based only on one of the nodes' encodings, instead of aggreggating the whole graph.

I was hoping you could recommend what the best way of doing this would be, since none of the implemented pooling layers seem to do this.

Thanks!