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joeddav / devol

Genetic neural architecture search with Keras
MIT License
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Error while exectuing demo notebook #23

Open tchaton opened 6 years ago

tchaton commented 6 years ago

Genome encoding and accuracy data stored at Wed Jan 24 15:38:15 2018.csv

model 1/20 - generation 1/10:

An error occurred and the model could not train. Assigned poor score.

RuntimeError Traceback (most recent call last)

in () 6 7 devol = DEvol(genome_handler) ----> 8 model = devol.run(dataset, num_generations, population_size, num_epochs) 9 model.summary() 10 /home/thomas/dsa-experimental/thomas/imagification/Ensemble_Testing/devol/devol.py in run(self, dataset, num_generations, pop_size, epochs, fitness, metric) 79 print("\nmodel {0}/{1} - generation {2}/{3}:\n"\ 80 .format(i + 1, len(members), 1, num_generations)) ---> 81 res = self.evaluate(members[i], epochs) 82 v = res[metric_index] 83 del res /home/thomas/dsa-experimental/thomas/imagification/Ensemble_Testing/devol/devol.py in evaluate(self, genome, epochs) 143 pass 144 self.bssf = met --> 145 model.save('best-model.h5') 146 147 return model, loss, accuracy /usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in save(self, filepath, overwrite, include_optimizer) 2554 """ 2555 from ..models import save_model -> 2556 save_model(self, filepath, overwrite, include_optimizer) 2557 2558 def save_weights(self, filepath, overwrite=True): /usr/local/lib/python2.7/dist-packages/keras/models.pyc in save_model(model, filepath, overwrite, include_optimizer) 114 else: 115 model_layers = model.layers --> 116 topology.save_weights_to_hdf5_group(model_weights_group, model_layers) 117 118 if include_optimizer and hasattr(model, 'optimizer'): /usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in save_weights_to_hdf5_group(f, layers) 2850 g = f.create_group(layer.name) 2851 symbolic_weights = layer.weights -> 2852 weight_values = K.batch_get_value(symbolic_weights) 2853 weight_names = [] 2854 for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)): /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.pyc in batch_get_value(ops) 2200 """ 2201 if ops: -> 2202 return get_session().run(ops) 2203 else: 2204 return [] /usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata) 887 try: 888 result = self._run(None, fetches, feed_dict, options_ptr, --> 889 run_metadata_ptr) 890 if run_metadata: 891 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) /usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata) 1047 raise RuntimeError('Attempted to use a closed Session.') 1048 if self.graph.version == 0: -> 1049 raise RuntimeError('The Session graph is empty. Add operations to the ' 1050 'graph before calling run().') 1051 RuntimeError: The Session graph is empty. Add operations to the graph before calling run().
davidADSP commented 6 years ago

I had the same issue - for me what solved it was ensuring the response is in one-hot encoded form (i.e. for binary problem, the input is [[0,1],[1,0]] instead of [1,0])