jliphard / deepevolve Goto Github PK

$ git clone [email protected]:jliphard/DeepEvolve.git
$ git log -1 --pretty=format:"%H"
e9bf389a658aacf52a1bdacf4697ae4a704c0452
$ python3 brute.py 
Using TensorFlow backend.
Traceback (most recent call last):
  File "brute.py", line 98, in <module>
    main()
  File "brute.py", line 93, in main
    genomes = generate_genome_list(all_possible_genes)
  File "brute.py", line 75, in generate_genome_list
    genome_obj.set_genes_to(genome, 0, 0)
  File "/nevis/milne/files/gd2392/research/projects/DeepEvolve/genome.py", line 102, in set_genes_to
    self.update_hash()
  File "/nevis/milne/files/gd2392/research/projects/DeepEvolve/genome.py", line 43, in update_hash
    + str(self.geneparam['nb_layers']) + self.geneparam['optimizer']
  File "/nevis/milne/files/gd2392/research/projects/DeepEvolve/genome.py", line 143, in nb_neurons
    nb_neurons[i] = self.geneparam['nb_neurons_' + str(i+1)]
KeyError: 'nb_neurons_1'

def compile_model_cnn(genome, nb_classes, input_shape):
    """Compile a sequential model.

    Args:
        genome (dict): the parameters of the genome

    Returns:
        a compiled network.

    """
    # Get our network parameters.
    nb_layers  = genome.geneparam['nb_layers' ]
    nb_neurons = genome.nb_neurons()

    activation = genome.geneparam['activation']
    optimizer  = genome.geneparam['optimizer' ]

    logging.info("Architecture:%s,%s,%s,%d" % (str(nb_neurons), activation, optimizer, nb_layers))

    model = Sequential()
    # Add each layer.
    for i in range(0,nb_layers):
        # Need input shape for first layer.
        if i == 0:
            model.add(Conv2D(nb_neurons[i], kernel_size = (3, 3), activation = activation, padding='same', input_shape = input_shape))
        else:
            model.add(Conv2D(nb_neurons[i], kernel_size = (3, 3), activation = activation))
        
        if i < 2: #otherwise we hit zero
            model.add(MaxPooling2D(pool_size=(2, 2)))
        
        model.add(Dropout(0.2))

    model.add(Flatten())
    # always use last nb_neurons value for dense layer
    model.add(Dense(nb_neurons[len(nb_neurons) - 1], activation = activation))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes, activation = 'softmax'))

    #BAYESIAN CONVOLUTIONAL NEURAL NETWORKS WITH BERNOULLI APPROXIMATE VARIATIONAL INFERENCE
    #need to read this paper

    model.compile(loss='categorical_crossentropy',
              optimizer=optimizer,
              metrics=['accuracy'])

    return model

Getting Keras datasets
Compling Keras model
Architecture:[64, 16, 128, 16, 64, 128],relu,nadam,5
  7%|▋         | 1/15 [00:17<04:09, 17.81s/it]Traceback (most recent call last):
  File "/home/shehzikhan/Projects/DeepWork/generaldeepevolution/main.py", line 377, in <module>
    main(dataset,nb_classes,batch_size,epochs,mode,population,generations,network,project_dir=project_dir)
  File "/home/shehzikhan/Projects/DeepWork/generaldeepevolution/main.py", line 339, in main
    generate(evolution_params, dataset,nb_classes,batch_size,epochs,run,network,project_dir=project_dir)
  File "/home/shehzikhan/Projects/DeepWork/generaldeepevolution/main.py", line 232, in generate
    train_genomes(genomes, dataset,i+1,run,nb_classes,batch_size,epochs,mode,network,project_dir=project_dir)
  File "/home/shehzikhan/Projects/DeepWork/generaldeepevolution/main.py", line 31, in train_genomes
    genome.train(dataset,gen,run,nb_classes,batch_size,epochs,mode,network,project_dir=project_dir)
  File "/home/shehzikhan/Projects/DeepWork/generaldeepevolution/genome.py", line 123, in train
    self.training_history,self.test_score,self.model_name = train_and_score(self, dataset,mode,gen,run,nb_classes,batch_size,epochs,network,project_dir=project_dir)
  File "/home/shehzikhan/Projects/DeepWork/generaldeepevolution/train.py", line 407, in train_and_score
    model = compile_model_cnn(genome, nb_classes, input_shape,mode)
  File "/home/shehzikhan/Projects/DeepWork/generaldeepevolution/train.py", line 259, in compile_model_cnn
    model.add(Conv2D(nb_neurons[i], kernel_size = (3, 3), activation = activation))
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/keras/engine/sequential.py", line 185, in add
    output_tensor = layer(self.outputs[0])
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/keras/engine/base_layer.py", line 457, in __call__
    output = self.call(inputs, **kwargs)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/keras/layers/convolutional.py", line 168, in call
    dilation_rate=self.dilation_rate)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 3565, in conv2d
    data_format=tf_data_format)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 780, in convolution
    return op(input, filter)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 868, in __call__
    return self.conv_op(inp, filter)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 520, in __call__
    return self.call(inp, filter)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 204, in __call__
    name=self.name)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 956, in conv2d
    data_format=data_format, dilations=dilations, name=name)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 454, in new_func
    return func(*args, **kwargs)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3155, in create_op
    op_def=op_def)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1731, in __init__
    control_input_ops)
  File "/home/shehzikhan/pythonenvs/deepwork/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1579, in _create_c_op
    raise ValueError(str(e))
ValueError: Negative dimension size caused by subtracting 3 from 2 for 'conv2d_5/convolution' (op: 'Conv2D') with input shapes: [?,2,2,16], [3,3,16,64].
Exception KeyError: KeyError(<weakref at 0x7f249b920e68; to 'tqdm' at 0x7f242b4391d0>,) in <object repr() failed> ignored