I'm trying to run GA for mnist but getting Negative Dimension error randomly in different network combinations. Some models would compile fine and some would not.
Your code for compiling CNN model remains same as given below.
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
Below is the error report.
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
I'm trying to run GA for mnist but getting Negative Dimension error randomly in different network combinations. Some models would compile fine and some would not.
Your code for compiling CNN model remains same as given below.
Below is the error report.