def __init__(self, model_layers, supervised=True):
self.model_layers = model_layers
self.placeholders = {}
self.placeholders['inputs'] = []
self.lastlayer = ''
self.num_dropout = 0
self.hidden_feed_dict = {}
self.is_training = tf.placeholder(tf.bool, name='is_training')
self.hidden_feed_dict[self.is_training] = True
self.network = OrderedDict()
self.build_layers()
if supervised:
targets = utils.placeholder(shape=(None, model_layers[-1]['num_units']), name='output')
self.placeholders['targets'] = targets
self.network['output'] = self.network[self.lastlayer]
else:
self.placeholders['targets'] = self.placeholders['inputs']
def model(input_shape, num_labels=None):
# design a neural network model
# placeholders
inputs = utils.placeholder(shape=input_shape, name='input')
is_training = tf.placeholder(tf.bool, name='is_training')
keep_prob_conv = tf.placeholder(tf.float32, name='keep_prob_conv')
keep_prob_dense = tf.placeholder(tf.float32, name='keep_prob_dense')
targets = utils.placeholder(shape=(None, num_labels), name='output')
# placeholder dictionary
placeholders = {
'inputs': inputs,
'targets': targets,
'keep_prob_conv': keep_prob_conv,
'keep_prob_dense': keep_prob_dense,
'is_training': is_training
}
# create model
layer1 = {'layer': 'input', 'inputs': inputs, 'name': 'input'}
layer2 = {
'layer': 'conv2d',
'num_filters': 32,
'filter_size': (1, 5),
'batch_norm': is_training,
'activation': 'prelu',
'dropout': keep_prob_conv,
'name': 'conv1'
}
layer3 = {
'layer': 'residual-conv2d',
'function': 'prelu',
'filter_size': (1, 5),
'dropout': keep_prob_conv,
'batch_norm': is_training,
'name': 'resid1'
}
layer4 = {
'layer': 'conv2d',
'num_filters': 64,
'filter_size': (4, 5),
'batch_norm': is_training,
'activation': 'prelu',
'dropout': keep_prob_conv,
'name': 'conv2'
}
layer5 = {
'layer': 'residual-conv2d',
'function': 'prelu',
'filter_size': (1, 5),
'batch_norm': is_training,
'dropout': keep_prob_conv,
'pool_size': (1, 10),
'name': 'resid2'
}
layer6 = {
'layer': 'conv2d',
'num_filters': 128,
'filter_size': (1, 1),
'batch_norm': is_training,
'activation': 'prelu',
'dropout': keep_prob_conv,
'name': 'conv3'
}
layer7 = {
'layer': 'dense',
'num_units': 256,
'activation': 'prelu',
'dropout': keep_prob_dense,
'name': 'dense1'
}
layer8 = {
'layer': 'residual-dense',
'function': 'prelu',
'batch_norm': is_training,
'dropout': keep_prob_dense,
'name': 'resid3'
}
layer9 = {
'layer': 'dense',
'num_units': num_labels,
'activation': 'softmax',
'name': 'dense2'
}
#from tfomics import build_network
model_layers = [
layer1, layer2, layer3, layer4, layer5, layer6, layer7, layer8, layer9
]
net = build_network(model_layers)
# optimization parameters
optimization = {
"objective": "categorical",
"optimizer": "adam",
"learning_rate": 0.001,
"l2": 1e-6,
# "l1": 0,
}
return net, placeholders, optimization
def model(input_shape, num_labels=None):
# design a neural network model
# placeholders
inputs = utils.placeholder(shape=input_shape, name='input')
targets = utils.placeholder(shape=(None, num_labels), name='output')
# placeholder dictionary
placeholders = {
'inputs': inputs,
'targets': targets,
'keep_prob': keep_prob,
'is_training': is_training
}
# create model
layer1 = {'layer': 'input', 'inputs': inputs, 'name': 'input'}
layer2 = {
'layer': 'conv2d',
'num_filters': 18,
'filter_size': (2, 5),
'batch_norm': is_training,
'activation': 'leaky_relu',
'name': 'conv1'
}
layer3 = {
'layer': 'conv2d',
'num_filters': 40,
'filter_size': (2, 5),
'batch_norm': is_training,
'activation': 'leaky_relu',
'pool_size': (1, 10),
'name': 'conv2'
}
layer4 = {
'layer': 'conv2d',
'num_filters': 15,
'filter_size': (1, 1),
'batch_norm': is_training,
'activation': 'leaky_relu',
'name': 'conv3'
}
layer5 = {
'layer': 'dense',
'num_units': 100,
'activation': 'leaky_relu',
'dropout': keep_prob,
'name': 'dense1'
}
layer6 = {
'layer': 'dense',
'num_units': num_labels,
'activation': 'softmax',
'name': 'dense2'
}
#from tfomics import build_network
model_layers = [layer1, layer2, layer3, layer4, layer5, layer6]
net = build_network(model_layers)
# optimization parameters
optimization = {
"objective": "categorical",
"optimizer": "adam",
"learning_rate": 0.001,
"l2": 1e-6,
# "l1": 0,
}
return net, placeholders, optimization
def model(input_shape, num_labels):
# placeholders
inputs = utils.placeholder(shape=input_shape, name='input')
is_training = tf.placeholder(tf.bool, name='is_training')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
targets = utils.placeholder(shape=(None, num_labels), name='output')
# placeholder dictionary
placeholders = {
'inputs': inputs,
'targets': targets,
'keep_prob': keep_prob,
'is_training': is_training
}
# create model
layer1 = {'layer': 'input', 'inputs': inputs, 'name': 'input'}
layer2 = {
'layer': 'conv1d',
'num_filters': {
'start': 20,
'bounds': [1, 200],
'scale': 20
},
'filter_size': {
'start': 5,
'bounds': [3, 19],
'scale': 6,
'multiples': 2,
'offset': 0
},
'batch_norm': is_training,
'padding': 'SAME',
'activation': 'relu',
'pool_size': {
'start': 4,
'bounds': [1, 10],
'scale': 6,
'multiples': 4
},
'name': 'conv1'
}
layer3 = {
'layer': 'conv1d',
'num_filters': {
'start': 20,
'bounds': [1, 200],
'scale': 20
},
'filter_size': {
'start': 5,
'bounds': [3, 19],
'scale': 6,
'multiples': 2,
'offset': 0
},
'batch_norm': is_training,
'padding': 'SAME',
'activation': 'relu',
'pool_size': {
'start': 4,
'bounds': [1, 10],
'scale': 6,
'multiples': 4
},
'dropout': keep_prob,
'name': 'conv2'
}
layer4 = {
'layer': 'dense',
'num_units': {
'start': 120,
'bounds': [20, 1000],
'scale': 100,
'multiples': 10
},
'activation': 'sigmoid',
'dropout': keep_prob,
'name': 'dense1'
}
layer5 = {
'layer': 'dense',
'num_units': num_labels,
'activation': 'sigmoid',
'name': 'dense2'
}
#from tfomics import build_network
model_layers = [layer1, layer2, layer3, layer4, layer5]
# optimization parameters
optimization = {
"objective": "binary",
"optimizer": "adam",
"learning_rate": {
'start': -3,
'bounds': [-4, -1],
'scale': 1.5,
'transform': 'log'
},
"l2": {
'start': -6,
'bounds': [-8, -2],
'scale': 3,
'transform': 'log'
},
# "l1": 0,
}
return model_layers, placeholders, optimization