def classifier_model(input_shape, output_shape, num_filters):
# create model
layer1 = {'layer': 'input',
'input_shape': input_shape
}
layer2 = {'layer': 'conv1d',
'num_filters': num_filters,
'filter_size': 11,
'activation': 'sigmoid',
'W': init.HeUniform(),
'padding': 'SAME',
'global_pool': 'max',
}
layer3 = {'layer': 'reshape',
'reshape': [-1, num_filters],
}
layer4 = {'layer': 'reduce_mean',
}
layer5 = {'layer': 'reshape',
'reshape': [-1, 1],
}
#from tfomics import build_network
model_layers = [layer1, layer2, layer3, layer4, layer5]
# optimization parameters
optimization = {"objective": "binary",
"optimizer": "adam",
"learning_rate": 0.001,
#"momentum": 0.9,
#"l2": 1e-6,
}
return model_layers, optimization
def conv2d_residual_block(self, model_layer, name): last_layer = self.last_layer filter_size = model_layer['filter_size'] if 'function' in model_layer: activation = model_layer['function'] else: activation = 'relu' # original residual unit shape = self.network[last_layer].get_output_shape() num_filters = shape[-1].value if not isinstance(filter_size, (list, tuple)): filter_size = (filter_size, filter_size) if 'W' not in model_layer.keys(): W = init.HeUniform(**self.seed) else: W = model_layer['W'] self.network[name+'_1resid'] = layers.Conv2DLayer(self.network[last_layer], num_filters=num_filters, filter_size=filter_size, W=W, padding='SAME') self.network[name+'_1resid_norm'] = layers.BatchNormLayer(self.network[name+'_1resid'], self.placeholders['is_training']) self.network[name+'_1resid_active'] = layers.ActivationLayer(self.network[name+'_1resid_norm'], function=activation) if 'dropout_block' in model_layer: placeholder_name = 'keep_prob_'+str(self.num_dropout) self.placeholders[placeholder_name] = tf.placeholder(tf.float32, name=placeholder_name) self.feed_dict[placeholder_name] = 1-model_layer['dropout_block'] self.num_dropout += 1 self.network[name+'_dropout1'] = layers.DropoutLayer(self.network[name+'_1resid_active'], keep_prob=self.placeholders[placeholder_name]) lastname = name+'_dropout1' else: lastname = name+'_1resid_active' self.network[name+'_2resid'] = layers.Conv2DLayer(self.network[lastname], num_filters=num_filters, filter_size=filter_size, W=W, padding='SAME') self.network[name+'_2resid_norm'] = layers.BatchNormLayer(self.network[name+'_2resid'], self.placeholders['is_training']) self.network[name+'_resid_sum'] = layers.ElementwiseSumLayer([self.network[last_layer], self.network[name+'_2resid_norm']]) self.network[name+'_resid'] = layers.ActivationLayer(self.network[name+'_resid_sum'], function=activation) self.last_layer = name+'_resid'
def dense_residual_block(self, model_layer, name): last_layer = self.last_layer if 'function' in model_layer: activation = model_layer['function'] else: activation = 'relu' # original residual unit shape = self.network[last_layer].get_output_shape() num_units = shape[-1].value if 'W' not in model_layer.keys(): W = init.HeUniform(**self.seed) else: W = model_layer['W'] self.network[name+'_1resid'] = layers.DenseLayer(self.network[last_layer], num_units=num_units, W=W, b=None, **self.seed) self.network[name+'_1resid_norm'] = layers.BatchNormLayer(self.network[name+'_1resid'], self.placeholders['is_training']) self.network[name+'_1resid_active'] = layers.ActivationLayer(self.network[name+'_1resid_norm'], function=activation) if 'dropout_block' in model_layer: placeholder_name = 'keep_prob_'+str(self.num_dropout) self.placeholders[placeholder_name] = tf.placeholder(tf.float32, name=placeholder_name) self.feed_dict[placeholder_name] = 1-model_layer['dropout_block'] self.num_dropout += 1 self.network[name+'_dropout1'] = layers.DropoutLayer(self.network[name+'_1resid_active'], keep_prob=self.placeholders[placeholder_name]) lastname = name+'_dropout1' else: lastname = name+'_1resid_active' self.network[name+'_2resid'] = layers.DenseLayer(self.network[lastname], num_units=num_units, W=W, b=None, **self.seed) self.network[name+'_2resid_norm'] = layers.BatchNormLayer(self.network[name+'_2resid'], self.placeholders['is_training']) self.network[name+'_resid_sum'] = layers.ElementwiseSumLayer([self.network[last_layer], self.network[name+'_2resid_norm']]) self.network[name+'_resid'] = layers.ActivationLayer(self.network[name+'_resid_sum'], function=activation) self.last_layer = name+'_resid'