def dense_residual_block(self, model_layer, name):
with tf.name_scope('dense_residual_block') as scope:
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
self.network[name + '_1resid'] = layers.DenseLayer(
self.network[last_layer],
num_units=num_units,
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,
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'
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 build_layers(self, model_layers, supervised=True, use_scope=False):
self.network = OrderedDict()
name_gen = NameGenerator()
self.num_dropout = 0
self.num_inputs = 0
self.last_layer = ''
# loop to build each layer of network
for model_layer in model_layers:
layer = model_layer['layer']
# name of layer
if 'name' in model_layer:
name = model_layer['name']
else:
name = name_gen.generate_name(layer)
if layer == "input":
# add input layer
self.single_layer(model_layer, name)
else:
if layer == 'conv1d_residual':
self.conv1d_residual_block(model_layer, name)
elif layer == 'conv2d_residual':
self.conv2d_residual_block(model_layer, name)
elif layer == 'dense_residual':
self.dense_residual_block(model_layer, name)
elif layer == 'variational':
self.network['encode_mu'] = layers.DenseLayer(self.network[self.last_layer], num_units=model_layer['num_units'], **self.seed)
self.network['encode_logsigma'] = layers.DenseLayer(self.network[self.last_layer], num_units=model_layer['num_units'], **self.seed)
self.network['Z'] = layers.VariationalSampleLayer(self.network['encode_mu'], self.network['encode_logsigma'])
self.last_layer = 'Z'
else:
# add core layer
self.single_layer(model_layer, name)
# add Batch normalization layer
if 'norm' in model_layer:
if 'batch' in model_layer['norm']:
new_layer = name + '_batch' #str(counter) + '_' + name + '_batch'
self.network[new_layer] = layers.BatchNormLayer(self.network[self.last_layer], self.placeholders['is_training'])
self.last_layer = new_layer
else:
if (model_layer['layer'] == 'dense') | (model_layer['layer'] == 'conv1d') | (model_layer['layer'] == 'conv2d'):
if 'b' in model_layer:
if model_layer['b'] != None:
b = init.Constant(0.05)
new_layer = name+'_bias'
self.network[new_layer] = layers.BiasLayer(self.network[self.last_layer], b=b)
self.last_layer = new_layer
elif 'norm' not in model_layer:
b = init.Constant(0.05)
new_layer = name+'_bias'
self.network[new_layer] = layers.BiasLayer(self.network[self.last_layer], b=b)
self.last_layer = new_layer
else:
if 'b' in model_layer:
if model_layer['b'] != None:
b = init.Constant(0.05)
new_layer = name+'_bias'
self.network[new_layer] = layers.BiasLayer(self.network[self.last_layer], b=b)
self.last_layer = new_layer
# add activation layer
if 'activation' in model_layer:
new_layer = name+'_active'
self.network[new_layer] = layers.ActivationLayer(self.network[self.last_layer], function=model_layer['activation'])
self.last_layer = new_layer
# add max-pooling layer ### Modified this from the older pool_size
if 'max_pool' in model_layer:
new_layer = name+'_maxpool' # str(counter) + '_' + name+'_pool'
if len(self.network[self.last_layer].output_shape) == 4:
if isinstance(model_layer['max_pool'], (tuple, list)):
self.network[new_layer] = layers.MaxPool2DLayer(self.network[self.last_layer], pool_size=model_layer['max_pool'])
else:
self.network[new_layer] = layers.MaxPool2DLayer(self.network[self.last_layer], pool_size=(model_layer['max_pool'], 1))
self.last_layer = new_layer
# add mean-pooling layer
elif 'mean_pool' in model_layer:
new_layer = name+'_meanpool' # str(counter) + '_' + name+'_pool'
if isinstance(model_layer['mean_pool'], (tuple, list)):
self.network[new_layer] = layers.MeanPool2DLayer(self.network[self.last_layer], pool_size=model_layer['mean_pool'], name=name+'_meanpool')
else:
self.network[new_layer] = layers.MeanPool1DLayer(self.network[self.last_layer], pool_size=model_layer['mean_pool'], name=name+'_meanpool')
self.last_layer = new_layer
# add global-pooling layer
elif 'global_pool' in model_layer:
print('global_pool')
new_layer = name+'_globalpool'
self.network[new_layer] = layers.GlobalPoolLayer(self.network[self.last_layer], func=model_layer['global_pool'], name=name+'_globalpool')
self.last_layer = new_layer
# add dropout layer
if 'dropout' in model_layer:
new_layer = name+'_dropout' # str(counter) + '_' + name+'_dropout'
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']
self.num_dropout += 1
self.network[new_layer] = layers.DropoutLayer(self.network[self.last_layer], keep_prob=self.placeholders[placeholder_name])
self.last_layer = new_layer
if supervised:
self.network['output'] = self.network.pop(self.last_layer)
shape = self.network['output'].get_output_shape()
targets = utils.placeholder(shape=shape, name='output')
self.placeholders['targets'] = targets
self.feed_dict['targets'] = []
else:
self.network['X'] = self.network.pop(self.last_layer)
self.placeholders['targets'] = self.placeholders['inputs'][0]
self.feed_dict['targets'] = []
self.feed_dict['KL_weight'] = 1.0
self.placeholders['KL_weight'] = tf.placeholder(tf.float32)
return self.network, self.placeholders, self.feed_dict
def build_layers(self, model_layers, supervised=True):
self.network = OrderedDict()
name_gen = NameGenerator()
self.num_dropout = 0
self.num_inputs = 0
self.last_layer = ''
# loop to build each layer of network
for model_layer in model_layers:
layer = model_layer['layer']
# name of layer
if 'name' in model_layer:
name = model_layer['name']
else:
name = name_gen.generate_name(layer)
# set scope for each layer
with tf.name_scope(name) as scope:
if layer == "input":
# add input layer
self.single_layer(model_layer, name)
elif layer == 'embedding':
vocab_size = model_layer['vocab_size']
embedding_size = model_layer['embedding_size']
if 'max_norm' in model_layer:
max_norm = model_layer['max_norm']
else:
max_norm = None
self.network[name] = layers.EmbeddingLayer(
self.network[self.last_layer], vocab_size,
embedding_size, max_norm)
self.last_layer = name
elif (layer == 'variational') | (layer
== 'variational_normal'):
if 'name' in model_layer:
name = model_layer['name']
else:
name = 'Z'
self.network[name + '_mu'] = layers.DenseLayer(
self.network[self.last_layer],
num_units=model_layer['num_units'],
b=init.GlorotUniform(),
**self.seed)
self.network[name + '_logvar'] = layers.DenseLayer(
self.network[self.last_layer],
num_units=model_layer['num_units'],
b=init.GlorotUniform(),
**self.seed)
self.network[name +
'_sample'] = layers.VariationalSampleLayer(
self.network[name + '_mu'],
self.network[name + '_logvar'])
self.last_layer = name + '_sample'
elif layer == 'variational_softmax':
if 'hard' in model_layer:
hard = model_layer['hard']
else:
hard = False
num_categories, num_classes = model_layer['shape']
if 'temperature' in model_layer:
temperature = model_layer['temperature']
else:
temperature = 5.0
self.feed_dict['temperature'] = temperature
self.placeholders['temperature'] = tf.placeholder(
dtype=tf.float32, name="temperature")
if 'name' in model_layer:
name = model_layer['name']
else:
name = 'Z'
self.network[name + '_logits'] = layers.DenseLayer(
self.network[self.last_layer],
num_units=num_categories * num_classes,
b=init.GlorotUniform())
self.network[name +
'_logits_reshape'] = layers.ReshapeLayer(
self.network[name + '_logits'],
shape=[-1, num_classes])
self.network[name] = layers.ActivationLayer(
self.network[name + '_logits_reshape'],
function='softmax')
self.network[name +
'_sample'] = layers.CategoricalSampleLayer(
self.network[name + '_logits_reshape'],
temperature=temperature,
hard=hard)
self.network[name +
'_logits_reshape'] = layers.ReshapeLayer(
self.network[name + '_logits'],
shape=[-1, num_categories, num_classes])
self.network[name + '_softmax'] = layers.Softmax2DLayer(
self.network[name + '_logits_reshape'])
self.network[name +
'_sample'] = layers.CategoricalSampleLayer(
self.network[name + '_logits_reshape'],
temperature=temperature,
hard=hard)
self.network[name] = layers.ReshapeLayer(
self.network[name + '_softmax'],
shape=[-1, num_categories * num_classes])
self.last_layer = name
else:
if layer == 'conv1d_residual':
self.conv1d_residual_block(model_layer, name)
elif layer == 'conv2d_residual':
self.conv2d_residual_block(model_layer, name)
elif layer == 'dense_residual':
self.dense_residual_block(model_layer, name)
else:
# add core layer
self.single_layer(model_layer, name)
# add Batch normalization layer
if 'norm' in model_layer:
if 'batch' in model_layer['norm']:
with tf.name_scope("norm") as scope:
new_layer = name + '_batch' #str(counter) + '_' + name + '_batch'
self.network[
new_layer] = layers.BatchNormLayer(
self.network[self.last_layer],
self.placeholders['is_training'])
self.last_layer = new_layer
else:
if (model_layer['layer'] == 'dense') | (
model_layer['layer'] == 'conv1d') | (
model_layer['layer'] == 'conv2d'):
if 'b' in model_layer:
if model_layer['b'] != None:
with tf.name_scope("bias") as scope:
b = init.Constant(model_layer['b'])
new_layer = name + '_bias'
self.network[
new_layer] = layers.BiasLayer(
self.network[self.last_layer],
b=b)
self.last_layer = new_layer
elif 'norm' not in model_layer:
with tf.name_scope("bias") as scope:
b = init.GlorotUniform()
new_layer = name + '_bias'
self.network[new_layer] = layers.BiasLayer(
self.network[self.last_layer], b=b)
self.last_layer = new_layer
# add activation layer
if 'activation' in model_layer:
new_layer = name + '_active'
self.network[new_layer] = layers.ActivationLayer(
self.network[self.last_layer],
function=model_layer['activation'],
name=scope)
self.last_layer = new_layer
# add max-pooling layer
if 'max_pool' in model_layer:
new_layer = name + '_maxpool' # str(counter) + '_' + name+'_pool'
if isinstance(model_layer['max_pool'], (tuple, list)):
self.network[new_layer] = layers.MaxPool2DLayer(
self.network[self.last_layer],
pool_size=model_layer['max_pool'],
name=name + '_maxpool')
else:
self.network[new_layer] = layers.MaxPool1DLayer(
self.network[self.last_layer],
pool_size=model_layer['max_pool'],
name=name + '_maxpool')
self.last_layer = new_layer
# add mean-pooling layer
elif 'mean_pool' in model_layer:
new_layer = name + '_meanpool' # str(counter) + '_' + name+'_pool'
if isinstance(model_layer['mean_pool'], (tuple, list)):
self.network[new_layer] = layers.MeanPool2DLayer(
self.network[self.last_layer],
pool_size=model_layer['mean_pool'],
name=name + '_meanpool')
else:
self.network[new_layer] = layers.MeanPool1DLayer(
self.network[self.last_layer],
pool_size=model_layer['mean_pool'],
name=name + '_meanpool')
self.last_layer = new_layer
# add global-pooling layer
elif 'global_pool' in model_layer:
new_layer = name + '_globalpool'
self.network[new_layer] = layers.GlobalPoolLayer(
self.network[self.last_layer],
func=model_layer['global_pool'],
name=name + '_globalpool')
self.last_layer = new_layer
# add dropout layer
if 'dropout' in model_layer:
new_layer = name + '_dropout' # str(counter) + '_' + name+'_dropout'
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']
self.num_dropout += 1
self.network[new_layer] = layers.DropoutLayer(
self.network[self.last_layer],
keep_prob=self.placeholders[placeholder_name],
name=name + '_dropout')
self.last_layer = new_layer
if ('reshape' in model_layer) & (layer != 'reshape'):
new_layer = name + '_reshape'
self.network[new_layer] = layers.ReshapeLayer(
self.network[self.last_layer], model_layer['reshape'])
self.last_layer = new_layer
if supervised:
self.network['output'] = self.network.pop(self.last_layer)
shape = self.network['output'].get_output_shape()
targets = utils.placeholder(shape=shape, name='output')
self.placeholders['targets'] = targets
self.feed_dict['targets'] = []
else:
self.network['X'] = self.network.pop(self.last_layer)
self.placeholders['targets'] = self.placeholders['inputs']
self.feed_dict['targets'] = []
return self.network, self.placeholders, self.feed_dict