def __init__(self,
input_shape,
output_dim,
hidden_dim,
hidden_nonlinearity=LN.rectify,
output_nonlinearity=None,
output_W_init=LI.GlorotUniform(),
name=None,
input_var=None,
input_layer=None,
trunc_steps=20,
output_gain=1):
if input_layer is None:
l_in = L.InputLayer(shape=(None, None) + input_shape,
input_var=input_var,
name="input")
else:
l_in = input_layer
l_step_input = L.InputLayer(shape=(None, ) + input_shape)
l_step_prev_hidden = L.InputLayer(shape=(None, hidden_dim))
l_gru = GRULayer(l_in,
num_units=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
hidden_init_trainable=False,
trunc_steps=trunc_steps)
l_gru_flat = L.ReshapeLayer(l_gru, shape=(-1, hidden_dim))
l_output_flat = L.DenseLayer(
l_gru_flat,
num_units=output_dim,
nonlinearity=output_nonlinearity,
W=output_W_init,
)
l_output = OpLayer(
l_output_flat,
op=lambda flat_output, l_input: flat_output.reshape(
(l_input.shape[0], l_input.shape[1], -1)),
shape_op=lambda flat_output_shape, l_input_shape:
(l_input_shape[0], l_input_shape[1], flat_output_shape[-1]),
extras=[l_in])
l_step_hidden = l_gru.get_step_layer(l_step_input, l_step_prev_hidden)
l_step_output = L.DenseLayer(
l_step_hidden,
num_units=output_dim,
nonlinearity=output_nonlinearity,
W=l_output_flat.W,
b=l_output_flat.b,
)
self._l_in = l_in
self._hid_init_param = l_gru.h0
self._l_gru = l_gru
self._l_out = l_output
self._l_step_input = l_step_input
self._l_step_prev_hidden = l_step_prev_hidden
self._l_step_hidden = l_step_hidden
self._l_step_output = l_step_output
def __init__(self,
output_dim,
hidden_sizes,
hidden_nonlinearity,
output_nonlinearity,
hidden_W_init=LI.GlorotUniform(),
hidden_b_init=LI.Constant(0.),
output_W_init=LI.GlorotUniform(),
output_b_init=LI.Constant(0.),
name=None,
input_var=None,
input_layer=None,
input_shape=None,
batch_norm=False):
Serializable.quick_init(self, locals())
if name is None:
prefix = ""
else:
prefix = name + "_"
if input_layer is None:
l_in = L.InputLayer(shape=(None, ) + input_shape,
input_var=input_var)
else:
l_in = input_layer
self._layers = [l_in]
l_hid = l_in
for idx, hidden_size in enumerate(hidden_sizes):
l_hid = L.DenseLayer(
l_hid,
num_units=hidden_size,
nonlinearity=hidden_nonlinearity,
name="%shidden_%d" % (prefix, idx),
W=hidden_W_init,
b=hidden_b_init,
)
if batch_norm:
l_hid = L.batch_norm(l_hid)
self._layers.append(l_hid)
l_out = L.DenseLayer(
l_hid,
num_units=output_dim,
nonlinearity=output_nonlinearity,
name="%soutput" % (prefix, ),
W=output_W_init,
b=output_b_init,
)
self._layers.append(l_out)
self._l_in = l_in
self._l_out = l_out
# self._input_var = l_in.input_var
self._output = L.get_output(l_out)
LasagnePowered.__init__(self, [l_out])
def _build_network(self, input_var):
"""Builds actor network, scaling outputs based on action bounds."""
tanh = lasagne.nonlinearities.ScaledTanH(1,
scale_out=self.action_scale)
network = nn.InputLayer((None, ) + self.state_shape, input_var)
network = nn.DenseLayer(network, 30, W_init, b_init, relu)
network = nn.DenseLayer(network, 30, W_init, b_init, relu)
return nn.DenseLayer(network, self.num_actions, W_out, b_out, tanh)
def build_pi_model():
log.i('BUILDING RASBPERRY PI MODEL...')
# Random Seed
lasagne_random.set_rng(cfg.getRandomState())
# Input layer for images
net = l.InputLayer((None, cfg.IM_DIM, cfg.IM_SIZE[1], cfg.IM_SIZE[0]))
# Convolutinal layer groups
for i in range(len(cfg.FILTERS)):
# 3x3 Convolution + Stride
net = batch_norm(
l.Conv2DLayer(net,
num_filters=cfg.FILTERS[i],
filter_size=cfg.KERNEL_SIZES[i],
num_groups=cfg.NUM_OF_GROUPS[i],
pad='same',
stride=2,
W=initialization(cfg.NONLINEARITY),
nonlinearity=nonlinearity(cfg.NONLINEARITY)))
log.i(('\tGROUP', i + 1, 'OUT SHAPE:', l.get_output_shape(net)))
# Fully connected layers + dropout layers
net = l.DenseLayer(net,
cfg.DENSE_UNITS,
nonlinearity=nonlinearity(cfg.NONLINEARITY),
W=initialization(cfg.NONLINEARITY))
net = l.DropoutLayer(net, p=cfg.DROPOUT)
net = l.DenseLayer(net,
cfg.DENSE_UNITS,
nonlinearity=nonlinearity(cfg.NONLINEARITY),
W=initialization(cfg.NONLINEARITY))
net = l.DropoutLayer(net, p=cfg.DROPOUT)
# Classification Layer (Softmax)
net = l.DenseLayer(net,
len(cfg.CLASSES),
nonlinearity=nonlinearity('softmax'),
W=initialization('softmax'))
log.i(("\tFINAL NET OUT SHAPE:", l.get_output_shape(net)))
log.i("...DONE!")
# Model stats
log.i(("MODEL HAS",
(sum(hasattr(layer, 'W')
for layer in l.get_all_layers(net))), "WEIGHTED LAYERS"))
log.i(("MODEL HAS", l.count_params(net), "PARAMS"))
return net
def __init__(self, incomings, num_units_hidden_common, dim_z, beta):
'''
params:
incomings: input layers, [image, label]
num_units_hidden_common: num_units_hidden for all BasicLayers.
dim_z: the dimension of z and num_units_output for encoders BaiscLayer
'''
super(SemiVAE, self).__init__(incomings)
self.mrg_srng = MRG_RandomStreams()
# random generator
self.incomings = incomings
self.num_classes = incomings[1].output_shape[1]
self.num_units_hidden_common = num_units_hidden_common
self.dim_z = dim_z
self.beta = beta
self.concat_xy = layers.ConcatLayer(self.incomings, axis=1)
self.encoder = BasicLayer(
self.concat_xy,
num_units_hidden=self.num_units_hidden_common,
)
self.encoder_mu = layers.DenseLayer(
self.encoder, self.dim_z, nonlinearity=nonlinearities.identity)
self.encoder_log_var = layers.DenseLayer(
self.encoder, self.dim_z, nonlinearity=nonlinearities.identity)
[image_input, label_input] = self.incomings
self.dim_image = image_input.output_shape[1]
print('dim_image: ', self.dim_image)
# merge encoder_mu and encoder_log_var to get z.
self.sampler = SamplerLayer([self.encoder_mu, self.encoder_log_var])
self.concat_yz = layers.ConcatLayer([label_input, self.sampler],
axis=1)
self.decoder = BasicLayer(
self.concat_yz, num_units_hidden=self.num_units_hidden_common)
self.decoder_x = layers.DenseLayer(self.decoder,
num_units=self.dim_image,
nonlinearity=nonlinearities.sigmoid)
self.classifier_helper = BasicLayer(
self.incomings[0], num_units_hidden=self.num_units_hidden_common)
self.classifier = layers.DenseLayer(
self.classifier_helper,
num_units=self.num_classes,
nonlinearity=nonlinearities.softmax,
)
def arch_class_00(dim_desc, dim_labels, param_arch, logger):
logger.info('Architecture:')
# input layers
desc = LL.InputLayer(shape=(None, dim_desc))
patch_op = LL.InputLayer(input_var=Tsp.csc_fmatrix('patch_op'),
shape=(None, None))
logger.info(' input : dim = %d' % dim_desc)
# layer 1: dimensionality reduction to 16
n_dim = 16
net = LL.DenseLayer(desc, n_dim)
logger.info(' layer 1: FC%d' % n_dim)
# layer 2: anisotropic convolution layer with 16 filters
n_filters = 16
net = CL.GCNNLayer([net, patch_op], n_filters, nrings=5, nrays=16)
string = ' layer 2: IC%d' % n_filters
if param_arch['flag_batchnorm'] is True:
net = LL.batch_norm(net)
string = string + ' + batch normalization'
logger.info(string)
# layer 3: anisotropic convolution layer with 32 filters
n_filters = 32
net = CL.GCNNLayer([net, patch_op], n_filters, nrings=5, nrays=16)
string = ' layer 3: IC%d' % n_filters
if param_arch['flag_batchnorm'] is True:
net = LL.batch_norm(net)
string = string + ' + batch normalization'
logger.info(string)
# layer 4: anisotropic convolution layer with 64 filters
n_filters = 64
net = CL.GCNNLayer([net, patch_op], n_filters, nrings=5, nrays=16)
string = ' layer 4: IC%d' % n_filters
if param_arch['flag_batchnorm'] is True:
net = LL.batch_norm(net)
string = string + ' + batch normalization'
logger.info(string)
# layer 5: fully connected layer with 256 filters
n_dim = 256
net = LL.DenseLayer(net, n_dim)
string = ' layer 5: FC%d' % n_dim
if param_arch['flag_batchnorm'] is True:
net = LL.batch_norm(net)
string = string + ' + batch normalization'
logger.info(string)
# layer 6: softmax layer producing a probability on the labels
if param_arch['flag_nonlinearity'] == 'softmax':
cla = LL.DenseLayer(net, dim_labels, nonlinearity=LN.softmax)
string = ' layer 6: softmax'
elif param_arch['flag_nonlinearity'] == 'log_softmax':
cla = LL.DenseLayer(net, dim_labels, nonlinearity=log_softmax)
string = ' layer 6: log-softmax'
else:
raise Exception('[e] the chosen non-linearity is not supported!')
logger.info(string)
# outputs
return desc, patch_op, cla, net, logger
def fcn_transfer(params):
""""""
assert 'inputs' in params
layers = L.InputLayer((None, 256 * len(params['inputs'])))
layers = L.dropout(layers)
layers = L.DenseLayer(layers, 1024, nonlinearity=nl.elu)
layers = L.dropout(layers)
layers = L.DenseLayer(layers, 16, nonlinearity=nl.softmax)
return layers
def build_instrument_model(self, n_vars, **kwargs):
targets = TT.vector()
instrument_vars = TT.matrix()
instruments = layers.InputLayer((None, n_vars), instrument_vars)
instruments = layers.DropoutLayer(instruments, p=0.2)
dense_layer = layers.DenseLayer(instruments,
kwargs['dense_size'],
nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(dense_layer,
kwargs['dense_size'],
nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.5)
self.instrument_output = layers.DenseLayer(
dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.instrument_output)
prediction = layers.get_output(self.instrument_output,
deterministic=False)
test_prediction = layers.get_output(self.instrument_output,
deterministic=True)
# flexible here, endog variable can be categorical, continuous, etc.
l2_cost = regularization.regularize_network_params(
self.instrument_output, regularization.l2)
loss = objectives.squared_error(
prediction.flatten(), targets.flatten()).mean() + 1e-4 * l2_cost
loss_total = objectives.squared_error(prediction.flatten(),
targets.flatten()).mean()
params = layers.get_all_params(self.instrument_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._instrument_train_fn = theano.function([
targets,
instrument_vars,
],
loss,
updates=param_updates)
self._instrument_loss_fn = theano.function([
targets,
instrument_vars,
], loss_total)
self._instrument_output_fn = theano.function([instrument_vars],
test_prediction)
return init_params
def buildModel():
print "BUILDING MODEL TYPE..."
#default settings
filters = 16
first_stride = 2
last_filter_multiplier = 4
#input layer
net = l.InputLayer((None, IM_DIM, IM_SIZE[1], IM_SIZE[0]))
#conv layers
net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters , filter_size=7, pad='same', stride=first_stride, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
net = l.MaxPool2DLayer(net, pool_size=2)
net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 2 , filter_size=5, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
net = l.MaxPool2DLayer(net, pool_size=2)
net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 4 , filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
net = l.MaxPool2DLayer(net, pool_size=2)
net = l.DropoutLayer(net, DROPOUT)
#net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 8 , filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
#net = l.MaxPool2DLayer(net, pool_size=2)
#net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 16 , filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
#net = l.MaxPool2DLayer(net, pool_size=2)
#net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 32 , filter_size=7, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
#net = l.MaxPool2DLayer(net, pool_size=2)
#print "\tFINAL POOL OUT SHAPE:", l.get_output_shape(net)
#dense layers
net = l.batch_norm(l.DenseLayer(net, 512, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
net = l.DropoutLayer(net, DROPOUT)
net = l.batch_norm(l.DenseLayer(net, 512, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
net = l.DropoutLayer(net, DROPOUT)
#Classification Layer
if MULTI_LABEL:
net = l.DenseLayer(net, NUM_CLASSES, nonlinearity=nonlinearities.sigmoid, W=init.HeNormal(gain=1))
else:
net = l.DenseLayer(net, NUM_CLASSES, nonlinearity=nonlinearities.softmax, W=init.HeNormal(gain=1))
print "...DONE!"
#model stats
print "MODEL HAS", (sum(hasattr(layer, 'W') for layer in l.get_all_layers(net))), "WEIGHTED LAYERS"
print "MODEL HAS", l.count_params(net), "PARAMS"
return net
def get_model(inp, patch_op):
icnn1 = batch_norm(utils_lasagne.GCNNLayer([inp, patch_op], 16, nrings=5, nrays=16))
ffn1 = LL.DenseLayer(icnn1, 512)
icnn2 = batch_norm(utils_lasagne.GCNNLayer([icnn1, patch_op], 32, nrings=5, nrays=16))
ffn2 = LL.DenseLayer(icnn2, 512)
icnn3 = batch_norm(utils_lasagne.GCNNLayer([icnn2, patch_op], 64, nrings=5, nrays=16))
ffn3 = LL.DenseLayer(icnn3, 512)
ffn4 = LL.ConcatLayer([inp,ffn1,ffn2,ffn3],axis=1, cropping=None);
ffn = LL.DenseLayer(ffn4, nclasses, nonlinearity=utils_lasagne.log_softmax)
return ffn
def __init__(self, args):
self.args = args
rng = np.random.RandomState(self.args.seed) # fixed random seeds
theano_rng = MRG_RandomStreams(rng.randint(2 ** 15))
lasagne.random.set_rng(np.random.RandomState(rng.randint(2 ** 15)))
data_rng = np.random.RandomState(self.args.seed_data)
''' specify pre-trained generator E '''
self.enc_layers = [LL.InputLayer(shape=(None, 3, 32, 32), input_var=None)]
enc_layer_conv1 = dnn.Conv2DDNNLayer(self.enc_layers[-1], 64, (5,5), pad=0, stride=1, W=Normal(0.01), nonlinearity=nn.relu)
self.enc_layers.append(enc_layer_conv1)
enc_layer_pool1 = LL.MaxPool2DLayer(self.enc_layers[-1], pool_size=(2, 2))
self.enc_layers.append(enc_layer_pool1)
enc_layer_conv2 = dnn.Conv2DDNNLayer(self.enc_layers[-1], 128, (5,5), pad=0, stride=1, W=Normal(0.01), nonlinearity=nn.relu)
self.enc_layers.append(enc_layer_conv2)
enc_layer_pool2 = LL.MaxPool2DLayer(self.enc_layers[-1], pool_size=(2, 2))
self.enc_layers.append(enc_layer_pool2)
self.enc_layer_fc3 = LL.DenseLayer(self.enc_layers[-1], num_units=256, nonlinearity=T.nnet.relu)
self.enc_layers.append(self.enc_layer_fc3)
self.enc_layer_fc4 = LL.DenseLayer(self.enc_layers[-1], num_units=10, nonlinearity=T.nnet.softmax)
self.enc_layers.append(self.enc_layer_fc4)
''' load pretrained weights for encoder '''
weights_toload = np.load('pretrained/encoder.npz')
weights_list_toload = [weights_toload['arr_{}'.format(k)] for k in range(len(weights_toload.files))]
LL.set_all_param_values(self.enc_layers[-1], weights_list_toload)
''' input tensor variables '''
#self.G_weights
#self.D_weights
self.dummy_input = T.scalar()
self.G_layers = []
self.z = theano_rng.uniform(size=(self.args.batch_size, self.args.z0dim))
self.x = T.tensor4()
self.meanx = T.tensor3()
self.Gen_x = T.tensor4()
self.D_layers = []
self.D_layer_adv = []
self.D_layer_z_recon = []
self.gen_lr = T.scalar() # learning rate
self.disc_lr = T.scalar() # learning rate
self.y = T.ivector()
self.y_1hot = T.matrix()
self.Gen_x_list = []
self.y_recon_list = []
self.mincost = T.scalar()
#self.enc_layer_fc3 = self.get_enc_layer_fc3()
self.real_fc3 = LL.get_output(self.enc_layer_fc3, self.x, deterministic=True)
def build_network(input_var, image_size=28, output_dim=10):
nonlin = lasagne.nonlinearities.rectify
W_init = lasagne.init.GlorotUniform()
b_init = lasagne.init.Constant(0.)
input_shape = (None, 1, image_size, image_size)
network = nn.InputLayer(input_shape, input_var)
network = nn.Conv2DLayer(network,
num_filters=64,
filter_size=(3, 3),
nonlinearity=nonlin,
W=W_init,
b=b_init)
network = nn.Conv2DLayer(network,
num_filters=64,
filter_size=(3, 3),
nonlinearity=nonlin,
W=W_init,
b=b_init)
network = nn.MaxPool2DLayer(network, pool_size=(2, 2))
network = nn.Conv2DLayer(network,
num_filters=128,
filter_size=(3, 3),
W=W_init,
b=b_init,
nonlinearity=nonlin)
network = nn.Conv2DLayer(network,
num_filters=128,
filter_size=(3, 3),
W=W_init,
b=b_init,
nonlinearity=nonlin)
network = nn.MaxPool2DLayer(network, pool_size=(2, 2))
network = nn.dropout(network, p=0.5)
network = nn.DenseLayer(network,
num_units=256,
W=W_init,
b=b_init,
nonlinearity=nonlin)
network = nn.dropout(network, p=0.5)
network = nn.DenseLayer(network,
num_units=output_dim,
W=W_init,
b=b_init,
nonlinearity=None)
return network
def test_stochastic_layer_network():
learning_rate = 0.1
momentum = 0.9
num_epoch = 1000
input = T.fmatrix('input')
output = T.fmatrix('output')
print 'FF-Layer: (Batch_size, n_features)'
print 'Building stochastic layer model'
l_in = L.InputLayer(shape=(1, 10), input_var=input)
l_2 = L.DenseLayer(l_in,
num_units=10,
nonlinearity=lasagne.nonlinearities.softmax,
W=lasagne.init.Constant(0.))
print 'Input Layer shape: ', L.get_output_shape(l_in)
print 'Dense Layer shape: ', L.get_output_shape(l_2)
l_stochastic_layer = StochasticLayer(l_2, estimator='ST')
print 'Stochastic Layer shape: ', L.get_output_shape(l_stochastic_layer)
l_out = L.DenseLayer(l_stochastic_layer,
num_units=10,
b=lasagne.init.Constant(0.))
print 'Final Dense Layer shape: ', L.get_output_shape(l_out)
network_output = L.get_output(l_out)
print 'Building loss function...'
loss = lasagne.objectives.squared_error(network_output, output)
loss = loss.mean()
params = L.get_all_params(l_out, trainable=True)
updates = nesterov_momentum(loss, params, learning_rate, momentum)
train = theano.function([input, output],
loss,
updates=updates,
allow_input_downcast=True)
output_fn = theano.function([input],
network_output,
allow_input_downcast=True)
test_X = np.ones((1, 10))
test_Y = np.ones((1, 10))
losses = []
mean_losses = []
for epoch in range(num_epoch):
print 'Epoch number: ', epoch
losses.append(train(test_X, test_Y))
print 'epoch {} mean loss {}'.format(epoch, np.mean(losses))
print 'Current Output: ', output_fn(test_X)
mean_losses.append(np.mean(losses))
plt.title("Mean loss")
plt.xlabel("Training examples")
plt.ylabel("Loss")
plt.plot(mean_losses, label="train")
plt.grid()
plt.legend()
plt.draw()
def get_model(inp, patch_op):
icnn = LL.DenseLayer(inp, 16)
icnn = batch_norm(
utils_lasagne.GCNNLayer([icnn, patch_op], 16, nrings=4, nrays=8))
icnn = batch_norm(
utils_lasagne.GCNNLayer([icnn, patch_op], 32, nrings=4, nrays=8))
icnn = batch_norm(
utils_lasagne.GCNNLayer([icnn, patch_op], 64, nrings=4, nrays=8))
ffn = batch_norm(LL.DenseLayer(icnn, 512))
ffn = LL.DenseLayer(icnn, nclasses, nonlinearity=utils_lasagne.log_softmax)
return ffn
def dec_net(_incoming, output_channels, nonlinearity=None):
_fc1 = L.DenseLayer(_incoming,
4 * _incoming.output_shape[1],
W=I.Normal(0.02),
b=None,
nonlinearity=NL.rectify)
_fc2 = L.DenseLayer(_fc1,
output_channels,
W=I.Normal(0.02),
b=I.Constant(0),
nonlinearity=nonlinearity)
return _fc2
def Network2(input_var=None,output_num=None,l1 = None,l2=None,l3=None,input_size = None): l_in = L.InputLayer(shape=(None,1,1,input_size),input_var=input_var) l_hid1 = L.DenseLayer(l_in,num_units=l1,nonlinearity=lasagne.nonlinearities.tanh) l_hid2 = L.DenseLayer(l_hid1,num_units = l2,nonlinearity = lasagne.nonlinearities.tanh) l_hid3 = L.DenseLayer(l_hid2,num_units = l3,nonlinearity = lasagne.nonlinearities.tanh) l_out = L.DenseLayer(l_hid3,num_units = output_num,nonlinearity=None) return l_out
def __init__(self,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=NL.rectify,
hidden_w_init=LI.HeUniform(),
hidden_b_init=LI.Constant(0.),
output_nonlinearity=NL.tanh,
output_w_init=LI.Uniform(-3e-3, 3e-3),
output_b_init=LI.Uniform(-3e-3, 3e-3),
bn=False):
assert isinstance(env_spec.action_space, Box)
Serializable.quick_init(self, locals())
l_obs = L.InputLayer(shape=(None, env_spec.observation_space.flat_dim))
l_hidden = l_obs
if bn:
l_hidden = batch_norm(l_hidden)
for idx, size in enumerate(hidden_sizes):
l_hidden = L.DenseLayer(
l_hidden,
num_units=size,
W=hidden_w_init,
b=hidden_b_init,
nonlinearity=hidden_nonlinearity,
name="h%d" % idx)
if bn:
l_hidden = batch_norm(l_hidden)
l_output = L.DenseLayer(
l_hidden,
num_units=env_spec.action_space.flat_dim,
W=output_w_init,
b=output_b_init,
nonlinearity=output_nonlinearity,
name="output")
# Note the deterministic=True argument. It makes sure that when getting
# actions from single observations, we do not update params in the
# batch normalization layers
action_var = L.get_output(l_output, deterministic=True)
self._output_layer = l_output
self._f_actions = tensor_utils.compile_function([l_obs.input_var],
action_var)
super(DeterministicMLPPolicy, self).__init__(env_spec)
LasagnePowered.__init__(self, [l_output])
def build_network(self):
l_char1_in = L.InputLayer(shape=(None, None, self.max_word_len),
input_var=self.inps[0])
l_char2_in = L.InputLayer(shape=(None, None, self.max_word_len),
input_var=self.inps[1])
l_mask1_in = L.InputLayer(shape=(None, None, self.max_word_len),
input_var=self.inps[2])
l_mask2_in = L.InputLayer(shape=(None, None, self.max_word_len),
input_var=self.inps[3])
l_char_in = L.ConcatLayer([l_char1_in, l_char2_in],
axis=1) # B x (ND+NQ) x L
l_char_mask = L.ConcatLayer([l_mask1_in, l_mask2_in], axis=1)
shp = (self.inps[0].shape[0],
self.inps[0].shape[1] + self.inps[1].shape[1],
self.inps[1].shape[2])
l_index_reshaped = L.ReshapeLayer(l_char_in,
(shp[0] * shp[1], shp[2])) # BN x L
l_mask_reshaped = L.ReshapeLayer(l_char_mask,
(shp[0] * shp[1], shp[2])) # BN x L
l_lookup = L.EmbeddingLayer(l_index_reshaped, self.num_chars,
self.char_dim) # BN x L x D
l_fgru = L.GRULayer(l_lookup,
2 * self.char_dim,
grad_clipping=10,
gradient_steps=-1,
precompute_input=True,
only_return_final=True,
mask_input=l_mask_reshaped)
l_bgru = L.GRULayer(l_lookup,
2 * self.char_dim,
grad_clipping=10,
gradient_steps=-1,
precompute_input=True,
backwards=True,
only_return_final=True,
mask_input=l_mask_reshaped) # BN x 2D
l_fwdembed = L.DenseLayer(l_fgru,
self.embed_dim / 2,
nonlinearity=None) # BN x DE
l_bckembed = L.DenseLayer(l_bgru,
self.embed_dim / 2,
nonlinearity=None) # BN x DE
l_embed = L.ElemwiseSumLayer([l_fwdembed, l_bckembed], coeffs=1)
l_char_embed = L.ReshapeLayer(l_embed,
(shp[0], shp[1], self.embed_dim / 2))
l_embed1 = L.SliceLayer(l_char_embed,
slice(0, self.inps[0].shape[1]),
axis=1)
l_embed2 = L.SliceLayer(l_char_embed,
slice(-self.inps[1].shape[1], None),
axis=1)
return l_embed1, l_embed2
def BatchNormRecurrentLayer(incoming,
num_units,
nonlinearity=None,
gradient_steps=-1,
grad_clipping=0,
layer_type=layers.CustomRecurrentLayer,
name='',
**kwargs):
"""
Helper method to define a Vanilla Recurrent Layer with batch normalization
"""
input_shape = incoming.output_shape
# Define input to hidden connections
in_to_hid_rf = layers.InputLayer((None, ) + input_shape[2:])
in_to_hid_rf = layers.DenseLayer(in_to_hid_rf,
num_units,
b=None,
nonlinearity=None,
name='ith_{0}'.format(name))
in_to_hid_rf_W = in_to_hid_rf.W
# Use batch normalization in the input to hidden connections
in_to_hid_rf = layers.BatchNormLayer(in_to_hid_rf,
name='ith_bn_{0}'.format(name))
# Define hidden to hidden connections
hid_to_hid_rf = layers.InputLayer((None, num_units))
hid_to_hid_rf = layers.DenseLayer(hid_to_hid_rf,
num_units,
b=None,
nonlinearity=None,
name='hth_{0}'.format(name))
l_r_f = layer_type(incoming,
input_to_hidden=in_to_hid_rf,
hidden_to_hidden=hid_to_hid_rf,
gradient_steps=gradient_steps,
grad_clipping=grad_clipping,
nonlinearity=nonlinearity,
name='l_r_{0}'.format(name),
**kwargs)
# Make layer parameters intuitively accessible
l_r_f.W_in_to_hid = in_to_hid_rf_W
l_r_f.W_hid_to_hid = hid_to_hid_rf.W
l_r_f.beta = in_to_hid_rf.beta
l_r_f.gamma = in_to_hid_rf.gamma
l_r_f.mean = in_to_hid_rf.mean
l_r_f.inv_std = in_to_hid_rf.inv_std
l_r_f.hid_init = l_r_f.hid_init
return l_r_f
def get_discriminator(self):
''' specify discriminator D0 '''
"""
disc0_layers = [LL.InputLayer(shape=(self.args.batch_size, 3, 32, 32))]
disc0_layers.append(LL.GaussianNoiseLayer(disc0_layers[-1], sigma=0.05))
disc0_layers.append(dnn.Conv2DDNNLayer(disc0_layers[-1], 96, (3,3), pad=1, W=Normal(0.02), nonlinearity=nn.lrelu))
disc0_layers.append(nn.batch_norm(dnn.Conv2DDNNLayer(disc0_layers[-1], 96, (3,3), pad=1, stride=2, W=Normal(0.02), nonlinearity=nn.lrelu))) # 16x16
disc0_layers.append(LL.DropoutLayer(disc0_layers[-1], p=0.1))
disc0_layers.append(nn.batch_norm(dnn.Conv2DDNNLayer(disc0_layers[-1], 192, (3,3), pad=1, W=Normal(0.02), nonlinearity=nn.lrelu)))
disc0_layers.append(nn.batch_norm(dnn.Conv2DDNNLayer(disc0_layers[-1], 192, (3,3), pad=1, stride=2, W=Normal(0.02), nonlinearity=nn.lrelu))) # 8x8
disc0_layers.append(LL.DropoutLayer(disc0_layers[-1], p=0.1))
disc0_layers.append(nn.batch_norm(dnn.Conv2DDNNLayer(disc0_layers[-1], 192, (3,3), pad=0, W=Normal(0.02), nonlinearity=nn.lrelu))) # 6x6
disc0_layer_shared = LL.NINLayer(disc0_layers[-1], num_units=192, W=Normal(0.02), nonlinearity=nn.lrelu) # 6x6
disc0_layers.append(disc0_layer_shared)
disc0_layer_z_recon = LL.DenseLayer(disc0_layer_shared, num_units=50, W=Normal(0.02), nonlinearity=None)
disc0_layers.append(disc0_layer_z_recon) # also need to recover z from x
disc0_layers.append(LL.GlobalPoolLayer(disc0_layer_shared))
disc0_layer_adv = LL.DenseLayer(disc0_layers[-1], num_units=10, W=Normal(0.02), nonlinearity=None)
disc0_layers.append(disc0_layer_adv)
return disc0_layers, disc0_layer_adv, disc0_layer_z_recon
"""
disc_x_layers = [LL.InputLayer(shape=(None, 3, 32, 32))]
disc_x_layers.append(LL.GaussianNoiseLayer(disc_x_layers[-1], sigma=0.2))
disc_x_layers.append(dnn.Conv2DDNNLayer(disc_x_layers[-1], 96, (3,3), pad=1, W=Normal(0.01), nonlinearity=nn.lrelu))
disc_x_layers.append(nn.batch_norm(dnn.Conv2DDNNLayer(disc_x_layers[-1], 96, (3,3), pad=1, stride=2, W=Normal(0.01), nonlinearity=nn.lrelu)))
disc_x_layers.append(LL.DropoutLayer(disc_x_layers[-1], p=0.5))
disc_x_layers.append(nn.batch_norm(dnn.Conv2DDNNLayer(disc_x_layers[-1], 192, (3,3), pad=1, W=Normal(0.01), nonlinearity=nn.lrelu)))
disc_x_layers.append(nn.batch_norm(dnn.Conv2DDNNLayer(disc_x_layers[-1], 192, (3,3), pad=1, stride=2, W=Normal(0.01), nonlinearity=nn.lrelu)))
disc_x_layers.append(LL.DropoutLayer(disc_x_layers[-1], p=0.5))
disc_x_layers.append(nn.batch_norm(dnn.Conv2DDNNLayer(disc_x_layers[-1], 192, (3,3), pad=0, W=Normal(0.01), nonlinearity=nn.lrelu)))
disc_x_layers_shared = LL.NINLayer(disc_x_layers[-1], num_units=192, W=Normal(0.01), nonlinearity=nn.lrelu)
disc_x_layers.append(disc_x_layers_shared)
disc_x_layer_z_recon = LL.DenseLayer(disc_x_layers_shared, num_units=self.args.z0dim, nonlinearity=None)
disc_x_layers.append(disc_x_layer_z_recon) # also need to recover z from x
# disc_x_layers.append(nn.MinibatchLayer(disc_x_layers_shared, num_kernels=100))
disc_x_layers.append(LL.GlobalPoolLayer(disc_x_layers_shared))
disc_x_layer_adv = LL.DenseLayer(disc_x_layers[-1], num_units=10, W=Normal(0.01), nonlinearity=None)
disc_x_layers.append(disc_x_layer_adv)
#output_before_softmax_x = LL.get_output(disc_x_layer_adv, x, deterministic=False)
#output_before_softmax_gen = LL.get_output(disc_x_layer_adv, gen_x, deterministic=False)
# temp = LL.get_output(gen_x_layers[-1], deterministic=False, init=True)
# temp = LL.get_output(disc_x_layers[-1], x, deterministic=False, init=True)
# init_updates = [u for l in LL.get_all_layers(gen_x_layers)+LL.get_all_layers(disc_x_layers) for u in getattr(l,'init_updates',[])]
return disc_x_layers, disc_x_layer_adv, disc_x_layer_z_recon
def build_network(self, ra_input_var, mc_input_var):
print('Building raw network with parameters:')
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(self.net_opts)
ra_network_1 = layers.InputLayer((None, 1, None), ra_input_var)
ra_network_1 = self.set_conv_layer(ra_network_1,
'ra_conv_1',
dropout=False,
pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_1')
ra_network_1 = self.set_conv_layer(ra_network_1,
'ra_conv_2',
pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_2')
ra_network_1 = self.set_conv_layer(ra_network_1,
'ra_conv_3',
pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_3')
ra_network_1 = self.set_conv_layer(ra_network_1,
'ra_conv_4',
pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_4')
concat_list = [ra_network_1]
mc_input = layers.InputLayer((None, 2, None), mc_input_var)
concat_list.append(mc_input)
network = layers.ConcatLayer(concat_list,
axis=1,
cropping=[None, None, 'center'])
network = self.set_conv_layer(network, 'conv_1')
network = self.set_pool_layer(network, 'pool_1')
network = self.set_conv_layer(network, 'conv_2')
network = self.set_pool_layer(network, 'pool_2')
network = self.set_conv_layer(network, 'conv_3')
network = layers.GlobalPoolLayer(
network, getattr(T, self.net_opts['global_pool_func']))
# print(layers.get_output_shape(network))
# network = layers.DenseLayer(layers.dropout(network, p=self.net_opts['dropout_p']),
# self.net_opts['dens_1'],
# nonlinearity=lasagne.nonlinearities.rectify)
network = layers.DenseLayer(
layers.dropout(network, p=self.net_opts['dropout_p']),
self.net_opts['dens_2'],
nonlinearity=lasagne.nonlinearities.rectify)
network = layers.DenseLayer(
layers.dropout(network, p=self.net_opts['dropout_p']),
self.net_opts['num_class'],
nonlinearity=lasagne.nonlinearities.softmax)
# print(layers.get_output_shape(network))
self.network = network
return self.network
def build_architecture(self):
l_out = layers.InputLayer(shape = tuple([None] + self.input_dim), input_var = self.input_var)
l_out = layers.DenseLayer(l_out, num_units = 10, nonlinearity = lasagne.nonlinearities.sigmoid)
self.l_out1 = l_out
# 3 output classes
# l_out = layers.DenseLayer(l_out, num_units = 3, nonlinearity = lasagne.nonlinearities.softmax)
print self.b.get_value()
l_out = layers.DenseLayer(incoming = l_out, num_units = 3, b = self.b, nonlinearity = lasagne.nonlinearities.softmax)
# print type(self.b)
self.l_out = l_out
def __build_24_calib_net__(self):
network = layers.InputLayer((None, 3, 24, 24),
input_var=self.__input_var__)
network = layers.Conv2DLayer(network,
num_filters=32,
filter_size=(5, 5),
stride=1,
nonlinearity=relu)
network = layers.MaxPool2DLayer(network, pool_size=(3, 3), stride=2)
network = layers.DenseLayer(network, num_units=64, nonlinearity=relu)
network = layers.DenseLayer(network,
num_units=45,
nonlinearity=softmax)
return network
def __build_12_net__(self):
network = layers.InputLayer((None, 3, 12, 12),
input_var=self.__input_var__)
network = layers.Conv2DLayer(network,
num_filters=16,
filter_size=(3, 3),
stride=1,
nonlinearity=relu)
network = layers.MaxPool2DLayer(network, pool_size=(3, 3), stride=2)
network = layers.DropoutLayer(network)
network = layers.DenseLayer(network, num_units=16, nonlinearity=relu)
#network = layers.Conv2DLayer(network,num_filters=16,filter_size=(1,1),stride=1,nonlinearity=relu)
network = layers.DenseLayer(network, num_units=2, nonlinearity=softmax)
return network
def build_network(state_shape, num_actions):
"""Builds a network with input size state_shape & num_actions output.
As problem is discrete, we predict the Q-value for all possible actions.
"""
input_shape = (None, ) + state_shape
W_init = lasagne.init.GlorotUniform()
nonlin = lasagne.nonlinearities.rectify
network = nn.InputLayer(input_shape, input_var=None)
network = nn.DenseLayer(network,50, W=W_init, nonlinearity=nonlin)
network = nn.DenseLayer(network, num_actions, W=W_init, nonlinearity=None)
return network
def buildModel():
#this is our input layer with the inputs (None, dimensions, width, height)
l_input = layers.InputLayer((None, 3, 32, 32))
#first convolutional layer, has l_input layer as incoming and is followed by a pooling layer
l_conv1 = layers.Conv2DLayer(l_input,
num_filters=32,
filter_size=3,
pad='same',
nonlinearity=tanh)
l_pool1 = layers.MaxPool2DLayer(l_conv1, pool_size=2)
#second convolution (l_pool1 is incoming), let's increase the number of filters
l_conv2 = layers.Conv2DLayer(l_pool1,
num_filters=64,
filter_size=3,
pad='same',
nonlinearity=tanh)
l_pool2 = layers.MaxPool2DLayer(l_conv2, pool_size=2)
#third convolution (l_pool2 is incoming), even more filters
l_conv3 = layers.Conv2DLayer(l_pool2,
num_filters=128,
filter_size=3,
pad='same',
nonlinearity=tanh)
l_pool3 = layers.MaxPool2DLayer(l_conv3, pool_size=2)
#fourth and final convolution
l_conv4 = layers.Conv2DLayer(l_pool3,
num_filters=256,
filter_size=3,
pad='same',
nonlinearity=tanh)
l_pool4 = layers.MaxPool2DLayer(l_conv4, pool_size=2)
#our cnn contains 3 dense layers, one of them is our output layer
l_dense1 = layers.DenseLayer(l_pool4, num_units=128, nonlinearity=tanh)
l_dense2 = layers.DenseLayer(l_dense1, num_units=128, nonlinearity=tanh)
#the output layer has 5 units which is exactly the count of our class labels
#it has a softmax activation function, its values represent class probabilities
l_output = layers.DenseLayer(l_dense2, len(tagMap), nonlinearity=softmax)
#let's see how many params our net has
print("MODEL HAS", layers.count_params(l_output), "PARAMS")
#we return the layer stack as our network by returning the last layer
return l_output
def buildModel():
# Theinput layer with the inputs (None, dimensions, width, height)
l_input = layers.InputLayer((None, 3, 32, 32))
# First convolutional layer, has l_input layer as incoming and is followed by a pooling layer, filters = 16
l_conv1 = layers.Conv2DLayer(l_input,
num_filters=16,
filter_size=3,
pad='same',
nonlinearity=tanh)
l_pool1 = layers.MaxPool2DLayer(l_conv1, pool_size=2)
# The second convolution (l_pool1 is incoming), filters = 32
l_conv2 = layers.Conv2DLayer(l_pool1,
num_filters=32,
filter_size=3,
pad='same',
nonlinearity=tanh)
l_pool2 = layers.MaxPool2DLayer(l_conv2, pool_size=2)
# The third convolution (l_pool2 is incoming), filters = 64
l_conv3 = layers.Conv2DLayer(l_pool2,
num_filters=64,
filter_size=3,
pad='same',
nonlinearity=tanh)
l_pool3 = layers.MaxPool2DLayer(l_conv3, pool_size=2)
# The fourth and final convolution, filters = 128
l_conv4 = layers.Conv2DLayer(l_pool3,
num_filters=128,
filter_size=3,
pad='same',
nonlinearity=tanh)
l_pool4 = layers.MaxPool2DLayer(l_conv4, pool_size=2)
# The CNN contains 3 dense layers, one of them is the output layer
l_dense1 = layers.DenseLayer(l_pool4, num_units=64, nonlinearity=tanh)
l_dense2 = layers.DenseLayer(l_dense1, num_units=64, nonlinearity=tanh)
# The output layer has 43 units which is exactly the count of our class labels
# It has a softmax activation function, its values represent class probabilities
l_output = layers.DenseLayer(l_dense2, num_units=43, nonlinearity=softmax)
#print "The CNN model has ", layers.count_params(l_output), "parameters"
# Returning the layer stack by returning the last layer
return l_output
def __init__(self,
input_shape,
output_dim,
hidden_sizes,
hidden_nonlinearity,
output_nonlinearity,
hidden_W_init=LI.GlorotUniform(),
hidden_b_init=LI.Constant(0.),
output_W_init=LI.GlorotUniform(),
output_b_init=LI.Constant(0.),
name=None,
input_var=None,
input_layer=None):
if name is None:
prefix = ""
else:
prefix = name + "_"
if input_layer is None:
l_in = L.InputLayer(shape=(None, ) + input_shape,
input_var=input_var)
else:
l_in = input_layer
self._layers = [l_in]
l_hid = l_in
for idx, hidden_size in enumerate(hidden_sizes):
l_hid = L.DenseLayer(
l_hid,
num_units=hidden_size,
nonlinearity=hidden_nonlinearity,
name="%shidden_%d" % (prefix, idx),
W=hidden_W_init,
b=hidden_b_init,
)
self._layers.append(l_hid)
l_out = L.DenseLayer(
l_hid,
num_units=output_dim,
nonlinearity=output_nonlinearity,
name="%soutput" % (prefix, ),
W=output_W_init,
b=output_b_init,
)
self._layers.append(l_out)
self._l_in = l_in
self._l_out = l_out
self._input_var = l_in.input_var
self._output = L.get_output(l_out)
def enc_net(_incoming, output_channels, drop_rate=0.3, nonlinearity=None):
# #_noise = L.GaussianNoiseLayer(_incoming, sigma=0.1)
_drop1 = L.DropoutLayer(_incoming, p=drop_rate, rescale=True)
_fc1 = L.DenseLayer(_drop1,
4 * output_channels,
W=I.Normal(0.02),
b=I.Constant(0.1),
nonlinearity=NL.rectify)
_drop2 = L.DropoutLayer(_fc1, p=drop_rate, rescale=True)
_fc2 = L.DenseLayer(_drop2,
output_channels,
W=I.Normal(0.02),
b=I.Constant(0.1),
nonlinearity=nonlinearity)
return _fc2
def _forward(self):
net = {}
net['input'] = layers.InputLayer(shape=(None, 1, 28, 28),
input_var=self.X)
net['dense'] = layers.DenseLayer(net['input'],
num_units=2048,
W=init.GlorotUniform(),
b=init.Constant(0.),
nonlinearity=nonlinearities.rectify)
net['out'] = layers.DenseLayer(net['dense'],
num_units=10,
W=init.GlorotUniform(),
b=None,
nonlinearity=nonlinearities.softmax)
return net
