def setup_model(configs):
tensor5 = theano.tensor.TensorType(config.floatX, (False,) * 5)
# shape: T x B x C x X x Y
input_ = tensor5("features")
tensor3 = theano.tensor.TensorType(config.floatX, (False,) * 3)
locs = tensor3("locs")
# shape: B x Classes
target = T.ivector("targets")
model = LSTMAttention(configs, weights_init=Glorot(), biases_init=Constant(0))
model.initialize()
(h, c, location, scale, alpha, patch, downn_sampled_input, conved_part_1, conved_part_2, pre_lstm) = model.apply(
input_, locs
)
model.location = location
model.scale = scale
model.alpha = location
model.patch = patch
classifier = MLP(
[Rectifier(), Softmax()], configs["classifier_dims"], weights_init=Glorot(), biases_init=Constant(0)
)
classifier.initialize()
probabilities = classifier.apply(h[-1])
cost = CategoricalCrossEntropy().apply(target, probabilities)
cost.name = "CE"
error_rate = MisclassificationRate().apply(target, probabilities)
error_rate.name = "ER"
model.cost = cost
model.error_rate = error_rate
model.probabilities = probabilities
if configs["load_pretrained"]:
blocks_model = Model(model.cost)
all_params = blocks_model.parameters
with open("VGG_CNN_params.npz") as f:
loaded = np.load(f)
all_conv_params = loaded.keys()
for param in all_params:
if param.name in loaded.keys():
assert param.get_value().shape == loaded[param.name].shape
param.set_value(loaded[param.name])
all_conv_params.pop(all_conv_params.index(param.name))
print "the following parameters did not match: " + str(all_conv_params)
if configs["test_model"]:
print "TESTING THE MODEL: CHECK THE INPUT SIZE!"
cg = ComputationGraph(model.cost)
f = theano.function(cg.inputs, [model.cost], on_unused_input="ignore", allow_input_downcast=True)
data = configs["get_streams"](configs["batch_size"])[0].get_epoch_iterator().next()
f(data[1], data[0], data[2])
print "Test passed! ;)"
model.monitorings = [cost, error_rate]
return model
def setup_model(configs):
tensor5 = theano.tensor.TensorType(config.floatX, (False,) * 5)
# shape: T x B x C x X x Y
input_ = tensor5('features')
# shape: B x Classes
target = T.lmatrix('targets')
model = LSTMAttention(
configs,
weights_init=Glorot(),
biases_init=Constant(0))
model.initialize()
(h, c, location, scale, patch, downn_sampled_input,
conved_part_1, conved_part_2, pre_lstm) = model.apply(input_)
classifier = MLP(
[Rectifier(), Logistic()],
configs['classifier_dims'],
weights_init=Glorot(),
biases_init=Constant(0))
classifier.initialize()
probabilities = classifier.apply(h[-1])
cost = BinaryCrossEntropy().apply(target, probabilities)
cost.name = 'CE'
error_rate = MisclassificationRate().apply(target, probabilities)
error_rate.name = 'ER'
model.cost = cost
if configs['load_pretrained']:
blocks_model = Model(model.cost)
all_params = blocks_model.parameters
with open('VGG_CNN_params.npz') as f:
loaded = np.load(f)
all_conv_params = loaded.keys()
for param in all_params:
if param.name in loaded.keys():
assert param.get_value().shape == loaded[param.name].shape
param.set_value(loaded[param.name])
all_conv_params.pop(all_conv_params.index(param.name))
print "the following parameters did not match: " + str(all_conv_params)
if configs['test_model']:
cg = ComputationGraph(model.cost)
f = theano.function(cg.inputs, [model.cost],
on_unused_input='ignore',
allow_input_downcast=True)
data = np.random.randn(10, 40, 3, 224, 224)
targs = np.random.randn(40, 101)
f(data, targs)
print "Test passed! ;)"
model.monitorings = [cost, error_rate]
return model
def setup_model():
# shape: T x B x F
input_ = T.tensor3('features')
# shape: B
target = T.lvector('targets')
model = LSTMAttention(dim=256,
mlp_hidden_dims=[256, 4],
batch_size=100,
image_shape=(64, 64),
patch_shape=(16, 16),
weights_init=Glorot(),
biases_init=Constant(0))
model.initialize()
h, c, location, scale = model.apply(input_)
classifier = MLP([Rectifier(), Softmax()], [256 * 2, 200, 10],
weights_init=Glorot(),
biases_init=Constant(0))
model.h = h
model.c = c
model.location = location
model.scale = scale
classifier.initialize()
probabilities = classifier.apply(T.concatenate([h[-1], c[-1]], axis=1))
cost = CategoricalCrossEntropy().apply(target, probabilities)
error_rate = MisclassificationRate().apply(target, probabilities)
model.cost = cost
location_x_0_avg = T.mean(location[0, :, 0])
location_x_0_avg.name = 'location_x_0_avg'
location_x_10_avg = T.mean(location[10, :, 0])
location_x_10_avg.name = 'location_x_10_avg'
location_x_20_avg = T.mean(location[-1, :, 0])
location_x_20_avg.name = 'location_x_20_avg'
scale_x_0_avg = T.mean(scale[0, :, 0])
scale_x_0_avg.name = 'scale_x_0_avg'
scale_x_10_avg = T.mean(scale[10, :, 0])
scale_x_10_avg.name = 'scale_x_10_avg'
scale_x_20_avg = T.mean(scale[-1, :, 0])
scale_x_20_avg.name = 'scale_x_20_avg'
monitorings = [
error_rate, location_x_0_avg, location_x_10_avg, location_x_20_avg,
scale_x_0_avg, scale_x_10_avg, scale_x_20_avg
]
model.monitorings = monitorings
return model
def setup_model():
# shape: T x B x F
input_ = T.tensor3('features')
# shape: B
target = T.lvector('targets')
model = LSTMAttention(dim=256,
mlp_hidden_dims=[256, 4],
batch_size=100,
image_shape=(64, 64),
patch_shape=(16, 16),
weights_init=Glorot(),
biases_init=Constant(0))
model.initialize()
h, c, location, scale = model.apply(input_)
classifier = MLP([Rectifier(), Softmax()], [256 * 2, 200, 10],
weights_init=Glorot(),
biases_init=Constant(0))
model.h = h
model.c = c
model.location = location
model.scale = scale
classifier.initialize()
probabilities = classifier.apply(T.concatenate([h[-1], c[-1]], axis=1))
cost = CategoricalCrossEntropy().apply(target, probabilities)
error_rate = MisclassificationRate().apply(target, probabilities)
model.cost = cost
location_x_0_avg = T.mean(location[0, :, 0])
location_x_0_avg.name = 'location_x_0_avg'
location_x_10_avg = T.mean(location[10, :, 0])
location_x_10_avg.name = 'location_x_10_avg'
location_x_20_avg = T.mean(location[-1, :, 0])
location_x_20_avg.name = 'location_x_20_avg'
scale_x_0_avg = T.mean(scale[0, :, 0])
scale_x_0_avg.name = 'scale_x_0_avg'
scale_x_10_avg = T.mean(scale[10, :, 0])
scale_x_10_avg.name = 'scale_x_10_avg'
scale_x_20_avg = T.mean(scale[-1, :, 0])
scale_x_20_avg.name = 'scale_x_20_avg'
monitorings = [error_rate,
location_x_0_avg, location_x_10_avg, location_x_20_avg,
scale_x_0_avg, scale_x_10_avg, scale_x_20_avg]
model.monitorings = monitorings
return model
def setup_model(configs):
tensor5 = theano.tensor.TensorType(config.floatX, (False,) * 5)
# shape: T x B x C x X x Y
input_ = tensor5('features')
tensor3 = theano.tensor.TensorType(config.floatX, (False,) * 3)
locs = tensor3('locs')
# shape: B x Classes
target = T.ivector('targets')
model = LSTMAttention(
configs,
weights_init=Glorot(),
biases_init=Constant(0))
model.initialize()
(h, c, location, scale, alpha, patch, downn_sampled_input,
conved_part_1, conved_part_2, pre_lstm) = model.apply(input_, locs)
model.location = location
model.scale = scale
model.alpha = location
model.patch = patch
classifier = MLP(
[Rectifier(), Softmax()],
configs['classifier_dims'],
weights_init=Glorot(),
biases_init=Constant(0))
classifier.initialize()
probabilities = classifier.apply(h[-1])
cost = CategoricalCrossEntropy().apply(target, probabilities)
cost.name = 'CE'
error_rate = MisclassificationRate().apply(target, probabilities)
error_rate.name = 'ER'
model.cost = cost
model.error_rate = error_rate
model.probabilities = probabilities
if configs['load_pretrained']:
blocks_model = Model(model.cost)
all_params = blocks_model.parameters
with open('VGG_CNN_params.npz') as f:
loaded = np.load(f)
all_conv_params = loaded.keys()
for param in all_params:
if param.name in loaded.keys():
assert param.get_value().shape == loaded[param.name].shape
param.set_value(loaded[param.name])
all_conv_params.pop(all_conv_params.index(param.name))
print "the following parameters did not match: " + str(all_conv_params)
if configs['test_model']:
print "TESTING THE MODEL: CHECK THE INPUT SIZE!"
cg = ComputationGraph(model.cost)
f = theano.function(cg.inputs, [model.cost],
on_unused_input='ignore',
allow_input_downcast=True)
data = configs['get_streams'](configs[
'batch_size'])[0].get_epoch_iterator().next()
f(data[1], data[0], data[2])
print "Test passed! ;)"
model.monitorings = [cost, error_rate]
return model