def __init__(self, inputs, bs, max_time, classes, feature_dim, hidden_size, levels, N=1, pool=None, seed=12345):
self._inputs = inputs
self.N = N
self.batch_size = bs
self.classes = classes
self.max_time = max_time
self.levels = levels
self.feature_dim = feature_dim
self.pool = pool
self.dropout = True
# create a pyramid of filters
self.temporal_pyramid = []
for l in range(self.levels):
for f in range(2**l):
tf = TemporalAttentionLayer(batch_size=bs, N=N, channels=feature_dim,
name='temporal-attention-layer-'+str(l)+'-filter-'+str(f))
tf.test = True
tf.d = theano.shared(value=np.asarray([1./2**(l+1)]).astype('float32'), name='d', borrow=True,
broadcastable=[True])
tf.g = theano.shared(value=np.asarray([((1./2**l)+(2*f/2.**l))]).astype('float32'), name='g', borrow=True,
broadcastable=[True])
tf.sigma = theano.shared(value=np.asarray([5.0]).astype('float32'), name='sigma', borrow=True,
broadcastable=[True])
self.temporal_pyramid.append(tf)
input_size = feature_dim*N*(len(self.temporal_pyramid) if pool == None else 1)
self.hidden = HiddenLayer(input_size=input_size, hidden_size=hidden_size, activation=act.LeakyRelu(),
batch_size=bs, name='hidden', dropout=0.5)
self.softmax = SoftmaxLayer(input_size=hidden_size, classes=self.classes,
batch_size=bs, name='softmax', dropout=0.5)
def __init__(self,
inputs,
bs,
max_time,
classes,
feature_dim,
hidden_size,
method='max',
seed=12345):
self._inputs = inputs
self.method = method
self.batch_size = bs
self.classes = classes
self.max_time = max_time
self.feature_dim = feature_dim
self.dropout = True
self.hidden = HiddenLayer(input_size=feature_dim,
hidden_size=hidden_size,
batch_size=bs,
name='hidden',
dropout=0.5,
activation=act.LeakyRelu())
self.softmax = SoftmaxLayer(input_size=hidden_size,
classes=self.classes,
batch_size=bs,
name='softmax',
dropout=0.5)
def __init__(self, param_dict):
self.param_dict = param_dict
self.training_batch_size = param_dict['training_batch_size']
nkerns = param_dict['nkerns']
recept_width = param_dict['recept_width']
pool_width = param_dict['pool_width']
stride = param_dict['stride']
dropout_prob = param_dict['dropout_prob']
weight_decay = param_dict['l2_reg']
activation = param_dict['activation']
weights_variance = param_dict['weights_variance']
n_channels = param_dict['n_channels']
n_timesteps = param_dict['n_timesteps']
n_fbins = param_dict['n_fbins']
global_pooling = param_dict['global_pooling']
rng = np.random.RandomState(23455)
self.training_mode = T.iscalar('training_mode')
self.x = T.tensor4('x')
self.y = T.bvector('y')
self.batch_size = theano.shared(self.training_batch_size)
self.input = self.x.reshape((self.batch_size, 1, n_channels * n_fbins, n_timesteps))
self.feature_extractor = FeatureExtractor(rng, self.input, nkerns, recept_width, pool_width, stride,
self.training_mode,
dropout_prob[0],
activation, weights_variance, n_channels, n_timesteps, n_fbins,
global_pooling)
self.classifier = SoftmaxLayer(rng=rng, input=self.feature_extractor.output, n_in=nkerns[-1],
training_mode=self.training_mode, dropout_prob=dropout_prob[-1])
self.weights = self.feature_extractor.weights + self.classifier.weights
# ---------------------- BACKPROP
self.cost = self.classifier.cross_entropy_cost(self.y)
self.cost = self.classifier.cross_entropy_cost(self.y)
L2_sqr = sum((weight ** 2).sum() for weight in self.weights[::2])
self.grads = T.grad(self.cost + weight_decay * L2_sqr, self.weights)
self.updates = self.adadelta_updates(self.grads, self.weights)
# self.updates = self.nesterov_momentum(self.grads, self.weights)
# --------------------- FUNCTIONS
self.train_model = theano.function([self.x, self.y, Param(self.training_mode, default=1)],
outputs=self.cost,
updates=self.updates)
self.validate_model = theano.function([self.x, self.y, Param(self.training_mode, default=0)],
self.cost)
self.test_model = theano.function([self.x, Param(self.training_mode, default=0)],
self.classifier.p_y_given_x[:, 1])
def __init__(self,
inputs,
bs,
max_time,
classes,
feature_dim,
hidden_size,
filters,
N=1,
pool=None,
lstm_dim=4096,
steps=8,
seed=12345):
self._inputs = inputs
self.N = N
self.batch_size = bs
self.classes = classes
self.max_time = max_time
self.filters = filters
self.feature_dim = feature_dim
self.pool = pool
self.dropout = True
self.steps = steps
self.temporal_filters = []
for f in range(filters):
tf = TemporalAttentionLayer(
batch_size=bs,
N=N,
channels=feature_dim,
input_hidden_size=lstm_dim,
name='temporal-attention-layer-filter-' + str(f))
self.temporal_filters.append(tf)
input_size = feature_dim * len(
self.temporal_filters) * (N if pool == None else 1)
self.lstm_in = HiddenLayer(input_size=input_size,
hidden_size=lstm_dim * 4,
batch_size=bs)
self.lstm = LSTMLayer(input_size=lstm_dim, hidden_size=lstm_dim)
self.hidden = HiddenLayer(input_size=lstm_dim,
hidden_size=hidden_size,
activation=act.relu,
batch_size=bs,
name='hidden',
dropout=0.5)
self.softmax = SoftmaxLayer(input_size=hidden_size,
classes=self.classes,
batch_size=bs,
name='softmax',
dropout=0.5)
def add_softmax_layer(self):
l = SoftmaxLayer(self.layers[-1])
self.layers.append(l)
class ConvNet(object):
def __init__(self, param_dict):
self.param_dict = param_dict
self.training_batch_size = param_dict['training_batch_size']
nkerns = param_dict['nkerns']
recept_width = param_dict['recept_width']
pool_width = param_dict['pool_width']
stride = param_dict['stride']
dropout_prob = param_dict['dropout_prob']
weight_decay = param_dict['l2_reg']
activation = param_dict['activation']
weights_variance = param_dict['weights_variance']
n_channels = param_dict['n_channels']
n_timesteps = param_dict['n_timesteps']
n_fbins = param_dict['n_fbins']
global_pooling = param_dict['global_pooling']
rng = np.random.RandomState(23455)
self.training_mode = T.iscalar('training_mode')
self.x = T.tensor4('x')
self.y = T.bvector('y')
self.batch_size = theano.shared(self.training_batch_size)
self.input = self.x.reshape((self.batch_size, 1, n_channels * n_fbins, n_timesteps))
self.feature_extractor = FeatureExtractor(rng, self.input, nkerns, recept_width, pool_width, stride,
self.training_mode,
dropout_prob[0],
activation, weights_variance, n_channels, n_timesteps, n_fbins,
global_pooling)
self.classifier = SoftmaxLayer(rng=rng, input=self.feature_extractor.output, n_in=nkerns[-1],
training_mode=self.training_mode, dropout_prob=dropout_prob[-1])
self.weights = self.feature_extractor.weights + self.classifier.weights
# ---------------------- BACKPROP
self.cost = self.classifier.cross_entropy_cost(self.y)
self.cost = self.classifier.cross_entropy_cost(self.y)
L2_sqr = sum((weight ** 2).sum() for weight in self.weights[::2])
self.grads = T.grad(self.cost + weight_decay * L2_sqr, self.weights)
self.updates = self.adadelta_updates(self.grads, self.weights)
# self.updates = self.nesterov_momentum(self.grads, self.weights)
# --------------------- FUNCTIONS
self.train_model = theano.function([self.x, self.y, Param(self.training_mode, default=1)],
outputs=self.cost,
updates=self.updates)
self.validate_model = theano.function([self.x, self.y, Param(self.training_mode, default=0)],
self.cost)
self.test_model = theano.function([self.x, Param(self.training_mode, default=0)],
self.classifier.p_y_given_x[:, 1])
def train(self, train_set, max_iter):
print 'training for', max_iter, 'iterations'
self.batch_size.set_value(self.training_batch_size)
train_set_iterator = RandomTrainIterator(train_set, self.training_batch_size)
done_looping = False
iter = 0
while not done_looping:
for train_x, train_y in train_set_iterator:
self.train_model(train_x, train_y)
# if iter % 10 == 0:
# self.batch_size.set_value(train_set[0].shape[0])
# print self.validate_model(train_set[0], train_set[1])
# self.batch_size.set_value(self.training_batch_size)
if iter > max_iter:
done_looping = True
break
iter += 1
def validate(self, train_set, valid_set, valid_freq, max_iter, fname_out):
train_set_iterator = RandomTrainIterator(train_set, self.training_batch_size)
valid_set_size = len(valid_set[1])
f_out = open(fname_out, 'w')
# ------------------------------ TRAINING
epoch = 0
iter = 0
best_ce = np.inf
best_iter_ce = 0
best_auc = 0
best_iter_auc = 0
done_looping = False
patience = 100000
patience_increase = 2
improvement_threshold = 0.995
while iter < max_iter and not done_looping:
epoch += 1
for train_x, train_y in train_set_iterator:
self.train_model(train_x, train_y)
iter += 1
# ------------------------ VALIDATION
if iter % valid_freq == 0:
self.batch_size.set_value(valid_set_size)
cost_valid = self.validate_model(valid_set[0], valid_set[1])
auc_valid = self.get_auc(valid_set)
# print "%4s %7s %15s %15s %10s " % (
# epoch, iter, auc_valid, cost_valid,
# patience)
f_out.write("%s \t %s \t %s \n" % (
iter, auc_valid, cost_valid))
self.batch_size.set_value(self.training_batch_size)
if cost_valid <= best_ce:
if cost_valid < best_ce * improvement_threshold:
patience = max(patience, iter * patience_increase)
best_iter_ce = iter
best_ce = cost_valid
if auc_valid >= best_auc:
best_iter_auc = iter
best_auc = auc_valid
if patience <= iter:
done_looping = True
print 'best_iter_cost:', best_iter_ce, 'best_cost:', best_ce
print 'best_iter_auc:', best_iter_auc, 'best_auc:', best_auc
f_out.close()
return max(best_iter_ce, best_iter_auc)
def get_auc(self, data_xy):
x, y = data_xy[0], data_xy[1]
p_y_given_x = self.get_test_proba(x)
fpr, tpr, thresholds = roc_curve(y, p_y_given_x, pos_label=1)
roc_auc = auc(fpr, tpr)
return roc_auc
def get_test_proba(self, x_test):
self.batch_size.set_value(len(x_test))
p_y_given_x = self.test_model(x_test)
return p_y_given_x
def nesterov_momentum(self, grads, weights, learning_rate=0.001, momentum=0.9):
updates = []
for param_i, grad_i in zip(weights, grads):
mparam_i = theano.shared(np.zeros(param_i.get_value().shape, dtype=theano.config.floatX))
v = momentum * mparam_i - learning_rate * grad_i
w = param_i + momentum * v - learning_rate * grad_i
updates.append((mparam_i, v))
updates.append((param_i, w))
return updates
def adadelta_updates(self, grads, weights, learning_rate=0.01, rho=0.95, epsilon=1e-6):
accumulators = [theano.shared(np.zeros_like(param_i.get_value())) for param_i in weights]
delta_accumulators = [theano.shared(np.zeros_like(param_i.get_value())) for param_i in weights]
updates = []
for param_i, grad_i, acc_i, acc_delta_i in zip(weights, grads, accumulators, delta_accumulators):
acc_i_new = rho * acc_i + (1 - rho) * grad_i ** 2
updates.append((acc_i, acc_i_new))
update_i = grad_i * T.sqrt(acc_delta_i + epsilon) / T.sqrt(acc_i_new + epsilon)
updates.append((param_i, param_i - learning_rate * update_i))
acc_delta_i_new = rho * acc_delta_i + (1 - rho) * update_i ** 2
updates.append((acc_delta_i, acc_delta_i_new))
return updates
def get_state(self):
state = {}
state['params'] = self.param_dict
weights_vals = []
for p in self.weights:
weights_vals.append(p.get_value())
state['weights'] = weights_vals
return state
def set_weights(self, weights_vals):
for i, w in enumerate(weights_vals):
self.weights[i].set_value(w)
# One hot encoding number 3 will become [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
y_train_enc = one_hot(y_train)
# number of classes / pixels per image
num_classes = y_train_enc.shape[0]
num_pixels = x_train.shape[0]
# Create our NN structure
net = NeuralNetwork()
net.add(FCLayer(num_pixels, 100, activation=TanH(), optimizer=Adam()))
net.add(DropOut(rate=0.0))
net.add(FCLayer(100, 50, activation=TanH(), optimizer=Adam()))
net.add(DropOut(rate=0.0))
net.add(FCLayer(50, 25, activation=TanH(), optimizer=Adam()))
net.add(DropOut(rate=0.0))
net.add(SoftmaxLayer(25, num_classes, activation=Softmax(), optimizer=Adam()))
# train
net.use(loss=MultiClassCrossEntropy(), regularizer=L2Regularizer(lambd=0.01))
net.train(x_train, y_train_enc, epochs=50, learning_rate=0.001, batch_size=256)
# check training accuracy
train_results = net.predict(x_train)
train_results = np.argmax(train_results, axis=0)
print("Accuracy on training set:",
np.mean(train_results == y_train) * 100, "%")
# Check our model on the test set
x_test = normalize_images(x_test)
test_results = net.predict(x_test)
class TemporalModel(Model):
def __init__(self,
inputs,
bs,
max_time,
classes,
feature_dim,
hidden_size,
method='max',
seed=12345):
self._inputs = inputs
self.method = method
self.batch_size = bs
self.classes = classes
self.max_time = max_time
self.feature_dim = feature_dim
self.dropout = True
self.hidden = HiddenLayer(input_size=feature_dim,
hidden_size=hidden_size,
batch_size=bs,
name='hidden',
dropout=0.5,
activation=act.LeakyRelu())
self.softmax = SoftmaxLayer(input_size=hidden_size,
classes=self.classes,
batch_size=bs,
name='softmax',
dropout=0.5)
@property
def params(self):
return self.softmax.params + self.hidden.params
@property
def inputs(self):
return self._inputs
@property
def outputs(self):
return self._outputs
@property
def updates(self):
return self._updates
@property
def test_algorithm(self):
if not hasattr(self, '_talgorithm'):
d = self.dropout
self.dropout = False
o = self.run(*self.inputs)
for i, ot in enumerate(self.outputs):
o[i].name = ot.name
self._talgorithm = theano.function(inputs=self.inputs,
outputs=o,
on_unused_input='warn')
self.dropout = d
return self._talgorithm
def run(self, x, mask, y):
# get the max/mean/sum of x for each feature
# from all frame
if self.method == 'max':
m = (-100 * (1 - mask)).dimshuffle([0, 1, 'x'])
x = T.max(x + m, axis=1)
elif self.method == 'sum' or self.method == 'mean':
x = T.sum(x, axis=1)
elif self.method == 'mean':
x = x / T.sum(mask, axis=1).dimshuffle([0, 'x'])
x = x.astype(theano.config.floatX)
x = self.hidden.run(x, self.dropout)
prob, pred = self.softmax.run(x, self.dropout)
y = y.reshape((y.shape[0], ))
loss = self.softmax.loss(prob, y) + T.sum(
self.hidden.w**2) * 0.001 + T.sum(self.softmax.w**2) * 0.0001
y = T.extra_ops.to_one_hot(y, 51)
error = self.softmax.error(pred, y)
acc = 1 - error
return prob, pred, loss, error, acc
class TemporalModel(Model):
def __init__(self, inputs, bs, max_time, classes, feature_dim, hidden_size, levels, N=1, pool=None, seed=12345):
self._inputs = inputs
self.N = N
self.batch_size = bs
self.classes = classes
self.max_time = max_time
self.levels = levels
self.feature_dim = feature_dim
self.pool = pool
self.dropout = True
# create a pyramid of filters
self.temporal_pyramid = []
for l in range(self.levels):
for f in range(2**l):
tf = TemporalAttentionLayer(batch_size=bs, N=N, channels=feature_dim,
name='temporal-attention-layer-'+str(l)+'-filter-'+str(f))
tf.test = True
tf.d = theano.shared(value=np.asarray([1./2**(l+1)]).astype('float32'), name='d', borrow=True,
broadcastable=[True])
tf.g = theano.shared(value=np.asarray([((1./2**l)+(2*f/2.**l))]).astype('float32'), name='g', borrow=True,
broadcastable=[True])
tf.sigma = theano.shared(value=np.asarray([5.0]).astype('float32'), name='sigma', borrow=True,
broadcastable=[True])
self.temporal_pyramid.append(tf)
input_size = feature_dim*N*(len(self.temporal_pyramid) if pool == None else 1)
self.hidden = HiddenLayer(input_size=input_size, hidden_size=hidden_size, activation=act.LeakyRelu(),
batch_size=bs, name='hidden', dropout=0.5)
self.softmax = SoftmaxLayer(input_size=hidden_size, classes=self.classes,
batch_size=bs, name='softmax', dropout=0.5)
@property
def params(self):
return self.softmax.params+self.hidden.params#+[p for f in self.temporal_filters for p in f.params]
@property
def inputs(self):
return self._inputs
@property
def outputs(self):
return self._outputs
@property
def updates(self):
return self._updates
@property
def test_algorithm(self):
if not hasattr(self, '_talgorithm'):
d = self.dropout
self.dropout = False
o = self.run(*self.inputs)
for i,ot in enumerate(self.outputs):
o[i].name = ot.name
self._talgorithm = theano.function(inputs=self.inputs,
outputs=o, on_unused_input='warn')
self.dropout = d
return self._talgorithm
def run(self, x, mask, y):
# use temporal filters
results = []
# make x to be batch x features x time
x = x.transpose([0,2,1])
for tf in self.temporal_pyramid:
# results is batch x features x N
# flatten to batch x features*N
res, (g,s2,d) = tf.run(x, mask)
if self.pool == None:
results.append(res.reshape((x.shape[0], self.feature_dim*self.N)))
else:
results.append(res.reshape((x.shape[0], 1, self.feature_dim*self.N)))
# concatenate on axis 1 to get batch x features*N*filters
x = T.concatenate(results, axis=1)
if self.pool == 'max':
x = T.max(x, axis=1)
elif self.pool == 'sum':
x = T.sum(x, axis=1)
elif self.pool == 'mean':
x = T.mean(x, axis=1)
x = self.hidden.run(x, self.dropout)
prob, pred = self.softmax.run(x, self.dropout)
loss = self.softmax.loss(prob, y)
error = self.softmax.error(pred, y)
acc = 1-error
return prob, pred, loss, error, acc
11,
11,
48,
2,
4,
Relu(),
0.05,
momentum_rate=0.0,
decay_rate=0.1)
# CLh(net, 2, 2, 10, 1, 2, Relu(), 0.05)
LrnLayer(net, 2, 0.0001, 5, 0.75)
MaxPoolingLayer(net, 3, 3, 2)
FcLayer(net, 5, Relu(), momentum_rate=0.0, decay_rate=0.1)
DropoutLayer(net, dropout_prob=0.5)
SoftmaxLayer(net, 10, momentum_rate=0.0, decay_rate=0.1)
# net1 = Network()
#
sp = SciPlot('Curve of softmax output')
# CLM(net1, 25, 25, 3, 11, 11, 48, 2, 4, Relu(), 0.05)
# CLMh(net1, 2, 2, 10, 1, 2, Relu(), 0.05)
# LrnLayer(net1, 2, 0.0001, 5, 0.75)
# MaxPoolingLayer(net1, 3, 3, 2)
# #
# FcLayer(net1, 5, Relu())
# DropoutLayer(net1, dropout_prob=0.5)
# SoftmaxLayer(net1, 10)
sp.plot(net.predict(fake_image, training=False), desc='episode-' + str(0))
for i in range(0, 5):
net.train_one_sample(fake_label, fake_image, 1)
class TemporalModel(Model):
def __init__(self,
inputs,
bs,
max_time,
classes,
feature_dim,
hidden_size,
filters,
N=1,
pool=None,
lstm_dim=4096,
steps=8,
seed=12345):
self._inputs = inputs
self.N = N
self.batch_size = bs
self.classes = classes
self.max_time = max_time
self.filters = filters
self.feature_dim = feature_dim
self.pool = pool
self.dropout = True
self.steps = steps
self.temporal_filters = []
for f in range(filters):
tf = TemporalAttentionLayer(
batch_size=bs,
N=N,
channels=feature_dim,
input_hidden_size=lstm_dim,
name='temporal-attention-layer-filter-' + str(f))
self.temporal_filters.append(tf)
input_size = feature_dim * len(
self.temporal_filters) * (N if pool == None else 1)
self.lstm_in = HiddenLayer(input_size=input_size,
hidden_size=lstm_dim * 4,
batch_size=bs)
self.lstm = LSTMLayer(input_size=lstm_dim, hidden_size=lstm_dim)
self.hidden = HiddenLayer(input_size=lstm_dim,
hidden_size=hidden_size,
activation=act.relu,
batch_size=bs,
name='hidden',
dropout=0.5)
self.softmax = SoftmaxLayer(input_size=hidden_size,
classes=self.classes,
batch_size=bs,
name='softmax',
dropout=0.5)
@property
def params(self):
return self.softmax.params + self.hidden.params + self.lstm_in.params + self.lstm.params + [
p for f in self.temporal_filters for p in f.params
]
@property
def inputs(self):
return self._inputs
@property
def outputs(self):
return self._outputs
@property
def updates(self):
return self._updates
@property
def test_algorithm(self):
if not hasattr(self, '_talgorithm'):
d = self.dropout
self.dropout = False
o = self.run(*self.inputs)
for i, ot in enumerate(self.outputs):
o[i].name = ot.name
self._talgorithm = theano.function(inputs=self.inputs,
outputs=o,
on_unused_input='warn')
self.dropout = d
return self._talgorithm
def run(self, x, mask, y):
# use temporal filters
# make x to be batch x features x time
x = x.transpose([0, 2, 1])
h, c = self.lstm.get_initial_hidden(x)
outputs_info = [
dict(initial=h, taps=[-1]), # h
dict(initial=c, taps=[-1])
] # c
[h, c], _ = theano.scan(fn=self.step,
non_sequences=[x, mask],
outputs_info=outputs_info,
n_steps=self.steps)
x = self.hidden.run(h[-1], self.dropout)
prob, pred = self.softmax.run(x, self.dropout)
loss = self.softmax.loss(prob, y)
error = self.softmax.error(pred, y)
acc = 1 - error
return prob, pred, loss, error, acc
def step(self, h, c, x, mask):
results = []
for tf in self.temporal_filters:
# results is batch x features x N
# flatten to batch x features*N
res, (g, s2, d) = tf.run(x, h, mask)
if self.pool == None:
results.append(
res.reshape((x.shape[0], self.feature_dim * self.N)))
elif self.pool == 'max':
results.append(
T.max(res, axis=2).reshape((x.shape[0], self.feature_dim)))
elif self.pool == 'sum':
results.append(
T.sum(res, axis=2).reshape((x.shape[0], self.feature_dim)))
elif self.pool == 'mean':
results.append(
T.mean(res, axis=2).reshape(
(x.shape[0], self.feature_dim)))
# concatenate on axis 1 to get batch x features*N*filters
x = T.concatenate(results, axis=1)
x = self.lstm_in.run(x)
h, c = self.lstm.run(x, h, c)
return h, c
