def __init__(self, ref_data, output_dim):
input_dim = ref_data.shape[1]
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name="ref_data")
# Construct the model
j = tensor.lvector("j")
r = ref_data_sh[j, :]
x = tensor.fmatrix("x")
y = tensor.ivector("y")
# input_dim must be nr
mlp0 = MLP(activations=activation_functions_0, dims=[input_dim] + hidden_dims_0, name="e0")
mlp0vs = MLP(activations=[None], dims=[hidden_dims_0[-1], input_dim], name="de0")
mlp1 = MLP(
activations=activation_functions_1, dims=[hidden_dims_0[-1]] + hidden_dims_1 + [n_inter], name="inter_gen"
)
mlp2 = MLP(
activations=activation_functions_2 + [None], dims=[n_inter] + hidden_dims_2 + [output_dim], name="end_mlp"
)
encod = mlp0.apply(r)
rprime = mlp0vs.apply(encod)
inter_weights = mlp1.apply(encod)
ibias = Bias(n_inter)
ibias.biases_init = Constant(0)
ibias.initialize()
inter = inter_act_fun.apply(ibias.apply(tensor.dot(x, inter_weights)))
final = mlp2.apply(inter)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
error_rate = tensor.neq(y, pred).mean()
# Initialize parameters
for brick in [mlp0, mlp0vs, mlp1, mlp2]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
# apply regularization
cg = ComputationGraph([cost, error_rate])
if r_dropout != 0:
# - dropout on input vector r : r_dropout
cg = apply_dropout(cg, [r], r_dropout)
if s_dropout != 0:
# - dropout on intermediate layers of first mlp : s_dropout
s_dropout_vars = list(
set(VariableFilter(bricks=[Tanh], name="output")(ComputationGraph([inter_weights])))
- set([inter_weights])
)
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if i_dropout != 0:
# - dropout on input to second mlp : i_dropout
cg = apply_dropout(cg, [inter], i_dropout)
if a_dropout != 0:
# - dropout on hidden layers of second mlp : a_dropout
a_dropout_vars = list(
set(VariableFilter(bricks=[Tanh], name="output")(ComputationGraph([final])))
- set([inter_weights])
- set(s_dropout_vars)
)
cg = apply_dropout(cg, a_dropout_vars, a_dropout)
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
[cost_reg, error_rate_reg] = cg.outputs
# add reconstruction penalty for AE part
penalty_val = tensor.sqrt(((r - rprime) ** 2).sum(axis=1)).mean()
cost_reg = cost_reg + reconstruction_penalty * penalty_val
self.cost = cost
self.cost_reg = cost_reg
self.error_rate = error_rate
self.error_rate_reg = error_rate_reg
self.pred = pred
self.confidence = confidence
def __init__(self, ref_data, output_dim):
input_dim = ref_data.shape[1]
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data')
rng = RandomStreams()
ae_bricks = []
ae_input = ref_data_sh
ae_costs = []
for i, (idim, odim) in enumerate(zip([input_dim] + ae_dims[:-1], ae_dims)):
ae_mlp = MLP(activations=[ae_activations[i]],
dims=[idim, odim],
name='enc%i'%i)
enc = ae_mlp.apply(ae_input)
enc_n = ae_mlp.apply(ae_input + rng.normal(size=ae_input.shape, std=ae_f_noise_std))
ae_mlp_dec = MLP(activations=[ae_activations[i]],
dims=[odim, idim],
name='dec%i'%i)
dec = ae_mlp_dec.apply(enc_n)
cost = tensor.sqrt(((ae_input - dec) ** 2).sum(axis=1)).mean() + \
ae_l1_pen * abs(enc).sum(axis=1).mean()
ae_costs.append(cost)
ae_input = enc
ae_bricks = ae_bricks + [ae_mlp, ae_mlp_dec]
self.ae_costs = ae_costs
ref_data_enc = ae_input
# Construct the model
j = tensor.lvector('j')
r = ref_data_enc[j, :]
x = tensor.fmatrix('x')
y = tensor.ivector('y')
# input_dim must be nr
mlp = MLP(activations=activation_functions,
dims=[ae_dims[-1]] + hidden_dims + [n_inter], name='inter_gen')
mlp2 = MLP(activations=activation_functions_2 + [None],
dims=[n_inter] + hidden_dims_2 + [output_dim],
name='end_mlp')
inter_weights = mlp.apply(r)
if inter_bias == None:
ibias = Bias(n_inter)
ibias.biases_init = Constant(0)
ibias.initialize()
inter = ibias.apply(tensor.dot(x, inter_weights))
else:
inter = tensor.dot(x, inter_weights) - inter_bias
inter = inter_act_fun.apply(inter)
final = mlp2.apply(inter)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
# error_rate = tensor.neq(y, pred).mean()
ber = balanced_error_rate.ber(y, pred)
# Initialize parameters
for brick in ae_bricks + [mlp, mlp2]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
# apply regularization
cg = ComputationGraph([cost, ber])
if r_dropout != 0:
# - dropout on input vector r : r_dropout
cg = apply_dropout(cg, [r], r_dropout)
if x_dropout != 0:
cg = apply_dropout(cg, [x], x_dropout)
if s_dropout != 0:
# - dropout on intermediate layers of first mlp : s_dropout
s_dropout_vars = list(set(VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([inter_weights])))
- set([inter_weights]))
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if i_dropout != 0:
# - dropout on input to second mlp : i_dropout
cg = apply_dropout(cg, [inter], i_dropout)
if a_dropout != 0:
# - dropout on hidden layers of second mlp : a_dropout
a_dropout_vars = list(set(VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([final])))
- set([inter_weights]) - set(s_dropout_vars))
cg = apply_dropout(cg, a_dropout_vars, a_dropout)
if r_noise_std != 0:
cg = apply_noise(cg, [r], r_noise_std)
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
[cost_reg, ber_reg] = cg.outputs
if s_l1pen != 0:
s_weights = VariableFilter(bricks=mlp.linear_transformations, roles=[WEIGHT])(cg)
cost_reg = cost_reg + s_l1pen * sum(abs(w).sum() for w in s_weights)
if i_l1pen != 0:
cost_reg = cost_reg + i_l1pen * abs(inter).sum()
if a_l1pen != 0:
a_weights = VariableFilter(bricks=mlp2.linear_transformations, roles=[WEIGHT])(cg)
cost_reg = cost_reg + a_l1pen * sum(abs(w).sum() for w in a_weights)
self.cost = cost
self.cost_reg = cost_reg
self.ber = ber
self.ber_reg = ber_reg
self.pred = pred
self.confidence = confidence
def __init__(self, ref_data, output_dim):
input_dim = ref_data.shape[1]
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32),
name='ref_data')
rng = RandomStreams()
ae_bricks = []
ae_input = ref_data_sh
ae_costs = []
for i, (idim,
odim) in enumerate(zip([input_dim] + ae_dims[:-1], ae_dims)):
ae_mlp = MLP(activations=[ae_activations[i]],
dims=[idim, odim],
name='enc%i' % i)
enc = ae_mlp.apply(ae_input)
enc_n = ae_mlp.apply(
ae_input + rng.normal(size=ae_input.shape, std=ae_f_noise_std))
ae_mlp_dec = MLP(activations=[ae_activations[i]],
dims=[odim, idim],
name='dec%i' % i)
dec = ae_mlp_dec.apply(enc_n)
cost = tensor.sqrt(((ae_input - dec) ** 2).sum(axis=1)).mean() + \
ae_l1_pen * abs(enc).sum(axis=1).mean()
ae_costs.append(cost)
ae_input = enc
ae_bricks = ae_bricks + [ae_mlp, ae_mlp_dec]
self.ae_costs = ae_costs
ref_data_enc = ae_input
# Construct the model
j = tensor.lvector('j')
r = ref_data_enc[j, :]
x = tensor.fmatrix('x')
y = tensor.ivector('y')
# input_dim must be nr
mlp = MLP(activations=activation_functions,
dims=[ae_dims[-1]] + hidden_dims + [n_inter],
name='inter_gen')
mlp2 = MLP(activations=activation_functions_2 + [None],
dims=[n_inter] + hidden_dims_2 + [output_dim],
name='end_mlp')
inter_weights = mlp.apply(r)
if inter_bias == None:
ibias = Bias(n_inter)
ibias.biases_init = Constant(0)
ibias.initialize()
inter = ibias.apply(tensor.dot(x, inter_weights))
else:
inter = tensor.dot(x, inter_weights) - inter_bias
inter = inter_act_fun.apply(inter)
final = mlp2.apply(inter)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
# error_rate = tensor.neq(y, pred).mean()
ber = balanced_error_rate.ber(y, pred)
# Initialize parameters
for brick in ae_bricks + [mlp, mlp2]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
# apply regularization
cg = ComputationGraph([cost, ber])
if r_dropout != 0:
# - dropout on input vector r : r_dropout
cg = apply_dropout(cg, [r], r_dropout)
if x_dropout != 0:
cg = apply_dropout(cg, [x], x_dropout)
if s_dropout != 0:
# - dropout on intermediate layers of first mlp : s_dropout
s_dropout_vars = list(
set(
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([inter_weights]))) -
set([inter_weights]))
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if i_dropout != 0:
# - dropout on input to second mlp : i_dropout
cg = apply_dropout(cg, [inter], i_dropout)
if a_dropout != 0:
# - dropout on hidden layers of second mlp : a_dropout
a_dropout_vars = list(
set(
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([final]))) - set([inter_weights]) -
set(s_dropout_vars))
cg = apply_dropout(cg, a_dropout_vars, a_dropout)
if r_noise_std != 0:
cg = apply_noise(cg, [r], r_noise_std)
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
[cost_reg, ber_reg] = cg.outputs
if s_l1pen != 0:
s_weights = VariableFilter(bricks=mlp.linear_transformations,
roles=[WEIGHT])(cg)
cost_reg = cost_reg + s_l1pen * sum(
abs(w).sum() for w in s_weights)
if i_l1pen != 0:
cost_reg = cost_reg + i_l1pen * abs(inter).sum()
if a_l1pen != 0:
a_weights = VariableFilter(bricks=mlp2.linear_transformations,
roles=[WEIGHT])(cg)
cost_reg = cost_reg + a_l1pen * sum(
abs(w).sum() for w in a_weights)
self.cost = cost
self.cost_reg = cost_reg
self.ber = ber
self.ber_reg = ber_reg
self.pred = pred
self.confidence = confidence
def __init__(self, ref_data, output_dim):
input_dim = ref_data.shape[1]
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32),
name='ref_data')
# Construct the model
j = tensor.lvector('j')
r = ref_data_sh[j, :]
x = tensor.fmatrix('x')
y = tensor.ivector('y')
# input_dim must be nr
mlp0 = MLP(activations=activation_functions_0,
dims=[input_dim] + hidden_dims_0,
name='e0')
mlp0vs = MLP(activations=[None],
dims=[hidden_dims_0[-1], input_dim],
name='de0')
mlp1 = MLP(activations=activation_functions_1,
dims=[hidden_dims_0[-1]] + hidden_dims_1 + [n_inter],
name='inter_gen')
mlp2 = MLP(activations=activation_functions_2 + [None],
dims=[n_inter] + hidden_dims_2 + [output_dim],
name='end_mlp')
encod = mlp0.apply(r)
rprime = mlp0vs.apply(encod)
inter_weights = mlp1.apply(encod)
ibias = Bias(n_inter)
ibias.biases_init = Constant(0)
ibias.initialize()
inter = inter_act_fun.apply(ibias.apply(tensor.dot(x, inter_weights)))
final = mlp2.apply(inter)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
error_rate = tensor.neq(y, pred).mean()
# Initialize parameters
for brick in [mlp0, mlp0vs, mlp1, mlp2]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
# apply regularization
cg = ComputationGraph([cost, error_rate])
if r_dropout != 0:
# - dropout on input vector r : r_dropout
cg = apply_dropout(cg, [r], r_dropout)
if s_dropout != 0:
# - dropout on intermediate layers of first mlp : s_dropout
s_dropout_vars = list(
set(
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([inter_weights]))) -
set([inter_weights]))
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if i_dropout != 0:
# - dropout on input to second mlp : i_dropout
cg = apply_dropout(cg, [inter], i_dropout)
if a_dropout != 0:
# - dropout on hidden layers of second mlp : a_dropout
a_dropout_vars = list(
set(
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([final]))) - set([inter_weights]) -
set(s_dropout_vars))
cg = apply_dropout(cg, a_dropout_vars, a_dropout)
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
[cost_reg, error_rate_reg] = cg.outputs
# add reconstruction penalty for AE part
penalty_val = tensor.sqrt(((r - rprime)**2).sum(axis=1)).mean()
cost_reg = cost_reg + reconstruction_penalty * penalty_val
self.cost = cost
self.cost_reg = cost_reg
self.error_rate = error_rate
self.error_rate_reg = error_rate_reg
self.pred = pred
self.confidence = confidence
def __init__(self, ref_data, output_dim):
if pca_dims is not None:
covmat = numpy.dot(ref_data.T, ref_data)
ev, evec = numpy.linalg.eig(covmat)
best_i = ev.argsort()[-pca_dims:]
best_evecs = evec[:, best_i]
best_evecs = best_evecs / numpy.sqrt(
(best_evecs**2).sum(axis=0)) #normalize
ref_data = numpy.dot(ref_data, best_evecs)
input_dim = ref_data.shape[1]
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32),
name='ref_data')
# Construct the model
j = tensor.lvector('j')
r = ref_data_sh[j, :]
x = tensor.fmatrix('x')
y = tensor.ivector('y')
# input_dim must be nr
mlp = MLP(activations=activation_functions,
dims=[input_dim] + hidden_dims + [n_inter],
name='inter_gen')
mlp2 = MLP(activations=activation_functions_2 + [None],
dims=[n_inter] + hidden_dims_2 + [output_dim],
name='end_mlp')
inter_weights = mlp.apply(r)
if inter_bias == None:
ibias = Bias(n_inter)
ibias.biases_init = Constant(0)
ibias.initialize()
inter = ibias.apply(tensor.dot(x, inter_weights))
else:
inter = tensor.dot(x, inter_weights) - inter_bias
inter = inter_act_fun.apply(inter)
final = mlp2.apply(inter)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
# error_rate = tensor.neq(y, pred).mean()
ber = balanced_error_rate.ber(y, pred)
# Initialize parameters
for brick in [mlp, mlp2]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
# apply regularization
cg = ComputationGraph([cost, ber])
if r_dropout != 0:
# - dropout on input vector r : r_dropout
cg = apply_dropout(cg, [r], r_dropout)
if x_dropout != 0:
cg = apply_dropout(cg, [x], x_dropout)
if s_dropout != 0:
# - dropout on intermediate layers of first mlp : s_dropout
s_dropout_vars = list(
set(
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([inter_weights]))) -
set([inter_weights]))
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if i_dropout != 0:
# - dropout on input to second mlp : i_dropout
cg = apply_dropout(cg, [inter], i_dropout)
if a_dropout != 0:
# - dropout on hidden layers of second mlp : a_dropout
a_dropout_vars = list(
set(
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([final]))) - set([inter_weights]) -
set(s_dropout_vars))
cg = apply_dropout(cg, a_dropout_vars, a_dropout)
if r_noise_std != 0:
cg = apply_noise(cg, [r], r_noise_std)
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
[cost_reg, ber_reg] = cg.outputs
if s_l1pen != 0:
s_weights = VariableFilter(bricks=mlp.linear_transformations,
roles=[WEIGHT])(cg)
cost_reg = cost_reg + s_l1pen * sum(
abs(w).sum() for w in s_weights)
if i_l1pen != 0:
cost_reg = cost_reg + i_l1pen * abs(inter).sum()
if a_l1pen != 0:
a_weights = VariableFilter(bricks=mlp2.linear_transformations,
roles=[WEIGHT])(cg)
cost_reg = cost_reg + a_l1pen * sum(
abs(w).sum() for w in a_weights)
self.cost = cost
self.cost_reg = cost_reg
self.ber = ber
self.ber_reg = ber_reg
self.pred = pred
self.confidence = confidence
