def __contrastive_divergence_rsm__(self, vis, hid, D):
neg_vis = dot(hid, self.weights.T) + self.visible_biases
softmax_value = dbn.softmax(neg_vis)
neg_vis *= 0
for i in xrange(len(vis)):
neg_vis[i] = random.multinomial(D[i], softmax_value[i], size=1)
D = sum(neg_vis, axis=1)
perplexity = nansum(vis * log(softmax_value))
neg_hid_prob = dbn.sigmoid(dot(neg_vis, self.weights) + outer(D, self.hidden_biases))
return neg_vis, neg_hid_prob, D, perplexity
def __contrastive_divergence_rsm__(self, vis, hid, D):
neg_vis = dot(hid, self.weights.T) + self.visible_biases
softmax_value = dbn.softmax(neg_vis)
neg_vis *= 0
for i in xrange(len(vis)):
neg_vis[i] = random.multinomial(D[i], softmax_value[i], size=1)
D = sum(neg_vis, axis=1)
perplexity = nansum(vis * log(softmax_value))
neg_hid_prob = dbn.sigmoid(
dot(neg_vis, self.weights) + outer(D, self.hidden_biases))
return neg_vis, neg_hid_prob, D, perplexity
def generate_output_data(x,
weight_matrices_added_biases,
binary_output=False,
sampled_noise=None):
"""
Compute forwards-pass in the deep autoencoder and compute the output.
@param x: The BOW.
@param weight_matrices_added_biases: The weight matrices added biases.
@param binary_output: If the output of the DBN must be binary. If so, Gaussian noise will be added to bottleneck.
@param sampled_noise: The gaussian noise matrix in case of binary output units.
"""
z_values = []
NN = sum(x, axis=1)
for i in range(len(weight_matrices_added_biases) - 1):
if i == 0:
z = dbn.sigmoid(
dot(x[:, :-1], weight_matrices_added_biases[i][:-1, :]) +
outer(NN, weight_matrices_added_biases[i][-1, :]))
elif i == (len(weight_matrices_added_biases) / 2) - 1:
act = dot(z_values[i - 1], weight_matrices_added_biases[i])
if binary_output and not sampled_noise is None:
z = act + sampled_noise
else:
z = act
else:
z = dbn.sigmoid(
dot(z_values[i - 1], weight_matrices_added_biases[i]))
z = append(z, ones((len(x), 1), dtype=float64), axis=1)
z_values.append(z)
neg_vis = dot(z_values[-1], weight_matrices_added_biases[-1])
softmax_value = dbn.softmax(neg_vis)
xout = softmax_value
if len(xout[xout == 0]) > 0:
w = where(xout == 0)
for i in range(len(w[0])):
row = w[0][i]
col = w[1][i]
xout[row, col] = finfo(float).eps
return xout, z_values
def generate_output_data(x, weight_matrices_added_biases, binary_output=False, sampled_noise=None):
"""
Compute forwards-pass in the deep autoencoder and compute the output.
@param x: The BOW.
@param weight_matrices_added_biases: The weight matrices added biases.
@param binary_output: If the output of the DBN must be binary. If so, Gaussian noise will be added to bottleneck.
@param sampled_noise: The gaussian noise matrix in case of binary output units.
"""
z_values = []
NN = sum(x, axis=1)
for i in range(len(weight_matrices_added_biases) - 1):
if i == 0:
z = dbn.sigmoid(dot(x[:, :-1], weight_matrices_added_biases[i][:-1, :]) + outer(NN,
weight_matrices_added_biases[
i][-1, :]))
elif i == (len(weight_matrices_added_biases) / 2) - 1:
act = dot(z_values[i - 1], weight_matrices_added_biases[i])
if binary_output and not sampled_noise is None:
z = act + sampled_noise
else:
z = act
else:
z = dbn.sigmoid(dot(z_values[i - 1], weight_matrices_added_biases[i]))
z = append(z, ones((len(x), 1), dtype=float64), axis=1)
z_values.append(z)
neg_vis = dot(z_values[-1], weight_matrices_added_biases[-1])
softmax_value = dbn.softmax(neg_vis)
xout = softmax_value
if len(xout[xout == 0]) > 0:
w = where(xout == 0)
for i in range(len(w[0])):
row = w[0][i]
col = w[1][i]
xout[row, col] = finfo(float).eps
return xout, z_values
