def __contrastive_divergence_rbm__(self, vis, hid, linear):
neg_vis = dbn.sigmoid(dot(hid, self.weights.T) + tile(self.visible_biases, (len(vis), 1)))
if linear:
neg_hid_prob = dot(neg_vis, self.weights) + tile(self.hidden_biases, (len(vis), 1))
else:
neg_hid_prob = dbn.sigmoid(dot(neg_vis, self.weights) + tile(self.hidden_biases, (len(vis), 1)))
return neg_vis, neg_hid_prob
def __contrastive_divergence_rbm__(self, vis, hid, linear):
neg_vis = dbn.sigmoid(
dot(hid, self.weights.T) +
tile(self.visible_biases, (len(vis), 1)))
if linear:
neg_hid_prob = dot(neg_vis, self.weights) + tile(
self.hidden_biases, (len(vis), 1))
else:
neg_hid_prob = dbn.sigmoid(
dot(neg_vis, self.weights) +
tile(self.hidden_biases, (len(vis), 1)))
return neg_vis, neg_hid_prob
def rbm_learn(self, epochs, first_layer=False, linear=False):
"""
The learning of the RBMs. The higher value of epochs will result in more training.
@param epochs: The number of epochs.
"""
if linear:
self.learning_rate = self.learning_rate * 0.01
for epoch in range(epochs):
errsum = 0
batch_index = 0
for _ in self.batches:
# Positive phase - generate data from visible to hidden units.
pos_vis = self.__get_input_data__(batch_index, first_layer=first_layer)
batch_size = len(pos_vis)
if linear:
pos_hid_prob = dot(pos_vis, self.weights) + tile(self.hidden_biases, (batch_size, 1))
else:
pos_hid_prob = dbn.sigmoid(dot(pos_vis, self.weights) + tile(self.hidden_biases, (batch_size, 1)))
self.__save_output__(batch_index, pos_hid_prob) # Serialize the output of the RBM
# If probabilities are higher than randomly generated, the states are 1
randoms = rand.rand(batch_size, self.num_hid)
pos_hid = array(randoms < pos_hid_prob, dtype=int)
# Negative phase - generate data from hidden to visible units and then again to hidden units.
neg_vis = pos_vis
neg_hid_prob = pos_hid
for i in range(self.gibbs_steps): # There is only 1 step of contrastive divergence
neg_vis, neg_hid_prob = self.__contrastive_divergence_rbm__(neg_vis, pos_hid_prob, linear)
# Set the error
errsum += sum(((pos_vis) - neg_vis) ** 2) / len(pos_vis)
# Update weights and biases
self.delta_weights = self.momentum * self.delta_weights + self.learning_rate * (
(dot(pos_vis.T, pos_hid_prob) - dot(neg_vis.T, neg_hid_prob)) / batch_size
- self.weight_cost * self.weights
) # TODO: RE-EVALUATE THE LAST LEARNING RATE
self.delta_visible_biases = self.momentum * self.delta_visible_biases + (
self.learning_rate / batch_size
) * (sum(pos_vis, axis=0) - sum(neg_vis, axis=0))
self.delta_hidden_biases = self.momentum * self.delta_hidden_biases + (
self.learning_rate / batch_size
) * (sum(pos_hid_prob, axis=0) - sum(neg_hid_prob, axis=0))
self.weights += self.delta_weights
self.visible_biases += self.delta_visible_biases
self.hidden_biases += self.delta_hidden_biases
batch_index += 1
# Output error scores
e = errsum / len(self.batches)
err_str = "Epoch[%2d]: Error = %.07f" % (epoch + 1, e)
self.fout(err_str)
self.error += [e]
def rsm_learn(self,epochs):
"""
The learning of the first layer RBM (Replicated Softmax Model). The higher value of epochs will result in
more training.
@param epochs: The number of epochs.
"""
for epoch in range(epochs):
perplexity = 0
batch_index = 0
for _ in self.batches:
# Positive phase - generate data from visible to hidden units.
pos_vis = self.__get_input_data__(batch_index,first_layer=True)
batch_size = len(pos_vis)
D = sum(pos_vis,axis = 1)
if epoch == 0:
self.words += sum(pos_vis) # Calculate the number of words in order to calculate the perplexity.
pos_hid_prob = dbn.sigmoid(dot(pos_vis,self.weights)+outer(D, self.hidden_biases))
self.__save_output__(batch_index, pos_hid_prob) # Serialize the output of the RBM
# If probabilities are higher than randomly generated, the states are 1
randoms = rand.rand(batch_size,self.num_hid)
pos_hid = array(randoms < pos_hid_prob,dtype = int)
# Negative phase - generate data from hidden to visible units and then again to hidden units.
neg_vis = pos_vis
neg_hid_prob = pos_hid
for i in range(100): # There is only 1 step of contrastive divergence
neg_vis,neg_hid_prob,D,p = self.__contrastive_divergence_rsm__(neg_vis, pos_hid_prob, D)
if i == 0:
perplexity+=p
pos_products = dot(pos_vis.T,pos_hid_prob)
pos_visible_bias_activation = sum(pos_vis,axis = 0)
pos_hidden_bias_activation = sum(pos_hid_prob,axis = 0)
neg_products = dot(neg_vis.T,neg_hid_prob)
neg_visibe_bias_activation = sum(neg_vis,axis = 0)
neg_hidden_bias_activation = sum(neg_hid_prob,axis = 0)
# Update the weights and biases
self.delta_weights = self.momentum * self.delta_weights + self.learning_rate * ((pos_products-neg_products)/batch_size - self.weight_cost * self.weights)
self.delta_visible_biases = (self.momentum * self.delta_visible_biases + (pos_visible_bias_activation-neg_visibe_bias_activation))*(self.learning_rate/batch_size)
self.delta_hidden_biases = (self.momentum * self.delta_hidden_biases + (pos_hidden_bias_activation-neg_hidden_bias_activation))*(self.learning_rate/batch_size)
self.weights += self.delta_weights
self.visible_biases += self.delta_visible_biases
self.hidden_biases += self.delta_hidden_biases
batch_index += 1
if not epoch == 0: # Output error score.
perplexity = exp(-perplexity/self.words)
err_str = "Epoch[%2d]: Perplexity = %.02f"%(epoch,perplexity)
self.fout(err_str)
self.error += [perplexity]
self.fprogress()
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 __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):
"""
Run through the deep autoencoder and compute the output.
@param x: The BOW.
@param weight_matrices_added_biases: The weight matrices added biases.
"""
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,weight_matrices_added_biases[i]))
elif i == (len(weight_matrices_added_biases)/2)-1:
z = dot(z_values[i-1],weight_matrices_added_biases[i])
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)
xout = dbn.sigmoid(dot(z_values[-1],weight_matrices_added_biases[-1]))
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
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 rsm_learn(self, epochs):
"""
The learning of the first layer RBM (Replicated Softmax Model). The higher value of epochs will result in
more training.
@param epochs: The number of epochs.
"""
for epoch in range(epochs):
perplexity = 0
batch_index = 0
for _ in self.batches:
# Positive phase - generate data from visible to hidden units.
pos_vis = self.__get_input_data__(batch_index,
first_layer=True)
batch_size = len(pos_vis)
D = sum(pos_vis, axis=1)
if epoch == 0:
self.words += sum(
pos_vis
) # Calculate the number of words in order to calculate the perplexity.
pos_hid_prob = dbn.sigmoid(
dot(pos_vis, self.weights) + outer(D, self.hidden_biases))
self.__save_output__(
batch_index,
pos_hid_prob) # Serialize the output of the RBM
# If probabilities are higher than randomly generated, the states are 1
randoms = rand.rand(batch_size, self.num_hid)
pos_hid = array(randoms < pos_hid_prob, dtype=int)
# Negative phase - generate data from hidden to visible units and then again to hidden units.
neg_vis = pos_vis
neg_hid_prob = pos_hid
for i in range(
self.gibbs_steps
): # There is only 1 step of contrastive divergence
neg_vis, neg_hid_prob, D, p = self.__contrastive_divergence_rsm__(
neg_vis, pos_hid_prob, D)
if i == 0:
perplexity += p
pos_products = dot(pos_vis.T, pos_hid_prob)
pos_visible_bias_activation = sum(pos_vis, axis=0)
pos_hidden_bias_activation = sum(pos_hid_prob, axis=0)
neg_products = dot(neg_vis.T, neg_hid_prob)
neg_visibe_bias_activation = sum(neg_vis, axis=0)
neg_hidden_bias_activation = sum(neg_hid_prob, axis=0)
# Update the weights and biases
self.delta_weights = self.momentum * self.delta_weights + self.learning_rate * (
(pos_products - neg_products) / batch_size -
self.weight_cost * self.weights)
self.delta_visible_biases = (
self.momentum * self.delta_visible_biases +
(pos_visible_bias_activation - neg_visibe_bias_activation)
) * (self.learning_rate / batch_size)
self.delta_hidden_biases = (
self.momentum * self.delta_hidden_biases +
(pos_hidden_bias_activation - neg_hidden_bias_activation)
) * (self.learning_rate / batch_size)
self.weights += self.delta_weights
self.visible_biases += self.delta_visible_biases
self.hidden_biases += self.delta_hidden_biases
batch_index += 1
if not epoch == 0: # Output error score.
perplexity = exp(-perplexity / self.words)
err_str = "Epoch[%2d]: Perplexity = %.02f" % (epoch,
perplexity)
self.fout(err_str)
self.error += [perplexity]
def rbm_learn(self, epochs, first_layer=False, linear=False):
"""
The learning of the RBMs. The higher value of epochs will result in more training.
@param epochs: The number of epochs.
"""
if linear:
self.learning_rate = self.learning_rate * 0.01
for epoch in range(epochs):
errsum = 0
batch_index = 0
for _ in self.batches:
# Positive phase - generate data from visible to hidden units.
pos_vis = self.__get_input_data__(batch_index,
first_layer=first_layer)
batch_size = len(pos_vis)
if linear:
pos_hid_prob = dot(pos_vis, self.weights) + tile(
self.hidden_biases, (batch_size, 1))
else:
pos_hid_prob = dbn.sigmoid(
dot(pos_vis, self.weights) +
tile(self.hidden_biases, (batch_size, 1)))
self.__save_output__(
batch_index,
pos_hid_prob) # Serialize the output of the RBM
# If probabilities are higher than randomly generated, the states are 1
randoms = rand.rand(batch_size, self.num_hid)
pos_hid = array(randoms < pos_hid_prob, dtype=int)
# Negative phase - generate data from hidden to visible units and then again to hidden units.
neg_vis = pos_vis
neg_hid_prob = pos_hid
for i in range(
self.gibbs_steps
): # There is only 1 step of contrastive divergence
neg_vis, neg_hid_prob = self.__contrastive_divergence_rbm__(
neg_vis, pos_hid_prob, linear)
# Set the error
errsum += sum(((pos_vis) - neg_vis)**2) / len(pos_vis)
# Update weights and biases
self.delta_weights = self.momentum * self.delta_weights + self.learning_rate * (
(dot(pos_vis.T, pos_hid_prob) -
dot(neg_vis.T, neg_hid_prob)) / batch_size -
self.weight_cost * self.weights
) # TODO: RE-EVALUATE THE LAST LEARNING RATE
self.delta_visible_biases = self.momentum * self.delta_visible_biases + (
self.learning_rate / batch_size) * (sum(pos_vis, axis=0) -
sum(neg_vis, axis=0))
self.delta_hidden_biases = self.momentum * self.delta_hidden_biases + (
self.learning_rate / batch_size) * (
sum(pos_hid_prob, axis=0) - sum(neg_hid_prob, axis=0))
self.weights += self.delta_weights
self.visible_biases += self.delta_visible_biases
self.hidden_biases += self.delta_hidden_biases
batch_index += 1
# Output error scores
e = errsum / len(self.batches)
err_str = "Epoch[%2d]: Error = %.07f" % (epoch + 1, e)
self.fout(err_str)
self.error += [e]
