def train(self, trainingData):
rbmList = [] # list RBM's weights
tempData = trainingData
# start RBM's training and get the respective weights
for n in range(self.nEncoderLayers):
if (n == 0 or n == (self.nEncoderLayers - 1)):
rbm = RBM(tempData, self.sEncoderLayers[n], rbmType='GBRBM')
else:
rbm = RBM(tempData, self.sEncoderLayers[n], rbmType='BBRBM')
print('Start %d RBM training' % (n + 1))
rbm.train(batchSize=100)
[weights, visBias, hidBias] = rbm.getWeights()
rbmList.append(RBM_Weights(weights, visBias, hidBias))
data = tf.convert_to_tensor(tempData,
dtype=tf.float32,
name='data')
probHid = tf.sigmoid(tf.matmul(data, weights) + hidBias)
hid = tf.cast(tf.greater(
probHid,
tf.random_uniform(tf.shape(probHid),
minval=0,
maxval=1,
dtype=tf.float32)),
dtype=tf.float32)
with tf.Session() as sess:
if ((self.nEncoderLayers - 1) == n):
tempData = sess.run(probHid)
else:
tempData = sess.run(hid)
# start the fine tuning process
return self.fineTuning(rbmList, trainingData)
