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Unsupervised Learning
Let us try stacking our RBM class. To be able to make the DBN we will need to define
one more function in the RBM class, the output of the hidden of one RBM needs to be
fed to the next RBM:
#Create expected output for our DBN
def rbm_output(self, X):
out = tf.nn.sigmoid(tf.matmul(X, self.w) + self.hb)
return out
Now we can just use the RBM class to create a stacked RBM structure. In the
following code we create an RBM stack: the first RBM will have 500 hidden units,
the second will have 200 hidden units, and the third will have 50 hidden units:
RBM_hidden_sizes = [500, 200 , 50 ] #create 2 layers of RBM with size
400 and 100
#Since we are training, set input as training data
inpX = train_data
#Create list to hold our RBMs
rbm_list = []
#Size of inputs is the number of inputs in the training set
input_size = train_data.shape[1]
#For each RBM we want to generate
for i, size in enumerate(RBM_hidden_sizes):
print ('RBM: ',i,' ',input_size,'->', size)
rbm_list.append(RBM(input_size, size))
input_size = size
---------------------------------------------------------------------
RBM: 0 784 -> 500
RBM: 1 500 -> 200
RBM: 2 200 -> 50
For the first RBM, the MNIST data is the input. The output of the first RBM is then
fed as input to the second RBM, and so on through the consecutive RBM layers:
#For each RBM in our list
for rbm in rbm_list:
print ('New RBM:')
#Train a new one
rbm.train(tf.cast(inpX,tf.float32))
#Return the output layer
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