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Unsupervised Learning
#Find the error rate
err = tf.reduce_mean(tf.square(batch - v1))
print ('Epoch: %d' % epoch,'reconstruction error: %f' % err)
loss.append(err)
return loss
Now that our class is ready, we instantiate an object of RBM and train it on the MNIST
dataset:
(train_data, _), (test_data, _) = tf.keras.datasets.mnist.load_data()
train_data = train_data/np.float32(255)
train_data = np.reshape(train_data, (train_data.shape[0], 784))
test_data = test_data/np.float32(255)
test_data = np.reshape(test_data, (test_data.shape[0], 784))
#Size of inputs is the number of inputs in the training set
input_size = train_data.shape[1]
rbm = RBM(input_size, 200)
err = rbm.train(train_data,50)
In the following code, you can see the learning curve of our RBM:
plt.plot(err)
plt.xlabel('epochs')
plt.ylabel('cost')
Figure 8: Learning curve for the RBM model
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