leaveInData.append(trX[i])
leaveInLabel.append(label)
leaveInData = np.stack(leaveInData,axis=0)
leaveInLabel = np.stack(leaveInLabel,axis=0)
removeData = np.stack(removeData,axis=0)
removeLabel = np.stack(removeLabel,axis=0)
visibleDim = 28*28
batchSize = 100
stepSize = 0.005
for i in range(5):
matricies = {}
for hiddenDim in range(10,101,5):
tf.reset_default_graph()
testRBM = RBM(visibleDim,hiddenDim)
train = testRBM.contrastiveDivergenceN(1,stepSize)
X,Y = testRBM.placeholders()
A = testRBM.getWeightsPointer()
bvis = testRBM.getVisibleBiasPointer()
bhid = testRBM.getHiddenBiasPointer()
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
tr_x, tr_y = mnist.train.next_batch(batchSize)
mse = testRBM.mse(tf.cast(tr_x, tf.float32))
for k in range(1, 10001):
tr_x, tr_y = mnist.train.next_batch(batchSize)
sess.run(train, feed_dict={X: tr_x})
