Implement RNN-based (seq2seq) VAEs about tars HOT 4 CLOSED

x = lasagne.layers.InputLayer((None, dim_x))
q1 = lasagne.layers.ReshapeLayer(x, (-1, timesteps, dim_x))
q2 = lasagne.layers.GRULayer(q1,num_units=dim_z, grad_clipping=100)
q3 = lasagne.layers.SliceLayer(q2,indices=-1,axis=1)
mean = lasagne.layers.DenseLayer(q3,2,nonlinearity=lasagne.nonlinearities.linear)
var = lasagne.layers.DenseLayer(q3,2,nonlinearity=lasagne.nonlinearities.softplus)
q = Gaussian(mean,var,given=[x])

z = lasagne.layers.InputLayer((None, dim_z))
p1 = RepeatLayer(z, n=timesteps)
p2 = lasagne.layers.GRULayer(p1, num_units=dim_x, grad_clipping=100)
p3 = lasagne.layers.ReshapeLayer(p2, (-1, [2]))
mean = lasagne.layers.DenseLayer(p3, dim_x, nonlinearity=lasagne.nonlinearities.softmax)
p = Categorical(mean,given=[z])

model = VAE(q,p,batch_size*20,lasagne.updates.adam,random=1234)

x = lasagne.layers.InputLayer((None, timesteps, dim_x))
q1 = lasagne.layers.GRULayer(x, num_units=dim_z, grad_clipping=100)
q2 = lasagne.layers.SliceLayer(q1, indices=-1, axis=1)
mean = lasagne.layers.DenseLayer(q2,2,nonlinearity=lasagne.nonlinearities.linear)
var = lasagne.layers.DenseLayer(q2,2,nonlinearity=lasagne.nonlinearities.softplus)
q = Gaussian(mean,var,given=[x])

z = lasagne.layers.InputLayer((None, dim_z))
p1 = RepeatLayer(z, n=timesteps)
p2 = lasagne.layers.GRULayer(p1, num_units=dim_x, grad_clipping=100)
p3 = lasagne.layers.ReshapeLayer(p2, (-1, [2]))
p4 = lasagne.layers.DenseLayer(p3, dim_x, nonlinearity=lasagne.nonlinearities.softmax)
mean = lasagne.layers.ReshapeLayer(p4, (-1, timesteps, dim_x))
p = Categorical(mean,given=[z])

model = VAE(q,p,batch_size,adam,random=1234)