tensorflow - Using states as predictors in a Recurrent Neural Network -


i'm using lstm (long short term memory) network in tensorflow linear layer final layer. using concatenation of input , lstm output input final layer. add states of lstm predictors.
difficulty tf.nn.dynamic_rnn() produces last state output, i've resorted using loop , running tf.nn.dynamic_rnn() 1 time step , outputting , running 1 more time step...

however i'm still getting errors code. here is. input 30*238*3 tensor of [observations/batches,timesteps, predictors]: 

testinput = tf.placeholder(tf.float64, [none,238,3])     weightin = tf.placeholder(tf.float64, [none])     target = tf.placeholder(tf.float64, [1, none, 238])     mask = tf.placeholder(tf.float64, [1,none,238])     lin_weight = tf.variable(numpy.concatenate((numpy.reshape(numpy.array([0.12504494, 0.326449906, -0.192413488]),         (1,3,1)), numpy.random.normal(0,1/((3000000000.0)**(1/2.0)),[1,3*neurons,1])), axis = 1),dtype=tf.float64)#[0.12504494, 0.326449906, -0.192413488]     bias = tf.variable(1.76535047076, dtype=tf.float64, )#1.76535047076     lstm = tf.contrib.rnn.basiclstmcell(neurons)     state = lstm.zero_state(1,tf.float64)     out1 =[0.0 each2 in range(238)]     = [0.0 each2 in range(238)]     b = [0.0 each2 in range(238)]     k in range(238):         out1[k],state = tf.nn.dynamic_rnn(lstm, testinput, intial_state = state, sequence_length = [1], dtype=tf.float64)         (a[k],b[k]) =  state     print(out1)     out1 = tf.reshape(numpy.array(out1),[-1,238,4])     = tf.reshape(numpy.array(a),[-1,238,4])     b = tf.reshape(numpy.array(b),[-1,238,4])     lineinput = tf.concat([testinput,out1,a,b], 2)     output = tf.squeeze(tf.tensordot(lin_weight, lineinput, [[1],[2]]) + bias, [0])     sqerror = tf.square(tf.subtract(output, target))      masklayer  = tf.multiply(sqerror,mask)     useweight = tf.tensordot(masklayer ,weightin,[[1],[0]])     meansquared = tf.reduce_sum(useweight)     #meansquared = tf.reduce_mean(tf.tensordot(tf.multiply(tf.square(tf.subtract(output, target)), mask),weightin,[[1],[0]]))     optimizer = tf.train.adamoptimizer()     minimize1 = optimizer.minimize(meansquared)     init_op = tf.global_variables_initializer()     sess = tf.session()     sess.run(init_op)     print(sess.run(a, {testinput: [xtesting[0,:,:]]}))     batch_size = 5     no_of_batches = int(30/batch_size)     run = 0     maxerror = 10000000000     flag = true     each in range(10000):         if flag:             ptr = 0             j in range(no_of_batches):                 inp, out, win, maskin= xtesting[ptr:ptr+batch_size,:,:], ytesting[:,ptr:ptr+batch_size,:], weights2[ptr:ptr+batch_size], bmask[:,ptr:ptr+batch_size,:]                 ptr+=batch_size                 sess.run(minimize1, {testinput: inp, target: out, weightin: win, mask: maskin})             validerror = sess.run(meansquared, {testinput: xtesting, target: ytesting, weightin: weights2, mask: bmaskvalidate})             print(sess.run(meansquared, {testinput: xtesting, target: ytesting, weightin: weights2, mask: bmask}))             print(validerror)             print(each)             if  validerror < maxerror:                 run = each                 maxerror = validerror             if each > run + 25:                 flag = false     print(sess.run(output, {testinput: [xtesting[0,:,:]]}))     print(sess.run(meansquared, {testinput: [xtesting[0,:,:]], target: [ytesting[:,0,:]], weightin: [weights2[0]], mask: [bmask2[:,0,:]]})/24.0)

the error is: typeerror: expected binary or unicode string, got < tf.tensor 'rnn/transpose:0' shape=(?, 238, 4) dtype=float64 >. error caused line out1 = tf.reshape(numpy.array(out1),[-1,238,4]).


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