Using RBM with midi files in Tensor Flow, getting some errors

Question

I am trying to keep track of this laptop, the problem is that it is written for Py 2.7, and I am trying to port it to Py 3.6. Fortunately, someone ported the MIDI library to Py 3 https://github.com/louisabraham/python3-midi , and I was able to use this to parse midi files into a numpy array, Now my problem is that I get these errors.

https://github.com/bhaktipriya/Blues/blob/master/Music.ipynb

---

TypeError                                 Traceback (most recent call last)
<ipython-input-62-f35c20bfe55b> in <module>()
      1 #backward pass, x samples drawn from prob distribution defn by (hk,w,bv)
----> 2 x_sample=gibbs_sample(2)
      3 print(x_sample)
      4 #h sampled from prob distrib defn by (x,w,bh)
      5 h=sample(tf.sigmoid(tf.matmul(x, W) + bh))

<ipython-input-57-943cbc813622> in gibbs_sample(k)
     13     #Gibbs sample(done for k iterations) is used to approximate the distribution of the RBM(defined by W, bh, bv)
     14     ct=tf.constant(0)
---> 15     [_, _, x_sample]=control_flow_ops.while_loop(lambda count, num_iter, *args: count < num_iter,gibbs_step, [ct, tf.constant(k), x], 1, False)
     16     #to stop tensorflow from propagating gradients back through the gibbs step
     17     x_sample=tf.stop_gradient(x_sample)

c:\users\ali\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\ops\control_flow_ops.py in while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name, maximum_iterations)
   3051       raise TypeError("body must be callable.")
   3052     if parallel_iterations < 1:
-> 3053       raise TypeError("parallel_iterations must be a positive integer.")
   3054 
   3055     if maximum_iterations is not None:

TypeError: parallel_iterations must be a positive integer.

I also get weird errors with the numpy array form in the training phase

size_tr=tf.cast(tf.shape(x)[0], tf.float32)
eta=lr/size_tr
W_upd=tf.multiply(eta, tf.subtract(tf.matmul(tf.transpose(x), h), tf.matmul(tf.transpose(x_sample), h_sample)))
bv_upd=tf.multiply(eta, tf.reduce_sum(tf.subtract(x, x_sample), 0, True))
bh_upd=tf.multiply(eta, tf.reduce_sum(tf.subtract(h, h_sample), 0, True))
updt=[W.assign_add(W_upd), bv.assign_add(bv_upd), bh.assign_add(bh_upd)]
sess=tf.Session()
init=tf.initialize_all_variables()
sess.run(init)
for epoch in tqdm(range(epochs)):
            for song in songs:
                song=np.array(song)
                #reshaping song into chunks of timestep size
                chunks=song.shape[0]/timesteps
                chunks = int(np.floor(chunks))
                dur=chunks*timesteps
                dur = int(np.floor(dur))

                song=song[:dur]
                song=np.reshape(song, [chunks, song.shape[1]*timesteps])
                #Train the RBM on batch_size examples at a time
                for i in range(1, len(song), batch_size): 
                    tr_x=song[i:i+batch_size]
                    sess.run(updt, feed_dict={x: tr_x})
Error is:
    InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'x_7' with dtype float and shape [?,2340]
         [[Node: x_7 = Placeholder[dtype=DT_FLOAT, shape=[?,2340], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Answer 1

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