AttributeError: 'module' object has no attribute 'While'

[chuck-park]

I am stuck in first part of your README.md

I have download importing things from pandas to python-midi by pip
What is that I was missed?

[gc-cloud]

@AKADark I'm also experimenting with this and got the same error. For what I've read, control_flow_ops.While has been deprecated since tensorflow 11. Here is a workaround using a virtual environment ( assuming you have anaconda installed)

Create and activate a conda environment with python 2.7

$ conda create -n tf10_py2.7 python=2.7

$ source activate tf10_py2.7

Install tensorflow 10

If you have a Mac:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0-py2-none-any.whl

If you are using linux
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl

$ pip install --upgrade $TF_BINARY_URL

Regenerate protobuf

$pip2 install --force-reinstall --upgrade protobuf

At this point you should be able to generate music

[chuck-park]

@gc-cloud Thanks to your answer, I solved the problem well but I ran into same issue as yours maybe haha. This is my error... If you have any information to help to fix it, re-comment please!

chuck@chuck-PC:~/Downloads/Music_RNN_RBM-master$ python rnn_rbm_generate.py parameter_checkpoints/pretrained.ckpt
Traceback (most recent call last):
  File "rnn_rbm_generate.py", line 45, in <module>
    main(sys.argv[1])
  File "rnn_rbm_generate.py", line 32, in main
    song_primer = midi_manipulation.get_song(primer_song) 
  File "/home/chuck/Downloads/Music_RNN_RBM-master/midi_manipulation.py", line 20, in get_song
    song = song[:np.floor(song.shape[0]/num_timesteps)*num_timesteps]
TypeError: slice indices must be integers or None or have an __index__ method

[gc-cloud]

@AKADark change line 20 in midi_manipulation.py to this
song = song[:int(np.floor(song.shape[0]/num_timesteps)*num_timesteps)]

With that, you will probably get another float vs integer error, which is what I'm dealing with right now

[chuck-park]

@gc-cloud I changed a lot of things that I could. So I can't write that in here... I'm so sorry about that. But If you want, I can send you my files that were modified. Please give me your email address. my address is [email protected]

[gc-cloud]

I think I managed to make this work with Tensorflow 1.1. Here are all the steps:

1. First, you need to upgrade your python scripts using the tensorflow upgrade utility

2. In midi_manipulation.py change get_song to this

def get_song(path):
    #Load the song and reshape it to place multiple timesteps next to each other
    song = np.array(midiToNoteStateMatrix(path))
    song = song[:int(np.floor(song.shape[0]/num_timesteps)*num_timesteps)]
    song = np.reshape(song, [song.shape[0]/num_timesteps, song.shape[1]*num_timesteps])
    return song

3. In RBM.py replace the call to deprecated control_flow_ops

#[_, _, x_sample] = control_flow_ops.While(lambda count, num_iter, *args: count < num_iter,
    #                                     gibbs_step, [ct, tf.constant(k), x], 1, False)
    # [_, _, x_sample] = tf.while_loop(lambda count, num_iter, *args: count < num_iter, gibbs_step,


    [_, _, x_sample] = tf.while_loop(lambda count, num_iter, *args: count < num_iter, gibbs_step,
                                     [ct, tf.constant(k), x], parallel_iterations=1, back_prop=False)

4. In rnn_rbm.py update generate to look like this

   def generate(num, x=x, size_bt=size_bt, u0=u0, n_visible=n_visible, prime_length=100):
        """
            This function handles generating music. This function is one of the outputs of the build_rnnrbm function
            Args:
                num (int): The number of time steps to generate
                x (tf.placeholder): The data vector. We can use feed_dict to set this to the music primer. 
                size_bt (tf.float32): The batch size
                u0 (tf.Variable): The initial state of the RNN
                n_visible (int): The size of the data vectors
                prime_length (int): The number of times teps into the primer song that we use befoe beginning to generate music
            Returns:
                The generated music, as a tf.Tensor

        """
        Uarr = tf.scan(rnn_recurrence, x, initializer=u0)
        # U = Uarr[np.floor(prime_length/midi_manipulation.num_timesteps), :, :]
        U = Uarr[int(np.floor(prime_length / midi_manipulation.num_timesteps)), :, :]
        # [_, _, _, _, _, music] = control_flow_ops.While(lambda count, num_iter, *args: count < num_iter,
        #                                                  generate_recurrence, [tf.constant(1, tf.int32), tf.constant(num), U,
        #                                                  tf.zeros([1, n_visible], tf.float32), x,
        #                                                 tf.zeros([1, n_visible],  tf.float32)])

        time_steps = tf.constant(1, tf.int32)
        iterations = tf.constant(num)
        u_t = tf.zeros([1, n_visible], tf.float32)
        music = tf.zeros([1, n_visible], tf.float32)
        loop_vars = [time_steps, iterations, U, u_t, x, music]

        [_, _, _, _, _, music] = tf.while_loop(lambda count, num_iter, *args: count < num_iter, generate_recurrence,
                                               loop_vars,
                                               shape_invariants=[time_steps.get_shape(), iterations.get_shape(),
                                                                 U.get_shape(), u_t.get_shape(),
                                                                 x.get_shape(), tf.TensorShape([None, 780])])

        return music

[chuck-park]

Thank you for your information!! But, unfortunately, My laptop was broken.... I will do it as soon as possible! Thanks very much!

…

On Sun, Apr 30, 2017 at 5:36 PM, Gerardo Castaneda ***@***.*** > wrote: I think I managed to make this work with Tensorflow 1.1. Here are all the steps: 1. First, you need to upgrade your python scripts using the tensorflow upgrade utility <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/compatibility> 2. In midi_manipulation.py change get_song to this def get_song(path): #Load the song and reshape it to place multiple timesteps next to each other song = np.array(midiToNoteStateMatrix(path)) song = song[:int(np.floor(song.shape[0]/num_timesteps)*num_timesteps)] song = np.reshape(song, [song.shape[0]/num_timesteps, song.shape[1]*num_timesteps]) return song 1. In RBM.py replace the call to deprecated control_flow_ops #[_, _, x_sample] = control_flow_ops.While(lambda count, num_iter, *args: count < num_iter, # gibbs_step, [ct, tf.constant(k), x], 1, False) # [_, _, x_sample] = tf.while_loop(lambda count, num_iter, *args: count < num_iter, gibbs_step, [_, _, x_sample] = tf.while_loop(lambda count, num_iter, *args: count < num_iter, gibbs_step, [ct, tf.constant(k), x], parallel_iterations=1, back_prop=False) 1. In rnn_rbm.py update generate to look like this def generate(num, x=x, size_bt=size_bt, u0=u0, n_visible=n_visible, prime_length=100): """ This function handles generating music. This function is one of the outputs of the build_rnnrbm function Args: num (int): The number of time steps to generate x (tf.placeholder): The data vector. We can use feed_dict to set this to the music primer. size_bt (tf.float32): The batch size u0 (tf.Variable): The initial state of the RNN n_visible (int): The size of the data vectors prime_length (int): The number of times teps into the primer song that we use befoe beginning to generate music Returns: The generated music, as a tf.Tensor """ Uarr = tf.scan(rnn_recurrence, x, initializer=u0) # U = Uarr[np.floor(prime_length/midi_manipulation.num_timesteps), :, :] U = Uarr[int(np.floor(prime_length / midi_manipulation.num_timesteps)), :, :] # [_, _, _, _, _, music] = control_flow_ops.While(lambda count, num_iter, *args: count < num_iter, # generate_recurrence, [tf.constant(1, tf.int32), tf.constant(num), U, # tf.zeros([1, n_visible], tf.float32), x, # tf.zeros([1, n_visible], tf.float32)]) time_steps = tf.constant(1, tf.int32) iterations = tf.constant(num) u_t = tf.zeros([1, n_visible], tf.float32) music = tf.zeros([1, n_visible], tf.float32) loop_vars = [time_steps, iterations, U, u_t, x, music] [_, _, _, _, _, music] = tf.while_loop(lambda count, num_iter, *args: count < num_iter, generate_recurrence, loop_vars, shape_invariants=[time_steps.get_shape(), iterations.get_shape(), U.get_shape(), u_t.get_shape(), x.get_shape(), tf.TensorShape([None, 780])]) return music — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#4 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/APMnHhy_XIlFNACQgqdDIcBAKXHAtFmnks5r1Q0AgaJpZM4NMATo> .

[chuck-park]

I got my new laptop! do you have something special? On Sun, Apr 30, 2017 at 7:10 PM, Chungmin Park <[email protected]> wrote:

…

Thank you for your information!! But, unfortunately, My laptop was broken.... I will do it as soon as possible! Thanks very much! On Sun, Apr 30, 2017 at 5:36 PM, Gerardo Castaneda < ***@***.***> wrote: > I think I managed to make this work with Tensorflow 1.1. Here are all the > steps: > > 1. > > First, you need to upgrade your python scripts using the tensorflow > upgrade utility > <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/compatibility> > 2. > > In midi_manipulation.py change get_song to this > > def get_song(path): > #Load the song and reshape it to place multiple timesteps next to each other > song = np.array(midiToNoteStateMatrix(path)) > song = song[:int(np.floor(song.shape[0]/num_timesteps)*num_timesteps)] > song = np.reshape(song, [song.shape[0]/num_timesteps, song.shape[1]*num_timesteps]) > return song > > > 1. In RBM.py replace the call to deprecated control_flow_ops > > #[_, _, x_sample] = control_flow_ops.While(lambda count, num_iter, *args: count < num_iter, > # gibbs_step, [ct, tf.constant(k), x], 1, False) > # [_, _, x_sample] = tf.while_loop(lambda count, num_iter, *args: count < num_iter, gibbs_step, > > > [_, _, x_sample] = tf.while_loop(lambda count, num_iter, *args: count < num_iter, gibbs_step, > [ct, tf.constant(k), x], parallel_iterations=1, back_prop=False) > > > 1. In rnn_rbm.py update generate to look like this > > def generate(num, x=x, size_bt=size_bt, u0=u0, n_visible=n_visible, prime_length=100): > """ > This function handles generating music. This function is one of the outputs of the build_rnnrbm function > Args: > num (int): The number of time steps to generate > x (tf.placeholder): The data vector. We can use feed_dict to set this to the music primer. > size_bt (tf.float32): The batch size > u0 (tf.Variable): The initial state of the RNN > n_visible (int): The size of the data vectors > prime_length (int): The number of times teps into the primer song that we use befoe beginning to generate music > Returns: > The generated music, as a tf.Tensor > > """ > Uarr = tf.scan(rnn_recurrence, x, initializer=u0) > # U = Uarr[np.floor(prime_length/midi_manipulation.num_timesteps), :, :] > U = Uarr[int(np.floor(prime_length / midi_manipulation.num_timesteps)), :, :] > # [_, _, _, _, _, music] = control_flow_ops.While(lambda count, num_iter, *args: count < num_iter, > # generate_recurrence, [tf.constant(1, tf.int32), tf.constant(num), U, > # tf.zeros([1, n_visible], tf.float32), x, > # tf.zeros([1, n_visible], tf.float32)]) > > time_steps = tf.constant(1, tf.int32) > iterations = tf.constant(num) > u_t = tf.zeros([1, n_visible], tf.float32) > music = tf.zeros([1, n_visible], tf.float32) > loop_vars = [time_steps, iterations, U, u_t, x, music] > > [_, _, _, _, _, music] = tf.while_loop(lambda count, num_iter, *args: count < num_iter, generate_recurrence, > loop_vars, > shape_invariants=[time_steps.get_shape(), iterations.get_shape(), > U.get_shape(), u_t.get_shape(), > x.get_shape(), tf.TensorShape([None, 780])]) > > return music > > — > You are receiving this because you were mentioned. > Reply to this email directly, view it on GitHub > <#4 (comment)>, > or mute the thread > <https://github.com/notifications/unsubscribe-auth/APMnHhy_XIlFNACQgqdDIcBAKXHAtFmnks5r1Q0AgaJpZM4NMATo> > . >

[marialaura73]

I've got the same error, and I've partially solved thanks to the modifications suggested by gc-cloud in midi_modifications.py, RNN.py and rnn_rbm.py. Now, my problem is the generation of polyphonies (rnn_rbm_generate.py). In fact, if I use main(sys.argv[1]) the answer is IndexError: list index out of range. If I utilize main(sys.argv[1:]), I obtain:

])

Maybe, is it necessary to change also this file? gc-cloud, please, may you help me? I'm not expert in Python and TensorFlow...
Laura

[hasagar97]

Same Error as @marialaura73 . @marialaura73 did you get it solved?
Thanks in advanced
Hrishikesh