def get_train_data_stats(options):
encoder_inputs, target_labels, num_examples, words, decoder_inputs, \
target_labels_lengths, encoder_inputs_lengths, decoder_inputs_lengths = get_split3(options)
number_of_steps_per_epoch = 5 #num_examples // options['batch_size'] + 1
sess = start_interactive_session()
eim = []
eivar = []
tlm = []
tlvar = []
eilm = []
for i in range(number_of_steps_per_epoch):
print("step %d of %d" % (i + 1, number_of_steps_per_epoch))
ei, tl, eil = sess.run([
tf.nn.moments(encoder_inputs, [0, 1]),
tf.nn.moments(target_labels, [0, 1]),
tf.reduce_mean(encoder_inputs_lengths)
])
eim.append(ei[0])
eivar.append(ei[1])
tlm.append(tl[0])
tlvar.append(tl[1])
eilm.append(eil)
eim = np.stack(eim, axis=0) #.mean(axis=0)
eivar = np.stack(eivar, axis=0) #.mean(axis=0)
tlm = np.stack(tlm, axis=0) #.mean(axis=0)
tlvar = np.stack(tlvar, axis=0) #.mean(axis=0)
eilm = np.max(eilm)
return eim, eivar, tlm, tlvar, eilm
'save_graph': False,
'save_dir': "/data/mat10/Projects/audio23d/Models/rnn_plus/summaries",
'save_summaries': True
}
#from data_provider import get_split
#raw_audio, mfcc, target_labels, \
#num_examples, word, decoder_inputs, \
#label_lengths, mfcc_lengths, decoder_inputs_lengths = get_split(options)
#raw_audio, mfcc, label, num_examples, word = get_split()
if True:
model = RNNplusModel(options)
sess = start_interactive_session()
if options['save_graph']:
model.save_graph(sess)
if options['restore']:
model.restore_model(sess)
if options['is_training']:
model.train(sess)
else:
loss = model.eval(sess, return_words=False)
if False:
losses = {}
for ep in range(1, 54):
options['restore_model'] = "/data/mat10/Projects/audio23d/Models/rnn_plus/rnnplus_all_melf_era1_epoch%d_step302" % ep
model = RNNplusModel(options)
sess = start_interactive_session()
