ValueError：Tensor Tensor（ "Const：0"、shape =（）、dtype = float32）にtf.placeholderを入力しないでください

Question

私はtensorflowで音声認識システムを作ろうとしています。

• 入力データサイズ400 X 1

入力およびマッピングのnumpyのアレイ50000 X 1

出力データ（マッピングデータ）サイズのnumpyのアレイでありますデータは2のバッチでリストに渡されます。

私はthisチュートリアルを使用してニューラルネットワークを設計しました。 RNNのために

：

input_data = tf.placeholder(tf.float32, [batch_size, sound_constants.MAX_ROW_SIZE_IN_DATA, sound_constants.MAX_COLUMN_SIZE_IN_DATA], name="train_input") 
target = tf.placeholder(tf.float32, [batch_size, sound_constants.MAX_ROW_SIZE_IN_TXT, sound_constants.MAX_COLUMN_SIZE_IN_TXT], name="train_output") 

fwd_cell = tf.nn.rnn_cell.BasicLSTMCell(num_hidden, state_is_tuple=True, forget_bias=1.0) 

# creating one backward cell 
bkwd_cell = tf.nn.rnn_cell.BasicLSTMCell(num_hidden, state_is_tuple=True, forget_bias=1.0) 

# creating bidirectional RNN 
val, _, _ = tf.nn.static_bidirectional_rnn(fwd_cell, bkwd_cell, tf.unstack(input_data), dtype=tf.float32) 

供給するデータの場合：コードスニペットを以下に示します

Traceback (most recent call last): 
    File "/home/wolborg/PycharmProjects/speech-to-text-rnn/src/rnn_train_1.py", line 205, in <module> 
    tr_losses, te_losses = train_network(g) 
    File "/home/wolborg/PycharmProjects/speech-to-text-rnn/src/rnn_train_1.py", line 177, in train_network 
    accuracy_, _ = sess.run([g['accuracy'], g['ts']], feed_dict=feed) 
    File "/home/wolborg/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 895, in run 
    run_metadata_ptr) 
    File "/home/wolborg/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1102, in _run 
    raise ValueError('Tensor %s may not be fed.' % subfeed_t) 
ValueError: Tensor Tensor("Const:0", shape=(), dtype=float32) may not be fed. 

Process finished with exit code 1 

以前：私は、コードを実行したとき、私はこのエラーを得た

feed = {g['input_data'] : trb[0], g['target'] : trb[1], g['dropout'] : 0.6} 
accuracy_, _ = sess.run([g['accuracy'], g['ts']], feed_dict=feed) 
accuracy += accuracy_ 

私はtf.sparse_placeholderでこの問題に直面していましたが、ブラウジング後に入力タイプをtf.placeholderに変更して関連付けを行いましたdが変化する。今私はどこでエラーが出ているのかわからない。

データをどのようにフィードするかをお勧めします。

全体コード：

import tensorflow as tf 

# for taking MFCC and label input 
import numpy as np 
import rnn_input_data_1 
import sound_constants 

# input constants 

# Training Parameters 
num_input = 10 # mfcc data input 
training_data_size = 8 # determines number of files in training and testing module 
testing_data_size = num_input - training_data_size 

# Network Parameters 
learning_rate = 0.0001 # for large training set, it can be set 0.001 
num_hidden = 200 # number of hidden layers 
num_classes = 28 # total alphabet classes (a-z) + extra symbols (', ' ') 
epoch = 1 # number of iterations 
batch_size = 2 # number of batches 

mfcc_coeffs, text_data = rnn_input_data_1.mfcc_and_text_encoding() 

class DataGenerator: 
    def __init__(self, data_size): 
    self.ptr = 0 
    self.epochs = 0 
    self.data_size = data_size 

    def next_batch(self): 
    self.ptr += batch_size 
    if self.ptr > self.data_size: 
     self.epochs += 1 
     self.ptr = 0 

    return mfcc_coeffs[self.ptr-batch_size : self.ptr], text_data[self.ptr-batch_size : self.ptr] 

def reset_graph(): 
    if 'sess' in globals() and sess: 
    sess.close() 
    tf.reset_default_graph() 

def struct_network(): 

    print ('Inside struct network !!') 

    reset_graph() 

    input_data = tf.placeholder(tf.float32, [batch_size, sound_constants.MAX_ROW_SIZE_IN_DATA, sound_constants.MAX_COLUMN_SIZE_IN_DATA], name="train_input") 
    target = tf.placeholder(tf.float32, [batch_size, sound_constants.MAX_ROW_SIZE_IN_TXT, sound_constants.MAX_COLUMN_SIZE_IN_TXT], name="train_output") 

    keep_prob = tf.constant(1.0) 

    fwd_cell = tf.nn.rnn_cell.BasicLSTMCell(num_hidden, state_is_tuple=True, forget_bias=1.0) 

    # creating one backward cell 
    bkwd_cell = tf.nn.rnn_cell.BasicLSTMCell(num_hidden, state_is_tuple=True, forget_bias=1.0) 

    # creating bidirectional RNN 
    val, _, _ = tf.nn.static_bidirectional_rnn(fwd_cell, bkwd_cell, tf.unstack(input_data), dtype=tf.float32) 

    # adding dropouts 
    val = tf.nn.dropout(val, keep_prob) 

    val = tf.transpose(val, [1, 0, 2]) 
    last = tf.gather(val, int(val.get_shape()[0]) - 1) 

    # creating bidirectional RNN 
    print ('BiRNN created !!') 


    print ('Last Size: ', last.get_shape()) 

    weight = tf.Variable(tf.truncated_normal([num_hidden * 2, sound_constants.MAX_ROW_SIZE_IN_TXT])) 
    bias = tf.Variable(tf.constant(0.1, shape=[sound_constants.MAX_ROW_SIZE_IN_TXT])) 

    # mapping to 28 output classes 
    logits = tf.matmul(last, weight) + bias 
    prediction = tf.nn.softmax(logits) 
    prediction = tf.reshape(prediction, shape = [batch_size, sound_constants.MAX_ROW_SIZE_IN_TXT, sound_constants.MAX_COLUMN_SIZE_IN_TXT]) 

    # getting probability distribution 
    mat1 = tf.cast(tf.argmax(prediction,1),tf.float32) 
    correct = tf.equal(prediction, target) 
    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) 

    logits = tf.reshape(logits, shape=[batch_size, sound_constants.MAX_ROW_SIZE_IN_TXT, sound_constants.MAX_COLUMN_SIZE_IN_TXT]) 


    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=target)) 
    train_step = tf.train.AdamOptimizer(1e-4).minimize(loss) 

    # returning components as dictionary elements 
    return {'input_data' : input_data, 
     'target' : target, 
     'dropout': keep_prob, 
     'loss': loss, 
     'ts': train_step, 
     'preds': prediction, 
     'accuracy': accuracy 
     } 


def train_network(graph): 


    # initialize tensorflow session and all variables 
    # tf_gpu_config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = True) 
    # tf_gpu_config.gpu_options.allow_growth = True 


    # with tf.Session(config = tf_gpu_config) as sess: 
    with tf.Session() as sess: 

    train_instance = DataGenerator(training_data_size) 
    test_instance = DataGenerator(testing_data_size) 

    print ('Training data size: ', train_instance.data_size) 
    print ('Testing data size: ', test_instance.data_size) 

    sess.run(tf.global_variables_initializer()) 
    print ('Starting session...') 
    step, accuracy = 0, 0 
    tr_losses, te_losses = [], [] 
    current_epoch = 0 
    while current_epoch < epoch: 
     step += 1 
     trb = train_instance.next_batch() 

     feed = {g['input_data'] : trb[0], g['target'] : trb[1], g['dropout'] : 0.6} 
     accuracy_, _ = sess.run([g['accuracy'], g['ts']], feed_dict=feed) 
     accuracy += accuracy_ 

     if train_instance.epochs > current_epoch: 
      current_epoch += 1 
      tr_losses.append(accuracy/step) 
      step, accuracy = 0, 0 

      #eval test set 
      te_epoch = test_instance.epochs 
      while test_instance.epochs == te_epoch: 
       step += 1 
       print ('Testing round ', step) 
       trc = test_instance.next_batch() 
       feed = {g['input_data']: trc[0], g['target']: trc[1]} 
       accuracy_ = sess.run([g['accuracy']], feed_dict=feed)[0] 
       accuracy += accuracy_ 

      te_losses.append(accuracy/step) 
      step, accuracy = 0,0 
      print("Accuracy after epoch", current_epoch, " - tr:", tr_losses[-1], "- te:", te_losses[-1]) 

    return tr_losses, te_losses 

g = struct_network() 
tr_losses, te_losses = train_network(g) 

Answer 1

あなたはtf.constantとしてkeep_probを定義しますが、それに値を供給しようとしています。 keep_prob = tf.constant(1.0)をkeep_prob = tf.placeholder(tf.float32,[])またはkeep_prob = tf.placeholder_with_default(1.0,[])