tensorflow - Invalid argument : You must feed a value for placeholder tensor with dtype float and shape [64,1] -

i got unexpected error when running below code :

ith tf.session(graph=graph) session: tf.initialize_all_variables().run() print('initialized') mean_loss = 0 step in xrange(num_steps) :   print ("train data ",len(train_data))   batch_inputs, batch_labels = generate_batches(train_dataset, batch_size=64, unrollings=5)    feed_dict = dict()   in range(unrollings):      batch_labels[i] = np.reshape(batch_labels[i], (batch_size, 1))      batch_inputs[i] = np.array(batch_inputs[i]).astype('int32')      batch_labels[i] = batch_labels[i].astype('float32')      print (train_inputs[i], train_labels[i])      feed_dict = {train_inputs[i] : batch_inputs[i],  train_labels[i] : batch_labels[i]}      _, l, predictions, lr = session.run([optimizer, loss, train_prediction, learning_rate], feed_dict=feed_dict)      mean_loss += l  

here generating batches, lstm cell , calculating loss code :

def generate_batches(raw_data, batch_size, unrollings):  global data_index  data_len = len(raw_data)  num_batches = data_len // batch_size  inputs = []  labels = []  label = np.zeros(shape=(batch_size, 1), dtype=np.float)  print (num_batches, data_len, batch_size)  j in xrange(unrollings) :      inputs.append([])     labels.append([])     in xrange(batch_size) :            inputs[j].append(raw_data[i + data_index])         label[i, 0] = raw_data[i + data_index + 1]     data_index = (data_index + 1) % len(raw_data)     print (len(inputs), len(inputs[0]), len(labels), label.shape)     labels[j].append(label.tolist())  return inputs, labels         embedding_size = 128 num_nodes = 32 graph = tf.graph() graph.as_default():     # parameters:   # input,forget,candidate,output gate: input, previous output, , bias.   ifcox = tf.variable(tf.truncated_normal([embedding_size, num_nodes*4], -0.1, 0.1))   ifcom = tf.variable(tf.truncated_normal([num_nodes, num_nodes*4], -0.1, 0.1))   ifcob = tf.variable(tf.zeros([1, num_nodes*4]))    # variables saving state across unrollings.   saved_output = tf.variable(tf.zeros([batch_size, num_nodes]), trainable=false)   saved_state = tf.variable(tf.zeros([batch_size, num_nodes]), trainable=false)   # classifier weights , biases.   w = tf.variable(tf.truncated_normal([num_nodes, 1], -0.1, 0.1))   b = tf.variable(tf.zeros([1]))    # definition of cell computation.   def lstm_cell(i, o, state):     embeddings = tf.variable(         tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))     embed = tf.nn.embedding_lookup(embeddings, i)      = tf.to_float(embed)     print (i.get_shape())     combined = tf.matmul(i, ifcox) + tf.matmul(o, ifcom) + ifcob     input_gate = tf.sigmoid(combined[:, 0:num_nodes])     forget_gate = tf.sigmoid(combined[:, num_nodes:2*num_nodes])     update = tf.sigmoid(combined[:, 2*num_nodes:3*num_nodes])     state = forget_gate * state + input_gate * tf.tanh(update)     output_gate = tf.sigmoid(combined[:, 3*num_nodes:4*num_nodes])     return output_gate * tf.tanh(state), state     train_data = list()   train_label = list()   _ in range(unrollings) :     train_data.append(tf.placeholder(shape=[batch_size], dtype=tf.int32))     train_label.append(tf.placeholder(shape=[batch_size, 1], dtype=tf.float32))   train_inputs = train_data[:unrollings]   train_labels = train_label[:unrollings]   print (train_inputs, train_labels)   outputs = list()   output = saved_output   state = saved_state    in train_inputs :     output, state = lstm_cell(i, output, state)     outputs.append(output)    # state saving across unrollings.   tf.control_dependencies([saved_output.assign(output),saved_state.assign(state)]):     # classifier.     logits = tf.nn.xw_plus_b(tf.concat(0, outputs), w, b)     loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf.to_float(tf.concat(0, train_labels))))    # optimizer.   global_step = tf.variable(0)   learning_rate = tf.train.exponential_decay(10.0, global_step, 5000, 0.1, staircase=true)   optimizer = tf.train.adamoptimizer(learning_rate)   gradients, v = zip(*optimizer.compute_gradients(loss))   gradients, _ = tf.clip_by_global_norm(gradients, 1.25)   optimizer = optimizer.apply_gradients(zip(gradients, v), global_step=global_step 

the error :

    tensorflow/core/framework/op_kernel.cc:940] invalid argument: must feed value placeholder tensor 'placeholder_3' dtype float , shape [64,1]      [[node: placeholder_3 = placeholder[dtype=dt_float, shape=[64,1], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] traceback (most recent call last):   file "ptb_rnn.py", line 232, in <module>     _, l, predictions, lr = session.run([optimizer, loss, train_prediction, learning_rate], feed_dict=feed_dict)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 710, in run     run_metadata_ptr)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 908, in _run     feed_dict_string, options, run_metadata)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 958, in _do_run     target_list, options, run_metadata)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 978, in _do_call     raise type(e)(node_def, op, message) tensorflow.python.framework.errors.invalidargumenterror: must feed value placeholder tensor 'placeholder_3' dtype float , shape [64,1]      [[node: placeholder_3 = placeholder[dtype=dt_float, shape=[64,1], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] caused op u'placeholder_3', defined at:   file "ptb_rnn.py", line 163, in <module>     train_label.append(tf.placeholder(shape=[batch_size, 1], dtype=tf.float32))   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1212, in placeholder     name=name)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1530, in _placeholder     name=name)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 703, in apply_op     op_def=op_def)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2317, in create_op     original_op=self._default_original_op, op_def=op_def)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1239, in __init__     self._traceback = _extract_stack() 

it looks not feeding using feed_dict value tensor train_labels[i] size (64, 1). when print using batch_labels[i].shape size (64,1) , dtype of both float32.
train_input has size (64,) , batch_inputs has size (64, ) , both of same dtype.
so, error in code?

p.s. think error in line reshaping batch_labels batch_labels[i] = np.reshape(batch_labels[i], (batch_size, 1)). dimension or rank gets changed ? 1 reason train_labels[i] not accept size of batch_labels (64, 1) dimension , rank may not same.as 3-d batch_labels[i] gets converted 2-d (batch_size, 1) rank increase? below generating batch output of 1 unrolling : ([9976, 9980, 9981, 9982, 9983, 9984, 9986, 9987, 9988, 9989, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9998, 9999, 2, 9256, 1, 3, 72, 393, 33, 2133, 0, 146, 19, 6, 9207, 276, 407, 3, 2, 23, 1, 13, 141, 4, 1, 5465, 0, 3081, 1596, 96, 2, 7682, 1, 3, 72, 393, 8, 337, 141, 4, 2477, 657, 2170, 955, 24, 521, 6], [[[9980.0], [9981.0], [9982.0], [9983.0], [9984.0], [9986.0], [9987.0], [9988.0], [9989.0], [9991.0], [9992.0], [9993.0], [9994.0], [9995.0], [9996.0], [9997.0], [9998.0], [9999.0], [2.0], [9256.0], [1.0], [3.0], [72.0], [393.0], [33.0], [2133.0], [0.0], [146.0], [19.0], [6.0], [9207.0], [276.0], [407.0], [3.0], [2.0], [23.0], [1.0], [13.0], [141.0], [4.0], [1.0], [5465.0], [0.0], [3081.0], [1596.0], [96.0], [2.0], [7682.0], [1.0], [3.0], [72.0], [393.0], [8.0], [337.0], [141.0], [4.0], [2477.0], [657.0], [2170.0], [955.0], [24.0], [521.0], [6.0], [9207.0]]])