LSTM Input 0 of layer basic_lstm_cell_1 in tensorflow

Question

I am working with lstm using tensor flow when I am running the code it is showing me the error. the code is running fine but when I am running the function tf.nn.dynamic_rnn(lstmCell, data, dtype=tf.float64) it is showing Value ERROR

import tensorflow as tf

wordsList = np.load('urduwords.npy')
wordVectors = np.load('urduwordsMatrix.npy')

batchSize = 24
lstmUnits = 64
numClasses = 2
iterations = 10000

tf.reset_default_graph()


labels = tf.placeholder(tf.float32, [batchSize, numClasses])
input_data = tf.placeholder(tf.int32, [batchSize, maxSeqLength])

print(labels)

data = tf.Variable(tf.zeros([batchSize, maxSeqLength, numDimensions]),dtype=tf.float32)
print(data)


data = tf.nn.embedding_lookup(wordVectors,input_data)
print(data)


lstmCell = tf.contrib.rnn.BasicLSTMCell(lstmUnits)
lstmCell = tf.contrib.rnn.DropoutWrapper(cell=lstmCell, output_keep_prob=0.1)

value, _ = tf.nn.dynamic_rnn(lstmCell, data, dtype=tf.float64)

How to resolve this error using tensor flow.

ValueError: Input 0 of layer basic_lstm_cell_1 is incompatible with the layer: expected ndim=2, found ndim=3. Full shape received: [24, 1, 2]

the shape of the input_data is

(24, 30, 1, 2)

and the shape of wordVector is

(24053, 1, 2)

Answer 1

Since you have not provided a standalone code to reproduce the bug, i have a sample working code as shown below:

VOCAB_SIZE = 128
HIDDEN_SIZE = 200

wordVectors = tf.Variable(tf.random_uniform([VOCAB_SIZE, HIDDEN_SIZE], -1, 1))

labels = tf.random_normal([batchSize, numClasses])
input_data = tf.random_uniform([batchSize, maxSeqLength], maxval=120, dtype=tf.int32)

data = tf.Variable(tf.zeros([batchSize, maxSeqLength, numDimensions]),dtype=tf.float32)

data = tf.nn.embedding_lookup(wordVectors,input_data)

lstmCell = tf.contrib.rnn.BasicLSTMCell(lstmUnits)
lstmCell = tf.contrib.rnn.DropoutWrapper(cell=lstmCell, output_keep_prob=0.1)

value, _ = tf.nn.dynamic_rnn(lstmCell, data, dtype=tf.float32)

I have changed the data type of tf.nn.dynamic_rnn to tf.float32 to fix the data type error.

Answer 2

the label shape is 4 dimension because of you feed the wrong type of data to tf,

please try to use NumberPy array or List