TensorFlow sparse_softmax_cross_entropy rank error - tensorflow

Question

I'm trying to build an RNN with LSTM on TensorFlow. Both the input and output are 5000 by 2 matrices, where the columns represent the features. Those matrices are then fed to the batchX and batchY placeholders which enable the backpropagation. The main definition of the code is at the bottom. I am getting the following error :
"Rank mismatch: Rank of labels (received 2) should equal rank of logits minus 1 (received 2)."
I have checked both logits_series and labels_series and they seem to both contain backpropagation amount of tensors of the shape of [batch_size, num_features]
The thing I am confused about is the following: since logits are predictions of labels, shouldn't they have the same dimensions?
'''
RNN definitions
input_dimensions = [batch_size, truncated_backprop_length, num_features_input]
output_dimensions = [batch_size, truncated_backprop_length, num_features_output]
state_dimensions = [batch_size, state_size]
'''
batchX_placeholder = tf.placeholder(tf.float32, (batch_size, truncated_backprop_length, num_features_input))
batchY_placeholder = tf.placeholder(tf.int32, (batch_size, truncated_backprop_length, num_features_output))
init_state = tf.placeholder(tf.float32, (batch_size, state_size))
inputs_series = tf.unstack(batchX_placeholder, axis=1)
labels_series = tf.unstack(batchY_placeholder, axis=1)
w = tf.Variable(np.random.rand(num_features_input+state_size,state_size), dtype = tf.float32)
b = tf.Variable(np.zeros((batch_size, state_size)), dtype = tf.float32)
w2 = tf.Variable(np.random.rand(state_size, num_features_output), dtype = tf.float32)
b2 = tf.Variable(np.zeros((batch_size, num_features_output)), dtype=tf.float32)
#calculate state and output variables
state_series = []
output_series = []
current_state = init_state
#iterate over each truncated_backprop_length
for current_input in inputs_series:
current_input = tf.reshape(current_input,[batch_size, num_features_input])
input_and_state_concatenated = tf.concat([current_input,current_state], 1)
next_state = tf.tanh(tf.matmul(input_and_state_concatenated, w) + b)
state_series.append(next_state)
current_state = next_state
output = tf.matmul(current_state, w2)+b2
output_series.append(output)
#calculate expected output for each state
logits_series = [tf.matmul(state, w2) + b2 for state in state_series]
#print(logits_series)
predictions_series = [tf.nn.softmax(logits) for logits in logits_series]
'''
batchY_placeholder = np.zeros((batch_size,truncated_backprop_length))
for i in range(batch_size):
for j in range(truncated_backprop_length):
batchY_placeholder[i,j] = batchY1_placeholder[j, i, 0]+batchY1_placeholder[j, i, 1]
'''
print("logits_series", logits_series)
print("labels_series", labels_series)
#calculate losses given each actual and calculated output
losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(logits = logits, labels = labels) for logits, labels in zip(logits_series,labels_series)]
total_loss = tf.reduce_mean(losses)

Answer 1

Thanks to Maosi Chen, I found the issue. It was because the
tf.nn.sparse_softmax_cross_entropy_with_logits
Requires labels to have one less dimension than logits. Specifically, the labels argument takes values of the shape [batch_size] and the dtype int32 or int64
I solved the issue by enumerating the one hot encoded labels I had, reducing the dimension
However, it was also possible to use
tf.nn.softmax_cross_entropy_with_logits
Which does not have the dimension reduction requirement, as it takes labels values with shape [batch_size, num_classes] and dtype float32 or float64.