ValueError: Error converting shape to a TensorShape: Dimension -5 must be >= 0 - tensorflow

Question

I have no idea how this error is arising. I'm trying to change the input format to an RNN and have printed out the tensors in the original version (which works) and the modified version (which crashes).
FUNCTIONAL:
LABEL= Tensor("concat_1:0", shape=(?, 2), dtype=float32, device=/device:CPU:0) (?, 2)
inputs=Tensor("concat:0", shape=(?, 8), dtype=float32, device=/device:CPU:0)
x=[<tf.Tensor 'split:0' shape=(?, 1) dtype=float32>,
<tf.Tensor 'split:1' shape=(?, 1) dtype=float32>,
<tf.Tensor 'split:2' shape=(?, 1) dtype=float32>,
<tf.Tensor 'split:3' shape=(?, 1) dtype=float32>,
<tf.Tensor 'split:4' shape=(?, 1) dtype=float32>,
<tf.Tensor 'split:5' shape=(?, 1) dtype=float32>,
<tf.Tensor 'split:6' shape=(?, 1) dtype=float32>,
<tf.Tensor 'split:7' shape=(?, 1) dtype=float32>]
last outputs=Tensor("rnn/rnn/basic_lstm_cell/mul_23:0", shape=(?, 3), dtype=float32)
PREDICTION Tensor("add:0", shape=(?, 2), dtype=float32)
LOSS Tensor("mean_squared_error/value:0", shape=(), dtype=float32)
BROKEN:
X= 5 Tensor("Const:0", shape=(49, 10), dtype=float32, device=/device:CPU:0)
labels= Tensor("Const_5:0", shape=(49, 10), dtype=float32)
OUTPUTS Tensor("rnn/rnn/basic_lstm_cell/mul_14:0", shape=(49, 5), dtype=float32)
PREDICTIONS Tensor("add:0", shape=(49, 10), dtype=float32)
LABELS Tensor("Const_5:0", shape=(49, 10), dtype=float32)
LOSS Tensor("mean_squared_error/value:0", shape=(), dtype=float32)
Here is the code for the model, which is the same for each of them:
lstm_cell = rnn.BasicLSTMCell(LSTM_SIZE, forget_bias=1.0)
outputs, _ = tf.nn.static_rnn(lstm_cell, x, dtype=tf.float32)
outputs = outputs[-1]
print('-->OUTPUTS', outputs)
weight = tf.Variable(tf.random_normal([LSTM_SIZE, N_OUTPUTS]))
bias = tf.Variable(tf.random_normal([N_OUTPUTS]))
predictions = tf.matmul(outputs, weight) + bias
print('-->PREDICTIONS', predictions)
print('-->LABELS', labels)
loss = tf.losses.mean_squared_error(labels, predictions)
print('-->LOSS', loss)
train_op = tf.contrib.layers.optimize_loss(loss=loss, global_step=tf.train.get_global_step(), learning_rate=0.01, optimizer="SGD")
eval_metric_ops = {"rmse": tf.metrics.root_mean_squared_error(labels, predictions)}

Answer 1

TL;DR: Use x = tf.split( x, 10, axis = -1 ) to split x before feeding it.
TS;WM:
The error probably happens at tf.nn_static_rnn(), in the second line of your code (would have been nice had you posted the error line number):
outputs, _ = tf.nn.static_rnn(lstm_cell, x, dtype=tf.float32)
The "broken" version tries to feed a tensor with shape ( 49, 10 ) whereas the working version is feeding a list of 8 tensors with shape ( ?, 1 ). The documentation says:
inputs: A length T list of inputs, each a Tensor of shape [batch_size, input_size], or a nested tuple of such elements.
In the previous line you define the lstm_cell with tf.contrib.rnn.BasicLSTMCell.__init__() (presumably, because the import lines are omitted from your code), which has the num_units argument filled by LSTM_SIZE (which is, again, omitted from your code):
lstm_cell = rnn.BasicLSTMCell(LSTM_SIZE, forget_bias=1.0)
So you have to get your ducks in a row. x has to be a list of ( batch_size, 1 ) tensors, which you can achieve with tf.split():
x = tf.split( x, 10, axis = -1 )
where I presume 10 to be the length of the data you're trying to feed, just based on the output you pasted.