I am trying to design a simple lstm in tensorflow. I want to classify a sequence of data into classes from 1 to 10.

I have *10 timestamps* and data X. I am only taking one sequence for now, so my batch size = 1.

At every epoch, a new sequence is generated. For example X is a numpy array like this-

```
X [[ 2.52413028 2.49449348 2.46520466 2.43625973 2.40765466 2.37938545
2.35144815 2.32383888 2.29655379 2.26958905]]
```

To make it suitable for lstm input, I first converted in to a tensor and then reshaped it (batch_size, sequence_lenght, input dimension) –

```
X= np.array([amplitude * np.exp(-t / tau)])
print 'X', X
#Sorting out the input
train_input = X
train_input = tf.convert_to_tensor(train_input)
train_input = tf.reshape(train_input,[1,10,1])
print 'ti', train_input
```

For output I am generating a one hot encoded label within a class range of 1 to 10.

```
#------------sorting out the output
train_output= [int(math.ceil(tau/resolution))]
train_output= one_hot(train_output, num_labels=10)
print 'label', train_output
train_output = tf.convert_to_tensor(train_output)
>>label [[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]]
```

Then I created the placeholders for tensorflow graph, made the lstm cell and gave weights and bias-

```
data = tf.placeholder(tf.float32, shape= [batch_size,len(t),1])
target = tf.placeholder(tf.float32, shape = [batch_size, num_classes])
cell = tf.nn.rnn_cell.LSTMCell(num_hidden)
output, state = rnn.dynamic_rnn(cell, data, dtype=tf.float32)
weight = tf.Variable(tf.random_normal([batch_size, num_classes, 1])),
bias = tf.Variable(tf.random_normal([num_classes]))
#training
prediction = tf.nn.softmax(tf.matmul(output,weight) + bias)
cross_entropy = -tf.reduce_sum(target * tf.log(prediction))
optimizer = tf.train.AdamOptimizer()
minimize = optimizer.minimize(cross_entropy)
```

I have written the code this far and got error at the training step. Is it to do with the input shapes? Here is the traceback—

Traceback (most recent call last):

```
File "/home/raisa/PycharmProjects/RNN_test1/test3.py", line 66, in <module>
prediction = tf.nn.softmax(tf.matmul(output,weight) + bias)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1036, in matmul
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 911, in _mat_mul
transpose_b=transpose_b, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2156, in create_op
set_shapes_for_outputs(ret)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1612, in set_shapes_for_outputs
shapes = shape_func(op)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/common_shapes.py", line 81, in matmul_shape
a_shape = op.inputs[0].get_shape().with_rank(2)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 625, in with_rank
raise ValueError("Shape %s must have rank %d" % (self, rank))
ValueError: Shape (1, 10, 5) must have rank 2
```

If you are using TF >= 1.0, you can take advantage of the `tf.contrib.rnn`

library and the `OutputProjectionWrapper`

to add a fully connected layer to the output of your RNN. Something like:

```
# Network definition.
cell = tf.contrib.rnn.LSTMCell(num_hidden)
cell = tf.contrib.rnn.OutputProjectionWrapper(cell, num_classes) # adds an output FC layer for you
output, state = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)
# Training.
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=targets)
cross_entropy = tf.reduce_sum(cross_entropy)
optimizer = tf.train.AdamOptimizer()
minimize = optimizer.minimize(cross_entropy)
```

Note I’m using `softmax_cross_entropy_with_logits`

instead of using your `prediction`

op and calculating cross entropy manually. It is supposed to be more efficient and robust.

The `OutputProjectionWrapper`

basically does the same thing, but it might help alleviate some headaches.

Looking at your code, your rnn output should have a dimension of `batch_size x 1 x num_hidden`

while your w has dimension `batch_size x num_classes x 1`

however you want multiplication of those two to be `batcH_size x num_classes`

.

Can you try `output = tf.reshape(output, [batch_size, num_hidden])`

and `weight = tf.Variable(tf.random_normal([num_hidden, num_classes]))`

and let me know how that goes?