# Prediction on timeseries data using tensorflow

I have an input and output of below format:

(X) = [[ 0  1  2]
[ 1  2  3]]

y   = [ 3  4 ]


It's timeseries data. The task is to predict the next number. Basically, the input was crafted by the below snippet:

 def split_sequence(arr,timesteps):
arr_len = len(arr)
X,y = [],[]
for i in range(arr_len):
end_idx = i + timesteps
if end_idx > arr_len-1:
break
input_component,output_component =  arr[i:end_idx],arr[end_idx]
X.append(input_component)
y.append(output_component)

return np.array(X), np.array(y)


Now, I would like to train the model on the input and predict the next number. For instance, x = [81,82,83] and the predicted output would be y = 84. I learned how to do this in keras. However, I would like to try to do this in tensorflow as well.

Below is the code in tensorflow:

 # Data generator
def generate_batch(X,y,batch_size):
m = X.shape
indexes = range(m)
n_batches = m // batch_size
for batch_index in np.array_split(indexes,n_batches):
yield X[batch_index],y[batch_index]

# parameters
n_inputs = 3
n_epochs = 1000
batch_size = 40
learning_rate = 0.01
n_steps = 3

# generate the input and output using split_sequence method
input, output = split_sequence(range(1000),n_steps)

# Define the input variables
X = tf.placeholder(tf.int32,shape=(None,n_inputs),name='X')
y = tf.placeholder(tf.float32,shape=(None),name='y')
theta = tf.Variable(tf.random_uniform([n_steps,1],-1.0,1.0),name='theta')

# predictions and error
y_predictions = tf.matmul(X,theta,name='predictions')
error = y_predictions - y
mse = tf.reduce_mean(tf.square(error),name='mse')

# train the model
training_op = optimizer.minimize(mse)

init = tf.global_variables_initializer()

with tf.Session() as session:
session.run(init)
for epoch in range(n_epochs):
for X_batch,y_batch in generate_batch(input,output,batch_size):
if epoch % 10 == 0:
print('epoch',epoch,'MSE=',mse.eval())
session.run(training_op,feed_dict={X:X_batch,y:y_batch})


To be honest, I am completely stuck with the below error:

You must feed a value for placeholder tensor 'X' with dtype float and shape [?,3].

My input is an integer, so that was the reason behind defining:

 X = tf.placeholder(tf.int32,shape=(None,n_inputs),name='X')


Can someone help me fix this? Also, if I wanted to add bias variable will I be able to achieve for the above input?

The error is caused by this line:

print('epoch',epoch,'MSE=',mse.eval())


This happens because the tensor mse also depends on the placeholders X and y. One way to fix this would be to change the training loop to be:

for X_batch,y_batch in generate_batch(input,output,batch_size):
mse_val, _ = session.run([mse, training_op],feed_dict={X:X_batch,y:y_batch})
if epoch % 10 == 0:
print('epoch',epoch,'MSE=',mse_val)


Also you will need to switch X back to tf.float32 since tf.matmul is not compatible with int and float. The data will automatically be casted once you feed it in.

To add a bias variable, you can do it similarly to how you define theta.

b = tf.Variable(0.0, dtype=tf.float32, name='b')
...
y_predictions += b

• Hi kenny. thanks for helping me. I was able to run the model, however my mse values are nan. Do you feel anything strange in my code. If so, can you let me know please ?. Oct 12, 2018 at 15:44