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I've been using matlab until now to classify a large number of labelled time series I have. This has been relatively successful but I'd like to try using Tensorflow to apply a Deep Learning paradigm instead.

I'm a complete noob at this and so I'm a bit overwhelmed with the literature as I'm struggling to generalise examples such as the 0-9 digits classification examples to my problem.

My current code reads in the 1064 time series (length 3125), reads in the labels, converts the labels to onehot_encoding and extracts training and validation sets.

```
#Not all code included
def read_data():
#Get labels from the labels.txt file
labels = pd.read_csv('labels.txt', header = None)
labels = labels.values
data = pd.read_csv('ts.txt',header = None)
return data, labels
data, labels = read_data()
# Default split is 75% train 25% test
ts_train, ts_test, labels_train, labels_test = train_test_split(data,labels)
onehot_encoder = OneHotEncoder(sparse=False)
labels_train = onehot_encoder.fit_transform(labels_train)
labels_test = onehot_encoder.fit_transform(labels_test)
#Construct the graph
batch_size = 100
seq_len = 3125
learning_rate = 0.0001
epochs = 1000
```

But now I need to construct the graph and I'm a bit lost. If someone could link me some useful examples I'd be very grateful thank you.

Thank you for the comprehensive reply. I'll investigate Keras and the links and report back. – user1147964 – 2018-02-05T17:05:27.147

Happy coding! Also, if you find this answer useful, please accept it. – Gaurav Kumar – 2018-02-05T18:17:24.030