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.