Simple script, no tabs, but needs assets and tflearn to be functional.
import tflearn from tflearn.data_utils import to_categorical, pad_sequences from tflearn.datasets import imdb # IMDB Dataset Loading train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000, valid_portion=0.001) #print(train) #quit() #trainX, trainY = train testX, testY = test # Data Preprocessing # Sequence padding #trainX = pad_sequences(trainX, maxlen=100, value=0.) testX = pad_sequences(testX, maxlen=100, value=0.) # converting labels to binary vectors #trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # Network building net = tflearn.input_data([None, 100]) net = tflearn.embedding(net, input_dim=10000, output_dim=128) net = tflearn.lstm(net, 128, dropout=0.8) net = tflearn.fully_connected(net, 2, activation='softmax') net = tflearn.regression(net, optimizer='adam', learning_rate=0.0001, loss='categorical_crossentropy') # Training model = tflearn.DNN(net, tensorboard_verbose=0) #model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True, batch_size=32) model.load("testModel.bla") print testX pred = model.predict(testX) print("test results? ", pred)