gistfile1.txt

    model = Sequential()
    model.add(Convolution2D(16, 8, 4, border_mode='valid',
                input_shape=(1, 401, 4)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    #model.add(MaxPooling2D(pool_size=(3,1)))
    model.add(Convolution2D(16, 3, 1))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(3,1)))
    model.add(Flatten())
    model.add(Dense(1))
    model.add(BatchNormalization())
    model.add(Activation('sigmoid'))

    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss='binary_crossentropy', optimizer=sgd)

    for epoch in range(100):
        X_train, Y_train = sklearn.utils.shuffle(X_train, Y_train)
        for x, y in utils.batch_input(X_train, Y_train):
            model.train_on_batch(x,y)

        scores = model.predict(X_dev)
        loss = model.evaluate(X_dev, Y_dev)
        auc = roc_auc_score(Y_dev, scores)
        if auc > best_auc:
            best_auc = auc
            model.save_weights('model.h5', overwrite=True)