adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output)) ValueError: shapes (3,2736,1) and (1,2736,1) not aligned: 1 (dim 2) != 2736 (dim 1)

Model | Trim | Cost
6     | 102  | 1200
8     | 105  | 1500
15    | 110  | 3000


df = pd.read_csv('fulldata.csv')
y = np.array(df[['Success']])
y.reshape(y.size, 1)

df.drop(['Success'],1 , inplace=True)

t_in = df.values.tolist()

class NeuralNetwork():
    def __init__(self):
        # Seed the random number generator
        random.seed(1)
        self.synaptic_weights = 2 * random.random((3,1)) - 1

    def __sigmoid(self, x):
        return 1 /(1 + exp(-x))

    def __sigmoid_derivative(self, x):
        return x * (1-x)

    def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
        for iteration in xrange(number_of_training_iterations):
            output = self.predict(training_set_inputs)

            error = training_set_outputs - output

            adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output))

            self.synaptic_weights += adjustment


    def predict(self, inputs):
        return self.__sigmoid(dot(inputs, self.synaptic_weights))


if __name__=="__main__":

    #initialize a single neuron neural network
    neural_network = NeuralNetwork()

    # training data inputs / outputs
    training_set_inputs = array([t_in])
    training_set_outputs = array([y]).T

    # number of iterations
    neural_network.train(training_set_inputs, training_set_outputs, 10000)

    # new input to predict
    my_input = array([6,102,3000])

    print 'New synaptic weights after training'
    print neural_network.synaptic_weights

    print 'Predicting'

    my_prediction =  neural_network.predict(my_input)
    print (my_prediction)

# t_in = [[6.0, 102.0, 0.0], [61.0, 138.0, 12414.0], [224.0, 291.0, 30309.0]]

#y = [[ 1.]
 [ 1.]
 [ 1.]
 ...,
 [ 0.]
 [ 0.]
 [ 0.]]

adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output)) ValueError: shapes (3,2736,1) and (1,2736,1) not aligned: 1 (dim 2) != 2736 (dim 1)