Backpropagation for Neural Network - Python

I am writing a program to do neural network in python I am trying to set up the backpropagation algorithm. The basic idea is that I look through 5,000 training examples and collect the errors and find out in which direction I need to move the thetas and then move them in that direction. There are the training examples, then I use one hidden layer, and then an output layer. However I am getting the gradient/derivative/error wrong here because I am not moving the thetas correct as they need to be moved. I put 8 hours into this today not sure what I'm doing wrong. Thanks for your help!!

x = 401x5000 matrix

y = 10x5000 matrix   # 10 possible output classes, so one column will look like [0, 0, 0, 1, 0... 0] to indicate the output class was 4

theta_1 = 25x401

theta_2 = 10x26


alpha=.01

    sigmoid= lambda theta, x: 1 / (1 + np.exp(-(theta*x)))


        #move thetas in right direction for each iteration
        for iter in range(0,1):
            all_delta_1, all_delta_2 = 0, 0
            #loop through each training example, 1...m    
            for t in range(0,5000):

                hidden_layer = np.matrix(np.concatenate((np.ones((1,1)),sigmoid(theta_1,x[:,t]))))
                output_layer = sigmoid(theta_2,hidden_layer)

                delta_3 = output_layer - y[:,t]
                delta_2= np.multiply((theta_2.T*delta_3),(np.multiply(hidden_layer,(1-hidden_layer))))

                #print type(delta_3), delta_3.shape, type(hidden_layer.T), hidden_layer.T.shape
                all_delta_2 += delta_3*hidden_layer.T
                all_delta_1 += delta_2[1:]*x[:,t].T



            delta_gradient_2 = (all_delta_2 / m)
            delta_gradient_1 = (all_delta_1 / m)
            theta_1 = theta_1- (alpha * delta_gradient_1)
            theta_2 = theta_2- (alpha * delta_gradient_2)