rnn的一个例子

直接po代码，简单的rnn的adder，改自github，不用TensorFlow等框架，可实现多位（超过8位）。

import copy, numpy as npnp.random.seed(0)def sigmoid(x):    output = 1 / (1 + np.exp(-x))    return outputdef sigmoid_output_to_derivative(output):    return output * (1 - output)int2binary = {}binary_dim = 16binary = []def generateInt2Binary(largest_number):    for i in range(largest_number):        c = (bin(i).replace('0b','').zfill(binary_dim))        binary.append([int(a) for a in c])largest_number = pow(2, binary_dim)generateInt2Binary(largest_number)for i in range(largest_number):    int2binary[i] = binary[i]alpha = 0.5input_dim = 2hidden_dim = 32output_dim = 1synapse_0 = 2 * np.random.random((input_dim, hidden_dim)) - 1synapse_1 = 2 * np.random.random((hidden_dim, output_dim)) - 1synapse_h = 2 * np.random.random((hidden_dim, hidden_dim)) - 1synapse_0_update = np.zeros_like(synapse_0)synapse_1_update = np.zeros_like(synapse_1)synapse_h_update = np.zeros_like(synapse_h)print('start train...')for j in range(10000):    a_int = np.random.randint(largest_number/2)    a = int2binary[a_int]    b_int = np.random.randint(largest_number/2)    b = int2binary[b_int]    c_int = a_int + b_int    c = int2binary[c_int]    d = np.zeros_like(c)    overallError = 0    layer_2_deltas = list()    layer_1_values = list()    layer_1_values.append(np.zeros(hidden_dim))    for position in range(binary_dim):        X = np.array([[a[binary_dim - position - 1], b[binary_dim - position - 1]]])        y = np.array([[c[binary_dim - position - 1]]]).T        layer_1 = sigmoid(np.dot(X, synapse_0) + np.dot(layer_1_values[-1], synapse_h))        layer_2 = sigmoid(np.dot(layer_1, synapse_1))        layer_2_error = y - layer_2        layer_2_deltas.append((layer_2_error) * sigmoid_output_to_derivative(layer_2))        overallError += np.abs(layer_2_error[0])        d[binary_dim - position - 1] = np.round(layer_2[0][0])        layer_1_values.append(copy.deepcopy(layer_1))    future_layer_1_delta = np.zeros(hidden_dim)    for position in range(binary_dim):        X = np.array([[a[position], b[position]]])        layer_1 = layer_1_values[-position - 1]        prev_layer_1 = layer_1_values[-position - 2]        layer_2_delta = layer_2_deltas[-position - 1]        layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + layer_2_delta.dot(            synapse_1.T)) * sigmoid_output_to_derivative(layer_1)        synapse_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta)        synapse_h_update += np.atleast_2d(prev_layer_1).T.dot(layer_1_delta)        synapse_0_update += X.T.dot(layer_1_delta)        future_layer_1_delta = layer_1_delta    synapse_0 += synapse_0_update * alpha    synapse_1 += synapse_1_update * alpha    synapse_h += synapse_h_update * alpha    synapse_0_update *= 0    synapse_1_update *= 0    synapse_h_update *= 0    if (j % 1000 == 0):        print('this is the %d times test...' % j)        print("ErrorRate is: %.2f" % overallError)        out = 0        for index, x in enumerate(reversed(d)):            out += x * pow(2, index)        print(str(a_int) + " + " + str(b_int) + " = " + str(out))        print('')print('training is over...')while True:    layer_1_values = list()    layer_1_values.append(np.zeros(hidden_dim))    a_int = int(input('please input the first number: '))    b_int = int(input('please input the first number: '))    a = int2binary[a_int]    b = int2binary[b_int]    c_true_int = a_int + b_int    c = int2binary[c_true_int]    d_bin = np.zeros_like(c)    for position in range(binary_dim):        # generate input and output        X = np.array([[a[binary_dim - position - 1], b[binary_dim - position - 1]]])        y = np.array([[c[binary_dim - position - 1]]]).T        layer_1 = sigmoid(np.dot(X, synapse_0) + np.dot(layer_1_values[-1], synapse_h))        layer_2 = sigmoid(np.dot(layer_1, synapse_1))        d_bin[binary_dim - position - 1] = np.round(layer_2[0][0])        layer_1_values.append(copy.deepcopy(layer_1))    out = 0    for index, x in enumerate(reversed(d_bin)):        out += x * pow(2, index)    print(str(a_int) + " + " + str(b_int) + " = " )    print('predict: ' + str(out))    print('true: ' + str(c_true_int))