NNDL 作业8：RNN-简单循环网络

目录

1.使用Numpy实现SRN

2. 在1的基础上，增加激活函数tanh

3. 分别使用nn.RNNCell、nn.RNN实现SRN

nn.RNNCell()

nn.RNN（）

4.分析“二进制加法” 源代码（选做） 

5. 实现“Character-Level Language Models”源代码（必做）

7. “编码器-解码器”的简单实现（必做）

总结

参考：

---

1.使用Numpy实现SRN

import numpy as np
 
inputs = np.array([[1., 1.],
                   [1., 1.],
                   [2., 2.]])  # 初始化输入序列
print('inputs is ', inputs)
 
state_t = np.zeros(2, )  # 初始化存储器
print('state_t is ', state_t)
 
w1, w2, w3, w4, w5, w6, w7, w8 = 1., 1., 1., 1., 1., 1., 1., 1.
U1, U2, U3, U4 = 1., 1., 1., 1.
print('--------------------------------------')
for input_t in inputs:
    print('inputs is ', input_t)
    print('state_t is ', state_t)
    in_h1 = np.dot([w1, w3], input_t) + np.dot([U2, U4], state_t)
    in_h2 = np.dot([w2, w4], input_t) + np.dot([U1, U3], state_t)
    state_t = in_h1, in_h2
    output_y1 = np.dot([w5, w7], [in_h1, in_h2])
    output_y2 = np.dot([w6, w8], [in_h1, in_h2])
    print('output_y is ', output_y1, output_y2)
    print('---------------')

运行结果：

2. 在1的基础上，增加激活函数tanh

import numpy as np
 
inputs = np.array([[1., 1.],
                   [1., 1.],
                   [2., 2.]])  # 初始化输入序列
print('inputs is ', inputs)
 
state_t = np.zeros(2, )  # 初始化存储器
print('state_t is ', state_t)
 
w1, w2, w3, w4, w5, w6, w7, w8 = 1., 1., 1., 1., 1., 1., 1., 1.
U1, U2, U3, U4 = 1., 1., 1., 1.
print('--------------------------------------')
for input_t in inputs:
    print('inputs is ', input_t)
    print('state_t is ', state_t)
    in_h1 = np.tanh(np.dot([w1, w3], input_t) + np.dot([U2, U4], state_t))
    in_h2 = np.tanh(np.dot([w2, w4], input_t) + np.dot([U1, U3], state_t))
    state_t = in_h1, in_h2
    output_y1 = np.dot([w5, w7], [in_h1, in_h2])
    output_y2 = np.dot([w6, w8], [in_h1, in_h2])
    print('output_y is ', output_y1, output_y2)
    print('---------------')

运行结果：

3. 分别使用nn.RNNCell、nn.RNN实现SRN

nn.RNNCell()

import torch
 
batch_size = 1
seq_len = 3  # 序列长度
input_size = 2  # 输入序列维度
hidden_size = 2  # 隐藏层维度
output_size = 2  # 输出层维度
 
# RNNCell
cell = torch.nn.RNNCell(input_size=input_size, hidden_size=hidden_size)
# 初始化参数 https://zhuanlan.zhihu.com/p/342012463
for name, param in cell.named_parameters():
    if name.startswith("weight"):
        torch.nn.init.ones_(param)
    else:
        torch.nn.init.zeros_(param)
# 线性层
liner = torch.nn.Linear(hidden_size, output_size)
liner.weight.data = torch.Tensor([[1, 1], [1, 1]])
liner.bias.data = torch.Tensor([0.0])
 
seq = torch.Tensor([[[1, 1]],
                    [[1, 1]],
                    [[2, 2]]])
hidden = torch.zeros(batch_size, hidden_size)
output = torch.zeros(batch_size, output_size)
 
for idx, input in enumerate(seq):
    print('=' * 20, idx, '=' * 20)
 
    print('Input :', input)
    print('hidden :', hidden)
 
    hidden = cell(input, hidden)
    output = liner(hidden)
    print('output :', output)

运行结果：

nn.RNN（）

import torch
 
batch_size = 1
seq_len = 3
input_size = 2
hidden_size = 2
num_layers = 1
output_size = 2
 
cell = torch.nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers)
for name, param in cell.named_parameters():  # 初始化参数
    if name.startswith("weight"):
        torch.nn.init.ones_(param)
    else:
        torch.nn.init.zeros_(param)
 
# 线性层
liner = torch.nn.Linear(hidden_size, output_size)
liner.weight.data = torch.Tensor([[1, 1], [1, 1]])
liner.bias.data = torch.Tensor([0.0])
 
inputs = torch.Tensor([[[1, 1]],
                       [[1, 1]],
                       [[2, 2]]])
hidden = torch.zeros(num_layers, batch_size, hidden_size)
out, hidden = cell(inputs, hidden)
 
print('Input :', inputs[0])
print('hidden:', 0, 0)
print('Output:', liner(out[0]))
print('--------------------------------------')
print('Input :', inputs[1])
print('hidden:', out[0])
print('Output:', liner(out[1]))
print('--------------------------------------')
print('Input :', inputs[2])
print('hidden:', out[1])
print('Output:', liner(out[2]))

运行结果：

4.分析“二进制加法” 源代码（选做） 

import copy, numpy as np
 
np.random.seed(0)
 
 
#定义sigmoid函数
def sigmoid(x):
    output = 1 / (1 + np.exp(-x))
    return output
 
 
#定义sigmoid导数
def sigmoid_output_to_derivative(output):
    return output * (1 - output)
 
 
#训练数据的产生
int2binary = {}
binary_dim = 8 #定义二进制位的长度
 
largest_number = pow(2, binary_dim)#定义数据的最大值
binary = np.unpackbits(
    np.array([range(largest_number)], dtype=np.uint8).T, axis=1)#函数产生包装所有符合的二进制序列
for i in range(largest_number):#遍历从0-256的值
    int2binary[i] = binary[i]#对于每个整型值赋值二进制序列
print(int2binary)
# 产生输入变量
alpha = 0.1         #设置更新速度（学习率）
input_dim = 2       #输入维度大小
hidden_dim = 16     #隐藏层维度大小
output_dim = 1      #输出维度大小
 
# 随机产生网络权重
synapse_0 = 2 * np.random.random((input_dim, hidden_dim)) - 1
synapse_1 = 2 * np.random.random((hidden_dim, output_dim)) - 1
synapse_h = 2 * np.random.random((hidden_dim, hidden_dim)) - 1
 
#梯度初始值设置为0
synapse_0_update = np.zeros_like(synapse_0)
synapse_1_update = np.zeros_like(synapse_1)
synapse_h_update = np.zeros_like(synapse_h)
 
#训练逻辑
for j in range(10000):
 
    # 产生一个简单的加法问题
    a_int = np.random.randint(largest_number / 2)  # 产生一个加法操作数
    a = int2binary[a_int]   # 找到二进制序列编码
 
    b_int = np.random.randint(largest_number / 2)  # 产生另一个加法操作数
    b = int2binary[b_int]   # 找到二进制序列编码
 
    # 计算正确值（标签值）
    c_int = a_int + b_int
    c = int2binary[c_int]   # 得到正确的结果序列
 
    # 设置存储器，存储中间值（记忆功能）
    d = np.zeros_like(c)
 
    overallError = 0        #设置误差
 
    layer_2_deltas = list()
    layer_1_values = list()
    layer_1_values.append(np.zeros(hidden_dim))
 
    # moving along the positions in the binary encoding
    for position in range(binary_dim):
        # 产生输入和输出
        X = np.array([[a[binary_dim - position - 1], b[binary_dim - position - 1]]])
        y = np.array([[c[binary_dim - position - 1]]]).T
 
        # 隐藏层计算
        layer_1 = sigmoid(np.dot(X, synapse_0) + np.dot(layer_1_values[-1], synapse_h))
 
        # 输出层
        layer_2 = sigmoid(np.dot(layer_1, synapse_1))
        # 计算差别
        layer_2_error = y - layer_2
        #计算每个梯度
        layer_2_deltas.append((layer_2_error) * sigmoid_output_to_derivative(layer_2))
        #计算所有损失
        overallError += np.abs(layer_2_error[0])
 
        # 编码记忆的中间值
        d[binary_dim - position - 1] = np.round(layer_2[0][0])
 
        # 拷贝副本
        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]
 
        # 输出层误差
        layer_2_delta = layer_2_deltas[-position - 1]
        # 隐藏层误差
        layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + layer_2_delta.dot(
            synapse_1.T)) * sigmoid_output_to_derivative(layer_1)
 
        # 计算梯度
        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
    #梯度下降
    synapse_0 += synapse_0_update * alpha
    synapse_1 += synapse_1_update * alpha
    synapse_h += synapse_h_update * alpha
    #重新初始化
    synapse_0_update *= 0
    synapse_1_update *= 0
    synapse_h_update *= 0
 
    # 打印训练过程
    if (j % 1000 == 0):
        print("Error:" + str(overallError))
        print("Pred:" + str(d))
        print("True:" + str(c))
        out = 0
        for index, x in enumerate(reversed(d)):
            out += x * pow(2, index)
        print(str(a_int) + " + " + str(b_int) + " = " + str(out))
        print("------------")

运行结果:

{0: array([0, 0, 0, 0, 0, 0, 0, 0], dtype=uint8), 1: array([0, 0, 0, 0, 0, 0, 0, 1], dtype=uint8), 2: array([0, 0, 0, 0, 0, 0, 1, 0], dtype=uint8), 3: array([0, 0, 0, 0, 0, 0, 1, 1], dtype=uint8), 4: array([0, 0, 0, 0, 0, 1, 0, 0], dtype=uint8), 5: array([0, 0, 0, 0, 0, 1, 0, 1], dtype=uint8), 6: array([0, 0, 0, 0, 0, 1, 1, 0], dtype=uint8), 7: array([0, 0, 0, 0, 0, 1, 1, 1], dtype=uint8), 8: array([0, 0, 0, 0, 1, 0, 0, 0], dtype=uint8), 9: array([0, 0, 0, 0, 1, 0, 0, 1], dtype=uint8), 10: array([0, 0, 0, 0, 1, 0, 1, 0], dtype=uint8), 11: array([0, 0, 0, 0, 1, 0, 1, 1], dtype=uint8), 12: array([0, 0, 0, 0, 1, 1, 0, 0], dtype=uint8), 13: array([0, 0, 0, 0, 1, 1, 0, 1], dtype=uint8), 14: array([0, 0, 0, 0, 1, 1, 1, 0], dtype=uint8), 15: array([0, 0, 0, 0, 1, 1, 1, 1], dtype=uint8), 16: array([0, 0, 0, 1, 0, 0, 0, 0], dtype=uint8), 17: array([0, 0, 0, 1, 0, 0, 0, 1], dtype=uint8), 18: array([0, 0, 0, 1, 0, 0, 1, 0], dtype=uint8), 19: array([0, 0, 0, 1, 0, 0, 1, 1], dtype=uint8), 20: array([0, 0, 0, 1, 0, 1, 0, 0], dtype=uint8), 21: array([0, 0, 0, 1, 0, 1, 0, 1], dtype=uint8), 22: array([0, 0, 0, 1, 0, 1, 1, 0], dtype=uint8), 23: array([0, 0, 0, 1, 0, 1, 1, 1], dtype=uint8), 24: array([0, 0, 0, 1, 1, 0, 0, 0], dtype=uint8), 25: array([0, 0, 0, 1, 1, 0, 0, 1], dtype=uint8), 26: array([0, 0, 0, 1, 1, 0, 1, 0], dtype=uint8), 27: array([0, 0, 0, 1, 1, 0, 1, 1], dtype=uint8), 28: array([0, 0, 0, 1, 1, 1, 0, 0], dtype=uint8), 29: array([0, 0, 0, 1, 1, 1, 0, 1], dtype=uint8), 30: array([0, 0, 0, 1, 1, 1, 1, 0], dtype=uint8), 31: array([0, 0, 0, 1, 1, 1, 1, 1], dtype=uint8), 32: array([0, 0, 1, 0, 0, 0, 0, 0], dtype=uint8), 33: array([0, 0, 1, 0, 0, 0, 0, 1], dtype=uint8), 34: array([0, 0, 1, 0, 0, 0, 1, 0], dtype=uint8), 35: array([0, 0, 1, 0, 0, 0, 1, 1], dtype=uint8), 36: array([0, 0, 1, 0, 0, 1, 0, 0], dtype=uint8), 37: array([0, 0, 1, 0, 0, 1, 0, 1], dtype=uint8), 38: array([0, 0, 1, 0, 0, 1, 1, 0], dtype=uint8), 39: array([0, 0, 1, 0, 0, 1, 1, 1], dtype=uint8), 40: array([0, 0, 1, 0, 1, 0, 0, 0], dtype=uint8), 41: array([0, 0, 1, 0, 1, 0, 0, 1], dtype=uint8), 42: array([0, 0, 1, 0, 1, 0, 1, 0], dtype=uint8), 43: array([0, 0, 1, 0, 1, 0, 1, 1], dtype=uint8), 44: array([0, 0, 1, 0, 1, 1, 0, 0], dtype=uint8), 45: array([0, 0, 1, 0, 1, 1, 0, 1], dtype=uint8), 46: array([0, 0, 1, 0, 1, 1, 1, 0], dtype=uint8), 47: array([0, 0, 1, 0, 1, 1, 1, 1], dtype=uint8), 48: array([0, 0, 1, 1, 0, 0, 0, 0], dtype=uint8), 49: array([0, 0, 1, 1, 0, 0, 0, 1], dtype=uint8), 50: array([0, 0, 1, 1, 0, 0, 1, 0], dtype=uint8), 51: array([0, 0, 1, 1, 0, 0, 1, 1], dtype=uint8), 52: array([0, 0, 1, 1, 0, 1, 0, 0], dtype=uint8), 53: array([0, 0, 1, 1, 0, 1, 0, 1], dtype=uint8), 54: array([0, 0, 1, 1, 0, 1, 1, 0], dtype=uint8), 55: array([0, 0, 1, 1, 0, 1, 1, 1], dtype=uint8), 56: array([0, 0, 1, 1, 1, 0, 0, 0], dtype=uint8), 57: array([0, 0, 1, 1, 1, 0, 0, 1], dtype=uint8), 58: array([0, 0, 1, 1, 1, 0, 1, 0], dtype=uint8), 59: array([0, 0, 1, 1, 1, 0, 1, 1], dtype=uint8), 60: array([0, 0, 1, 1, 1, 1, 0, 0], dtype=uint8), 61: array([0, 0, 1, 1, 1, 1, 0, 1], dtype=uint8), 62: array([0, 0, 1, 1, 1, 1, 1, 0], dtype=uint8), 63: array([0, 0, 1, 1, 1, 1, 1, 1], dtype=uint8), 64: array([0, 1, 0, 0, 0, 0, 0, 0], dtype=uint8), 65: array([0, 1, 0, 0, 0, 0, 0, 1], dtype=uint8), 66: array([0, 1, 0, 0, 0, 0, 1, 0], dtype=uint8), 67: array([0, 1, 0, 0, 0, 0, 1, 1], dtype=uint8), 68: array([0, 1, 0, 0, 0, 1, 0, 0], dtype=uint8), 69: array([0, 1, 0, 0, 0, 1, 0, 1], dtype=uint8), 70: array([0, 1, 0, 0, 0, 1, 1, 0], dtype=uint8), 71: array([0, 1, 0, 0, 0, 1, 1, 1], dtype=uint8), 72: array([0, 1, 0, 0, 1, 0, 0, 0], dtype=uint8), 73: array([0, 1, 0, 0, 1, 0, 0, 1], dtype=uint8), 74: array([0, 1, 0, 0, 1, 0, 1, 0], dtype=uint8), 75: array([0, 1, 0, 0, 1, 0, 1, 1], dtype=uint8), 76: array([0, 1, 0, 0, 1, 1, 0, 0], dtype=uint8), 77: array([0, 1, 0, 0, 1, 1, 0, 1], dtype=uint8), 78: array([0, 1, 0, 0, 1, 1, 1, 0], dtype=uint8), 79: array([0, 1, 0, 0, 1, 1, 1, 1], dtype=uint8), 80: array([0, 1, 0, 1, 0, 0, 0, 0], dtype=uint8), 81: array([0, 1, 0, 1, 0, 0, 0, 1], dtype=uint8), 82: array([0, 1, 0, 1, 0, 0, 1, 0], dtype=uint8), 83: array([0, 1, 0, 1, 0, 0, 1, 1], dtype=uint8), 84: array([0, 1, 0, 1, 0, 1, 0, 0], dtype=uint8), 85: array([0, 1, 0, 1, 0, 1, 0, 1], dtype=uint8), 86: array([0, 1, 0, 1, 0, 1, 1, 0], dtype=uint8), 87: array([0, 1, 0, 1, 0, 1, 1, 1], dtype=uint8), 88: array([0, 1, 0, 1, 1, 0, 0, 0], dtype=uint8), 89: array([0, 1, 0, 1, 1, 0, 0, 1], dtype=uint8), 90: array([0, 1, 0, 1, 1, 0, 1, 0], dtype=uint8), 91: array([0, 1, 0, 1, 1, 0, 1, 1], dtype=uint8), 92: array([0, 1, 0, 1, 1, 1, 0, 0], dtype=uint8), 93: array([0, 1, 0, 1, 1, 1, 0, 1], dtype=uint8), 94: array([0, 1, 0, 1, 1, 1, 1, 0], dtype=uint8), 95: array([0, 1, 0, 1, 1, 1, 1, 1], dtype=uint8), 96: array([0, 1, 1, 0, 0, 0, 0, 0], dtype=uint8), 97: array([0, 1, 1, 0, 0, 0, 0, 1], dtype=uint8), 98: array([0, 1, 1, 0, 0, 0, 1, 0], dtype=uint8), 99: array([0, 1, 1, 0, 0, 0, 1, 1], dtype=uint8), 100: array([0, 1, 1, 0, 0, 1, 0, 0], dtype=uint8), 101: array([0, 1, 1, 0, 0, 1, 0, 1], dtype=uint8), 102: array([0, 1, 1, 0, 0, 1, 1, 0], dtype=uint8), 103: array([0, 1, 1, 0, 0, 1, 1, 1], dtype=uint8), 104: array([0, 1, 1, 0, 1, 0, 0, 0], dtype=uint8), 105: array([0, 1, 1, 0, 1, 0, 0, 1], dtype=uint8), 106: array([0, 1, 1, 0, 1, 0, 1, 0], dtype=uint8), 107: array([0, 1, 1, 0, 1, 0, 1, 1], dtype=uint8), 108: array([0, 1, 1, 0, 1, 1, 0, 0], dtype=uint8), 109: array([0, 1, 1, 0, 1, 1, 0, 1], dtype=uint8), 110: array([0, 1, 1, 0, 1, 1, 1, 0], dtype=uint8), 111: array([0, 1, 1, 0, 1, 1, 1, 1], dtype=uint8), 112: array([0, 1, 1, 1, 0, 0, 0, 0], dtype=uint8), 113: array([0, 1, 1, 1, 0, 0, 0, 1], dtype=uint8), 114: array([0, 1, 1, 1, 0, 0, 1, 0], dtype=uint8), 115: array([0, 1, 1, 1, 0, 0, 1, 1], dtype=uint8), 116: array([0, 1, 1, 1, 0, 1, 0, 0], dtype=uint8), 117: array([0, 1, 1, 1, 0, 1, 0, 1], dtype=uint8), 118: array([0, 1, 1, 1, 0, 1, 1, 0], dtype=uint8), 119: array([0, 1, 1, 1, 0, 1, 1, 1], dtype=uint8), 120: array([0, 1, 1, 1, 1, 0, 0, 0], dtype=uint8), 121: array([0, 1, 1, 1, 1, 0, 0, 1], dtype=uint8), 122: array([0, 1, 1, 1, 1, 0, 1, 0], dtype=uint8), 123: array([0, 1, 1, 1, 1, 0, 1, 1], dtype=uint8), 124: array([0, 1, 1, 1, 1, 1, 0, 0], dtype=uint8), 125: array([0, 1, 1, 1, 1, 1, 0, 1], dtype=uint8), 126: array([0, 1, 1, 1, 1, 1, 1, 0], dtype=uint8), 127: array([0, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), 128: array([1, 0, 0, 0, 0, 0, 0, 0], dtype=uint8), 129: array([1, 0, 0, 0, 0, 0, 0, 1], dtype=uint8), 130: array([1, 0, 0, 0, 0, 0, 1, 0], dtype=uint8), 131: array([1, 0, 0, 0, 0, 0, 1, 1], dtype=uint8), 132: array([1, 0, 0, 0, 0, 1, 0, 0], dtype=uint8), 133: array([1, 0, 0, 0, 0, 1, 0, 1], dtype=uint8), 134: array([1, 0, 0, 0, 0, 1, 1, 0], dtype=uint8), 135: array([1, 0, 0, 0, 0, 1, 1, 1], dtype=uint8), 136: array([1, 0, 0, 0, 1, 0, 0, 0], dtype=uint8), 137: array([1, 0, 0, 0, 1, 0, 0, 1], dtype=uint8), 138: array([1, 0, 0, 0, 1, 0, 1, 0], dtype=uint8), 139: array([1, 0, 0, 0, 1, 0, 1, 1], dtype=uint8), 140: array([1, 0, 0, 0, 1, 1, 0, 0], dtype=uint8), 141: array([1, 0, 0, 0, 1, 1, 0, 1], dtype=uint8), 142: array([1, 0, 0, 0, 1, 1, 1, 0], dtype=uint8), 143: array([1, 0, 0, 0, 1, 1, 1, 1], dtype=uint8), 144: array([1, 0, 0, 1, 0, 0, 0, 0], dtype=uint8), 145: array([1, 0, 0, 1, 0, 0, 0, 1], dtype=uint8), 146: array([1, 0, 0, 1, 0, 0, 1, 0], dtype=uint8), 147: array([1, 0, 0, 1, 0, 0, 1, 1], dtype=uint8), 148: array([1, 0, 0, 1, 0, 1, 0, 0], dtype=uint8), 149: array([1, 0, 0, 1, 0, 1, 0, 1], dtype=uint8), 150: array([1, 0, 0, 1, 0, 1, 1, 0], dtype=uint8), 151: array([1, 0, 0, 1, 0, 1, 1, 1], dtype=uint8), 152: array([1, 0, 0, 1, 1, 0, 0, 0], dtype=uint8), 153: array([1, 0, 0, 1, 1, 0, 0, 1], dtype=uint8), 154: array([1, 0, 0, 1, 1, 0, 1, 0], dtype=uint8), 155: array([1, 0, 0, 1, 1, 0, 1, 1], dtype=uint8), 156: array([1, 0, 0, 1, 1, 1, 0, 0], dtype=uint8), 157: array([1, 0, 0, 1, 1, 1, 0, 1], dtype=uint8), 158: array([1, 0, 0, 1, 1, 1, 1, 0], dtype=uint8), 159: array([1, 0, 0, 1, 1, 1, 1, 1], dtype=uint8), 160: array([1, 0, 1, 0, 0, 0, 0, 0], dtype=uint8), 161: array([1, 0, 1, 0, 0, 0, 0, 1], dtype=uint8), 162: array([1, 0, 1, 0, 0, 0, 1, 0], dtype=uint8), 163: array([1, 0, 1, 0, 0, 0, 1, 1], dtype=uint8), 164: array([1, 0, 1, 0, 0, 1, 0, 0], dtype=uint8), 165: array([1, 0, 1, 0, 0, 1, 0, 1], dtype=uint8), 166: array([1, 0, 1, 0, 0, 1, 1, 0], dtype=uint8), 167: array([1, 0, 1, 0, 0, 1, 1, 1], dtype=uint8), 168: array([1, 0, 1, 0, 1, 0, 0, 0], dtype=uint8), 169: array([1, 0, 1, 0, 1, 0, 0, 1], dtype=uint8), 170: array([1, 0, 1, 0, 1, 0, 1, 0], dtype=uint8), 171: array([1, 0, 1, 0, 1, 0, 1, 1], dtype=uint8), 172: array([1, 0, 1, 0, 1, 1, 0, 0], dtype=uint8), 173: array([1, 0, 1, 0, 1, 1, 0, 1], dtype=uint8), 174: array([1, 0, 1, 0, 1, 1, 1, 0], dtype=uint8), 175: array([1, 0, 1, 0, 1, 1, 1, 1], dtype=uint8), 176: array([1, 0, 1, 1, 0, 0, 0, 0], dtype=uint8), 177: array([1, 0, 1, 1, 0, 0, 0, 1], dtype=uint8), 178: array([1, 0, 1, 1, 0, 0, 1, 0], dtype=uint8), 179: array([1, 0, 1, 1, 0, 0, 1, 1], dtype=uint8), 180: array([1, 0, 1, 1, 0, 1, 0, 0], dtype=uint8), 181: array([1, 0, 1, 1, 0, 1, 0, 1], dtype=uint8), 182: array([1, 0, 1, 1, 0, 1, 1, 0], dtype=uint8), 183: array([1, 0, 1, 1, 0, 1, 1, 1], dtype=uint8), 184: array([1, 0, 1, 1, 1, 0, 0, 0], dtype=uint8), 185: array([1, 0, 1, 1, 1, 0, 0, 1], dtype=uint8), 186: array([1, 0, 1, 1, 1, 0, 1, 0], dtype=uint8), 187: array([1, 0, 1, 1, 1, 0, 1, 1], dtype=uint8), 188: array([1, 0, 1, 1, 1, 1, 0, 0], dtype=uint8), 189: array([1, 0, 1, 1, 1, 1, 0, 1], dtype=uint8), 190: array([1, 0, 1, 1, 1, 1, 1, 0], dtype=uint8), 191: array([1, 0, 1, 1, 1, 1, 1, 1], dtype=uint8), 192: array([1, 1, 0, 0, 0, 0, 0, 0], dtype=uint8), 193: array([1, 1, 0, 0, 0, 0, 0, 1], dtype=uint8), 194: array([1, 1, 0, 0, 0, 0, 1, 0], dtype=uint8), 195: array([1, 1, 0, 0, 0, 0, 1, 1], dtype=uint8), 196: array([1, 1, 0, 0, 0, 1, 0, 0], dtype=uint8), 197: array([1, 1, 0, 0, 0, 1, 0, 1], dtype=uint8), 198: array([1, 1, 0, 0, 0, 1, 1, 0], dtype=uint8), 199: array([1, 1, 0, 0, 0, 1, 1, 1], dtype=uint8), 200: array([1, 1, 0, 0, 1, 0, 0, 0], dtype=uint8), 201: array([1, 1, 0, 0, 1, 0, 0, 1], dtype=uint8), 202: array([1, 1, 0, 0, 1, 0, 1, 0], dtype=uint8), 203: array([1, 1, 0, 0, 1, 0, 1, 1], dtype=uint8), 204: array([1, 1, 0, 0, 1, 1, 0, 0], dtype=uint8), 205: array([1, 1, 0, 0, 1, 1, 0, 1], dtype=uint8), 206: array([1, 1, 0, 0, 1, 1, 1, 0], dtype=uint8), 207: array([1, 1, 0, 0, 1, 1, 1, 1], dtype=uint8), 208: array([1, 1, 0, 1, 0, 0, 0, 0], dtype=uint8), 209: array([1, 1, 0, 1, 0, 0, 0, 1], dtype=uint8), 210: array([1, 1, 0, 1, 0, 0, 1, 0], dtype=uint8), 211: array([1, 1, 0, 1, 0, 0, 1, 1], dtype=uint8), 212: array([1, 1, 0, 1, 0, 1, 0, 0], dtype=uint8), 213: array([1, 1, 0, 1, 0, 1, 0, 1], dtype=uint8), 214: array([1, 1, 0, 1, 0, 1, 1, 0], dtype=uint8), 215: array([1, 1, 0, 1, 0, 1, 1, 1], dtype=uint8), 216: array([1, 1, 0, 1, 1, 0, 0, 0], dtype=uint8), 217: array([1, 1, 0, 1, 1, 0, 0, 1], dtype=uint8), 218: array([1, 1, 0, 1, 1, 0, 1, 0], dtype=uint8), 219: array([1, 1, 0, 1, 1, 0, 1, 1], dtype=uint8), 220: array([1, 1, 0, 1, 1, 1, 0, 0], dtype=uint8), 221: array([1, 1, 0, 1, 1, 1, 0, 1], dtype=uint8), 222: array([1, 1, 0, 1, 1, 1, 1, 0], dtype=uint8), 223: array([1, 1, 0, 1, 1, 1, 1, 1], dtype=uint8), 224: array([1, 1, 1, 0, 0, 0, 0, 0], dtype=uint8), 225: array([1, 1, 1, 0, 0, 0, 0, 1], dtype=uint8), 226: array([1, 1, 1, 0, 0, 0, 1, 0], dtype=uint8), 227: array([1, 1, 1, 0, 0, 0, 1, 1], dtype=uint8), 228: array([1, 1, 1, 0, 0, 1, 0, 0], dtype=uint8), 229: array([1, 1, 1, 0, 0, 1, 0, 1], dtype=uint8), 230: array([1, 1, 1, 0, 0, 1, 1, 0], dtype=uint8), 231: array([1, 1, 1, 0, 0, 1, 1, 1], dtype=uint8), 232: array([1, 1, 1, 0, 1, 0, 0, 0], dtype=uint8), 233: array([1, 1, 1, 0, 1, 0, 0, 1], dtype=uint8), 234: array([1, 1, 1, 0, 1, 0, 1, 0], dtype=uint8), 235: array([1, 1, 1, 0, 1, 0, 1, 1], dtype=uint8), 236: array([1, 1, 1, 0, 1, 1, 0, 0], dtype=uint8), 237: array([1, 1, 1, 0, 1, 1, 0, 1], dtype=uint8), 238: array([1, 1, 1, 0, 1, 1, 1, 0], dtype=uint8), 239: array([1, 1, 1, 0, 1, 1, 1, 1], dtype=uint8), 240: array([1, 1, 1, 1, 0, 0, 0, 0], dtype=uint8), 241: array([1, 1, 1, 1, 0, 0, 0, 1], dtype=uint8), 242: array([1, 1, 1, 1, 0, 0, 1, 0], dtype=uint8), 243: array([1, 1, 1, 1, 0, 0, 1, 1], dtype=uint8), 244: array([1, 1, 1, 1, 0, 1, 0, 0], dtype=uint8), 245: array([1, 1, 1, 1, 0, 1, 0, 1], dtype=uint8), 246: array([1, 1, 1, 1, 0, 1, 1, 0], dtype=uint8), 247: array([1, 1, 1, 1, 0, 1, 1, 1], dtype=uint8), 248: array([1, 1, 1, 1, 1, 0, 0, 0], dtype=uint8), 249: array([1, 1, 1, 1, 1, 0, 0, 1], dtype=uint8), 250: array([1, 1, 1, 1, 1, 0, 1, 0], dtype=uint8), 251: array([1, 1, 1, 1, 1, 0, 1, 1], dtype=uint8), 252: array([1, 1, 1, 1, 1, 1, 0, 0], dtype=uint8), 253: array([1, 1, 1, 1, 1, 1, 0, 1], dtype=uint8), 254: array([1, 1, 1, 1, 1, 1, 1, 0], dtype=uint8), 255: array([1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8)}
Error:[3.45638663]
Pred:[0 0 0 0 0 0 0 1]
True:[0 1 0 0 0 1 0 1]
9 + 60 = 1
------------
Error:[3.63389116]
Pred:[1 1 1 1 1 1 1 1]
True:[0 0 1 1 1 1 1 1]
28 + 35 = 255
------------
Error:[3.91366595]
Pred:[0 1 0 0 1 0 0 0]
True:[1 0 1 0 0 0 0 0]
116 + 44 = 72
------------
Error:[3.72191702]
Pred:[1 1 0 1 1 1 1 1]
True:[0 1 0 0 1 1 0 1]
4 + 73 = 223
------------
Error:[3.5852713]
Pred:[0 0 0 0 1 0 0 0]
True:[0 1 0 1 0 0 1 0]
71 + 11 = 8
------------
Error:[2.53352328]
Pred:[1 0 1 0 0 0 1 0]
True:[1 1 0 0 0 0 1 0]
81 + 113 = 162
------------
Error:[0.57691441]
Pred:[0 1 0 1 0 0 0 1]
True:[0 1 0 1 0 0 0 1]
81 + 0 = 81
------------
Error:[1.42589952]
Pred:[1 0 0 0 0 0 0 1]
True:[1 0 0 0 0 0 0 1]
4 + 125 = 129
------------
Error:[0.47477457]
Pred:[0 0 1 1 1 0 0 0]
True:[0 0 1 1 1 0 0 0]
39 + 17 = 56
------------
Error:[0.21595037]
Pred:[0 0 0 0 1 1 1 0]
True:[0 0 0 0 1 1 1 0]
11 + 3 = 14
------------

由代码中的注释便可知该代码运行的大体步骤为：

• 定义sigmoid函数和sigmoid导数函数；
• 初始化长度为8的二进制序列的编码；
• 随机产生网络权重；
• 每次训练随机产生两个数，找到对应的8位二进制序列，进行数据输入；
• 开始训练，每1000次查看一次中间结果，产生的结果和正确的结果进行误差计算，从而更新随机网络权重的参数，直至训练至10000次为止（或者可以设置误差小于多少停止）。

 NNDL 作业8：RNN - 简单循环网络_AI-2 刘子豪的博客-CSDN博客

5. 实现“Character-Level Language Models”源代码（必做）

import torch
 
# 使用RNN 有嵌入层和线性层
num_class = 4  # 4个类别
input_size = 4  # 输入维度是4
hidden_size = 8  # 隐层是8个维度
embedding_size = 10  # 嵌入到10维空间
batch_size = 1
num_layers = 2  # 两层的RNN
seq_len = 5  # 序列长度是5
 
# 准备数据
idx2char = ['e', 'h', 'l', 'o']  # 字典
x_data = [[1, 0, 2, 2, 3]]  # hello  维度（batch,seqlen）
y_data = [3, 1, 2, 3, 2]  # ohlol    维度 (batch*seqlen)
 
inputs = torch.LongTensor(x_data)
labels = torch.LongTensor(y_data)
 
 
# 构造模型
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.emb = torch.nn.Embedding(input_size, embedding_size)
        self.rnn = torch.nn.RNN(input_size=embedding_size, hidden_size=hidden_size, num_layers=num_layers,
                                batch_first=True)
        self.fc = torch.nn.Linear(hidden_size, num_class)
 
    def forward(self, x):
        hidden = torch.zeros(num_layers, x.size(0), hidden_size)
        x = self.emb(x)  # (batch,seqlen,embeddingsize)
        x, _ = self.rnn(x, hidden)
        x = self.fc(x)
        return x.view(-1, num_class)  # 转变维2维矩阵，seq*batchsize*numclass -》((seq*batchsize),numclass)
 
 
model = Model()
 
# 损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.05)  # lr = 0.01学习的太慢
 
# 训练
for epoch in range(15):
    optimizer.zero_grad()
    outputs = model(inputs)  # inputs是（seq,Batchsize,Inputsize） outputs是(seq,Batchsize,Hiddensize)
    loss = criterion(outputs, labels)  # labels是（seq，batchsize，1）
    loss.backward()
    optimizer.step()
 
    _, idx = outputs.max(dim=1)
    idx = idx.data.numpy()
    print("Predicted:", ''.join([idx2char[x] for x in idx]), end='')
    print(",Epoch {}/15 loss={:.3f}".format(epoch + 1, loss.item()))

运行结果:

7. “编码器-解码器”的简单实现（必做）

import torch
import numpy as np
import torch.nn as nn
import torch.utils.data as Data
 
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
letter = [c for c in 'SE?abcdefghijklmnopqrstuvwxyz']
letter2idx = {n: i for i, n in enumerate(letter)}
 
seq_data = [['man', 'women'], ['black', 'white'], ['king', 'queen'], ['girl', 'boy'], ['up', 'down'], ['high', 'low']]
 
# Seq2Seq Parameter
n_step = max([max(len(i), len(j)) for i, j in seq_data])  # max_len(=5)
n_hidden = 128
n_class = len(letter2idx)  # classfication problem
batch_size = 3
 
 
def make_data(seq_data):
    enc_input_all, dec_input_all, dec_output_all = [], [], []
 
    for seq in seq_data:
        for i in range(2):
            seq[i] = seq[i] + '?' * (n_step - len(seq[i]))  # 'man??', 'women'
 
        enc_input = [letter2idx[n] for n in (seq[0] + 'E')]  # ['m', 'a', 'n', '?', '?', 'E']
        dec_input = [letter2idx[n] for n in ('S' + seq[1])]  # ['S', 'w', 'o', 'm', 'e', 'n']
        dec_output = [letter2idx[n] for n in (seq[1] + 'E')]  # ['w', 'o', 'm', 'e', 'n', 'E']
 
        enc_input_all.append(np.eye(n_class)[enc_input])
        dec_input_all.append(np.eye(n_class)[dec_input])
        dec_output_all.append(dec_output)  # not one-hot
 
    # make tensor
    return torch.Tensor(enc_input_all), torch.Tensor(dec_input_all), torch.LongTensor(dec_output_all)
 
enc_input_all, dec_input_all, dec_output_all = make_data(seq_data)
 
 
class TranslateDataSet(Data.Dataset):
    def __init__(self, enc_input_all, dec_input_all, dec_output_all):
        self.enc_input_all = enc_input_all
        self.dec_input_all = dec_input_all
        self.dec_output_all = dec_output_all
 
    def __len__(self):  # return dataset size
        return len(self.enc_input_all)
 
    def __getitem__(self, idx):
        return self.enc_input_all[idx], self.dec_input_all[idx], self.dec_output_all[idx]
 
 
loader = Data.DataLoader(TranslateDataSet(enc_input_all, dec_input_all, dec_output_all), batch_size, True)
 
 
# Model
class Seq2Seq(nn.Module):
    def __init__(self):
        super(Seq2Seq, self).__init__()
        self.encoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5)  # encoder
        self.decoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5)  # decoder
        self.fc = nn.Linear(n_hidden, n_class)
 
    def forward(self, enc_input, enc_hidden, dec_input):
        enc_input = enc_input.transpose(0, 1)  # enc_input: [n_step+1, batch_size, n_class]
        dec_input = dec_input.transpose(0, 1)  # dec_input: [n_step+1, batch_size, n_class]
 
        # h_t : [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
        _, h_t = self.encoder(enc_input, enc_hidden)
        # outputs : [n_step+1, batch_size, num_directions(=1) * n_hidden(=128)]
        outputs, _ = self.decoder(dec_input, h_t)
 
        model = self.fc(outputs)  # model : [n_step+1, batch_size, n_class]
        return model
 
 
model = Seq2Seq().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
 
for epoch in range(5000):
    for enc_input_batch, dec_input_batch, dec_output_batch in loader:
        # make hidden shape [num_layers * num_directions, batch_size, n_hidden]
        h_0 = torch.zeros(1, batch_size, n_hidden).to(device)
 
        (enc_input_batch, dec_intput_batch, dec_output_batch) = (
        enc_input_batch.to(device), dec_input_batch.to(device), dec_output_batch.to(device))
        # enc_input_batch : [batch_size, n_step+1, n_class]
        # dec_intput_batch : [batch_size, n_step+1, n_class]
        # dec_output_batch : [batch_size, n_step+1], not one-hot
        pred = model(enc_input_batch, h_0, dec_intput_batch)
        # pred : [n_step+1, batch_size, n_class]
        pred = pred.transpose(0, 1)  # [batch_size, n_step+1(=6), n_class]
        loss = 0
        for i in range(len(dec_output_batch)):
            # pred[i] : [n_step+1, n_class]
            # dec_output_batch[i] : [n_step+1]
            loss += criterion(pred[i], dec_output_batch[i])
        if (epoch + 1) % 1000 == 0:
            print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
 
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 
 
# Test
def translate(word):
    enc_input, dec_input, _ = make_data([[word, '?' * n_step]])
    enc_input, dec_input = enc_input.to(device), dec_input.to(device)
    # make hidden shape [num_layers * num_directions, batch_size, n_hidden]
    hidden = torch.zeros(1, 1, n_hidden).to(device)
    output = model(enc_input, hidden, dec_input)
    # output : [n_step+1, batch_size, n_class]
 
    predict = output.data.max(2, keepdim=True)[1]  # select n_class dimension
    decoded = [letter[i] for i in predict]
    translated = ''.join(decoded[:decoded.index('E')])
 
    return translated.replace('?', '')
 
print('test')
print('man ->', translate('man'))
print('mans ->', translate('mans'))
print('king ->', translate('king'))
print('black ->', translate('black'))
print('up ->', translate('up'))
print('old ->', translate('old'))
print('high ->', translate('high'))

运行结果：
 

总结

本次作业动手实践了SRN，对比实践了使用nn.RNNCell（）和nn.RNN（）实现SRN，分析了二进制加法的源代码并实现，对Seq2Seq模型有了更深入的了解。对理解简单循环神经网络有很大帮助

参考：

NNDL 作业8：RNN - 简单循环网络_HBU_David的博客-CSDN博客

Seq2Seq的PyTorch实现 - mathor (wmathor.com)

完全图解RNN、RNN变体、Seq2Seq、Attention机制 - 知乎 (zhihu.com)

《PyTorch深度学习实践》完结合集_哔哩哔哩_bilibili