学习笔记CB012: LSTM 简单实现、完整实现、torch、小说训练word2vec lstm机器人
原创学习笔记CB012: LSTM 简单实现、完整实现、torch、小说训练word2vec lstm机器人
原创
真正掌握一种算法，最实际的方法，完全手写出来。
LSTM（Long Short Tem Memory）特殊递归神经网络，神经元保存历史记忆，解决自然语言处理统计方法只能考虑最近n个词语而忽略更久前词语的问题。用途：word representation（embedding）(词语向量)、sequence to sequence learning（输入句子预测句子）、机器翻译、语音识别等。
100多行原始python代码实现基于LSTM二进制加法器。https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/ ，翻译http://blog.csdn.net/zzukun/article/details/49968129 ：
import copy, numpy as npnp.random.seed(0)最开始引入numpy库，矩阵操作。
def sigmoid(x): output = 1/(1+np.exp(-x)) return output声明sigmoid激活函数，神经网络基础内容，常用激活函数sigmoid、tan、relu等，sigmoid取值范围0, 1，tan取值范围-1,1，x是向量，返回output是向量。
def sigmoid_output_to_derivative(output): return output*(1-output)声明sigmoid求导函数。
加法器思路：二进制加法是二进制位相加，记录满二进一进位，训练时随机c=a+b样本，输入a、b输出c是整个lstm预测过程，训练由a、b二进制向c各种转换矩阵和权重，神经网络。
int2binary = {}声明词典，由整型数字转成二进制，存起来不用随时计算，提前存好读取更快。
binary_dim = 8largest_number = pow(2,binary_dim)
声明二进制数字维度，8，二进制能表达最大整数2^8=256，largest_number。
binary = np.unpackbits( np.array([range(largest_number)],dtype=np.uint8).T,axis=1)for i in range(largest_number): int2binary[i] = binary[i]预先把整数到二进制转换词典存起来。
alpha = 0.1input_dim = 2hidden_dim = 16output_dim = 1设置参数，alpha是学习速度，input_dim是输入层向量维度，输入a、b两个数，是2，hidden_dim是隐藏层向量维度，隐藏层神经元个数，output_dim是输出层向量维度，输出一个c，是1维。从输入层到隐藏层权重矩阵是216维，从隐藏层到输出层权重矩阵是161维，隐藏层到隐藏层权重矩阵是16*16维：
synapse_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)) - 12x-1，np.random.random生成从0到1之间随机浮点数，2x-1使其取值范围在-1, 1。
synapse_0_update = np.zeros_like(synapse_0)synapse_1_update = np.zeros_like(synapse_1)synapse_h_update = np.zeros_like(synapse_h)声明三个矩阵更新，Delta。
for j in range(10000):进行10000次迭代。
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_intc = int2binary[c_int]随机生成样本，包含二进制a、b、c，c=a+b，a_int、b_int、c_int分别是a、b、c对应整数格式。
d = np.zeros_like(c)d存模型对c预测值。
overallError = 0全局误差，观察模型效果。
layer_2_deltas = list()
存储第二层(输出层)残差，输出层残差计算公式推导公式http://deeplearning.stanford.edu/wiki/index.php/%E5%8F%8D%E5%90%91%E4%BC%A0%E5%AF%BC%E7%AE%97%E6%B3%95 。
layer_1_values = list()layer_1_values.append(np.zeros(hidden_dim))存储第一层(隐藏层)输出值，赋0值作为上一个时间值。
for position in range(binary_dim):遍历二进制每一位。
X = np.array([[a[binary_dim - position - 1],b[binary_dim - position - 1]]])y = np.array([[c[binary_dim - position - 1]]]).TX和y分别是样本输入和输出二进制值第position位，X对于每个样本有两个值，分别是a和b对应第position位。把样本拆成每个二进制位用于训练，二进制加法存在进位标记正好适合利用LSTM长短期记忆训练，每个样本8个二进制位是一个时间序列。
layer_1 = sigmoid(np.dot(X,synapse_0) + np.dot(layer_1_values[-1],synapse_h))公式Ct = sigma(W0·Xt + Wh·Ct-1)
layer_2 = sigmoid(np.dot(layer_1,synapse_1))这里使用的公式是C2 = sigma(W1·C1)，
layer_2_error = y - layer_2计算预测值和真实值误差。
layer_2_deltas.append((layer_2_error)*sigmoid_output_to_derivative(layer_2))反向传导，计算delta，添加到数组layer_2_deltas
overallError += np.abs(layer_2_error[0])计算累加总误差，用于展示和观察。
d[binary_dim - position - 1] = np.round(layer_2[0][0])存储预测position位输出值。
layer_1_values.append(copy.deepcopy(layer_1))存储中间过程生成隐藏层值。
future_layer_1_delta = np.zeros(hidden_dim)存储下一个时间周期隐藏层历史记忆值，先赋一个空值。
for position in range(binary_dim):遍历二进制每一位。
X = np.array([[a[position],b[position]]])取出X值，从大位开始更新，反向传导按时序逆着一级一级更新。
layer_1 = layer_1_values[-position-1]取出位对应隐藏层输出。
prev_layer_1 = layer_1_values[-position-2]取出位对应隐藏层上一时序输出。
layer_2_delta = layer_2_deltas[-position-1]取出位对应输出层delta。
layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + layer_2_delta.dot(synapse_1.T)) * sigmoid_output_to_derivative(layer_1)神经网络反向传导公式，加上隐藏层?值。
synapse_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta)累加权重矩阵更新，对权重(权重矩阵)偏导等于本层输出与下一层delta点乘。
synapse_h_update += np.atleast_2d(prev_layer_1).T.dot(layer_1_delta)前一时序隐藏层权重矩阵更新，前一时序隐藏层输出与本时序delta点乘。
synapse_0_update += X.T.dot(layer_1_delta)输入层权重矩阵更新。
future_layer_1_delta = layer_1_delta记录本时序隐藏层delta。
synapse_0 += synapse_0_update * alphasynapse_1 += synapse_1_update * alphasynapse_h += synapse_h_update * alpha权重矩阵更新。
synapse_0_update *= 0synapse_1_update *= 0synapse_h_update *= 0更新变量归零。
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 "------------"每训练1000个样本输出总误差信息，运行时看收敛过程。
LSTM最简单实现，没有考虑偏置变量，只有两个神经元。
完整LSTM python实现。完全参照论文great intro paper实现,代码来源https://github.com/nicodjimenez/lstm ，作者解释http://nicodjimenez.github.io/2014/08/08/lstm.html ，具体过程参考http://colah.github.io/posts/2015-08-Understanding-LSTMs/ 图。
import randomimport numpy as npimport mathdef sigmoid(x): return 1. / (1 + np.exp(-x))声明sigmoid函数。
def rand_arr(a, b, *args): np.random.seed(0) return np.random.rand(*args) * (b - a) + a生成随机矩阵，取值范围[a,b)，shape用args指定。
class LstmParam: def __init__(self, mem_cell_ct, x_dim): self.mem_cell_ct = mem_cell_ct self.x_dim = x_dim concat_len = x_dim + mem_cell_ct # weight matrices self.wg = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len) self.wi = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len) self.wf = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len) self.wo = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len) # bias terms self.bg = rand_arr(-0.1, 0.1, mem_cell_ct) self.bi = rand_arr(-0.1, 0.1, mem_cell_ct) self.bf = rand_arr(-0.1, 0.1, mem_cell_ct) self.bo = rand_arr(-0.1, 0.1, mem_cell_ct) # diffs (derivative of loss function w.r.t. all parameters) self.wg_diff = np.zeros((mem_cell_ct, concat_len)) self.wi_diff = np.zeros((mem_cell_ct, concat_len)) self.wf_diff = np.zeros((mem_cell_ct, concat_len)) self.wo_diff = np.zeros((mem_cell_ct, concat_len)) self.bg_diff = np.zeros(mem_cell_ct) self.bi_diff = np.zeros(mem_cell_ct) self.bf_diff = np.zeros(mem_cell_ct) self.bo_diff = np.zeros(mem_cell_ct)LstmParam类传递参数，mem_cell_ct是lstm神经元数目，x_dim是输入数据维度，concat_len是mem_cell_ct与x_dim长度和，wg是输入节点权重矩阵，wi是输入门权重矩阵，wf是忘记门权重矩阵，wo是输出门权重矩阵，bg、bi、bf、bo分别是输入节点、输入门、忘记门、输出门偏置，wg_diff、wi_diff、wf_diff、wo_diff分别是输入节点、输入门、忘记门、输出门权重损失，bg_diff、bi_diff、bf_diff、bo_diff分别是输入节点、输入门、忘记门、输出门偏置损失，初始化按照矩阵维度初始化，损失矩阵归零。
def apply_diff(self, lr = 1): self.wg -= lr * self.wg_diff self.wi -= lr * self.wi_diff self.wf -= lr * self.wf_diff self.wo -= lr * self.wo_diff self.bg -= lr * self.bg_diff self.bi -= lr * self.bi_diff self.bf -= lr * self.bf_diff self.bo -= lr * self.bo_diff # reset diffs to zero self.wg_diff = np.zeros_like(self.wg) self.wi_diff = np.zeros_like(self.wi) self.wf_diff = np.zeros_like(self.wf) self.wo_diff = np.zeros_like(self.wo) self.bg_diff = np.zeros_like(self.bg) self.bi_diff = np.zeros_like(self.bi) self.bf_diff = np.zeros_like(self.bf) self.bo_diff = np.zeros_like(self.bo)定义权重更新过程，先减损失，再把损失矩阵归零。
class LstmState: def __init__(self, mem_cell_ct, x_dim): self.g = np.zeros(mem_cell_ct) self.i = np.zeros(mem_cell_ct) self.f = np.zeros(mem_cell_ct) self.o = np.zeros(mem_cell_ct) self.s = np.zeros(mem_cell_ct) self.h = np.zeros(mem_cell_ct) self.bottom_diff_h = np.zeros_like(self.h) self.bottom_diff_s = np.zeros_like(self.s) self.bottom_diff_x = np.zeros(x_dim)LstmState存储LSTM神经元状态，包括g、i、f、o、s、h，s是内部状态矩阵(记忆)，h是隐藏层神经元输出矩阵。
class LstmNode: def __init__(self, lstm_param, lstm_state): # store reference to parameters and to activations self.state = lstm_state self.param = lstm_param # non-recurrent input to node self.x = None # non-recurrent input concatenated with recurrent input self.xc = NoneLstmNode对应样本输入，x是输入样本x，xc是用hstack把x和递归输入节点拼接矩阵（hstack是横拼矩阵，vstack是纵拼矩阵）。
def bottom_data_is(self, x, s_prev = None, h_prev = None): # if this is the first lstm node in the network if s_prev == None: s_prev = np.zeros_like(self.state.s) if h_prev == None: h_prev = np.zeros_like(self.state.h) # save data for use in backprop self.s_prev = s_prev self.h_prev = h_prev # concatenate x(t) and h(t-1) xc = np.hstack((x, h_prev)) self.state.g = np.tanh(np.dot(self.param.wg, xc) + self.param.bg) self.state.i = sigmoid(np.dot(self.param.wi, xc) + self.param.bi) self.state.f = sigmoid(np.dot(self.param.wf, xc) + self.param.bf) self.state.o = sigmoid(np.dot(self.param.wo, xc) + self.param.bo) self.state.s = self.state.g * self.state.i + s_prev * self.state.f self.state.h = self.state.s * self.state.o self.x = x self.xc = xcbottom和top是两个方向，输入样本从底部输入，反向传导从顶部向底部传导，bottom_data_is是输入样本过程，把x和先前输入拼接成矩阵，用公式wx+b分别计算g、i、f、o值，激活函数tanh和sigmoid。
每个时序神经网络有四个神经网络层(激活函数)，最左边忘记门，直接生效到记忆C，第二个输入门，依赖输入样本数据，按照一定“比例”影响记忆C，“比例”通过第三个层(tanh)实现，取值范围是-1,1可以正向影响也可以负向影响，最后一个输出门，每一时序产生输出既依赖输入样本x和上一时序输出，还依赖记忆C，设计模仿生物神经元记忆功能。
def top_diff_is(self, top_diff_h, top_diff_s): # notice that top_diff_s is carried along the constant error carousel ds = self.state.o * top_diff_h + top_diff_s do = self.state.s * top_diff_h di = self.state.g * ds dg = self.state.i * ds df = self.s_prev * ds # diffs w.r.t. vector inside sigma / tanh function di_input = (1. - self.state.i) * self.state.i * di df_input = (1. - self.state.f) * self.state.f * df do_input = (1. - self.state.o) * self.state.o * do dg_input = (1. - self.state.g ** 2) * dg # diffs w.r.t. inputs self.param.wi_diff += np.outer(di_input, self.xc) self.param.wf_diff += np.outer(df_input, self.xc) self.param.wo_diff += np.outer(do_input, self.xc) self.param.wg_diff += np.outer(dg_input, self.xc) self.param.bi_diff += di_input self.param.bf_diff += df_input self.param.bo_diff += do_input self.param.bg_diff += dg_input # compute bottom diff dxc = np.zeros_like(self.xc) dxc += np.dot(self.param.wi.T, di_input) dxc += np.dot(self.param.wf.T, df_input) dxc += np.dot(self.param.wo.T, do_input) dxc += np.dot(self.param.wg.T, dg_input) # save bottom diffs self.state.bottom_diff_s = ds * self.state.f self.state.bottom_diff_x = dxc[:self.param.x_dim] self.state.bottom_diff_h = dxc[self.param.x_dim:]反向传导，整个训练过程核心。假设在t时刻lstm输出预测值h(t)，实际输出值是y(t)，之间差别是损失，假设损失函数为l(t) = f(h(t), y(t)) = ||h(t) - y(t)||^2，欧式距离，整体损失函数是L(t) = ∑l(t)，t从1到T，T表示整个事件序列最大长度。最终目标是用梯度下降法让L(t)最小化，找到一个最优权重w使得L(t)最小，当w发生微小变化L(t)不再变化，达到局部最优，即L对w偏导梯度为0。
dL/dw表示当w发生单位变化L变化多少，dh(t)/dw表示当w发生单位变化h(t)变化多少，dL/dh(t)表示当h(t)发生单位变化时L变化多少，(dL/dh(t)) * (dh(t)/dw)表示第t时序第i个记忆单元w发生单位变化L变化多少，把所有由1到M的i和所有由1到T的t累加是整体dL/dw。
第i个记忆单元，h(t)发生单位变化，整个从1到T时序所有局部损失l的累加和，是dL/dh(t)，h(t)只影响从t到T时序局部损失l。
假设L(t)表示从t到T损失和，L(t) = ∑l(s)。
h(t)对w导数。
L(t) = l(t) + L(t+1)，dL(t)/dh(t) = dl(t)/dh(t) + dL(t+1)/dh(t)，用下一时序导数得出当前时序导数，规律推导，计算T时刻导数往前推，在T时刻，dL(T)/dh(T) = dl(T)/dh(T)。
class LstmNetwork(): def __init__(self, lstm_param): self.lstm_param = lstm_param self.lstm_node_list = [] # input sequence self.x_list = [] def y_list_is(self, y_list, loss_layer): """ Updates diffs by setting target sequence with corresponding loss layer. Will *NOT* update parameters. To update parameters, call self.lstm_param.apply_diff() """ assert len(y_list) == len(self.x_list) idx = len(self.x_list) - 1 # first node only gets diffs from label ... loss = loss_layer.loss(self.lstm_node_list[idx].state.h, y_list[idx]) diff_h = loss_layer.bottom_diff(self.lstm_node_list[idx].state.h, y_list[idx]) # here s is not affecting loss due to h(t+1), hence we set equal to zero diff_s = np.zeros(self.lstm_param.mem_cell_ct) self.lstm_node_list[idx].top_diff_is(diff_h, diff_s) idx -= 1 ### ... following nodes also get diffs from next nodes, hence we add diffs to diff_h ### we also propagate error along constant error carousel using diff_s while idx >= 0: loss += loss_layer.loss(self.lstm_node_list[idx].state.h, y_list[idx]) diff_h = loss_layer.bottom_diff(self.lstm_node_list[idx].state.h, y_list[idx]) diff_h += self.lstm_node_list[idx + 1].state.bottom_diff_h diff_s = self.lstm_node_list[idx + 1].state.bottom_diff_s self.lstm_node_list[idx].top_diff_is(diff_h, diff_s) idx -= 1 return lossdiff_h(预测结果误差发生单位变化损失L多少，dL(t)/dh(t)数值计算)，由idx从T往前遍历到1，计算loss_layer.bottom_diff和下一个时序bottom_diff_h和作为diff_h(第一次遍历即T不加bottom_diff_h)。
loss_layer.bottom_diff：
def bottom_diff(self, pred, label): diff = np.zeros_like(pred) diff[0] = 2 * (pred[0] - label) return diffl(t) = f(h(t), y(t)) = ||h(t) - y(t)||^2导数l'(t) = 2 * (h(t) - y(t))
。当s(t)发生变化，L(t)变化来源s(t)影响h(t)和h(t+1)，影响L(t)。
h(t+1)不会影响l(t)。
左边式子(dL(t)/dh(t)) * (dh(t)/ds(t))，由t+1到t来逐级反推dL(t)/ds(t)。
神经元self.state.h = self.state.s self.state.o，h(t) = s(t) o(t)，dh(t)/ds(t) = o(t)，dL(t)/dh(t)是top_diff_h。
top_diff_is，Bottom means input to the layer, top means output of the layer. Caffe also uses this terminology. bottom表示神经网络层输入，top表示神经网络层输出，和caffe概念一致。
def top_diff_is(self, top_diff_h, top_diff_s):
top_diff_h表示当前t时序dL(t)/dh(t), top_diff_s表示t+1时序记忆单元dL(t)/ds(t)。
ds = self.state.o * top_diff_h + top_diff_s do = self.state.s * top_diff_h di = self.state.g * ds dg = self.state.i * ds df = self.s_prev * ds前缀d表达误差L对某一项导数(directive)。
ds是在根据公式dL(t)/ds(t)计算当前t时序dL(t)/ds(t)。
do是计算dL(t)/do(t)，h(t) = s(t) o(t)，dh(t)/do(t) = s(t)，dL(t)/do(t) = (dL(t)/dh(t)) (dh(t)/do(t)) = top_diff_h * s(t)。
di是计算dL(t)/di(t)。s(t) = f(t) s(t-1) + i(t) g(t)。dL(t)/di(t) = (dL(t)/ds(t)) (ds(t)/di(t)) = ds g(t)。
dg是计算dL(t)/dg(t)，dL(t)/dg(t) = (dL(t)/ds(t)) (ds(t)/dg(t)) = ds i(t)。
df是计算dL(t)/df(t)，dL(t)/df(t) = (dL(t)/ds(t)) (ds(t)/df(t)) = ds s(t-1)。
di_input = (1. - self.state.i) * self.state.i * di df_input = (1. - self.state.f) * self.state.f * df do_input = (1. - self.state.o) * self.state.o * do dg_input = (1. - self.state.g ** 2) * dgsigmoid函数导数，tanh函数导数。di_input，(1. - self.state.i) * self.state.i，sigmoid导数，当i神经元输入发生单位变化时输出值有多大变化，再乘di表示当i神经元输入发生单位变化时误差L(t)发生多大变化，dL(t)/d i_input(t)。
self.param.wi_diff += np.outer(di_input, self.xc) self.param.wf_diff += np.outer(df_input, self.xc) self.param.wo_diff += np.outer(do_input, self.xc) self.param.wg_diff += np.outer(dg_input, self.xc) self.param.bi_diff += di_input self.param.bf_diff += df_input self.param.bo_diff += do_input self.param.bg_diff += dg_inputw_diff是权重矩阵误差，b_diff是偏置误差，用于更新。
dxc = np.zeros_like(self.xc) dxc += np.dot(self.param.wi.T, di_input) dxc += np.dot(self.param.wf.T, df_input) dxc += np.dot(self.param.wo.T, do_input) dxc += np.dot(self.param.wg.T, dg_input)累加输入xdiff，x在四处起作用，四处diff加和后作xdiff。
self.state.bottom_diff_s = ds * self.state.f self.state.bottom_diff_x = dxc[:self.param.x_dim] self.state.bottom_diff_h = dxc[self.param.x_dim:]bottom_diff_s是在t-1时序上s变化和t时序上s变化时f倍关系。dxc是x和h横向合并矩阵，分别取两部分diff信息bottom_diff_x和bottom_diff_h。
def x_list_clear(self): self.x_list = [] def x_list_add(self, x): self.x_list.append(x) if len(self.x_list) > len(self.lstm_node_list): # need to add new lstm node, create new state mem lstm_state = LstmState(self.lstm_param.mem_cell_ct, self.lstm_param.x_dim) self.lstm_node_list.append(LstmNode(self.lstm_param, lstm_state)) # get index of most recent x input idx = len(self.x_list) - 1 if idx == 0: # no recurrent inputs yet self.lstm_node_list[idx].bottom_data_is(x) else: s_prev = self.lstm_node_list[idx - 1].state.s h_prev = self.lstm_node_list[idx - 1].state.h self.lstm_node_list[idx].bottom_data_is(x, s_prev, h_prev)添加训练样本，输入x数据。
def example_0(): # learns to repeat simple sequence from random inputs np.random.seed(0) # parameters for input data dimension and lstm cell count mem_cell_ct = 100 x_dim = 50 concat_len = x_dim + mem_cell_ct lstm_param = LstmParam(mem_cell_ct, x_dim) lstm_net = LstmNetwork(lstm_param) y_list = [-0.5,0.2,0.1, -0.5] input_val_arr = [np.random.random(x_dim) for _ in y_list] for cur_iter in range(100): print "cur iter: ", cur_iter for ind in range(len(y_list)): lstm_net.x_list_add(input_val_arr[ind]) print "y_pred[%d] : %f" % (ind, lstm_net.lstm_node_list[ind].state.h[0]) loss = lstm_net.y_list_is(y_list, ToyLossLayer) print "loss: ", loss lstm_param.apply_diff(lr=0.1) lstm_net.x_list_clear()初始化LstmParam，指定记忆存储单元数为100，指定输入样本x维度是50。初始化LstmNetwork训练模型，生成4组各50个随机数，分别以-0.5,0.2,0.1, -0.5作为y值训练，每次喂50个随机数和一个y值，迭代100次。
lstm输入一串连续质数预估下一个质数。小测试，生成100以内质数，循环拿出50个质数序列作x，第51个质数作y，拿出10个样本参与训练1w次，均方误差由0.17973最终达到了1.05172e-06，几乎完全正确：
import numpy as npimport sysfrom lstm import LstmParam, LstmNetworkclass ToyLossLayer: """ Computes square loss with first element of hidden layer array. """ @classmethod def loss(self, pred, label): return (pred[0] - label) ** 2 @classmethod def bottom_diff(self, pred, label): diff = np.zeros_like(pred) diff[0] = 2 * (pred[0] - label) return diffclass Primes: def __init__(self): self.primes = list() for i in range(2, 100): is_prime = True for j in range(2, i-1): if i % j == 0: is_prime = False if is_prime: self.primes.append(i) self.primes_count = len(self.primes) def get_sample(self, x_dim, y_dim, index): result = np.zeros((x_dim+y_dim)) for i in range(index, index + x_dim + y_dim): result[i-index] = self.primes[i%self.primes_count]/100.0 return resultdef example_0(): mem_cell_ct = 100 x_dim = 50 concat_len = x_dim + mem_cell_ct lstm_param = LstmParam(mem_cell_ct, x_dim) lstm_net = LstmNetwork(lstm_param) primes = Primes() x_list = [] y_list = [] for i in range(0, 10): sample = primes.get_sample(x_dim, 1, i) x = sample[0:x_dim] y = sample[x_dim:x_dim+1].tolist()[0] x_list.append(x) y_list.append(y) for cur_iter in range(10000): if cur_iter % 1000 == 0: print "y_list=", y_list for ind in range(len(y_list)): lstm_net.x_list_add(x_list[ind]) if cur_iter % 1000 == 0: print "y_pred[%d] : %f" % (ind, lstm_net.lstm_node_list[ind].state.h[0]) loss = lstm_net.y_list_is(y_list, ToyLossLayer) if cur_iter % 1000 == 0: print "loss: ", loss lstm_param.apply_diff(lr=0.01) lstm_net.x_list_clear()if __name__ == "__main__": example_0()质数列表全都除以100，这个代码训练数据必须是小于1数值。
torch是深度学习框架。1）tensorflow，谷歌主推，时下最火，小型试验和大型计算都可以，基于python，缺点是上手相对较难，速度一般；2）torch，facebook主推，用于小型试验，开源应用较多，基于lua，上手较快，网上文档较全，缺点是lua语言相对冷门；3）mxnet，Amazon主推，主要用于大型计算，基于python和R，缺点是网上开源项目较少；4）caffe，facebook主推，用于大型计算，基于c++、python，缺点是开发不是很方便；5）theano，速度一般，基于python，评价很好。
torch github上lstm实现项目比较多。
在mac上安装torch。https://github.com/torch/torch7/wiki/Cheatsheet#installing-and-running-torch 。
git clone https://github.com/torch/distro.git ~/torch --recursivecd ~/torch; bash install-deps;./install.shqt安装不成功问题，自己单独安装。
brew install cartr/qt4/qt安装后需要手工加到~/.bash_profile中。
. ~/torch/install/bin/torch-activatesource ~/.bash_profile后执行th使用torch。
安装itorch，安装依赖
brew install zeromqbrew install opensslluarocks install luacrypto OPENSSL_DIR=/usr/local/opt/openssl/git clone https://github.com/facebook/iTorch.gitcd iTorchluarocks make 用卷积神经网络实现图像识别。
创建pattern_recognition.lua：
require 'nn'require 'paths'if (not paths.filep("cifar10torchsmall.zip")) then os.execute('wget -c https://s3.amazonaws.com/torch7/data/cifar10torchsmall.zip') os.execute('unzip cifar10torchsmall.zip')endtrainset = torch.load('cifar10-train.t7')testset = torch.load('cifar10-test.t7')classes = {'airplane', 'automobile', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck'}setmetatable(trainset,{__index = function(t, i) return {t.data[i], t.label[i]}end});trainset.data = trainset.data:double() -- convert the data from a ByteTensor to a DoubleTensor.function trainset:size() return self.data:size(1)endmean = {} -- store the mean, to normalize the test set in the futurestdv = {} -- store the standard-deviation for the futurefor i=1,3 do -- over each image channel mean[i] = trainset.data[{ {}, {i}, {}, {} }]:mean() -- mean estimation print('Channel ' .. i .. ', Mean: ' .. mean[i]) trainset.data[{ {}, {i}, {}, {} }]:add(-mean[i]) -- mean subtraction stdv[i] = trainset.data[{ {}, {i}, {}, {} }]:std() -- std estimation print('Channel ' .. i .. ', Standard Deviation: ' .. stdv[i]) trainset.data[{ {}, {i}, {}, {} }]:div(stdv[i]) -- std scalingendnet = nn.Sequential()net:add(nn.SpatialConvolution(3, 6, 5, 5)) -- 3 input image channels, 6 output channels, 5x5 convolution kernelnet:add(nn.ReLU()) -- non-linearitynet:add(nn.SpatialMaxPooling(2,2,2,2)) -- A max-pooling operation that looks at 2x2 windows and finds the max.net:add(nn.SpatialConvolution(6, 16, 5, 5))net:add(nn.ReLU()) -- non-linearitynet:add(nn.SpatialMaxPooling(2,2,2,2))net:add(nn.View(16*5*5)) -- reshapes from a 3D tensor of 16x5x5 into 1D tensor of 16*5*5net:add(nn.Linear(16*5*5, 120)) -- fully connected layer (matrix multiplication between input and weights)net:add(nn.ReLU()) -- non-linearitynet:add(nn.Linear(120, 84))net:add(nn.ReLU()) -- non-linearitynet:add(nn.Linear(84, 10)) -- 10 is the number of outputs of the network (in this case, 10 digits)net:add(nn.LogSoftMax()) -- converts the output to a log-probability. Useful for classification problemscriterion = nn.ClassNLLCriterion()trainer = nn.StochasticGradient(net, criterion)trainer.learningRate = 0.001trainer.maxIteration = 5trainer:train(trainset)testset.data = testset.data:double() -- convert from Byte tensor to Double tensorfor i=1,3 do -- over each image channel testset.data[{ {}, {i}, {}, {} }]:add(-mean[i]) -- mean subtraction testset.data[{ {}, {i}, {}, {} }]:div(stdv[i]) -- std scalingendpredicted = net:forward(testset.data[100])print(classes[testset.label[100]])print(predicted:exp())for i=1,predicted:size(1) do print(classes[i], predicted[i])endcorrect = 0for i=1,10000 do local groundtruth = testset.label[i] local prediction = net:forward(testset.data[i]) local confidences, indices = torch.sort(prediction, true) -- true means sort in descending order if groundtruth == indices[1] then correct = correct + 1 endendprint(correct, 100*correct/10000 .. ' % ')class_performance = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}for i=1,10000 do local groundtruth = testset.label[i] local prediction = net:forward(testset.data[i]) local confidences, indices = torch.sort(prediction, true) -- true means sort in descending order if groundtruth == indices[1] then class_performance[groundtruth] = class_performance[groundtruth] + 1 endendfor i=1,#classes do print(classes[i], 100*class_performance[i]/1000 .. ' %')end执行th pattern_recognition.lua。
首先下载cifar10torchsmall.zip样本，有50000张训练用图片，10000张测试用图片，分别都标注，包括airplane、automobile等10种分类，对trainset绑定__index和size方法，兼容nn.Sequential使用，绑定函数看lua教程：http://tylerneylon.com/a/learn-lua/ ,trainset数据正规化，数据转成均值为1方差为1的double类型张量。初始化卷积神经网络模型，包括两层卷积、两层池化、一个全连接以及一个softmax层，进行训练，学习率为0.001，迭代5次，模型训练好后对测试机第100号图片做预测，打印出整体正确率以及每种分类准确率。https://github.com/soumith/cvpr2015/blob/master/Deep%20Learning%20with%20Torch.ipynb 。
torch可以方便支持gpu计算，需要对代码做修改。
比较流行的seq2seq基本都用lstm组成编码器解码器模型实现，开源实现大都基于one-hot embedding(没有词向量表达信息量大)。word2vec词向量 seq2seq模型，只有一个lstm单元机器人。
下载《甄环传》小说原文。上网随便百度“甄环传 txt”，下载下来，把文件转码成utf-8编码，把windows回车符都替换成n，以便后续处理。
对甄环传切词。切词工具word_segment.py到github下载，地址在https://github.com/warmheartli/ChatBotCourse/blob/master/word_segment.py 。
python ./word_segment.py zhenhuanzhuan.txt zhenhuanzhuan.segment生成词向量。用word2vec，word2vec源码 https://github.com/warmheartli/ChatBotCourse/tree/master/word2vec 。make编译即可执行。
./word2vec -train ./zhenhuanzhuan.segment -output vectors.bin -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -iter 15生成一个vectors.bin文件，基于甄环传原文生成的词向量文件。
训练代码。
# -*- coding: utf-8 -*-import sysimport mathimport tflearnimport chardetimport numpy as npimport structseq = []max_w = 50float_size = 4word_vector_dict = {}def load_vectors(input): """从vectors.bin加载词向量，返回一个word_vector_dict的词典，key是词，value是200维的向量 """ print "begin load vectors" input_file = open(input, "rb") # 获取词表数目及向量维度 words_and_size = input_file.readline() words_and_size = words_and_size.strip() words = long(words_and_size.split(' ')[0]) size = long(words_and_size.split(' ')[1]) print "words =", words print "size =", size for b in range(0, words): a = 0 word = '' # 读取一个词 while True: c = input_file.read(1) word = word + c if False == c or c == ' ': break if a < max_w and c != 'n': a = a + 1 word = word.strip() vector = [] for index in range(0, size): m = input_file.read(float_size) (weight,) = struct.unpack('f', m) vector.append(weight) # 将词及其对应的向量存到dict中 word_vector_dict[word.decode('utf-8')] = vector input_file.close() print "load vectors finish"def init_seq(): """读取切好词的文本文件，加载全部词序列 """ file_object = open('zhenhuanzhuan.segment', 'r') vocab_dict = {} while True: line = file_object.readline() if line: for word in line.decode('utf-8').split(' '): if word_vector_dict.has_key(word): seq.append(word_vector_dict[word]) else: break file_object.close()def vector_sqrtlen(vector): len = 0 for item in vector: len += item * item len = math.sqrt(len) return lendef vector_cosine(v1, v2): if len(v1) != len(v2): sys.exit(1) sqrtlen1 = vector_sqrtlen(v1) sqrtlen2 = vector_sqrtlen(v2) value = 0 for item1, item2 in zip(v1, v2): value += item1 * item2 return value / (sqrtlen1*sqrtlen2)def vector2word(vector): max_cos = -10000 match_word = '' for word in word_vector_dict: v = word_vector_dict[word] cosine = vector_cosine(vector, v) if cosine > max_cos: max_cos = cosine match_word = word return (match_word, max_cos)def main(): load_vectors("./vectors.bin") init_seq() xlist = [] ylist = [] test_X = None #for i in range(len(seq)-100): for i in range(10): sequence = seq[i:i+20] xlist.append(sequence) ylist.append(seq[i+20]) if test_X is None: test_X = np.array(sequence) (match_word, max_cos) = vector2word(seq[i+20]) print "right answer=", match_word, max_cos X = np.array(xlist) Y = np.array(ylist) net = tflearn.input_data([None, 20, 200]) net = tflearn.lstm(net, 200) net = tflearn.fully_connected(net, 200, activation='linear') net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='mean_square') model = tflearn.DNN(net) model.fit(X, Y, n_epoch=500, batch_size=10,snapshot_epoch=False,show_metric=True) model.save("model") predict = model.predict([test_X]) #print predict #for v in test_X: # print vector2word(v) (match_word, max_cos) = vector2word(predict[0]) print "predict=", match_word, max_cosmain()load_vectors从vectors.bin加载词向量，init_seq加载甄环传切词文本并存到一个序列里，vector2word求距离某向量最近词，模型只有一个lstm单元。
经过500个epoch训练，均方损失降到0.33673，以0.941794432002余弦相似度预测出下一个字。
强大gpu，调整参数，整篇文章都训练，修改代码predict部分，不断输出下一个字，自动吐出甄环体。基于tflearn实现，tflearn官方文档examples实现seq2seq直接调用tensorflow中的tensorflow/python/ops/seq2seq.py，基于one-hot embedding方法，一定没有词向量效果好。
参考资料：
《Python 自然语言处理》
http://www.shareditor.com/blogshow?blogId=116
http://www.shareditor.com/blogshow?blogId=117
http://www.shareditor.com/blogshow?blogId=118
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原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。