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lstm_predict.py
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lstm_predict.py
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# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from lstm import LstmParam, LstmNetwork
class 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 diff
def example_0():
np.random.seed(0)
mem_cell_ct = 100
x_dim = 1
lstm_param = LstmParam(mem_cell_ct, x_dim)
lstm_net = LstmNetwork(lstm_param)
"""
y_list = [-0.5, 0.2, 0.1, -0.5] # 五步
input_val_arr = [np.random.random(x_dim) for _ in y_list]
for i in range(len(input_val_arr)):
print "input_val_arr = ", input_val_arr[i]
"""
input_val_arr = [
[1, 2],
[2, 3],
[3, 4]
]
pre_x = [
[3, 4]
]
y_list = [0.03, 0.05, 0.07]
x = np.arange(-1, 1, 0.01)
xa = []
for i in range(len(x)):
xa.append([x[i]])
# y = 2 * np.sin(x * 2.3) + 0.5 * x ** 3
# y1 = y + 0.5 * (np.random.rand(len(x)) - 0.5)
y = ((x * x - 1) ** 3 + 1) * (np.cos(x * 2) + 0.6 * np.sin(x * 1.3))
y_list = y + (np.random.rand(len(x)) - 0.5)
# print "input_val_arr = ", input_val_arr
for cur_iter in range(len(x)):
for ind in range(len(y_list)):
lstm_net.x_list_add(xa[ind])
y_pred = [0] * len(y_list)
for i in range(len(y_list)):
y_pred[i] = lstm_net.lstm_node_list[i].state.h[0]
loss = lstm_net.y_list_is(y_list, ToyLossLayer)
# print("loss:", "%.3e" % loss)
lstm_param.apply_diff(lr=0.1) # 更新权重
lstm_net.x_list_clear()
plt.plot(xa, y_list)
plt.plot(xa, y_pred)
plt.show()
print "loss = ", loss
print "y_pred = ", y_pred
def test(data_x, day_flaovr_num):
np.random.seed(0)
mem_cell_ct = 100
x_dim = 7 # 7天一个维度
lstm_param = LstmParam(mem_cell_ct, x_dim)
lstm_net = LstmNetwork(lstm_param)
"""
input = []
y_list = []
# 处理数,将数据处理成 7天一段,逐天滚动
for i in range(len(data_x) - 7):
input.append(data_x[i: i+7])
# y_list.append(day_flaovr_num[i + 7][0])
for i in range(len(data_x) - 7):
y_list.append(day_flaovr_num[i + 7])
# y_list = [-0.5, 0.2, 0.1, -0.5] # 五步
# input_val_arr = [np.random.random(x_dim) for _ in y_list]
for i in range(len(input)):
print "input_val_arr = ", input[i]
for i in range(len(y_list)):
print "y_list =", y_list[i]
print "len(input) = ", len(input)
print "len(y_list) = ", len(y_list)
"""
input_val_arr = [
[1, 2, 3, 4, 5, 6, 7],
[2, 3, 4, 5, 6, 7, 8]
[3, 4, 5, 6, 7, 8, 9],
]
y_list = [1, 2, 3]
for cur_iter in range(100):
# print("iter", "%2s" % str(cur_iter), end=": ")
print "str(cur_iter) = ", str(cur_iter)
for ind in range(len(y_list)):
lstm_net.x_list_add(input_val_arr[ind])
# print("y_pred = [" +
# ", ".join(["% 2.5f" % lstm_net.lstm_node_list[ind].state.h[0] for ind in range(len(y_list))]) +
# "]", end=", ")
for i in range(len(y_list)):
print "y_pred = ", lstm_net.lstm_node_list[i].state.h[0]
loss = lstm_net.y_list_is(y_list, ToyLossLayer)
# print("loss:", "%.3e" % loss)
print "loss = ", loss
lstm_param.apply_diff(lr=0.1) # 更新权重
lstm_net.x_list_clear()
if __name__ == "__main__":
example_0()