import numpy as np
import matplotlib.pyplot as plt
import sys
server = None
net = Neural()
x = np.linspace(0, 4, 1000)
y = np.sin(x)
x_test = np.linspace(4, 8, 100)
x_test = y.reshape(y.shape[0], 1)
x = y.reshape(y.shape[0], 1)
y = x.reshape(x.shape[0], 1)
net.setparam(n_input=1, n_hidden=10, n_output=1, lr=0.00001)
net.init_weight()
net.train(x, y, 200)
net.accuracy()
cmd = sys.argv[1:]
if cmd:
if cmd[0] == 'server' or 'serve':
plt.plot(y)
plt.plot([net.predict(i) for i in x_test])
plt.show()
PRINT_FROM = 10
PRINT_TO = 50
STEP_SHOW = 3
RHO = 0.9
EPSILON = 1e-4
STEP = 50
print "test"
trX, teX = load.loadMel("afterLDA100.db")
# trY, teY = load.loadRes()
trZ, teZ = load.loadLeter()
print "loaded"
neur = Neural()
# neur.load_from_file(WEIGHTS_FILE)
neur.init_model(ARR_SHAPE)
neur.train(trX, teX, trZ, teZ, epochs=TIMES)
neur.save_to_file(WEIGHTS_FILE)
# trT = neur.result(trX)
# teT = neur.result(teX)
# print "start train"
# neur2 = Neural()
# neur2.init_model([(55, 300), (300, 28)])
# neur2.train(trT, teT, trZ, teZ, epochs=TIMES)
# neur = Neural()
# neur.load_from_file(DIRECTORY_PATH + WEIGHTS_FILE)
# mel, res = procces_file(FILE)
# print res
def run(self):
filename = self.file_path_label["text"]
if (filename == ""):
print("未選取檔案")
tk.messagebox.showinfo("Error", "未選取資料集")
return
df = pd.read_table(filename, sep=" ", header=None)
# 檢查是否二類問題
df_label = np.split(df, [len(df.columns) - 1], axis=1)[1]
if (len(df_label.groupby(len(df.columns) - 1).groups) != 2):
print("非二類問題")
tk.messagebox.showinfo("Error", "資料集非二類問題")
return
# 非二類問題
# label非0/1組合 改變label-> 0~1
if (0 not in df_label.groupby(len(df.columns) - 1).groups) or (
1 not in df_label.groupby(len(df.columns) - 1).groups):
df[len(df.columns) - 1] = df[len(df.columns) - 1] % 2
# split traning data and testing data
train_df = df.sample(frac=0.666666)
test_df = df.drop(train_df.index)
train_dfs = np.split(train_df, [len(train_df.columns) - 1], axis=1)
train_X_df = train_dfs[0]
train_y_df = train_dfs[1]
train_X = train_X_df.values.tolist()
train_y = train_y_df.values.reshape(-1, ).tolist()
test_dfs = np.split(test_df, [len(test_df.columns) - 1], axis=1)
test_X_df = test_dfs[0]
test_y_df = test_dfs[1]
test_X = test_X_df.values.tolist()
test_y = test_y_df.values.reshape(-1, ).tolist()
#learning_rate = 0.8
learning_rate = self.learning_rate.get()
# run training and show result
n = Neural(train_X, train_y, learning_rate)
train_result_list = []
print("### training start ###")
for i in range(int(self.epoch_spinbox.get())):
self.training_epoch_text_label["text"] = i + 1
train_result = n.train()
train_result_list.append(train_result)
if train_result["acc"] > float(self.early_stop_spinbox.get()):
break
print("### training end ###")
self.draw_training_acc_figure(train_result_list)
self.training_acc_text_label["text"] = train_result_list[
len(train_result_list) - 1]["acc"]
self.bias_text_label["text"] = train_result_list[len(train_result_list)
- 1]["bias"]
self.weight_text.delete(1.0, END)
self.weight_text.insert(
1.0, train_result_list[len(train_result_list) - 1]["weight"])
# run testing and show result
print("### predict start ###")
test_result = n.test(test_X, test_y)
print("### predict end ###")
self.testing_acc_text_label["text"] = test_result["acc"]
# draw training data and predict line
self.draw_training_data_figure(
df, test_df, train_result_list[len(train_result_list) - 1])
from neural import Neural from sklearn.datasets import load_iris net = Neural() data = load_iris() x, y = data.data, data.target net.setparam(n_input=4, n_hidden=41, n_output=1, lr=0.000001) net.init_weight() net.train(x, y.reshape(y.shape[0], 1), 20000) net.accuracy()
