嘗試1維卷積網絡運用於光譜近紅外分析，可能是樣本數太少，目前測試結果不是很理想。樣本數據：https://pan.baidu.com/s/1IuMSPOVmSD26IFgf2pCDqg 第一列是要擬合的水分含量， 後50列爲光譜數據。
import pathlib
import sys
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
from keras.layers import Reshape
from keras.layers import Embedding, Conv1D,MaxPooling1D,GlobalAveragePooling1D,Dense
from keras.models import Sequential
from keras.layers import Dropout
from matplotlib import rcParams
from keras.layers.normalization import BatchNormalization
column_names = ['water','1','2','3','4','5', '6', '7','8','9','10','11','12','13','14','15', '16', '17','18','19','20','21','22','23','24','25', '26', '27','28','29','30',
'31','32','33','34','35', '36', '37','38','39','40','41','42','43','44','45', '46', '47','48','49','50']
raw_dataset = pd.read_csv('./water.csv',names=column_names, sep=',',header = None, encoding='gkb')
dataset = raw_dataset.copy()
# 查看前3條數據
print(dataset.head(3))
dataset.isna().sum()
dataset = dataset.dropna()
train_dataset = dataset.sample(frac=0.8, random_state=22)
print("train:")
print(train_dataset)
test_dataset = dataset.drop(train_dataset.index)
print("test:")
print(test_dataset)
# 解決中文亂碼問題
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
train_stats = train_dataset.describe()
print(train_stats)
train_stats.pop("water")
train_stats = train_stats.transpose()
print(train_stats)
train_labels = train_dataset.pop('water')
print(train_labels)
test_labels = test_dataset.pop('water')
print("water:")
print(test_labels)
def norm(x):
return (x - train_stats['mean']) / train_stats['std']
normed_train_data = norm(train_dataset)
normed_test_data = norm(test_dataset)
#def build_model(): 全連接神經網絡
# input_dim = len(train_dataset.keys())
# model = keras.Sequential([
# layers.Dense(64, activation='relu', input_shape=[input_dim,]),
# layers.Dense(64, activation='relu'),
# layers.Dense(1)
# ])
# model.compile(loss='mse', metrics=['mae', 'mse'],
# optimizer=tf.keras.optimizers.RMSprop(0.001))
# return model
def build_model(): //1D卷積神經網絡
input_dim = len(train_dataset.keys())
model = Sequential()
model.add(Conv1D(10, 7, activation='relu',input_shape=[input_dim,1]))
model.add(MaxPooling1D(2))
model.add(Conv1D(6, 7, activation='relu'))
model.add(MaxPooling1D(2))
model.add(GlobalAveragePooling1D())
model.add(Dense(1,activation='linear'))
model.compile(loss='mse', metrics=['mae', 'mse'],
optimizer='adam')
return model
model = build_model()
# 打印模型的描述信息，每一層的大小、參數個數等
model.summary()
EPOCHS = 100
class ProgressBar(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs):
# 顯示進度條
self.draw_progress_bar(epoch + 1, EPOCHS)
def draw_progress_bar(self, cur, total, bar_len=50):
cur_len = int(cur / total * bar_len)
sys.stdout.write("\r")
sys.stdout.write("[{:<{}}] {}/{}".format("=" * cur_len, bar_len, cur, total))
sys.stdout.flush()
#early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)
history = model.fit(
np.expand_dims(normed_train_data,2), train_labels,
epochs=EPOCHS, validation_split=0.1, verbose=0,batch_size=4,shuffle=True)
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
hist.tail(3)
print(hist)
def plot_history(history):
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
plt.figure()
plt.xlabel('epoch')
plt.ylabel('metric - MSE')
plt.plot(hist['epoch'], hist['mse'], label='train')
plt.plot(hist['epoch'], hist['val_mse'], label = 'test')
plt.ylim([0, 500])
plt.legend()
plt.figure()
plt.xlabel('epoch')
plt.ylabel('metric - MAE')
plt.plot(hist['epoch'], hist['mae'], label='train')
plt.plot(hist['epoch'], hist['val_mae'], label = 'test')
plt.ylim([0, 50])
plt.legend()
plot_history(history)
loss, mae, mse = model.evaluate(np.expand_dims(normed_test_data,2), test_labels, verbose=0)
print("平均絕對誤差(MAE): {:5.2f} ".format(mae))
test_pred = model.predict(np.expand_dims(normed_test_data,2)).flatten()
print("!!!!!!!!!!!!!!!!!!")
print(test_labels)
print("~~~~~~~~~~~~~~~~~~")
print(test_pred)
plt.figure()
plt.scatter(test_labels, test_pred)
plt.xlabel('真實值')
plt.ylabel('預測值')
plt.axis('equal')
plt.axis('square')
plt.xlim([50,70])
plt.ylim([50,70])
plt.plot([-100, 100], [-100, 100])
rcParams['font.sans-serif'] = 'SimHei'
fig = plt.figure(figsize=(10,6))
plt.plot(range(test_labels.shape[0]),test_labels,color="blue", linewidth=1.5, linestyle="-")
plt.plot(range(test_labels.shape[0]),test_pred,color="red", linewidth=1.5, linestyle="-")
plt.xlim((0,200))
plt.ylim((50,70))
plt.legend(['real','pred'])
plt.show()
運行結果如下：
