-
Notifications
You must be signed in to change notification settings - Fork 1
/
img_ANN.py
54 lines (45 loc) · 1.47 KB
/
img_ANN.py
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
51
52
53
import numpy as np
import pandas as pd
import tensorflow
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from numpy import save
df=pd.read_csv('data.csv')
Y=df['Target_variable'].values
X=df.drop(['Re','Rs','Pd','PS','Target_variable'], axis=1)
print(X)
X=X.values
X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=.2,random_state=101)
scaler=StandardScaler()
X_train=scaler.fit_transform(X_train)
X_test=scaler.transform(X_test)
pca=PCA()
pca.fit(X_train)
X_train=pca.transform(X_train)
X_test=pca.transform(X_test)
print(pca.explained_variance_ratio_)
save("testing_data_final",X_test)
model=Sequential()
model.add(Dense(25,activation='relu'))
model.add(Dense(15,activation='relu'))
model.add(Dense(9,activation='relu'))
model.add(Dense(6,activation='relu'))
model.add(Dense(1, activation='linear'))
opt = keras.optimizers.Adam(learning_rate=0.0005)
model.compile(optimizer=opt,loss='mse')
model.fit(x=X_train,y=y_train,validation_data=(X_test,y_test),epochs=200)
#soln=model.predict(X_test)
#print(soln)
#plt.scatter(x=soln,y=y_test)
#plt.xlabel("Predicted Target")
#plt.ylabel("Actual Target")
#plt.show()
save("testing_data_finalANN",X_test)
save("target_data_finalANN",y_test)
model.save("modelANN_final.h5")
print("Saved model to disk")