-
Notifications
You must be signed in to change notification settings - Fork 0
/
RESNET.py
124 lines (102 loc) · 4.9 KB
/
RESNET.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
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
import data_utils
import model_utils
import plot_utils
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import sys
import os
import tensorflow as tf
import numpy as np
import random
from tensorflow import keras
os.environ['PYTHONHASHSEED'] = str(76)
random.seed(45)
np.random.seed(1)
tf.random.set_seed(346)
model_name = sys.argv[1]
#model_name = "resnet_3s3DH"
print(model_name)
div = model_name.split("_")[1]
if div[0:2] == "3D" or div[0:2] == "2D":
data_location = "../data/data_split/"
if div[0:2] == "n3" or div[0:2] == "n2":
data_location = "../data/data_split_n/"
if div[0:2] == "3s":
data_location = "../data/data_split_3s/"
print(data_location)
#data_location = "../data/data_split_n/"
if div[-3:] == "3DH":
ddhs = 0
if div[-3:] == "2DH":
ddhs = 1
train, test, train_targ, test_targ, train_ID, test_ID = data_utils.read_data_folders(data_location, 10000, 2000, ddh = ddhs)
train = train.reshape(len(train), 51, 51, 3)
test = test.reshape(len(test), 51, 51, 3)
print(train.shape, test.shape)
def premade(model_name, train, test, train_targ, test_targ, checkpoints=0):
random.seed(45)
np.random.seed(1)
tf.random.set_seed(346)
# premade = keras.applications.EfficientNetB7(include_top = False, weights = None)
# layer = keras.layers.ZeroPadding2D((197-51, 197-51), input_shape=(51, 51, 3))
premade = keras.applications.ResNet50V2(include_top = False, weights = None, input_shape=(51, 51, 3))
#premade = keras.applications.VGG16(include_top = False, weights = None, input_shape=(51, 51, 3))
layer7 = keras.layers.Dropout(0.4)
layer8 = keras.layers.Flatten()
layer9 = keras.layers.Dense(32, activation="relu")
layer10 = keras.layers.Dense(2, activation="softmax")
layers = [premade, layer7, layer8, layer9, layer10]
model = keras.Sequential(layers)
opt = keras.optimizers.SGD(learning_rate=0.01)
model.compile(optimizer=opt, loss="sparse_categorical_crossentropy", metrics=["accuracy"])
filepath = "../outputs/models/model_premade_%s.h5"%model_name
print(train.shape, test.shape)
print('unique test: {}'.format(np.unique(test_targ, return_counts=True)))
feat_te2 = test.copy()
feat_tr2 = train.copy()
model.summary()
# -- define the checkpoint
checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1,save_best_only=True, mode='max')
earlyst = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=0, patience=100)
# -- fit the model
if checkpoints == 0:
history = model.fit(feat_tr2, train_targ, validation_split=0.20, epochs=650, batch_size = 200,
callbacks=[checkpoint, earlyst])
# -- print the accuracy
loss_tr, acc_tr = model.evaluate(feat_tr2, train_targ, verbose=0)
loss_te, acc_te = model.evaluate(feat_te2, test_targ, verbose=0)
print("Training accuracy : {0:.4f}".format(acc_tr))
print("Testing accuracy : {0:.4f}".format(acc_te))
pd.DataFrame(history.history["loss"]).to_csv("trainc_loss%s"%model_name)
pd.DataFrame(history.history["val_loss"]).to_csv("testc_loss%s"%model_name)
pd.DataFrame(history.history["accuracy"]).to_csv("trainc_acc%s"%model_name)
pd.DataFrame(history.history["val_accuracy"]).to_csv("testc_acc%s"%model_name)
fig, ax = plt.subplots(2,1,figsize=(8, 8))
ax[0].plot(history.history["loss"], color = "#00441b")
ax[0].plot(history.history["val_loss"], color = "#40004b")
ax[0].legend(["train", "validation"], loc="upper right")
#ax[0].set_xlabel("epoch", fontsize=15)
ax[0].set_xticks([])
ax[0].set_ylabel("Loss", fontsize=15)
ax[1].plot(history.history["accuracy"], color = "#00441b")
ax[1].plot(history.history["val_accuracy"], color = "#40004b")
ax[1].legend(["train", "validation"], loc="lower right")
ax[1].set_xlabel("Epoch", fontsize=15)
ax[1].set_ylabel("Accuracy", fontsize=15)
if checkpoints == 0:
plt.savefig("../outputs/lossacc_model%s.pdf"%model_name,bbox_inches="tight")
else:
plt.savefig("../outputs/lossacc_model%s_checkpoint.pdf"%model_name, bbox_inches="tight")
y_pred_test = model_utils.predict_data(test, model, 0, test_ID,test_targ)
if checkpoints == 0:
plot_utils.create_confusion_matrix("test_%s"%model_name, y_pred_test, test_targ)
else:
plot_utils.create_confusion_matrix("test_%s_checkpoint"%model_name, y_pred_test, test_targ)
y_pred_train = model_utils.predict_data(train, model, 0, train_ID, train_targ)
if checkpoints == 0:
plot_utils.create_confusion_matrix("train_%s"%model_name, y_pred_train, train_targ)
else:
plot_utils.create_confusion_matrix("train_%s_checkpoint"%model_name, y_pred_train, train_targ)
return train, test, train_targ, test_targ, train_ID, test_ID
train, test, train_targ, test_targ, train_ID, test_ID = premade(model_name, train, test, train_targ, test_targ, checkpoints=0)