-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
215 lines (183 loc) · 7.98 KB
/
train.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
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
from datetime import datetime
import itertools
import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
import os
import pandas as pd
from def_lib import load_data
train_path = './data/train_5f.csv'
test_path = './data/test_5f.csv'
# Load the train data
X, y, class_names = load_data(train_path)
# Split training data (X, y) into (X_train, y_train) and (X_val, y_val)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.15, random_state=42)
# Load the test data
X_test, y_test, _ = load_data(test_path)
# Pre-process data
X_train = np.reshape(X_train,(X_train.shape[0],X_train.shape[3],X_train.shape[1]))
X_val = np.reshape(X_val,(X_val.shape[0],X_val.shape[3],X_val.shape[1]))
X_test = np.reshape(X_test,(X_test.shape[0],X_test.shape[3],X_test.shape[1]))
print( "X_train:", X_train.shape)
print( "y_train:", y_train.shape)
print( "\nX_val:", X_val.shape)
print( "y_val:", y_val.shape)
print( "\nX_test:", X_test.shape)
print( "y_test:", y_test.shape)
print( "\nclass_names:", class_names)
# Define the model
def LSTM():
inputs = tf.keras.Input(shape=(34, 5))
layer = keras.layers.LSTM(
32, activation=tf.nn.relu6, return_sequences=True)(inputs)
layer = keras.layers.Dropout(0.2)(layer)
layer = keras.layers.LSTM(32, activation=tf.nn.relu6)(layer)
layer = keras.layers.Dropout(0.2)(layer)
layer = keras.layers.Dense(16, activation=tf.nn.relu6)(layer)
layer = keras.layers.Dropout(0.2)(layer)
outputs = keras.layers.Dense(len(class_names), activation="softmax")(layer)
model = keras.Model(inputs, outputs)
model.summary()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
# def LSTM():
# inputs = tf.keras.Input(shape=(34, 5))
# layer = keras.layers.LSTM(
# 32, activation=tf.nn.relu6, return_sequences=True)(inputs)
# layer = keras.layers.Dropout(0.2)(layer)
# layer = keras.layers.LSTM(40, activation=tf.nn.relu6)(layer)
# layer = keras.layers.Dropout(0.2)(layer)
# layer = keras.layers.Dense(20, activation=tf.nn.relu6)(layer)
# layer = keras.layers.Dropout(0.2)(layer)
# outputs = keras.layers.Dense(len(class_names), activation="softmax")(layer)
# model = keras.Model(inputs, outputs)
# model.summary()
# model.compile(optimizer='adam',
# loss='categorical_crossentropy',
# metrics=['accuracy'])
# return model
#@title Make Model
model = LSTM()
# write Classification_report and Confusion_matrix to file
def plot_confusion_matrix(plot_confusion_matrix_path,cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""Plots the confusion matrix."""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=55)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
fig.savefig(plot_confusion_matrix_path)
plt.close()
result_path = './results/'
model_path = os.path.join(result_path,'model_fall.h5')
checkpoint_path = os.path.join(result_path,"weights.best.hdf5")
checkpoint = keras.callbacks.ModelCheckpoint(checkpoint_path,
monitor='val_accuracy',
verbose=1,
save_best_only=True,
mode='max')
earlystopping = keras.callbacks.EarlyStopping(monitor='val_accuracy',
patience=20)
# Start training
print('---------------------------------- TRAINING -------------------------------------------')
start = datetime.now()
history = model.fit(X_train, y_train,
epochs=100,
batch_size=64,
validation_data=(X_val, y_val),
callbacks=[checkpoint, earlystopping])
# Save model
model.save(model_path)
print('--------------------------------- EVALUATION -----------------------------------------')
loss_test, accuracy_test = model.evaluate(X_test, y_test)
print('LOSS TEST: ', loss_test)
print("ACCURACY TEST: ", accuracy_test)
loss_train, accuracy_train = model.evaluate(X_train, y_train)
duration = datetime.now() - start
print('LOSS TRAIN: ', loss_train)
print("ACCURACY TRAIN: ", accuracy_train)
print('TIME COMPLETED: ', duration)
data_eval = 'LOSS TEST: ' + str(loss_test) + ' / ACCURACY TEST: ' + str(accuracy_test) \
+ '\n' + 'LOSS TRAIN: ' + str(loss_train) + ' / ACCURACY TRAIN: ' + str(accuracy_train) \
+ '\n' + 'TIME COMPLETED: ' + str(duration)
'''--------------------------------------- STATISTC -------------------------------------------'''
hist_df = pd.DataFrame(history.history)
name_history = 'history.csv'
path_history = os.path.join(result_path , name_history)
with open(path_history, mode='w') as f:
hist_df.to_csv(f)
eval_path = 'Evaluation_LSTM_5f.txt'
path_s = os.path.join(result_path , eval_path)
with open(path_s, mode='w') as f:
f.writelines(data_eval)
f.close()
model_path_load = os.path.join(result_path,'model_fall.h5')
plot_confusion_matrix_nor_path = os.path.join(result_path,'confusion_matrix_nor.png')
plot_confusion_matrix_path = os.path.join(result_path,'confusion_matrix.png')
model = tf.keras.models.load_model(model_path_load)
y_pred = model.predict(X_test)
# Convert the prediction result to class name
y_pred_label = [class_names[i] for i in np.argmax(y_pred, axis=1)]
y_true_label = [class_names[i] for i in np.argmax(y_test, axis=1)]
# Plot the confusion matrix
cm = confusion_matrix(np.argmax(y_test, axis=1), np.argmax(y_pred, axis=1))
plot_confusion_matrix(plot_confusion_matrix_path,cm,
class_names,
title ='Confusion Matrix of Fall Detection Model')
plot_confusion_matrix(plot_confusion_matrix_nor_path,cm,
class_names,normalize=True,
title ='Normalized Confusion Matrix of Fall Detection Model')
# Print the classification report
print('\nClassification Report:\n', classification_report(y_true_label,
y_pred_label))
Classification_Report = os.path.join(result_path,'Classification_Report.txt')
with open(Classification_Report, mode='w') as f:
f.writelines(classification_report(y_true_label,y_pred_label))
f.close()
# Visualize the training history to see whether you're overfitting.
image_acc_path = os.path.join(result_path,'model_acc_LSTM.png')
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['train', 'val'], loc='best')
# plt.show()
plt.savefig(image_acc_path)
plt.close()
# Visualize the training history to see whether you're overfitting.
image_loss_path = os.path.join(result_path,'model_loss_LSTM.png')
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'val'], loc='best')
# plt.show()
plt.savefig(image_loss_path)
plt.close()