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train_test.py
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train_test.py
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import numpy as np
import csv
import json
from collections import defaultdict
import tensorflow as tf
from tensorflow.keras.models import Model, load_model
from sklearn.preprocessing import RobustScaler
from keras.layers import Dense
from sklearn.metrics import classification_report, confusion_matrix
from utils import add_gestures
from model import create_model
from loss import grad
def train_test_model(X_train_source, X_train_target, y_train_target, X_test_target, y_test_target,
model_cfg, label_maps):
"""
Train and evaluate the model in a few-shot continual learning way
Args:
X_train_source: data from control participants
X_train_target: train data from the impaired participant
y_train_target: true train labels for the gesture classes
X_test_target: test data from the impaired participant
y_test_target: true test labels for the gesture classes
model_cfg: config instance to access the hyperparameters
label_maps: gesture dictionary based on an order
"""
# Save average precision, recall and F1-score in a csv file
csvfile_prf = open('avg_metric_score_gesture.csv', 'w', encoding='utf-8')
csvfile_writer_prf = csv.writer(csvfile_prf)
csvfile_writer_prf.writerow(["number of samples", "gesture", "avg_precision", "avg_recall", "avg_f1_score"])
# Save average accuracy in a csv file
csvfile_acc = open('avg_acc_gesture.csv', 'w', encoding='utf-8')
csvfile_writer_acc = csv.writer(csvfile_acc)
csvfile_writer_acc.writerow(["number of samples", "number of gestures", "avg_accuracy"])
cm = defaultdict(list)
number_of_gestures = 6
start = 2 # initialize with two gestures
optimizer = tf.keras.optimizers.legacy.Adam(learning_rate=0.001)
model = load_model('./saved_model/pre-trained_model.h5')
intermediate_model = Model(inputs=model.input, outputs=model.layers[2].output)
embeddings_control = intermediate_model(X_train_source)
for cur in range(start, number_of_gestures + 1): # iterate over the gestures. starts using labels: 0 and 1.
copy_model = load_model('./saved_model/pre-trained_model.h5')
intermediate_copy_model = Model(inputs=copy_model.input, outputs=copy_model.layers[2].output)
gesture_labels = [i for i in range(cur)] # gesture labels in order
shots = []
cls_ges_pre = defaultdict(list)
cls_ges_rec = defaultdict(list)
cls_ges_f1 = defaultdict(list)
count = 1
X_train_n, y_train_n = add_gestures(X_train_target, y_train_target, cur)
X_test_n, y_test_n = add_gestures(X_test_target, y_test_target, cur)
target_names = list(label_maps.keys())[:cur]
while count: # adding 1 sample, 3 samples and 5 samples consecutively.
X_train_f = []
y_train_f = []
c = {}
for i in gesture_labels:
c[i] = 0
for label in range(len(y_train_n)):
if c[y_train_n[label]] != count:
X_train_f.append(X_train_n[label])
y_train_f.append(y_train_n[label])
c[y_train_n[label]] += 1
X_train_n_arr = np.array(X_train_f) # converting list to array (train set)
y_train_n_arr = np.array(y_train_f)
X_test_n_arr = np.array(X_test_n) # converting list to array (test set)
y_test_n_arr = np.array(y_test_n)
scaler = RobustScaler()
X_train_n_arr = scaler.fit_transform(X_train_n_arr.reshape(-1, X_train_n_arr.shape[-1])).reshape(
X_train_n_arr.shape)
X_test_n_arr = scaler.transform(X_test_n_arr.reshape(-1, X_test_n_arr.shape[-1])).reshape(
X_test_n_arr.shape)
# Temporary latent embedding of impaired samples
embeddings_impaired_fixed = intermediate_copy_model(X_train_n_arr)
cls_ges_sum_pre = defaultdict(list)
cls_ges_sum_rec = defaultdict(list)
cls_ges_sum_f1 = defaultdict(list)
cls_acc = []
for i in range(model_cfg['num_iteration']):
train_model = create_model(model_cfg['time_steps'], model_cfg['features'], model_cfg['dimension1'],
model_cfg['dropout'], model_cfg['dimension2'], model_cfg['num_classes'])
train_model.load_weights('./saved_model/pre-trained_model_weights.h5')
train_model.pop()
train_model.add(Dense(len(gesture_labels), activation='softmax'))
train_model.summary()
for epoch in range(model_cfg['epochs']):
intermediate_train_model = Model(inputs=train_model.input, outputs=train_model.layers[2].output)
embeddings_impaired = intermediate_train_model(X_train_n_arr)
loss_value, grads = grad(train_model, X_train_n_arr, y_train_n_arr, embeddings_control,
embeddings_impaired_fixed, embeddings_impaired)
optimizer.apply_gradients(zip(grads, train_model.trainable_variables))
y_pre = np.argmax(train_model.predict(X_test_n_arr), axis=-1)
cm[cur].append(confusion_matrix(y_test_n_arr, y_pre))
report = classification_report(y_test_n_arr, y_pre, target_names=target_names)
# Store all iterations' precision, recall and F1-score for each gesture from the classification report
for item in target_names:
precision = float(report.split(item)[1].split()[0])
recall = float(report.split(item)[1].split()[1])
f1_score = float(report.split(item)[1].split()[2])
cls_ges_sum_pre[item].append(precision)
cls_ges_sum_rec[item].append(recall)
cls_ges_sum_f1[item].append(f1_score)
cls_acc.append(float(report.split('accuracy')[1].split()[0]))
# Save the average precision, recall and F1-score for each gesture
for n in target_names:
cls_ges_pre[n].append(np.mean(cls_ges_sum_pre[n]))
cls_ges_rec[n].append(np.mean(cls_ges_sum_rec[n]))
cls_ges_f1[n].append(np.mean(cls_ges_sum_f1[n]))
avg_pre = np.mean(cls_ges_sum_pre[n])
avg_rec = np.mean(cls_ges_sum_rec[n])
avg_f1 = np.mean(cls_ges_sum_f1[n])
csv_line_prf = [count, n, avg_pre, avg_rec, avg_f1]
csvfile_writer_prf.writerow(csv_line_prf)
csv_line_acc = [count, len(gesture_labels), np.mean(cls_acc)]
csvfile_writer_acc.writerow(csv_line_acc)
shots.append(count)
count += 2
if count > 6:
break
csvfile_prf.close()
csvfile_acc.close()
# Save confusion matrices as a json file
for key, value in cm.items():
cm[key] = [arr.tolist() for arr in value]
with open('cm_file.json', 'w') as f:
json.dump(cm, f)