def evaluate(label_indices = {'1': 0, '2': 1, '3': 2 ,'4': 3,'5': 4}, channel_means = np.array([147.12697, 160.21092, 167.70029]), data_path = '../data_trial', minibatch_size = 32, num_batches_to_test = 10, checkpoint_dir = 'tf_data/sample_model'): print("1. Loading data") data = data_loader(label_indices = label_indices, channel_means = channel_means, train_test_split = 0.5, data_path = data_path) print("2. Instantiating the model") M = Model(mode = 'test') #Evaluate on test images: GT = Generator(data.test.X, data.test.y, minibatch_size = minibatch_size) num_correct = 0 num_total = 0 print("3. Evaluating on test images") for i in range(num_batches_to_test): GT.generate() yhat = M.predict(X = GT.X, checkpoint_dir = checkpoint_dir) correct_predictions = (np.argmax(yhat, axis = 1) == np.argmax(GT.y, axis = 1)) num_correct += np.sum(correct_predictions) num_total += len(correct_predictions) accuracy = round(num_correct/num_total,4) return accuracy
def do_run(self): for i in range(0, self.num_simulations): model = Model(self.model_config) model.initialize_agents() result = model.do_update() self.results.append(result) self.write_results(model)
def evaluate_with_path(image_path): im = cv2.imread(image_path) input_image_size = (227, 227) im = cv2.resize(im, (input_image_size[0], input_image_size[1])) channel_means = np.array([147.12697, 160.21092, 167.70029]) X = np.zeros((1, input_image_size[0], input_image_size[1], 3), dtype='float32') X[0, :, :, :] = im - channel_means M = Model(mode='test') #Evaluate on test images: # print(f'Predicted {}') return M.predict(X)
def evaluate(label_indices={ 'mountain_bikes': 1, 'road_bikes': 0 }, channel_means=np.array([147.12697, 160.21092, 167.70029]), data_path='test_data', minibatch_size=32, num_batches_to_test=10, checkpoint_dir='tf_data/sample_model'): print("1. Loading data") data = data_loader(label_indices=label_indices, channel_means=channel_means, train_test_split=0, data_path=data_path) print("2. Instantiating the model") M = Model(mode='test') #Evaluate on test images: accuracy = M.test(data) return accuracy
## ## Simple Training Script ## from sample_model import Model M = Model(mode='train') M.train()
import numpy as np import time from sample_model import Model from data_loader import data_loader from generator import Generator checkpoint_dir='tf_data/sample_model' X='C:/Users/Karthick/Desktop/cvproject/data/5/00000_00000.ppmspeed_2_.ppm' M = Model(mode = 'test') yhat = M.predict(X = X, checkpoint_dir = checkpoint_dir) # save_dir="C:/Users/Karthick/Desktop/cvproject/speedlimitckp/" # #saver = tf.train.Saver() # sess = tf.Session() # saver = tf.train.import_meta_graph('C:/Users/Karthick/Desktop/cvproject/src/tf_data/sample_model/model_epoch70.ckpt.meta') # saver.restore(sess,tf.train.latest_checkpoint('C:/Users/Karthick/Desktop/cvproject/src/tf_data/sample_model/')) # #checkpoint_name = tf.train.latest_checkpoint(save_dir) # #saver.restore(sess, checkpoint_name) # yhat_numpy = sess.run(yhat, feed_dict = {X : X, keep_prob: 1.0}) # print(yhat_numpy) # #C:/Users/Karthick/Desktop/cvproject/src/tf_data/sample_model