#!/usr/bin/python3 # -*- coding: utf-8 -*- import os from sklearn import manifold import numpy as np import matplotlib.pylab as plt from mpl_toolkits.mplot3d import Axes3D from model.utils import arg, Param if __name__ == '__main__': args = arg() config_path = args.cfg params = Param(config_path) vecs = np.loadtxt(os.path.join(config_path, f"emb/{args.epoch}vecs.tsv"), dtype=np.float, delimiter='\t') tsne = manifold.TSNE(n_components=3, learning_rate=100, n_iter=350) out = tsne.fit_transform(vecs) fig = plt.figure() ax = Axes3D(fig) for i in range(18): ax.scatter(out[i * 100:(i + 1) * 100, 0], out[i * 100:(i + 1) * 100, 1], out[i * 100:(i + 1) * 100, 2]) plt.show()
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU") except RuntimeError as e: print(e) from tensorflow.keras.applications import MobileNetV2 from tensorflow.keras.models import Model # from model.balance_input_fn import dataset_pipeline from model.input_fn import dataset_pipeline from model.triplet_loss import batch_hard_triplet_loss from model.utils import Param, arg if __name__ == "__main__": args = arg(True) config_path = args.cfg params = Param(config_path) # dataset train_ds, train_count = dataset_pipeline(params) test_ds, test_count = dataset_pipeline(params, True) # create model baseModel = MobileNetV2(include_top=False, weights='imagenet', input_shape=(224, 224, 3), pooling="avg") fc = tf.keras.layers.Dense(params.NUM_CLASSES, activation="softmax", name="dense_final")(baseModel.output)