kerasとtensorflowで実装したSiamese Networkを実行するとGPUの使用率が0~80%くらいを推移し、CPUの使用率が93%くらいになります。この値は正常なのでしょうか?
一応精度はそれぞれaccuracyが0.96、lossが0.03くらいは出ます。
GPUが使用できているかどうかと使えていない場合の原因が知りたいです。
ubuntu:18.04
GPU:GeForce GTX 1070 8GB
nvidia driver:495
cuda:10.0
cudnn:7.4
python:3.6
tensorflow:1.14.0
keras:2.2.5
tf.keras:2.2.4
import numpy as np import random import tensorflow as tf import keras from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Flatten, Dense, Dropout, Lambda from tensorflow.keras.optimizers import RMSprop from tensorflow.keras import backend as K from time import time t_start = time() # 開始時間 num_classes = 10 # 0~9 epochs = 20 def euclidean_distance(vects): x, y = vects sum_square = K.sum(K.square(x - y), axis=1, keepdims=True) return K.sqrt(K.maximum(sum_square, K.epsilon())) def eucl_dist_output_shape(shapes): shape1, shape2 = shapes return (shape1[0], 1) def contrastive_loss(y_true, y_pred): margin = 1 square_pred = K.square(y_pred) margin_square = K.square(K.maximum(margin - y_pred, 0)) return K.mean(y_true * square_pred + (1 - y_true) * margin_square) def create_pairs(x, digit_indices): '''Positive and negative pair creation. Alternates between positive and negative pairs. ''' pairs = [] labels = [] n = min([len(digit_indices[d]) for d in range(num_classes)]) - 1 for d in range(num_classes): for i in range(n): z1, z2 = digit_indices[d][i], digit_indices[d][i + 1] pairs += [[x[z1], x[z2]]] inc = random.randrange(1, num_classes) dn = (d + inc) % num_classes z1, z2 = digit_indices[d][i], digit_indices[dn][i] pairs += [[x[z1], x[z2]]] labels += [1, 0] return np.array(pairs), np.array(labels) def create_base_network(input_shape): '''Base network to be shared (eq. to feature extraction). ''' input = Input(shape=input_shape) x = Flatten()(input) x = Dense(128, activation='relu')(x) x = Dropout(0.1)(x) x = Dense(128, activation='relu')(x) x = Dropout(0.1)(x) x = Dense(128, activation='relu')(x) return Model(input, x) def compute_accuracy(y_true, y_pred): '''Compute classification accuracy with a fixed threshold on distances. ''' pred = y_pred.ravel() < 0.5 return np.mean(pred == y_true) def accuracy(y_true, y_pred): '''Compute classification accuracy with a fixed threshold on distances. ''' return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype))) # The data, split between train and test sets mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train = x_train / 255.0 x_test = x_test / 255.0 # input_shape = (28, 28, 1) input_shape = x_train.shape[1:] # (28, 28) # create training+test positive and negative pairs digit_indices = [np.where(y_train == i)[0] for i in range(num_classes)] tr_pairs, tr_y = create_pairs(x_train, digit_indices) digit_indices = [np.where(y_test == i)[0] for i in range(num_classes)] te_pairs, te_y = create_pairs(x_test, digit_indices) print(np.shape(tr_pairs)) base_network = create_base_network(input_shape) input_a = Input(shape=input_shape) input_b = Input(shape=input_shape) # because we re-use the same instance `base_network`, # the weights of the network # will be shared across the two branches processed_a = base_network(input_a) processed_b = base_network(input_b) distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([processed_a, processed_b]) model = Model([input_a, input_b], distance) model.summary() tf.keras.utils.plot_model(model, to_file = 'model.png', show_shapes = True, show_layer_names = True) # train tr_y = tf.cast(tr_y, dtype='float32') te_y = tf.cast(te_y, dtype='float32') rms = RMSprop() model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy]) H = model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y, batch_size=128, epochs=epochs, steps_per_epoch=1000 ) t_end = time() #終了時間 t_elapsed = t_end - t_start print("処理時間は{0}".format(t_elapsed))
どういう答えを期待しているのでしょう?
「はい/いいえ」ですか。